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26Sep, 2022

The 4 Types of Data Analysis and their difference

Businesses and organisations that continuously learn and adapt are the most successful. No matter what sector you work in, it’s critical to be aware of the recent past, the current state of affairs, and potential future developments. So how do businesses accomplish that?

Data analytics holds the solution. Most businesses constantly gather data, yet this data is meaningless in its unprocessed state. What you do with the data is what really matters. Data analytics is the act of examining raw data to identify patterns, trends, and insights that might provide valuable information about a certain business domain. Following that, informed judgments based on data are made using these insights.

AI and sophisticated analytics have gained popularity in recent years. There are a lot of blogs out there that discuss the benefits of employing sophisticated analytics in your company.

It is tempting to go right in and try to get advanced analytics straight immediately given the amount of value that they may provide. But these insights cannot be attained without the right foundations. What is the first step to obtaining these insightful data, then?

Advanced analytics success and the use of AI can be ensured by comprehending the analytics development and getting started in the proper spot.

56 percent of respondents claimed data analytics resulted in “faster, more effective decision-making” at their firms, according to MicroStrategy’s The Global State of Enterprise Analytics survey (pdf). Other advantages mentioned include:

increased productivity and efficiency (64 percent)
better financial results (51 percent)
Finding and generating new sources of revenue for products and services (46 percent)
enhanced client acquisition and retention (46 percent)
enhanced client experiences (44 percent)
advantage over rivals (43 percent)

Table of Contents

WHO IS IN NEED OF DATA ANALYSIS?

Any business professional who takes judgments must have a solid understanding of data analytics. Data access is easier to come by than ever. You may overlook significant possibilities or warning signs if you design strategies and make decisions without taking the facts into account.

Skills in data analytics can be useful for the following professions:

Marketers develop marketing plans by using information about customers, market trends, and the results of previous campaigns.
Product managers improve their companies’ goods by analysing market, industry, and user data.
Finance experts predict the financial trajectories of their organisations using historical performance data and market trends.
Human resources and diversity, equality, and inclusion specialists can use information on industry trends and employee perspectives, motivations, and behaviours to make significant organisational changes.

1. Descriptive Analytics

The foundation for all other types of analytics is descriptive analytics, which is the most basic type. It enables you to quickly summarise what occurred or is happening by drawing trends from the raw data.

What happened is answered by descriptive analytics.

Consider the scenario where you are studying the statistics for your business and discover that sales of one of your goods, a video game console, are increasing at a seasonal rate. Here, descriptive analytics can inform you, “Sales of this video game system increase each year in early December, early November, and October.”

Charts, graphs, and maps may clearly and understandably display data patterns, as well as dips and spikes, making data visualization a good choice for expressing descriptive analysis.

Data Aggregation:

Definition: Data aggregation involves combining and summarizing individual data points to form a higher-level overview. This can be done based on specific criteria such as time periods, locations, or categories.
Example: Aggregating daily sales data into monthly or yearly totals to understand overall performance.

Summary Statistics:

Mean: The average of a set of values.
Median: The middle value in a dataset when it is ordered.
Mode: The most frequently occurring value.
Standard Deviation: A measure of the amount of variation or dispersion in a set of values.
Range: The difference between the maximum and minimum values in a dataset.
Percentiles: Divides the data into 100 equal parts, helping understand the relative standing of a particular value.
Purpose: These statistics provide insights into the central tendency, spread, and distribution of the data.

Data Visualization:

Graphs and Charts: Represent data visually to identify patterns, trends, and relationships.
Examples: Bar charts for categorical data, line graphs for trends over time, scatter plots for relationships between two variables, and heat maps for visualizing variations in a matrix.
Purpose: Enhances understanding and communication of complex data patterns.

Data Exploration (EDA):

Techniques: Histograms, box plots, and summary statistics to identify data characteristics.
Outliers and Missing Values: Identification and handling of unusual or missing data points.
Purpose: Gain initial insights, understand the structure, and identify potential issues in the dataset.

Data Profiling:

Data Types: Understanding the types of data (numerical, categorical, etc.).
Data Integrity: Assessing the accuracy and consistency of data.
Completeness: Identifying missing values or incomplete records.
Anomalies: Detecting irregularities or outliers.
Purpose: Assess the overall quality and reliability of the dataset.

Segmentation:

Definition: Dividing data into meaningful groups or segments based on specific attributes or characteristics.
Example: Segmenting customers based on demographics, buying behavior, or preferences.
Purpose: Identifying different patterns and behaviors within the dataset for targeted analysis or decision-making.
Time Series Analysis:

Definition: Analysis of data collected over time to understand patterns, seasonality, and trends.
Techniques: Moving averages, trend analysis, and forecasting.
Purpose: Uncover temporal patterns and make predictions based on historical data.
In summary, descriptive analytics involves various techniques to organize, summarize, and visually represent data, providing valuable insights into its characteristics and patterns. These techniques serve as a foundation for more advanced analytics and decision-making processes.

2. Diagnostic Analytics

The following logical question, “Why did this happen?” is answered by diagnostic analytics.

This sort of analysis goes a step further by comparing current trends or movements, finding relationships between variables, and, when possible, establishing causal linkages.

Using the previous example, you might look at the demographics of video game console users and discover that they range in age from eight to 18 years old. The average age of the patrons, however, is between 35 and 55. Data from customer surveys that have been analysed show that buying a video game console as a present for kids is one of the main reasons people do so. The increase in sales throughout the fall and early winter may be attributed to the gift-giving holidays.

Using diagnostic analytics to identify

Root Cause Analysis:

Definition: Root cause analysis involves identifying the fundamental factors or reasons that contributed to a specific event or outcome. It aims to go beyond surface-level observations to uncover the underlying causes.
Techniques: Correlation analysis, regression analysis, and dependency analysis help in understanding relationships between variables and identifying potential causes.
Process: Investigating patterns and trends in the data to determine the root causes, which may involve exploring historical data, conducting interviews, and using various analytical tools.

Comparative Analysis:

Definition: Comparative analysis involves comparing different datasets or segments to identify variations, differences, and similarities in performance. It helps in understanding the relative performance of different entities.
Techniques: Comparative analysis may involve metrics such as percentages, ratios, and indices to quantify and compare performance. Visualization techniques like bar charts and heat maps are often used for easy comparison.
Purpose: To identify patterns, anomalies, and trends by comparing different dimensions, such as regions, time periods, customer segments, or product lines.

Hypothesis Testing:

Definition: Hypothesis testing is a statistical method used to validate or reject potential explanations (hypotheses) for observed outcomes. It involves formulating hypotheses, collecting data, and performing statistical tests to assess the significance of relationships.
Process: Formulating a null hypothesis and an alternative hypothesis, collecting data, conducting statistical tests (e.g., t-tests, chi-square tests), and interpreting the results to make informed decisions about the hypotheses.
Purpose: To provide evidence for or against proposed explanations and make data-driven decisions.

Drill-Down Analysis:

Definition: Drill-down analysis involves examining data at a detailed, granular level by exploring specific dimensions or attributes. It allows for a deeper understanding of the data and helps uncover insights that may be hidden when looking at higher levels of aggregation.
Techniques: Pivot tables, detailed reports, and interactive dashboards are often used to facilitate drill-down analysis. Users can navigate through layers of data to explore specific details.
Purpose: To identify specific trends, outliers, or patterns that may not be evident at a higher level of aggregation.

Cohort Analysis:

Definition: Cohort analysis involves studying groups of individuals or entities that share common characteristics or experiences. It helps in understanding how different cohorts behave over time and how they may differ from one another.
Techniques: Time-based segmentation is a common technique in cohort analysis. The behavior of specific cohorts is tracked over time to observe changes and trends.
Purpose: To gain insights into customer behavior, product performance, or employee performance by analyzing groups that share similar characteristics or experiences.

Data Mining and Machine Learning:

Definition: Data mining and machine learning involve using advanced algorithms to discover patterns, correlations, and predictive relationships in large datasets.
Techniques: Clustering, classification, regression, and association rule mining are common techniques in data mining and machine learning. These algorithms can uncover hidden insights and automate the discovery process.
Purpose: To extract valuable knowledge from data, predict future outcomes, and uncover complex relationships that may not be apparent through traditional analysis.
In summary, diagnostic analytics involves a set of techniques that go beyond describing data to understand why certain events or outcomes occurred. These techniques help organizations uncover insights, identify causation, and make informed decisions based on a deeper understanding of the data.

3. Predictive Analytics

In order to predict future trends or events or to provide a response to the question “What might happen in the future,” predictive analytics is utilised.

You can accurately estimate what the future may hold for your firm by examining historical data along with current industry trends.

For instance, knowing that, over the previous ten years, sales of video game consoles have peaked in October, November, and the first few weeks of December each year gives you enough information to forecast that the same trend will continue in 2016. This is a logical prediction, supported by upward trends in the video game industry as a whole.

Making forecasts about the future might assist your company in developing plans based on probable outcomes.

Here are some key aspects of predictive analytics:

Data Preparation: Preparing and cleansing the data to ensure its quality and suitability for analysis. This includes handling missing values, outliers, and transforming the data into a format suitable for modeling.
Feature Selection and Engineering: Identifying the most relevant features or variables that are likely to have predictive power. This involves selecting informative variables and creating new derived features that may enhance the prediction models.
Model Development: Building statistical or machine learning models based on the historical data. Common techniques used in predictive analytics include regression analysis, decision trees, random forests, support vector machines, neural networks, and ensemble methods.
Training and Evaluation: Training the predictive models using a portion of the historical data and evaluating their performance on another portion of the data. This is done to assess how well the models generalize to unseen data and to select the best-performing model.
Prediction and Forecasting: Applying the trained models to new or future data to generate predictions or forecasts. These predictions can be in the form of numerical values, classifications, or probabilities.
Model Validation: Validating the predictive models by comparing the predicted outcomes with the actual outcomes observed in real-world scenarios. This helps assess the accuracy and reliability of the models.

4. Prescriptive Analytics

Prescriptive analytics finally provides a response to the query, “What should we do next?”

Prescriptive analytics recommends actionable takeaways after considering all potential aspects in a circumstance. Making decisions based on data can be extremely helpful when using this kind of analytics.

To complete the video game illustration: What should your team decide to do in light of the anticipated seasonality trend brought on by the holiday gift-giving season? Perhaps you decide to do an A/B test with two adverts, one geared toward customers and the other towards the product’s end-users (children) (their parents). The results of that experiment can help determine how best to further capitalise on the seasonal rise and its purported cause. Or, perhaps you decide to step up your marketing initiatives in September with messages centred around the holidays to try to prolong the boost.

Here are some key aspects of prescriptive analytics:

Optimization Modeling: Developing mathematical or computational models that represent the problem domain and capture the relationships between variables, constraints, and objectives. These models aim to find the best solution that maximizes performance or minimizes costs based on predefined criteria.
Constraint Analysis: Identifying and incorporating constraints and limitations into the optimization models. These constraints could be related to resource availability, capacity limits, regulatory requirements, or business rules that need to be adhered to.
Scenario Analysis: Conducting “what-if” analysis by simulating different scenarios and evaluating their potential outcomes. This helps assess the impact of different decisions or actions on the desired outcomes and facilitates decision-making under various conditions.
Decision Support Systems: Developing decision support tools or systems that can assist decision-makers in evaluating alternative courses of action and selecting the optimal solution. These systems often provide interactive interfaces and visualizations to aid in the decision-making process.
Recommendation Engines: Implementing recommendation systems that utilize advanced algorithms to suggest the best actions or options based on user preferences, historical data, and real-time information. These engines are commonly used in personalized marketing, content recommendation, and product recommendation.
Continuous Learning and Adaptation: Prescriptive analytics systems can be designed to continuously learn from new data, feedback, and outcomes. This allows for the refinement and adaptation of models and recommendations over time, leading to improved decision-making and performance optimization.

24Sep, 2022

Top prime reasons for learning SQL for data analysis

The standard language for working with Relational Databases is called Structured Query Language (SQL), which is pronounced either “S-Q-L” or occasionally “See-Quel.” The use of SQL to insert, search, update, delete, and alter database records is effective. It doesn’t imply that SQL can’t perform tasks beyond those. In actuality, it is capable of a great deal more. That was a quick summary of SQL. You can read my article on SQL Tutorial if you want to learn more about SQL and its commands.

Knowing what SQL is helpful if you’re looking for the best motivations to devote time to learning SQL.

The most sought-after expertise for software developer jobs is SQL, and including SQL in your resume significantly increases your chance of receiving calls and job offers. In conclusion, learn SQL if you are unsure or if you have any doubts; you won’t regret it.

The database is an important part of modern development. Working with a database is not easy and becomes tougher if the technology or language being used is complicated. If you have any experience in backend development, you may encounter the term “SQL”. SQL stands for Structural Query language. It is used to manage relational databases.

High Paying Jobs

The job market appears promising for SQL enthusiasts, from startups to well-established businesses, and it is anticipated to increase significantly over the next few years. All geographical areas offer excellent chances for SQL programmers and developers. Take a look at the graph below to see the UK wage trend for SQL Developers.

Ease of Learning and Use

Declarative Language:

SQL is a declarative language, which means that you declare what you wish to get or manipulate from the database rather than how. This abstraction simplifies the process and allows you to concentrate on the desired output, making it easier for beginners to understand.

English-like Syntax:

SQL has a syntax that is similar to English language constructs, including commands like SELECT, FROM, WHERE, GROUP BY, ORDER BY, and so on. This natural language style makes it friendly to people who do not have significant programming skills.

Interactive Environment:

SQL is typically used in an interactive environment, where you can immediately see the results of your queries. This real-time feedback facilitates rapid learning and experimentation.

Small Set of Commands:

SQL has a relatively small set of commands to perform most data retrieval and manipulation tasks. Learning a handful of essential commands is often sufficient to start working with databases effectively.

Abstraction from Database Complexity:

SQL abstracts the underlying complexities of database management systems, allowing users to interact with data without worrying about the underlying database structure or implementation details.

Rich Online Resources:

Due to SQL’s widespread use, there is a wealth of learning resources available online, including tutorials, documentation, forums, and online courses. This abundance of resources makes it easier for individuals to self-learn SQL.

Code Reusability:

Once you learn SQL, the knowledge can be applied to various database management systems, as most follow the SQL standard with minor variations. This reusability saves time and effort when switching between different database platforms.

Instant Gratification:

As you write SQL queries, you get immediate results, which reinforces learning and helps you see the impact of your commands in real-time.

Visual Query Builders:

Many database management tools offer visual query builders, which allow users to build SQL queries by dragging and dropping elements. These tools can be helpful for beginners and make SQL more accessible.

Community Support:

SQL has a large and active community of developers and data analysts. The community provides support, shares knowledge, and helps newcomers with their questions, contributing to the ease of learning.

Despite its ease of learning, mastering SQL requires practice and experience, especially when dealing with complex queries and large datasets. However, with dedication and continuous use, even those with limited technical backgrounds can become proficient in SQL for data analysis.

How to learn sql step by step from scratch

1. Understand the Basics:

a. Introduction to Databases:

Familiarize yourself with the concept of databases and their importance in storing and managing data.

b. Relational Databases:

Learn about relational databases and how they organize data into tables.

c. Basic SQL Concepts:

Understand basic SQL concepts like tables, rows, columns, and relationships.

2. Set Up a Database:

a. Install a Database Management System (DBMS):
Choose a DBMS such as MySQL, PostgreSQL, SQLite, or Microsoft SQL Server.
Install and set up the chosen DBMS on your machine.
b. Create Your First Database:
Practice creating a simple database and tables.

3. Learn SQL Commands

a. Data Querying (SELECT):
Start with basic SELECT statements to retrieve data from tables.
b. Data Filtering (WHERE clause):
Learn how to filter data using the WHERE clause.
c. Sorting Data (ORDER BY):
Understand how to sort query results using the ORDER BY clause.
d. Data Modification (INSERT, UPDATE, DELETE):
Practice inserting, updating, and deleting data in tables.
e. Aggregate Functions (COUNT, SUM, AVG, MAX, MIN):
Explore functions for summarizing and aggregating data.
f. Grouping and Group Functions:
Learn about GROUP BY and HAVING clauses for grouping and filtering data.

4. Understand Joins:

a. Inner Joins:
Learn how to combine data from multiple tables using INNER JOIN.
b. Outer Joins:
Understand LEFT, RIGHT, and FULL OUTER JOIN for handling NULL values.

5. Advanced SQL Concepts:

a . Subqueries:
Explore subqueries and their use in SQL statements.
b. Indexes and Performance Optimization:
Learn about indexing for optimizing database performance.
c. Transactions and ACID Properties:
Understand transactions and the ACID properties (Atomicity, Consistency, Isolation, Durability).

d. Views:
Explore the creation and use of views for simplifying complex quer

6. Practice:

a. Use Sample Databases:
Work with sample databases to practice your skills.
b. Solve Real-world Problems:
Challenge yourself with real-world scenarios to apply your knowledge.

7. Online Resources and Courses:

a. Online Courses:
Enroll in online courses on platforms like Coursera, Udemy, or Khan Academy.
b. Documentation and Tutorials:
Refer to official documentation and tutorials provided by the chosen DBMS.

8. Join SQL Communities:

a. Forums and Q&A Sites:
Participate in SQL communities like Stack Overflow to learn from and help others.

9. Build Projects:

a. Apply Your Knowledge:
Build small projects to reinforce what you’ve learned.

23Sep, 2022

Best Data Analytics training in Dehradun Why to learn

20Sep, 2022

Data Cleaning With Python for data analytics

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Table of Contents

What is data cleaning

Data cleansing definition
Before conducting data analysis, a data set must be cleaned of any inaccurate, corrupt, or extraneous information.

By converting your messy, potentially troublesome data into clean data, data cleaning—also known as data cleansing, data scrubbing, and data preparation—serves to elaborate on the basic concept given above. That’s “clean data,” which is defined as information that the potent data analysis engines you invested in can truly utilize.

Additionally, and perhaps even more importantly, Python can be used to programme the great majority of datasets. Python’s significance is increased by the fact that data scientists use Numpy and Pandas, two Python libraries (i.e., pre-programmed toolsets), for data preparation and other types of analysis.What else is there to say? However, let’s get down to business and use these libraries to actually clean our data.

Python Data Cleaning

We will now guide you through the set of activities indicated below using Pandas and NumPy. We’ll offer a very brief overview of the assignment before describing the required code using the terms INPUT (what you should enter) and OUTPUT (what you should see as a result). Where applicable, we’ll also include notes and advice to assist you understand any confusing passages.

The basic data cleansing chores that we’ll take on are as follows:
Importing Libraries
Input Customer Feedback Dataset
Locate Missing Data
Check for Duplicates
Detect Outliers
Normalize Casing

Importing Libraries

Let’s get your Python script going with NumPy and Pandas installed.

INPUT:

import pandas as pd

import numpy as np

OUTPUT:

In this situation, the libraries should have been loaded into your script by this point. In our next step, you’ll input a dataset to verify whether this is the case.

Enter the Dataset of Customer Feedback

The feedback dataset is then read by our libraries. Let’s have a look at that.

INPUT:

data = pd.read_csv(‘feedback.csv’)

As you can see, the dataset you wish to look at is “feedback.csv”. And in this instance, we know we are utilising the Pandas library to read our dataset as we see “pd.read csv” as the prior function.

Locate Missing Data

The isnull function, a sophisticated Python hack, will then be used to find our data. Actually a common function, “isnull” aids in locating missing items in our collection. This information is helpful since it shows what has to be fixed throughout the data cleaning process.

data.isnull()

We get a collection of boolean values as our output result.

The list can provide us with a variety of insights. The first thing to consider is where the missing data is; any column with a ‘True’ reading denotes that the data file’s category for that column contains missing data.

Datapoint 1 has missing information in its Review section and Review ID section, for instance (both are marked true).

Each feature’s missing data can be expanded further by coding:

Dropping the data
Another choice will need to be made: to maintain the data in the set while simply dropping the missing values, or to completely remove the feature (the entire column) because there are so many missing datapoints that it is unusable for analysis.

You must go in and label the missing values as void in accordance with Pandas or NumBy standards if you want to remove them (see section below). However, this is the code to remove the full column:

INPUT:

remove = [‘Review ID’,’Date’]
data.drop(remove, inplace =True, axis =1)

2. Input any missing data
Technically speaking, adding individual values using Pandas or NumBy standards is the same as adding missing data; we refer to it as adding “No Review.” When entering missing data, you have two options: manually enter the right information or add “No Review” using the code below.

INPUT:

data[‘Review’] = data[‘Review’].fillna(‘No review’)

Check for Duplicates

Similar to missing data, duplicates are problematic and choke analytics tools. Let’s find them and get rid of them.

In order to find duplicates, we start with:

data.duplicated()

data.drop_duplicates()

Detect Outliers

Outliers are numerical values that lie significantly outside of the statistical norm. Cutting that down from unnecessary science garble – they are data points that are so out of range they are likely misreads.

They, like duplicates, need to be removed. Let’s sniff out an outlier by first, pulling up our dataset.

INPUT:

data[‘Rating’].describe()

Categories of SQL command to know for Data Analytics

19Sep, 2022

Categories of SQL command to know for Data Analysis

Relational databases can be managed and manipulated using the programming language SQL (Structured Query Language). Here are some crucial SQL command categories to be aware of for data analysis:

Data Definition Language (DDL)

what are the DDL commands?

DDL modifies the structure of the table by adding, removing, or changing tables, among other things.
All DDL commands are automatically committed, which permanently saves all database modifications.
The following commands are included in DDL:

Create

This command builds a new table and has a predefined syntax. The CREATE statement syntax is:

CREATE TABLE [table name] ([column definitions]) [table parameters];

For example:

CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    salary DECIMAL(10, 2)
);

CREATE TABLE employees:

This part of the statement indicates that you are creating a new table named “employees.”

(id INT PRIMARY KEY,):

This line defines the first column of the table, named “id.”
INT specifies that it is an integer data type, meaning it will store whole numbers.
PRIMARY KEY indicates that the “id” column will be the primary key for the table. A primary key is a unique identifier for each record in the table.

(name VARCHAR(100),):

This line defines the second column of the table, named “name.”
VARCHAR(100) specifies that it is a variable character field, capable of storing up to 100 characters. This column is typically used for storing names or textual information.

(salary DECIMAL(10, 2)):

This line defines the third column of the table, named “salary.”
DECIMAL(10, 2) specifies a decimal data type with a total of 10 digits, where 2 digits are reserved for the decimal part. This is commonly used for storing monetary values.

In summary, the SQL statement is creating a table named “employees” with three columns: “id” for unique identification, “name” for storing names, and “salary” for storing decimal values such as salaries. The “id” column is designated as the primary key, ensuring each record in the table has a unique identifier.

Alter

An alter command modifies an existing database table. This command can add up additional column, drop existing columns and even change the data type of columns involved in a database table.

An alter command syntax is:

ALTER object type object name parameters;

For example:

ALTER TABLE employees
ADD department VARCHAR(50);

In this example, we added a unique primary key to the table to add a constraint and enforce a unique value. The constraint “employee_pk” is a primary key and is on the Employee table.

Drop

A drop command is used to delete objects such as a table, index or view. A DROP statement cannot be rolled back, so once an object is destroyed, there’s no way to recover it.

Drop statement syntax is:

DROP object type object name;

For example:

DROP TABLE employees;

In this example, we’re deleting the Employee table.

Truncate

Similar to DROP, the TRUNCATE statement is used to quickly remove all records from a table. However, unlike DROP that completely destroys a table, TRUNCATE preserves its full structure to be reused later.Truncate statement syntax is:

TRUNCATE TABLE table_name;

For example:

TRUNCATE TABLE employees;

COMMENT:

This statement allows you to add comments to a table or column, which can be useful for documentation purposes.

COMMENT ON COLUMN employees.salary IS 'The salary of the employee';

DQL Command

Data Query Language DQL is used to fetch the data from the database. A group of SQL commands known as Data Query Language (DQL) are used to extract data from databases. The DQL command that is used the most is SELECT. Here are a few DQL command examples: Create table

CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    salary DECIMAL(10, 2)
);

Insert Data

INSERT INTO employees (id, name, salary) VALUES
    (1, 'John Doe', 50000.00),
    (2, 'Jane Smith', 60000.00),
    (3, 'Michael Johnson', 55000.00),
    (4, 'Emily Williams', 62000.00),
    (5, 'Robert Brown', 48000.00),
    (6, 'Lisa Davis', 53000.00),
    (7, 'William Wilson', 58000.00),
    (8, 'Linda Taylor', 51000.00),
    (9, 'James Anderson', 54000.00),
    (10, 'Mary Martinez', 57000.00);

This command will inser data

Step 3: Retrieve All Data

SELECT * FROM employees;

This query retrieves all rows and columns from the “employees” table.

Step 4: Retrieve Specific Columns

SELECT name, salary FROM employees;

This query retrieves only the “name” and “salary” columns from the “employees” table.

Step 5: Filter Data

SELECT * FROM employees WHERE salary > 55000;

DML COMMAND

The database can be changed by using DML commands. It is in charge of making any kind of database modifications. DML commands cannot permanently save all database changes since they are not auto-committed. They are rollbackable. The four primary DML commands in SQL are as follows:

INSERT Command
UPDATE Command
DELETE Command

UPDATE: Modify existing data.

-- Update John Doe's salary to $55000
UPDATE employees SET salary = 55000.00 WHERE name = 'John Doe';

DELETE: Remove rows from the table.

-- Delete the employee with ID 5
DELETE FROM employees WHERE id = 5;

INSERT INTO: Add new rows to the table.

-- Insert a new employee
INSERT INTO employees (id, name, salary) VALUES (11, 'Alex Johnson', 59000.00);

UPDATE Multiple Rows: You can update multiple rows at once.

-- Increase salary by 10% for employees with salary less than 55000
UPDATE employees SET salary = salary * 1.10 WHERE salary < 55000;

DELETE with Condition: Delete rows based on a condition.

-- Delete employees with a salary less than 50000
DELETE FROM employees WHERE salary < 50000;

INSERT Multiple Rows: Insert multiple rows at once.

-- Insert multiple employees
INSERT INTO employees (id, name, salary)
VALUES
    (12, 'Sarah Brown', 58000.00),
    (13, 'Thomas Miller', 53000.00);

Data Control Language (DCL)

To control access to database objects, use these instructions. GRANT and REVOKE are a couple of examples of commands in this group.DCL (Data Control Language) commands are used to control access to data within a database. They are primarily concerned with granting or revoking permissions and privileges to users. Here’s how you can use DCL commands on the “employees” table and dataset: A person or group of users can be granted special rights with this command. The GRANT command has the following syntax:

GRANT

-- Grant SELECT privilege on the employees table to a user or role
GRANT SELECT ON employees TO username_or_role;

REVOKE: Remove privileges from users or roles.

-- Revoke SELECT privilege on the employees table from a user or role
REVOKE SELECT ON employees FROM username_or_role;

DCL commands are used to manage permissions, access control, and security within the database. Keep in mind that the specific syntax and implementation might vary depending on the database system you are using.

Transaction Control Language (TCL)

The management of transactions, which are collections of database activities that are handled as a single unit of work, is done using these commands. The commands COMMIT and ROLLBACK are examples of this class. Transaction Control Language (TCL) consists of a set of SQL commands that control the transactions in a database. Transactions are sequences of one or more SQL statements that are executed as a single unit of work. TCL commands allow you to manage the changes made to a database during transactions. Here are the main TCL commands:

Commit

This command is used to permanently save changes made in the current transaction to the database. Once committed, the changes become permanent and visible to other transactions.

COMMIT;

ROLLBACK:

This command is used to undo changes made in the current transaction. It reverts the database to the state it was in before the transaction began.

ROLLBACK;

SAVEPOINT:

A savepoint marks a specific point within a transaction to which you can later roll back. It allows you to selectively undo parts of a transaction while leaving other parts intact.

SAVEPOINT sp1;

ROLLBACK TO SAVEPOINT:

This command undoes changes made after a specific savepoint within a transaction.

RELEASE SAVEPOINT sp1;

TCL commands are crucial for maintaining data integrity, managing concurrent transactions, and ensuring that changes to the database are controlled and consistent. By using these commands effectively, you can handle transactions in a way that aligns with your application’s requirements and maintains the reliability of your data.

What is the purpose of the SQL SELECT statement in data analysis?

Answer: The SQL SELECT statement is used to retrieve data from one or more tables. It allows you to specify which columns to retrieve and apply filtering conditions using the WHERE clause.

How can you filter rows from a table using SQL?

Answer: You can filter rows from a table using the WHERE clause in conjunction with the SELECT statement. The WHERE clause allows you to specify conditions that the rows must meet to be included in the result set.

What is the significance of the SQL GROUP BY clause?

Answer: The GROUP BY clause is used to group rows with similar values in specified columns. It’s often used in combination with aggregate functions to perform calculations on grouped data.

Explain the difference between the INNER JOIN and LEFT JOIN SQL commands.

Answer: The INNER JOIN returns only the matching rows from both tables based on the specified condition. On the other hand, the LEFT JOIN returns all the rows from the left table and the matching rows from the right table. If there’s no match, NULL values are returned for the right table’s columns.

How can you calculate the average of a specific column in a SQL table?

Answer: You can calculate the average of a specific column using the AVG aggregate function in combination with the SELECT statement and optionally the GROUP BY clause.

Question 6: What is the purpose of the SQL INSERT statement?

Answer: The SQL INSERT statement is used to add new rows into a table. It allows you to specify the values for each column in the new row.

Explain the concept of a primary key in SQL.

Answer: A primary key is a column or a set of columns that uniquely identify each row in a table. It ensures data integrity by preventing duplicate or null values in the key columns.

How does the SQL ORDER BY clause work?

Answer: The ORDER BY clause is used to sort the result set of a query based on one or more columns. You can specify the sorting order (ascending or descending) for each column.

What is the purpose of the SQL ALTER TABLE statement?

The SQL ALTER TABLE statement is used to modify the structure of an existing table, such as adding or dropping columns, changing data types, or adding constraints.

How can you use a subquery in SQL for data analysis?

A subquery is a nested query that can be used within another query. It can be employed to retrieve data that will be used for filtering, calculations, or comparisons in the outer query. Subqueries are enclosed in parentheses and can appear in various parts of a SQL statement, such as the WHERE clause or the FROM clause.

17Sep, 2022

Top 10 SQL Commands You Should Know

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There is an increasing demand for developers to efficiently use databases to handle the continuously expanding amounts of data that businesses and organisations are dealing with. The programming language known as SQL, or Structured Query Language, is used to manage data that is kept in relational databases (a popular type of database).

A developer can use SQL commands to build tables, update the data in those tables, search the database, and more. Ten fundamental SQL commands that are a requirement for developers working with SQL will be covered in this article. You can find a code snippet and a brief explanation of what each SQL command does.

A database is used by every large or dynamic website in some capacity, and when combined with Structured Query Language (SQL), the possibilities for data manipulation are virtually limitless.

Table of Contents

CREATE TABLE

CREATE TABLE table_name ( column_1 datatype_1, column_2 datatype_2, column_3 datatype_3 );

A new database or table can be created with this command; the previous example adds a new table with a title and column names.

CREATE TABLE table_name(
column1 datatype
column2 datatype,
column3 datatype,
…..
columnN datatype,
PRIMARY KEY( columnName )
);

Let’s utilise this syntax to make a table that contains information on employees for a business.

use mySchema;
CREATE TABLE employee(
empID int not null,
empName varchar(25) not null,
emailID varchar(25) not null,
PRIMARY KEY (empID)
);

ALTER TABLE

ALTER TABLE table_name
ADD column_name datatype;

Use this command to change the database’s structure—rather than its contents—for example, to add a new column to a table with a certain datatype.

DELETE

DELETE FROM table_name
WHERE some_condition = some_value;

Using the WHERE keyword, this command can delete data from your database based on certain criteria.

Delete is used to remove records entirely; it can be quite damaging if used in the wrong manner.

The delete command is used to completely delete records, but if it’s used improperly, it might cause significant damage.

DELETE FROM people WHERE name = ‘Mahesh’;

DROP

DROP TABLE table_name;

The DROP command deletes a database or table similarly to the create command. Use caution while executing this command because it will completely erase your table, including all of its data, indexes, and other elements.

ALTER TABLE table_name DROP COLUMN column_name;

The above ALTER TABLE and DROP instructions will remove a specific column from a table.

INSERT INTO

INSERT INTO table_name (column_1, column_2, column_3)
VALUES (value_1, value_2, value_3);

The INSERT INTO command can be used to add new records to your table. On one or more rows, you can use this command.

SELECT

SELECT column_name
FROM table_name;

SELECT is the first word in every query; it is used to retrieve data from your database. The most basic SQL query is that one. The keywords FROM, WHERE, and ORDER BY can be used after the SELECT statement to define a table, select with conditions, and sort your results, respectively.

UPDATE

UPDATE table_name
SET some_column = some_value
WHERE some_column = some_value;

With this command, you can modify the data in your table by changing it in accordance with the criteria listed behind the WHERE keyword.

AS

SELECT column_name AS ‘Alias’
FROM table_name;

When referring to a column or table, you can utilise a temporary alias by using the AS keyword.

COUNT

SELECT COUNT(column_name)
FROM table_name;

Add the total number of rows where the provided column is not NULL using the COUNT() method.

BETWEEN

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What Differs a Business Analyst from a Data Analyst

13Sep, 2022

Data Analyst vs. Business Analyst: What’s the Difference?

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Most people concur that the ultimate purpose of business and data analytics is to use technology and data to improve business performance. In a data-driven world where enterprises have access to an exponentially growing amount of information, the two tasks may even cooperate to increase efficiency, provide insightful data, and support the growth of businesses.

The differences between business and data analytics should be more clearly understood as a result of this side-by-side comparison.

Business analyst vs. data analyst: A comparison of roles

Both data analysts and business analysts use data. What they do with it is what makes a difference. Data is used by business analysts to make strategic business decisions. Data analysts acquire data, process it, extract information that is helpful, and distil their findings into easily understandable insights. Their final objective is data analysis.

Both roles call for candidates to have a passion for data, an analytical mindset, strong problem-solving abilities, and the capacity to see and work toward the greater picture. But it’s equally crucial to know how these two professional routes differ if you’re trying to choose between them.

While data analysts need strong business intelligence and data mining abilities as well as expertise with in-demand technologies like machine learning and AI, business analysts must be skilled in modelling and requirements gathering.

A strong foundation in business administration is a major asset for business analysts. Business management, business, IT, computer science, and other related subjects are the backgrounds of many business analysts. On the other hand, data analysts need to grasp intricate statistics, algorithms, and databases, so they would benefit from having a background in math or information technology.

An intro to business analytics

Business analytics (BA) is the iterative exploration of an organization’s data with the goal of revealing information that can help drive innovation and financial performance. Analytics-driven businesses view big data as a valuable corporate asset that fuels business planning and supports future strategies, and business analytics assists them in extracting the most value from this goldmine of insights.

Business analytics can be classified into three types: descriptive, predictive, and prescriptive. These are typically implemented in stages and, when combined, can answer or solve almost any question or problem that a company may have.

Descriptive analytics provides an answer to the question, “What happened?”

“This type of analytics examines historical data to gain insights into future planning.” Because self-service data access and discovery tools and dashboards are widely available, executives and non-technical professionals can benefit from big data insights to improve business performance.
The next step on the path to insight is predictive analytics. It employs machine learning and statistical techniques to assist businesses in predicting the likelihood of future events. However, because predictive analytics is probabilistic in nature, it cannot predict the future; it can only suggest the most likely outcome based on past events.
Prescriptive analytics investigates potential actions based on descriptive and predictive analysis results. This type of analytics combines mathematical models and business rules to optimise decision making by recommending multiple responses to various scenarios and tradeoffs.
Most commonly-used data analysis techniques have been automated to speed the analytical process. Thanks to the widespread availability of powerful analytics platforms, data analysts can sort through huge amounts of data in minutes or hours instead of days or weeks using:
- The majority of regularly used data analysis procedures have been automated in order to speed up the analytical process. Because powerful analytics tools are widely available, data analysts can go through massive amounts of data in minutes or hours rather than days or weeks using:
  Data mining is the process of searching through enormous data sets in order to uncover trends, patterns, and relationships.
  Predictive analytics collects and analyses historical data to assist firms in responding properly to future events such as customer behaviour and equipment problems.
  Machine learning: The use of statistical probability to educate computers to process data more quickly than traditional analytical modelling.
  Data mining, predictive analytics, and machine learning methods are used in big data analytics to translate data into business intelligence.
  Text mining is the detection of patterns and sentiments in papers, emails, and other text-based content.

Data Analyst VS Business Analyst

Data Analyst	Business Analyst
focuses on digging into and understanding vast datasets in order to find insights and trends.	Focuses on comprehending corporate processes and finding areas for development
Collects, cleans, and prepares data for analysis.	Assembles and records business requirements obtained from key players.
Utilizes statistical and data analysis techniques to identify trends and make data-driven recommendations.	Applies business analysis techniques to develop strategies and solutions for business challenges
Works primarily with quantitative data and employs tools like SQL, Python, or R for analysis	Works with qualitative and quantitative data, using tools like flowcharts and diagrams for process analysis.
locates data sources, performs data quality evaluations, and guarantees data accuracy.	performs gap analysis, confirms alignment with business goals, and validates the business needs.
develops reports and visualization’s to effectively convey findings.	creates documentation such as user stories and business requirement documents.
collaborates with stakeholders to grasp their analytical needs and provide practical information.	Collaborates with stakeholders to understand business goals and provide solutions for process improvement.
Develops predictive models and conducts forecasting to support strategic decision-making.	Facilitates requirements gathering sessions and leads discussions with stakeholders.
implements data-driven strategies to enhance productivity and business processes.	Implements business process improvements and supports change management initiatives
helps with data management and governance tasks to keep data integrity.	Assists in identifying and analyzing business risks and proposing risk mitigation strategies.
understand how to present data visually using tools like Tableau, Power BI, or matplotlib.	Proficient in business analysis tools like Microsoft Visio or Enterprise Architect for process mapping and modeling.
concentrates on the technical components of data analysis, such as data extraction, transformation, and loading (ETL)	focuses on comprehending and converting into technical specifications the functional needs of a system.
Monitors and tracks key performance indicators (KPIs) to measure the success of data-driven initiatives.	Assesses and monitors key performance indicators (KPIs) to measure the effectiveness of business processes.
Conducts exploratory data analysis to identify trends, outliers, and correlations within datasets.	Conducts business process analysis to identify bottlenecks, inefficiencies, and opportunities for improvement.
To ensure data accessibility and model quality, actively collaborate with data engineers and data scientists.	To guarantee that system requirements are satisfied and solutions are effectively deployed, collaborates with IT teams and stakeholders.
gives information on market trends, consumer behaviour, and product performance.	provides information on market opportunities, company strategy, and competition assessments.
uses A/B tests and data-driven experiments to put changes to the test.	supports process reengineering activities and business process improvement initiatives.
keeps abreast of the most recent technologies, techniques, and market trends in data analysis.	keeps up with the most recent frameworks, techniques, and best practises in business analysis
as well as good programming, statistical, and analytical abilities.	Can transition into roles like Product Manager, Project Manager, or Systems Analyst with additional training and experience.

Data Analyst 10 POINTS

1.Data Analysis:

ability to analyse massive datasets, spot trends, and draw out valuable insights to support data-driven decision-making.

2.Data Manipulation:

knowledgeable about the collection, cleaning, and conversion of raw data into formats that may be used, such as SQL, Python, or R.

3.Statistical Analysis:

knowledge of how to use statistical methods to help decision-making processes, including regression analysis and hypothesis testing.

4.Data Visualization:

ability to effectively convey findings to stakeholders by using technologies like Tableau, Power BI, or matplotlib to create visually appealing and educational data visualisations.

5.Data Quality Assurance:

able to assure data veracity, conduct data quality evaluations, and use data governance procedures to maintain data integrity.

6.Problem-Solving:

To identify difficult business problems, create hypotheses, and extract practical solutions based on data analysis, you need strong analytical and critical thinking skills.

7.Business Acumen:

Understanding of business goals and procedures makes it possible to translate data insights into actionable suggestions for process optimisation and strategic decision-making.

8.Programming Skills:

able to construct effective queries, automate data processes, and carry out sophisticated data analysis. Proficient in programming languages such as Python, SQL, or R.

9. Tools and Technologies:

the capacity to quickly learn new technologies and software, as well as familiarity with data analysis and visualisation tools like Excel, Tableau, Power BI, or Google Analytics.

10.Communication and Collaboration:

Strong interpersonal and communication abilities are necessary to work productively with cross-functional teams, convey findings to non-technical audiences, and succinctly explain difficult data concepts.

.

Business Analyst 10 POINTS

1.Business Requirements Gathering:

capable of conducting seminars, surveys, and interviews to elicit and document comprehensive business requirements from stakeholders.

2.Process Analysis:

capable of analysing and describing existing business processes, locating pain points, bottlenecks, and potential development areas.

3.Stakeholder Collaboration:

Strong ability to work together with stakeholders across departments to comprehend their wants, close communication gaps, and guarantee that provided solutions are aligned with business goals.

4.Business Process Modeling:

knowledge of how to develop process flowcharts, diagrams, and models that represent current and future business processes using software like Microsoft Visio or Enterprise Architect.

5. Requirement Documentation:

able to clearly and concisely document business requirements, user stories, use cases, and functional specifications.

6. Problem-Solving:

Strong analytical and problem-solving abilities to recognise business issues, analyse their causes, and put forth creative solutions that are in line with organisational objectives.

7.Change Management:

a working knowledge of change management principles and the capacity to influence organisational change by convincing stakeholders of the advantages and effects of suggested solutions.

8.Data Analysis:

Ability to work with data to find trends, correlations, and insights that help business decision-making. Basic understanding of data analysis concepts.

9.Business Case Development:

knowledgeable about creating business cases that detail the costs, advantages, and risks connected with suggested solutions, assisting stakeholders in making decisions.

10.Communication and Presentation:

Excellent communication and presentation skills to convey complex information, facilitate meetings, and deliver impactful presentations to both technical and non-technical audiences.

In summary

Data Analytics is about examining data to find patterns and insights.

Business Analytics takes those insights and applies them strategically to achieve specific business objectives.

Analogy:
Think of data analytics as a detective analyzing clues at a crime scene (data) to understand what happened. Business analytics is like the police chief using that information to make decisions on how to improve overall crime prevention strategies.

In essence, data analytics is about understanding the “what,” and business analytics is about understanding the “so what” and “now what” in a business context.

What is the primary focus of business analytics compared to data analytics?

Business analytics primarily focuses on applying data-driven insights to solve specific business problems and improve decision-making. Data analytics, on the other hand, is more centered on processing and analyzing data to uncover patterns, trends, and relationships.

How do business analytics and data analytics differ in terms of their objectives?

Business analytics aims to provide actionable recommendations for addressing business challenges, while data analytics aims to extract insights from data without necessarily prescribing specific actions.

What's the distinction between the problem-solving approach of business analytics and data analytics?

Business analytics often uses a problem-centric approach, focusing on answering specific business questions using data. Data analytics takes a more exploratory approach, seeking patterns and correlations in data that may not have a predefined question.

Which of the two, business analytics or data analytics, is more aligned with predicting future trends and outcomes?

While both can involve predictive modeling, data analytics tends to have a stronger emphasis on forecasting future trends and outcomes, as it seeks to uncover patterns that can lead to predictions.

How do business analytics and data analytics differ in terms of their role in decision-making?

Business analytics directly influences managerial decisions by providing actionable insights and recommendations for specific business problems. Data analytics provides insights that can inform decision-making but might not necessarily prescribe specific actions.

11Sep, 2022

Top 10 reason to Learn Power bi for Data Analysis

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Power BI is a business analytics tool that, in the words of Microsoft, “allows you to visualise your data, share insights across your organisation, or embed them in your app or website.”

One image can convey a thousand words. Your data are altered in this way by Power BI. Your data, one of your most precious raw materials, is transformed into rich graphics and statistics that are simple to understand.

Although there are many BI tools available, it would be difficult to find a superior choice. The offering from Microsoft is the best on the market and is affordable. These are the top ten reasons why Power BI is the business intelligence programme you need to pick:

Power BI additionally distinguishes itself through simplified publication and distribution features, as well as connects with other Microsoft products and services. Is it, however, the best option for your company?

Table of Contents

The Essentials of Power BI

The following qualities of Power BI contribute to its current level of market ubiquity:

Data selection: Power BI gives you the ability to filter datasets and produce pertinent little data chunks. You’ll be able to concentrate on specific datasets and carry out more insightful data analysis as a result.
Power BI relies on Navigation Trees and Dashboards that are incredibly responsive across all devices, including Android, iOS, etc. This enables you to view any Report or Graph in depth and without any errors.
Seamless Functionalities: Power BI provides you with straightforward drag-and-drop tools to build bespoke reports. In this way, you may simply examine and visualise your data even if you have little to no technical skills. The platform of Power BI also utilises Natural Language Queries. This protects you from investing time and money in learning a difficult query language.
Data connectivity is made possible by the abundance of connectors that Power BI has, which enable it to interface with many different external data sources. By doing so, you may conduct high-level data analytics and visualisation tasks by effortlessly connecting your favourite Data Sources to Power BI.

It’s the market leader

You merit the very best. And Microsoft Power BI is the best, easily the best. But don’t just believe what we say. Microsoft BI has held the top spot in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for 13 years running. Microsoft is frequently ranked by Gartner as the BI vendor you ought to use.

It has the lowest price in its class.

The prices of Power BI’s rivals are all at least three times higher. Tableau’s Explorer, for instance, starts at $35 per user per month. The Business licence for Qlik Sense costs $30 per user/month, whereas the Enterprise licence costs $40 per user/month. The base edition of Power BI Pro is even free, and the licencing for each user begins at $9.99 per month.

Power BI is easy to use

Excel, an application that is widely used and globally recognized, is the foundation of Power BI. This makes learning Power BI simple.

However, Power BI offers a very simple and user-friendly interface. Power BI may be used without any programming knowledge. It has intelligence built in that will assist you in choosing the finest characteristics for your reports.

Users may design stunning reports and dashboards with Power BI

Users may design stunning reports and dashboards with Power BI, which has a strong visual component.
One of the key factors contributing to Power BI’s appeal is its capacity to produce stunning dashboards and reports.

Users may engage with the interactive visuals in Power BI and discover new ways to use the data. Users’ unique demands may be catered for by customising the reports and dashboards.

The technologies, in my opinion, do not prevent analysts from producing attractive dashboards and reports.

Your data connection is simple.

Your data connection is simple.
You don’t need to be a data scientist to start connecting Power BI to the majority of the apps you already use because it comes with over 70 out-of-the-box connections. It’s true that Microsoft has made importing all of your data more easier.

It simplifies reporting.

It simplifies reporting.
The days of laboriously preparing PowerPoint slides or Excel documents at the end of each month are over. You may create a template for all the information you want, and it will be automatically exported in the required format. Packing board becomes simple. Additionally, you may combine all of your primary reports and KPIs into a single master dashboard. Then, anytime you need it, you can drill down to access granular-level data.

It has a lot of power.

It has a lot of power.
Power BI has access to Microsoft’s comprehensive machine learning and artificial intelligence engines through the Cortana Intelligence Suite because it is based on the Power Platform and Azure infrastructure of the software giant. Using cutting-edge analytics languages like Python and R inside the software, users may build machine learning models directly. There won’t be any issues with computing power either. Power BI Premium, for instance, can handle tables with more than 100 million records without breaking a sweat.

It is quite secure.

It is quite secure.
In terms of user and data security, Microsoft is the market leader. You shouldn’t have any worries about this as this is an enterprise product. Active Directory, which controls user access, contains all the security levels you’d anticipate and are accustomed to in Office 365. All of this guarantees that your data is secure, whether it is stored locally, on the cloud, or in a combination of both.

You've probably had a licence for it already.

You’ve probably had a licence for it already.
The professional edition of Power BI is already available to businesses with at least an E5 Microsoft Office 365 licence, despite the fact that Power BI may be licenced individually. So why spend money on extra licences for rival products when you undoubtedly already have access to Power BI?

Power BI has a huge online community,

The community is a fantastic resource for finding out more about Power BI and assistance with any issues you might have.

One of the largest and busiest communities is the Power BI community. There are numerous specialists prepared to assist you with any issues you may be experiencing.

Power BI has a tonne of debates going on, as you can see from the Power BI Community, and it seems to have a pretty wide community.

Power BI is available on many platforms

Power BI desktop is is available on Windows, but users can access the Power BI dashboards from various platforms (Windows, browsers, iOS, Android, etc.)

9Sep, 2022

5 Data Analytics Projects for Beginners

You probably already know the answer if you’re about to start a new profession as a data analyst. Job postings request experience, but how can someone who is applying for their first data analyst position get experience?

Your resume will be useful in this situation. The projects you include in your portfolio show recruiting managers and interviewers your abilities and expertise, even if it’s not from a previous data analytics employment. Even if you lack prior work experience, filling your portfolio with the appropriate projects will greatly increase your confidence that you are the suitable candidate for the position.

Ideas for data analysis projects

As a prospective data analyst, you should highlight a few crucial competencies in your portfolio. These project ideas for data analytics illustrate the duties that are frequently essential to many data analyst professions.

Table of Contents

1. Web scraping

Our daily lives are becoming ever more data-rich. This growth has made data analytics a crucial component of how businesses are conducted. Although data comes from a variety of sources, the internet is its largest repository. Companies require data analysts who can scrape the web in increasingly complex ways as the fields of big data analytics, artificial intelligence, and machine learning advance.

1. Web scraping – what is it?
Web scraping, also known as data scraping, is a method for gathering information and content from the internet. In order to be altered and examined as required, this data is typically saved in a local file. Web scraping is essentially what you would do on a very tiny scale if you had ever copied and pasted material from a website into an Excel spreadsheet.

But when people talk about “web scrapers,” they typically mean software programmes. Applications called “web scrapers” or “bots” are programmed to visit websites, grab the pertinent pages, and extract useful data. These bots extract enormous volumes of data extremely quickly by automating this procedure. This has clear advantages in the age of digital technology.

While there are many top-notch (and cost-free) public data sets available online, you might wish to demonstrate to potential employers that you can also locate and scrape your own data. Additionally, by learning how to scrape web data, you can locate and use data sets that are relevant to your interests, whether or not they have already been assembled.

If you are familiar with Python, you may search the web for relevant data using programmes like Beautiful Soup or Scrapy. Don’t worry if you don’t know how to code. Many of the solutions that automate the process, like Octoparse or ParseHub, provide a free trial.

Here are some websites with useful data possibilities if you don’t know where to start.

Reddit
Wikipedia
Job portals

Web scrapping for data analysis step by step

Web scraping for data analysis involves the process of collecting data from websites to perform various analytical tasks. Here are the steps to perform web scraping for data analysis:

Define Your Data Analysis Goals:

Clearly define what kind of data you need for your analysis and what insights you hope to gain from it.

Choose a Target Website:

Identify the website(s) from which you want to scrape data. Ensure that the website provides the data you need.
Check Website’s Terms of Service and Legality:

Review the website’s terms of service and legal restrictions to ensure compliance with their policies and any applicable laws.

Select a Web Scraping Tool or Library:

Choose a web scraping tool or library based on your programming skills and preferences. Popular options include Python with BeautifulSoup and Scrapy, or Node.js with Puppeteer.

Install Necessary Libraries:

Install the required libraries or packages for web scraping in your chosen programming language.

Inspect the Website:

Use your web browser’s developer tools to inspect the HTML structure of the web pages containing the data you want to analyze. This will help you understand the structure of the data.

Write Code to Fetch Web Pages:

Develop code to make HTTP requests to the target website and retrieve the HTML content of the pages you want to scrape.

Parse HTML Content:

Utilize your chosen library to parse the HTML content and extract the relevant data from the webpage.

Data Extraction and Cleaning:

Extract the data you need by identifying the HTML elements or attributes that contain it. Clean and preprocess the data as necessary, removing any irrelevant information and handling missing values.

Store Data for Analysis:

Save the extracted and cleaned data in a suitable format for analysis. Common formats include CSV, Excel, JSON, or a database.

Perform Data Analysis:

Import the collected data into your preferred data analysis tool (e.g., Python with pandas, R, or a data visualization tool like Tableau) to analyze and draw insights from the data.

Data Visualization:

Create visualizations (e.g., charts, graphs, plots) to help you understand the data and present your findings effectively.

Statistical Analysis and Modeling (if applicable):

If your analysis requires statistical analysis or modeling, perform these tasks to draw meaningful conclusions.

Interpret and Document Results:

Interpret the results of your analysis and document your findings, insights, and any actionable recommendations.

Repeat and Automate (if necessary):

If you need to regularly update and analyze data from the same website, consider automating the web scraping process using scheduled tasks or scripts.

Ethical Considerations:

Ensure that your web scraping activities are carried out ethically and legally, respecting the website’s terms of service and privacy considerations.
Monitoring and Maintenance:

Regularly check and update your scraping scripts to adapt to changes in the website’s structure or data format.

Data Privacy and Security:

Be cautious when handling sensitive or personal data, and ensure that you follow best practices for data privacy and security.

Backup Data:

Keep backup copies of the scraped data to avoid data loss in case of unexpected issues.

Share Your Findings:

If your analysis yields valuable insights, consider sharing your findings with others who can benefit from them.
Remember that web scraping should be conducted responsibly, respecting the website’s policies and the law. It’s essential to keep the purpose of your data analysis clear and ethical throughout the process.

2. Data cleaning

Data cleansing—also known as data cleaning, data scrubbing, or data rectification—is the process of correcting inaccurate, insufficient, duplicate, or other wrong data in a data set.

It entails locating data mistakes and then correcting them by modifying, updating, or eliminating data.
Data preparation work that prepares data sets for use in business intelligence (BI) and data science applications includes data cleansing, which is a crucial step in the overall data management process. Data quality analysts, engineers, and other data management experts are often the ones who carry it out. For their own applications, data scientists, BI analysts, and business users can also clean data or participate in the process.

Data cleaning enhances data quality and contributes to the provision of more precise, dependable, and consistent information for decision-making inside an organisation.

Cleaning data so that it is suitable for analysis is a big part of your job as a data analyst. The act of deleting inaccurate and duplicate data, handling any gaps in the data, and ensuring that the formatting of the data is consistent is known as data cleaning (also known as data scrubbing).

When looking for a data set to practise cleaning, aim for one that has a variety of files that were collected from various sources with little to no curation. These are several websites where you can access “dirty” data sets to work with:

CDC Wonder
Data.gov
World Bank
Data.world

/r/datasets

Data cleaning step by step guide for data analysis

Data cleaning is a critical step in the data analysis process to ensure that your dataset is accurate, consistent, and ready for analysis. Here’s a step-by-step guide on how to clean data for data analysis:

Understanding the Data:

Begin by thoroughly understanding the dataset you’re working with. This includes knowing the variables, data types, and the context in which the data was collected.

Data Profiling:

Perform initial data profiling to identify missing values, duplicate records, outliers, and any inconsistencies in the data. This step helps you get a sense of the data’s quality.

Handling Missing Data:

Identify missing data in your dataset and decide on an appropriate strategy for handling it. Common approaches include:

Removing rows with missing values.

Imputing missing values using methods like mean, median, or machine learning algorithms.
Using domain knowledge to fill missing data.

Handling Duplicates:

Detect and remove duplicate records from your dataset. Duplicates can skew your analysis results.

Data Type Conversion:

Ensure that data types are appropriate for each variable. Convert data types as needed to facilitate analysis.

Standardizing Data:

Standardize data by converting text to a consistent case (e.g., lowercase), removing leading/trailing spaces, and ensuring consistent date formats.

Handling Outliers:

Identify and decide how to handle outliers. Depending on the context, you can either remove outliers, transform the data, or treat them as special cases.

Data Validation:

Check for data integrity by validating values against expected ranges or patterns. Remove or correct data that doesn’t meet validation criteria.

Dealing with Inconsistent Data:

Address inconsistencies in categorical data by mapping synonymous categories to a common representation. For example, ‘Male’ and ‘M’ can be mapped to ‘Male.’

Feature Engineering:

Create new features or transform existing ones to make the data more suitable for analysis. This can include creating derived variables, aggregating data, or creating indicator variables.

Addressing Encoding Issues:

Handle character encoding issues that may lead to special characters or symbols appearing incorrectly.
Data Scaling and Normalization (if necessary):

Scale or normalize numerical data if it’s required for certain analysis techniques, like clustering or gradient descent in machine learning.

Handling Date and Time Data:

If your dataset contains date and time information, ensure it’s in a standardized format. Extract relevant information like day of the week, month, or year if needed.

Quality Assurance:

Perform checks to ensure that the data cleaning process hasn’t introduced new issues. Re-run data profiling and validation checks.

Documentation:

Document the data cleaning process, including the steps you’ve taken, decisions made, and any transformations applied. This documentation is crucial for reproducibility and collaboration.

Version Control:

If you’re working with a team, use version control (e.g., Git) to track changes to the dataset and analysis code.

Testing:

Test your cleaned dataset with preliminary analysis to ensure that it meets your goals and expectations.

Data Backup:

Keep a backup of your cleaned dataset, especially if you’re working with large datasets or making significant changes.

Iterate as Needed:

Data cleaning is often an iterative process. If you discover issues during analysis, go back to the cleaning stage and make necessary adjustments.

Final Data Export:

Once your data is cleaned to your satisfaction, export it in a format suitable for your analysis tool (e.g., CSV, Excel, database).
Data cleaning is a crucial step in the data analysis workflow, and the quality of your analysis is heavily dependent on the quality of your data. Careful and thorough data cleaning can lead to more accurate and meaningful insights from your data.

3.Exploratory data analysis (EDA)

There is no question that if your wolf pack decides to see a movie you haven’t heard of, it would leave you perplexed with many questions that need to be answered in order for you to make a decision. Being a good chieftain, your first query would be, “Who is the cast and crew of the movie?” As part of your routine, you would also watch the movie’s trailer on YouTube. In addition, you would research the audience’s ratings and reviews for the film.

Whatever research steps you would do before ultimately purchasing popcorn for your family at the theatre are nothing more than what data scientists refer to as “Exploratory Data Analysis” in their jargon.

Data analysis is all about using the data to answer questions. EDA, or exploratory data analysis, aids in the process of determining what questions to pose. This could be carried out independently of or alongside data cleaning. In either case, you must do the following tasks during these first inquiries.

Frequently enquire about the statistics.
Learn the data’s fundamental structure.
Analyze the data for trends, patterns, and abnormalities.
Validate assumptions regarding the data and test hypotheses.
Consider the problems that the data might help you solve.

Natural language processing (NLP) uses the technique of sentiment analysis to ascertain if textual input is neutral, positive, or negative. On the basis of a list of terms and their corresponding emotions, it can also be used to identify a specific emotion (known as a lexicon).

This kind of analysis works well with social media platforms and public review sites where people are likely to express public opinions on a range of topics.

Start with websites like: to learn more about how people feel about a particular issue.

Amazon (product reviews)
Red Tomato (movie reviews)
Facebook
Twitter
news websites

Step by step guide for Exploratory data analysis (EDA)

Exploratory Data Analysis (EDA) is an essential step in the data analysis process that involves examining and understanding your data before formal modeling or hypothesis testing. EDA helps you uncover patterns, relationships, and insights in your data. Here’s a step-by-step guide for performing EDA:

Load the Data:

Begin by loading your dataset into your chosen data analysis environment (e.g., Python with pandas, R, or a data visualization tool).

Understand the Data’s Structure:

Review the dataset’s structure by checking the number of rows and columns, data types, and column names.

Summary Statistics:

Calculate summary statistics for numerical variables (e.g., mean, median, standard deviation, quartiles) and categorical variables (e.g., counts, unique values, mode).

Data Visualization:

Create visualizations to get an initial sense of the data:
Histograms and density plots for numerical variables to understand their distributions.
Bar charts for categorical variables to visualize their frequency distributions.
Box plots to identify outliers and variations in numerical data.
Scatter plots to explore relationships between pairs of numerical variables.
Heatmaps to visualize correlations between variables.

Handle Missing Data:

Identify and address missing values in the dataset, either by imputing them or deciding on an appropriate strategy for handling them.

Data Distribution Analysis:

Examine the distribution of numerical variables for skewness and kurtosis. Consider transformations (e.g., log transformation) to make the data more symmetric if needed.

Outlier Detection and Treatment:

Identify and handle outliers in the data. Decide whether to remove them, transform the data, or treat them as special cases.

Feature Engineering:

Create new features or derive meaningful variables based on domain knowledge to enhance your analysis.

Explore Categorical Variables:

Analyze categorical variables by:
Visualizing their distributions using bar plots.
Examining relationships between categorical variables using contingency tables or chi-squared tests.
Checking for missing or inconsistent categories.

Bivariate Analysis:

Explore relationships between pairs of variables:
Use scatter plots for numerical vs. numerical relationships.
Use grouped bar plots or stacked bar plots for categorical vs. categorical relationships.
Use box plots or violin plots for numerical vs. categorical relationships.

Correlation Analysis:

Compute and visualize correlations between numerical variables using correlation matrices and heatmaps. Identify strong positive or negative correlations.

Time Series Analysis (if applicable):

If your data involves time series, explore patterns over time using line plots, seasonal decomposition, autocorrelation plots, and lag plots.

Hypothesis Testing (if relevant):

Perform statistical tests to test hypotheses or assumptions about the data, such as t-tests, ANOVA, or chi-squared tests.

Interactive Exploration (if available):

Utilize interactive visualization tools like Plotly or Tableau to create dynamic visualizations that allow for exploration at a deeper level.

Documentation:

Document your EDA process, including the visualizations you created, any data transformations, and key findings. This documentation is crucial for communicating your insights to others and for reproducibility.

Iteration:

EDA is often an iterative process. As you gain insights, you may need to revisit previous steps, refine your analysis, or explore new questions that arise.

Report and Presentation:

Prepare a report or presentation summarizing your EDA findings, insights, and any actionable recommendations. Visualizations and clear explanations should be included to convey your results effectively.
EDA is a creative and flexible process, and the specific steps you take may vary depending on your dataset and analysis goals. The primary objective is to gain a deep understanding of your data and generate hypotheses for further analysis or modeling.

4.Sentiment analysis

Sentiment analysis is contextual text mining that recognises and extracts subjective information from source material. It assists businesses in understanding the social sentiment of their brands, products, and services while keeping an eye on online discussions. However, simple sentiment analysis and count-based metrics are typically the only ones used in social media stream analysis. This is comparable to only scraping the surface and excluding those really valuable discoveries that are just waiting to be found. What then should a brand do to seize that easy-to-grab opportunity?

STEPS FOR SENTIMENT ANALYSIS PROCESS IN DATA ANALYTICS

Collect data:

The first step is to collect the data that you want to analyze. This data can come from a variety of sources, such as:

Social media:

Social media platforms like Twitter, Facebook, and Instagram are a great source of data for sentiment analysis. You can use social media APIs to collect data from these platforms.

Customer reviews:

Customer reviews on websites like Amazon and Yelp can also be a valuable source of data for sentiment analysis.

Surveys:

You can also collect data by conducting surveys with your customers or clients.
Text-based customer support tickets: Text-based customer support tickets can also be a good source of data for sentiment analysis.
Clean the data: Once you have collected your data, you need to clean it to ensure that it is in a format that can be easily analyzed. This may involve:

Removing punctuation and stop words.

Converting all text to lowercase.
Lemmatizing or stemming the words.
Removing any other irrelevant characters or data.

Identify the sentiment:

The next step is to identify the sentiment of the data. This can be done using a variety of methods, such as:

Machine learning:

Machine learning algorithms can be trained to identify the sentiment of text. This is a supervised learning task, so you will need to provide the machine learning algorithm with a labeled dataset of text and sentiment.

Lexicon-based approaches:

Lexicon-based approaches use a dictionary of words and their associated sentiment scores to identify the sentiment of text. This is an unsupervised learning task, so you do not need to provide the lexicon-based approach with a labeled dataset.
Analyze the sentiment: Once you have identified the sentiment of the data, you can start to analyze it. This may involve:

Looking at the most common sentiments.

Identifying trends over time.
Comparing the sentiment between different groups, such as different product categories or different customer segments.
Interpret the results: The final step is to interpret the results of your analysis. This may involve:
Drawing conclusions about the data.
Making recommendations based on the data.
Developing new hypotheses based on the data.
It is important to note that sentiment analysis is not a perfect science. There are a number of factors that can affect the accuracy of sentiment analysis, such as the quality of the data, the method used to identify sentiment, and the context in which the text was written.

However, sentiment analysis can be a valuable tool for businesses and organizations of all sizes. It can be used to track customer satisfaction, identify trends, and make better decisions.

Here are some examples of how sentiment analysis can be used in data analytics:

A company can use sentiment analysis to track customer satisfaction with its products or services. This information can then be used to improve the products or services, or to develop new marketing campaigns.
A political campaign can use sentiment analysis to track public opinion on various issues. This information can then be used to develop campaign messages and strategies.
A financial institution can use sentiment analysis to track investor sentiment towards different stocks or sectors. This information can then be used to make investment decisions.
Sentiment analysis is a powerful tool that can be used to gain insights from text data. By following the steps outlined above, you can use sentiment analysis to improve your data analytics and make better decisions.

5. Displaying data

People are visual beings. As a result, data visualisation is an effective tool for turning facts into an engaging narrative that motivates action. In addition to being enjoyable to produce, excellent visualisations may dramatically improve the appearance of your portfolio.

29Aug, 2022

Some important questions about the while loop in Python to practice

While loop execute a set a statement as long as test statement is true.
A relevant variable has to be declared at the beginning of the loop

Until a specified condition is met, a block of statements will be periodically executed in a Python while loop. And the line in the programme that follows the loop is run when the condition changes to false.

The while loop is an example of an infinite loop. When a loop is iterated indefinitely, the number of times the loop is run isn’t explicitly stated beforehand.

Example 1: Python While Loop

# while loop
count = 0
while (count < 3):
count = count + 1
print(“Vista Academy Data Science Institute”)

output

Vista Academy Data Science Institute

Vista Academy Data Science Institute

Write a program to print table of a number entered from the user.

a=int(input(“enter number”))
i=1
while i<10:
print(a,”*”,i,”=”,a*i)
i=i+1

Create a programme that uses a while loop to determine whether an integer is prime or not.

num1 = int(input(“Enter any number : “))
k=0
if num1 == 0 or num1 == 1:
print(“Not a prime number “)
else:
i = 2
while(i<num1):
if num1 % i == 0:
k = k+1
i = i+1
if k == 0 :
print( num1,”is prime number”)
else:
print(num1, “is not prime number”)
output
Enter any number : 67
67 is prime number

WHO IS IN NEED OF DATA ANALYSIS?

1. Descriptive Analytics

What happened is answered by descriptive analytics.

Data Aggregation:

Summary Statistics:

Data Visualization:

Data Exploration (EDA):

Data Profiling:

Segmentation:

2. Diagnostic Analytics

Root Cause Analysis:

Comparative Analysis:

Hypothesis Testing:

Drill-Down Analysis:

Cohort Analysis:

Data Mining and Machine Learning:

3. Predictive Analytics

4. Prescriptive Analytics

What is SQL?

High Paying Jobs

Ease of Learning and Use

Declarative Language:

English-like Syntax:

Interactive Environment:

Small Set of Commands:

Abstraction from Database Complexity:

Rich Online Resources:

Code Reusability:

Instant Gratification:

Visual Query Builders:

Community Support:

How to learn sql step by step from scratch

1. Understand the Basics:

a. Introduction to Databases:

b. Relational Databases:

c. Basic SQL Concepts:

2. Set Up a Database:

3. Learn SQL Commands

4. Understand Joins:

5. Advanced SQL Concepts:

6. Practice:

7. Online Resources and Courses:

8. Join SQL Communities:

9. Build Projects:

What is data cleaning

Python Data Cleaning

Importing Libraries

Enter the Dataset of Customer Feedback

Locate Missing Data

Check for Duplicates

Detect Outliers

SQL Command

Data Definition Language (DDL)

what are the DDL commands?

Create

CREATE TABLE employees:

(id INT PRIMARY KEY,):

(name VARCHAR(100),):

(salary DECIMAL(10, 2)):

Alter

Drop

Truncate

COMMENT:

DQL Command

Insert Data

Step 3: Retrieve All Data

Step 4: Retrieve Specific Columns

Step 5: Filter Data

DML COMMAND

UPDATE: Modify existing data.

DELETE: Remove rows from the table.

INSERT INTO: Add new rows to the table.

UPDATE Multiple Rows: You can update multiple rows at once.

DELETE with Condition: Delete rows based on a condition.

INSERT Multiple Rows: Insert multiple rows at once.

Data Control Language (DCL)

GRANT

REVOKE: Remove privileges from users or roles.

Transaction Control Language (TCL)