Data analysis project ideas

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.

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

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 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
  • World Bank


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

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?

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.

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