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
Toggle1. 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.
- 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 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.
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.
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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)
- 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.
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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.