28Nov, 2022

Beginner’s Guide to DAX Functions in Power BI DAX

Dax Introduction

The formula expression language DAX (Data Analysis Expressions) is applicable to a variety of BI and visualisation tools. Because the entire code is contained within a function, DAX is also known as a function language. There are two data types in DAX programming formulas: numeric and other. While Other includes string and binary object, Numeric includes integers, money, and decimals.

What is DAX?

The acronym DAX stands for Data Analysis Expressions. Microsoft created this language to interact with data in a number of their platforms, including Power BI, PowerPivot, and SSAS tabular models. It is intended to be straightforward and simple to learn while demonstrating the strength and adaptability of tabular models. You might liken it to Excel formulas taken to the next level.

Using DAX will truly unleash the capabilities of Power BI.

I have reached a point where I use a lot of the code in my daily work even though I am still far from a DAX expert and have coworkers who are beginning to ask more and more questions about it. I decided to write this article about why you should (or shouldn’t) use this tool from the data science/data analysis toolbox as a result.

Essential Qualities of DAX Functions

Measures:

To perform dynamic computations, DAX Functions contain formulas referred to as Measures. You can define your Measures using the DAX formula bar, and these Measures have the property of changing their values based on the situation. Additionally, measures give you the freedom to switch between tables without losing any functionality.

Row-level Security:

The row-level security of your data tables is always maintained by DAX Functions. A DAX evaluation always yields a boolean result in order to guarantee this (True or False). This limits the number of rows that can be set at once and prevents other users from accessing the assigned row. Additionally, DAX functions employ filters in order to maintain the relationship that is currently in use.

Calculated Columns:

The DAX library provides calculated columns for storing the results of computations you do on data. These procedures are typically performed during a data refresh and include two or more columns. Additionally, these Calculated Columns are made to carry out row-level calculations and then create a new column that contains the results.

DAX Functions

You can use several function types in Power BI to analyse data and add new columns and metrics. It consists of operations from several categories, including

Aggregate
Text
Date
Logical
Counting
Information

Aggregate Functions

DAX has a number of aggregate functions.

MIN
MAX
Average
SUM
SUMX

Counting Functions

Other counting functions in DAX include −

DISTINCTCOUNT
COUNT
COUNTA
COUNTROWS
COUNTBLANK
Logical Functions

Following are the collection of Logical functions −

AND
OR
NOT
IF
IFERROR

TEXT Functions

REPLACE
SEARCH
UPPER
FIXED
CONCATENATE
DATE Functions
DATE
HOUR
WEEKDAY
NOW
EOMONTH
INFORMATION Functions
ISBLANK
ISNUMBER
ISTEXT
ISNONTEXT
ISERROR

DAX Functions for Power BI: Date and Time Functions

DATE: The pre-specified date is returned by this function as output in datetime format. It works using the syntax shown below:
DATE(<year>, <month>, <day>)

DATEVALUE: This function transforms a date’s textual representation into a datetime format. It works using the syntax shown below:

DATEVALUE(date text)
TODAY: The following syntax is used when this function is in operation and returns the current date as output:
TODAY()

WEEKDAY: Using a date as its argument, this function produces a number (1–7) that represents the day of the week for that date. It works using the syntax shown below:
WEEKDAY(<date>, <return_type>)

HOUR: This function provides a number that represents the current hour ( 0 for 12:00 A.M. and 23 for 11:00 P.M.). It works using the syntax shown below:

HOUR(<datetime>)

Information Functions for DAX in Power BI

The output of DAX Information functions is either True or False after analysis of the input argument. This function class takes one or more cells or rows from your Power BI table as parameters. According to DAX, the following operations are classified as informational:

CONTAINS:

Depending on the values for the referred columns, this function either returns true or false. It returns true if all of the values are present; otherwise, it returns false. It works using the syntax shown below:

CONTAINS(<table>, <columnName>, <value>[, <columnName>, <value>]…)

CUSTOMDATA:

This function retrieves the value of the CustomData property from the connection string after reading it.

It uses the following procedures to function:

CUSTOMDATA()

LOOKUPVALUE:

This function locates the row that satisfies the search columnName and search value criteria in their entirety and returns the value from the result columnName. It works using the syntax shown below:

LOOKUPVALUE( <result_columnName>, <search_columnName>, <search_value>[, <search_columnName>, <search_value>]…)

USERNAME:

During connection setup, you provided the system with your domain and user name, which are returned by this function. It works using the syntax shown below:
USERNAME()

ISBLANK:

After examining the input value, this function returns true for blank and false for any other value. It works using the syntax shown below:
ISBLANK(<value>)

The other important functions under Information Functions are as follows:

ISERROR
ISEVEN
ISLOGICAL
ISNUMBER
SODDING
ISTEXT

DAX Functions for Power BI: Logical Functions

DAX Logical Functions evaluate a certain input expression containing logical operators and provide a True or False output. The Logical functions work on evaluating a logical expression and are different from the Information functions which test the input argument by matching it to the stored data. The following functions fall under the Logical classification in DAX:

AND:

This function returns true if both arguments present in the input expression are valid, else it returns false. It operates using the following syntax: AND(<logical1>,<logical2>)

NOT: The value of the input expression is changed by this function to its polar opposite. This suggests that it can switch between being true and false. It works with the syntax shown below:
NOT(<logical>)

OR: If any of the arguments present in the input expression are valid, this function returns true; otherwise, it returns false. It works with the syntax shown below:
OR(<logical1>,<logical2>)

SWITCH: Using a list of values and an input expression, this function compares the results and returns one of the possible results. It works using the syntax shown below:
SWITCH(“expression”, “value,” “result,” “value,” result,”…”, “else,”)

IF: If the first argument is true, this function checks the condition and, based on that, returns a true or false condition. It works using the syntax shown below:

If (logical test), “value if true,” ,”value if false,”

The following are some additional crucial logical functions:

TRUE
FALSE
IFERROR
IN

Trigonometric and mathematical DAX functions for Power BI

You may easily carry out fundamental mathematical operations using the DAX mathematical and trigonometric functions, which operate similarly to their Excel equivalents. The DAX category “Mathematical & Trigonometric” includes the following functions:

ABS :The following syntax is used by the ABS function to return the input number’s absolute value:

ABS(<number>)

ACOS :The inverse cosine value of a given input integer is returned by the ACOS function. The angle it returns is measured in radians, from 0 to pi. It works using the syntax shown below:

ACOS(number)

CEILING: This function returns the nearest roundup value (integer) of the given input using the following syntax:

CEILING(“number,” “importance”)

COMBIN:This function gives the total number of combinations that can be made from the input items. It works using the syntax shown below:

COMBIN(number, number_chosen)

CURRENCY: This function analyses the input and outputs the result as a data type for currency. It works using the syntax shown below:

CURRENCY(<value>)

The other important functions under Mathematical & Trigonometric are as follows:

ASIN
ATAN
COS
DEGREES
DIVIDE
EVEN
EXP
FACT

Power BI DAX Functions: Parent & Child Functions

You can manage data that is restricted in a parent-child hierarchy using the DAX Parent and Child functions. The Parent & Child category in DAX includes the following functions:

PATH: This function gives back a delimited text string containing all of the current identifiers’ parent identifiers. It works using the syntax shown below:
PATH(‘ID’ column, ‘parent’ column)

PATHITEM: This function extracts the item from a string at the specified position after reading the PATH function. It works using the syntax shown below:
PATHITEM(“path,” “position,” “type,”
The following are some additional crucial responsibilities under Parent & Child:

PATHITEMREVERSE
PATHLENGTH
PATHCONTAINS

DAX Functions for Power BI: Statistical Functions

Averages, approximations, and many other statistical formula operations are supported by DAX statistical functions. The statistical functions listed below are classified as such in DAX:

The ADDCOLUMNS function adds calculated columns to the table that is supplied. The syntax it uses is ADDCOLUMNS(table>, name>, expression>[, name>, expression>]…)

AVERAGE: Using the following syntax, this function determines and returns the arithmetic mean of all the values in a column of input data:
AVERAGE(<column>)

BETA.INV: This function returns the beta cumulative probability density function’s inverse value. It works using the syntax shown below:
BETA.INV(probability,alpha,beta,[A],[B])

The other important functions under Statistical are as follows:

CONFIDENCE.
ORM
COUNT
COUNTBLANK
COUNTROWS
CROSSJOIN
DISTINCTCOUNT
EXPON.DIST
GENERATE
GENERATEALL
GEOMEA
GEOMEANX
MAX

DAX Functions for Power BI: Text Functions

You can use the String data type while working with tables and columns using DAX Text methods. With the help of these functions, you may alter different aspects of a string, search for specific text inside strings, manage date formats, and much more. The Text category in DAX includes the following functions:

The syntax for this function, which just returns a blank, is as follows:
BLANK()
The first character in the input text string corresponds to the numeric value (code) that this function returns. The syntax for IT is as follows:

CODE(text)

CONCATENATE: Using the following syntax, this function joins two input text strings into one text string.
CONCATENATE(“text1” “text2”);

EXACT: This function compares two text strings and returns a true value if they are precisely the same. It works using the syntax shown below:

EXACT(<text1>,<text2>)

Find:A text string’s beginning within another input text string is returned by the FIND function. It works using the syntax shown below:
FIND(“find text,” “inside,” “start num,”
[, NotFindValue]])

The other important functions under Text are as follows:

COMBINEVALUES
FORMAT
LEFT
MID
REPLACE
RIGHT
SEARCH
SUBSTITUTE
TRIM
UNICHAR

DAX Functions for Power BI: Other Functions

DAX Functions for Power BI: Other Functions
This category contains DAX Functions that can not be grouped in a specific class due to their unique characteristic properties. The following functions fall under the Other classification in DAX:

DATATABLE
ERROR
EXCEPT
GENERATESERIES
GROUPBY
INTERSECT
ISEMPTY
ISSELECTEDME
SURE
NATURALINNE
JOIN
NATURALLEFT
UTERJOIN
SELECTEDMEASURE
FORMATSTRING
SUMMARIZECOLUMNS
UNION
WAS

Benefits of DAX Functions

By itself, Power BI offers you amazing functionality. However, adding DAX Functions to Power BI will further your efforts and give you the advantages listed below:

Many cutting-edge Power BI features can be used thanks to the DAX library. For instance, you can use DAX’s select, join, filter, and many other commands to dynamically change your data. The Power BI dashboard will then automatically generate data, including Calculated Columns, Tables, and Measures, based on the input that users provide.
You may add intelligence to your Power BI Dashboards by using DAX. Utilizing Power BI’s Calculated Columns and Measures to limit the data that your Dashboard will retrieve and visualise makes this possible. Your latency and complete data can both be increased by a well-written DAX expression.

21Nov, 2022

Step by Step complete A Complete Guide Power Query in Excel

Common data problems are simple to solve using Power Query. Most of our valuable time is frequently consumed by tedious manual procedures like cut and paste labour, column merging, and filtering. These operations are greatly simplified with the Power Query tool.

Describe Power Query.

An application for preparing and transforming data is called Power Query. With Power Query, you may perform transformations to data obtained from sources using a Power Query Editor and a graphical user interface. You may import data from a variety of sources, clean it up, convert it, and then reshape it to suit your needs using Power Query, a business intelligence tool provided by Microsoft Excel. This allows you to create a query just once and reuse it at a later time by simply refreshing.

Power Query, as its name suggests, is Excel 2010 and later’s most potent data automation tool. Data may be imported into Excel using Power Query from a variety of external sources, including Excel workbooks, CSV files, text files, and the web, to name a few.

Power Query includes a number of helpful capabilities, including the ability to append data and establish connections across various data sources. The joining of the data sets is what this is known as. The tool also allows us to group and condense data. Without a doubt, it is a very helpful tool.

How Can Power Query Be Enabled?

You may get Power Query for free as an add-in for Excel 2010 and 2013 from Microsoft’s website. You can access the URL here.

When you click the Download button, a dialogue box where you may select the best download option for your OS opens. Your machine will then download Power Query.

Starting with Excel 2016, it is a built-in tool that can be found in the Data Tab’s Get & Transforms Data Section.

Let’s continue and comprehend the Power Query idea.

What Can You Do With Power Query?

Get Data

Data can be imported from a variety of sources.

Excel files (Workbooks), text or CSV files, XML files, and JSON files can all be read from files.
SQL Server, Microsoft Access, and SQL Server Analysis Services are databases.

Excel Tables/Ranges, the Web, Microsoft Query, and OData feeds are some additional sources.

Transform Data

With the use of Power Query, we may change the data after importing it. You can modify data according to your needs with the help of the Power Query Editor.Let’s examine the editor and learn about its various parts.

The Power Query Editor has the following six key sections:

Ribbon for the Query Editor: This ribbon resembles the one found on the Excel user interface. Separate tabs are used to group different commands.
You can look through a list of all the queries in your current workbook by using the Query List section.
Formula Bar: The M language formula for the current transformation will be given here.
Data Preview: Based on the current transformation phase, you can view a preview of your data. By selecting the appropriate column header’s filter option or by right-clicking the column header, you can access a number of transformation commands.
Properties: A list of query stages is included in this section. You can name your inquiry right here. Naming a question is a crucial step in making it simple to recognise it.
Adopted actions: Here, we’ll keep track of every transformational step you make in reverse-chronological sequence. If necessary, you can alter, add, remove, or reorder the steps.

Follow the steps below to learn how to sort a table based on a single column.

First, load the data onto the Editor.
Then, select the column you want to sort.
Click on the filter icon, as shown in the image.

Output to Excel

We must output the editor’s operations to our Excel sheet after finishing them all. To accomplish this, select Close and Load from the Power Query Editor’s Ribbon menu.

Different Ways to Import Data to the Excel Sheet

Listed below is a detailed tutorial of importing data from various data sources.

1. Importing Data from a Text File

To import a text file using Power Query, follow these steps:

Click Text/CSV File under the Data tab.
After choosing “Text/CSV file,” a new “Import data” dialogue box appears.

Click Import after choosing the text file you want to import.
When a dialogue box is opened, a preview of the data inside is displayed.
To import the data, click Load at the very end.

Importing Data From a CSV File

You can use Power Query to import from CSV files by following the steps below:

Click on the Data tab –> Text/CSV File.
Once we have selected the “Text/CSV file” option, an “Import data” dialog box is opened.
Select the desired CSV file and click on import.
A dialog box is opened, which shows a preview of the data contained.
Finally, click on Load to import the data.

Importing a Multiple Data Source From an Excel Workbook

You can import various data sources from the Excel workbook by following these steps:

Select the Get data command after clicking the Data tab. When you click it, a drop-down menu appears. We can import our data using a variety of options from the drop-down menu. We choose “From File” and then “From Workbook” to import data from an Excel workbook.
Excel displays a dialogue box to aid in selecting and navigating the workbook.

Once we have found the worksheet, we can click on it and choose “Open” from the menu.
The navigation dialogue box appears as a result. You can access a number of data sources from the navigation dialogue box.

UPPERCASE

Step 1: Fill the Power Query Editor with the necessary data. To achieve this, choose the appropriate data source from the Excel Data tab’s Get & Transform Data section. This will launch the Editor, which enables data editing.

Step 2: After clicking the column name, select the “Transform” tab to get a range of possibilities. When you select the option to format text, a drop-down menu with the option to alter text in “UPPERCASE” will appear.

Finally, on selecting the UPPERCASE edit option, all the text in the given column will be converted to uppercase.

LOWERCASE

Step 1: Fill the Power Query Editor with the necessary data. Choose the appropriate data source from the Get & Transform Data section of the Excel data tab to accomplish this. This will launch the Editor, which enables data editing.

Step 2: Click the name of the column, then select the “Transform” tab to see a range of options. When you select the text formatting option, a drop-down menu with the text editing choice “LOWERCASE” will appear.

Trim

Step 1: Fill the Power Query Editor with the necessary data. Choose the appropriate data source from the Get & Transform Data section of the Excel data tab to accomplish this. The Power Query Editor will then be displayed, allowing us to alter the data.

Step 2: Click on the column name and then choose the “Transform” tab, which will present you with a number of options, to eliminate all extra white spaces from the data. When you select the “Format” option, a drop-down menu with the “Trim” text editing option will appear.

Finally, on selecting the Trim edit option, all the extra white spaces in the given column will be removed.

Splitting a Column Using Delimiters

Step 1: Fill the Power Query Editor with the necessary data. Choose the appropriate data source from the Get & Transform Data section of the Excel data tab to accomplish this. This will launch the Editor, which enables data editing.

Step 2: Select the “Transform” tab, then the “Split column” option, to divide the column using a delimiter from the data. A drop-down menu with the option to divide the data by delimiter will appear as a result.

Complete step by step guide to do Power Query in Excel

10Nov, 2022

What Is the Distinction Between Data Science and Artificial Intelligence?

What is Data Science in Hindi

Describe data science.

Today, data science is a dominant field in the IT sector that has dominated practically all industries. It is a vast area that mostly focuses on data systems and processes, and it works with these datasets to extract useful information from them. In this field, data serves as the fuel for gaining practical and insightful knowledge about businesses and spotting emerging market trends.

You now understand the fundamentals of data science. However, I am sure you are curious about the various steps involved in this technology. These steps include data extraction, manipulation, maintenance, and visualisation, which allow you to forecast future events.After learning about Data Science, you may be wondering, “What is the need to study and understand data?”

With data, Facebook is revolutionising social media.

Today, Facebook is the dominant social media platform. With millions of users signed up from all over the world, the platform conducts extensive research using Data Science to extract information about its users’ social interactions. It employs advanced Data Science techniques to comprehend user behaviour and, as a result, improve their product or application. This is just one of many possible applications for this technology. As a result, you can conclude that Data Science is one of the most widely used fields today.

What exactly is artificial intelligence (AI)?

In contrast to Data Science, Artificial Intelligence (AI) is the intelligence that machines can possess. It is inspired by natural human intelligence. You can impose and simulate human intelligence in machines using AI. This technology employs a number of algorithms to aid in autonomous action.

Traditional AI algorithms were given a set of objectives to achieve in order to grow. However, more recent and popular algorithms, such as Machine Learning and Deep Learning, enable you to understand the trends and patterns in the given data and, as a result, determine the data’s purpose.

Learning, reasoning, and self-correction are the three main steps in Artificial Intelligence. Furthermore, AI frameworks can be used for deep neural network computations

To gain a better understanding of this technology, consider a real-world application of AI. Let us now look at the application and implementation of AI in the field of personal security.

AI Applications in Personal Security Systems

Have you recently participated in a large public event or flown? You must have noticed the long lines for security checks if you did. This must have been inconvenient and frustrating for you at times, and with a pandemic like Covid-19 sweeping the globe, it can be frightening. False alarms can be eliminated using Machine Learning, a subset of AI. Furthermore, these machines can detect things that human scanners miss during the screening process in airports, concerts, and stadiums, among other places. This can help to speed up security processes and save a lot of time for security officers as well as passengers and event attendees.

Data Science and Artificial Intelligence (AI

scope and Purpose:

Data Science:

Data science is a multidisciplinary field that primarily deals with extracting insights and knowledge from data. It encompasses various techniques for data collection, cleaning, analysis, and visualization. Data scientists use statistical methods and machine learning to uncover patterns, trends, and correlations in data. The primary goal is to make data-driven decisions and solve real-world problems using data.

Artificial Intelligence (AI):

AI is a broader field that focuses on creating intelligent systems that can perform tasks typically requiring human intelligence, such as understanding natural language, recognizing patterns, making decisions, and solving problems. AI includes various subfields like machine learning, natural language processing, computer vision, and robotics. The primary goal of AI is to develop systems that can simulate human-like intelligence and reasoning.

Methods and Techniques:

Data Science:

Data scientists employ a wide range of techniques, including statistical analysis, data mining, data visualization, and machine learning. Machine learning is a subset of data science and plays a significant role in predictive modeling and pattern recognition.

Artificial Intelligence:

AI relies heavily on machine learning, deep learning (a subset of machine learning), and neural networks to develop models that can learn from data and make predictions or decisions. While AI encompasses other techniques, machine learning is a core component used to build AI models.

Applications:

Data Science:

Data science is used in a wide array of applications, including business analytics, marketing optimization, fraud detection, healthcare analytics, and more. It focuses on extracting insights and generating predictions from data to inform decision-making.

Artificial Intelligence:

AI applications include autonomous vehicles, chatbots, virtual assistants, recommendation systems, image recognition, language translation, and autonomous robotics. AI systems are designed to perform tasks with varying degrees of autonomy and human-like intelligence.

Data vs. Intelligence:

Data Science:

Data science is primarily concerned with working with data, both structured and unstructured, to extract valuable information and insights. It deals with the entire data pipeline from data collection to analysis and visualization.

Artificial Intelligence:

AI focuses on creating systems that can use data to make intelligent decisions or perform tasks that typically require human intelligence. While data is a crucial component of AI, it is just one element in the broader pursuit of achieving machine intelligence.

In summary, data science is a subset of AI, with a specific focus on data analysis and insights extraction, while AI encompasses a broader spectrum of intelligent system development. Both fields are interconnected and often work together, with data science providing the foundation for AI models and AI enabling data-driven decision-making through advanced automation and intelligence.

What Is the Distinction Between Data Science and Artificial Intelligence?

Parameters	Data science	Artificial Intelligence
Basics	Data Science is a comprehensive process that includes pre-processing analysis, visualisation, and prediction.	Artificial intelligence aids in the implementation of data and machine knowledge.
Skill	For development and design, you must employ statistical techniques.	You must use algorithms for development and design
Techniques	Data Science makes use of the Data Analytics technique	Deep Learning and Machine Learning techniques are used in AI.
Build	By using the concept of data science, we can build complex models about statistics and facts about data.	By using this we emulate cognition and human understanding to a certain level.
Technique	It uses the technique of data analysis and data analytics.	It uses a lot of machine learning techniques.
Use	Data science makes use of graphical representation.	Artificial intelligence makes use of algorithms and network node representation.
Knowledge	ts knowledge was established to find hidden patterns and trends in the data.	its knowledge is all about imparting some autonomy to a data model.
Examples of Tools	R, Python, etc. are the tools used in data science.	Tensor flow, sci-kit-learn, Kaffee, etc are the tools used in AI.
models	In Data Science, models are created to generate statistical insights for decision-making.	AI uses Deep Learning and Machine Learning techniques
observation	It looks for patterns in data to make well-informed decisions	It instils intelligence in machines through the use of data, causing them to respond in the same way that humans do.
Examples	Process optimization, Customer trends, and financial analysis are some examples.	Robots, Chatbots, online gaming, and voice assistants are some examples
appllication	Applications of Data Science are dominantly used in Internet search engines, such as Yahoo, Bing, Google, etc.	Applications of Data Science are dominantly used in Internet search engines, such as Yahoo, Bing, Google, etc.

choosing between a career in data science and analytics

1. Take into account your background.

To assist firms make more strategic decisions, data analysts analyse enormous data sets to find trends, build charts, and produce visual presentations. Analysts generally seek undergraduate STEM degrees, and perhaps even graduate degrees in analytics or a similar discipline, to ensure that their education is in line with these objectives. Additionally, they look for candidates with background in science, math, programming, databases, modelling, and predictive analytics.

Data scientists need to have “more of a foundation in computer science,” he adds, adding that they are “far more technical and mathematical [than data analysts].”

It’s crucial to study these educational prerequisites while deciding which job route is best for you. You will probably have the educational and professional background to pursue either path if you have already decided to invest in your career with an advanced degree. On the other hand, since employers are more likely to consider candidates without a master’s degree for these positions, you might be more inclined to stick with a data analytics role if you’re still debating whether returning to school is the right choice for you.

Consider your interests.

Data scientists need to have “more of a foundation in computer science,” he adds, adding that they are “far more technical and mathematical [than data analysts].”

It’s crucial to study these educational prerequisites while deciding which job route is best for you. You will probably have the educational and professional credentials to pursue either option if you have previously decided to invest in your career with an advanced degree. On the other hand, since companies are more likely to consider people without a master’s degree for these positions, you could be more motivated to continue with a data analytics role if you’re still debating whether returning to school is the correct choice for you.

3. Take into account the salary and career path you want.

Earning potential for data analysts is from $83,750 to $142,500. However, because these professionals mostly work with databases, they can raise their wages by learning new programming languages like R and Python.

Data analysts with more than ten years of experience, however, frequently take advantage of their earning potential and move on to other positions, according to PayScale. Following the completion of an advanced degree, two common career changes, according to Blake Angove, director of technology services at IT staffing firm LaSalle Network, include moving into a developer or data scientist position.

Data scientists are regarded as being more senior than data analysts because they frequently possess advanced skills, a doctorate degree, and more experience. As a result, they frequently receive more pay for their services. RHT estimates that the average annual salary for data scientists is between $105,750 and $180,250.

What data profession is best for you?

Given the considerable variances in function responsibilities, educational requirements, and career paths, data analysts and data scientists have job titles that are misleadingly identical.

Regardless of your perspective, Schedlbauer argues that qualified candidates for data-focused occupations are in high demand on the job market right now because of how strongly firms need to make sense of—and profit from—their data.

You can choose the career that is the best fit for you and get started on your path to success once you have taken into account factors like your background, interests, and desired salary.

What are the primary goals of data science and artificial intelligence?

Data science primarily aims to extract insights and knowledge from data to solve specific problems and make informed decisions. In contrast, artificial intelligence focuses on creating intelligent systems that can mimic human-like reasoning, learning, and decision-making.

What techniques and methods are commonly used in data science?

Data science involves techniques such as data collection, data cleaning, data visualization, statistical analysis, and machine learning. Machine learning is often used within data science to build predictive models.

What techniques and methods are commonly used in artificial intelligence?

Artificial intelligence relies heavily on machine learning, deep learning, neural networks, reinforcement learning, and natural language processing (NLP). These techniques enable AI systems to learn from data and perform intelligent tasks.

How does the scope of data science differ from that of artificial intelligence?

Data science has a broader scope and can be applied to various domains, while artificial intelligence often focuses on specific tasks, such as image recognition, speech recognition, and autonomous driving.

What is the role of data in data science and artificial intelligence?

In data science, data is the central focus, and the emphasis is on extracting insights and information from data. In artificial intelligence, data is used to train models and enable systems to make intelligent decisions.

Are data science and artificial intelligence interdisciplinary fields?

Yes, both fields draw from various disciplines. Data science incorporates elements of statistics, computer science, domain expertise, and data engineering. Artificial intelligence is a subfield of computer science and often requires specialized knowledge in machine learning, neural networks, and cognitive science.

Can data science be considered a component of artificial intelligence?

Yes, data science is often a component of artificial intelligence. AI systems rely on data and machine learning techniques, which are key elements of data science, to achieve their intelligent behavior.

What are some practical applications of data science?

Data science finds applications in various domains, including finance, healthcare, marketing, and more. It is used for tasks such as customer segmentation, fraud detection, and predictive maintenance.

What are some practical applications of artificial intelligence?

Artificial intelligence is applied in tasks such as image recognition, natural language processing, autonomous vehicles, recommendation systems, and playing games like chess and Go.

Monthly Archives: November 2022

Beginner’s Guide to DAX Functions in Power BI DAX

Dax Introduction

What is DAX?

Essential Qualities of DAX Functions

Measures:

Row-level Security:

Calculated Columns:

DAX Functions

Aggregate Functions

DAX has a number of aggregate functions.

Counting Functions

Other counting functions in DAX include −

Following are the collection of Logical functions −

TEXT Functions

DAX Functions for Power BI: Date and Time Functions

Information Functions for DAX in Power BI

DAX Functions for Power BI: Logical Functions

Trigonometric and mathematical DAX functions for Power BI

Power BI DAX Functions: Parent & Child Functions

DAX Functions for Power BI: Statistical Functions

DAX Functions for Power BI: Text Functions

DAX Functions for Power BI: Other Functions

Benefits of DAX Functions

Step by Step complete A Complete Guide Power Query in Excel

Describe Power Query.

How Can Power Query Be Enabled?

What Can You Do With Power Query?

Get Data

Transform Data

Output to Excel

Different Ways to Import Data to the Excel Sheet

Importing Data From a CSV File

Importing a Multiple Data Source From an Excel Workbook

UPPERCASE

LOWERCASE

Trim

Splitting a Column Using Delimiters

Complete step by step guide to do Power Query in Excel

What Is the Distinction Between Data Science and Artificial Intelligence?

What is Data Science in Hindi

Describe data science.

With data, Facebook is revolutionising social media.

What exactly is artificial intelligence (AI)?

AI Applications in Personal Security Systems

Data Science and Artificial Intelligence (AI

scope and Purpose:

Data Science:

Artificial Intelligence (AI):

Methods and Techniques:

Data Science:

Artificial Intelligence:

Applications:

Data Science:

Artificial Intelligence:

Data vs. Intelligence:

Data Science:

Artificial Intelligence:

What Is the Distinction Between Data Science and Artificial Intelligence?

choosing between a career in data science and analytics

1. Take into account your background.

Consider your interests.

3. Take into account the salary and career path you want.

What data profession is best for you?