Top 8 Exciting Data Analytics Project Ideas & Topics [For Freshers]

Doing data Science courses has been one of the best and most reassuring career options of this generation for quite some time now. If you are an aspiring data scientist, you should be focusing more on improving your technical capabilities. By doing so, you will be increasing your skill level as a data scientist. The best way to practice your art is to take up personal projects to boost your knowledge, skills, and confidence.

Analyzing data also plays a significant role in your career growth. It is mostly about discovering new insights that can help with your decision making process. Even if you ask a veteran analyst, he will tell you that the intuition we see as consumers results from hard work. And around 80% of all data analytics assignments start with the evaluation of data. So, a data scientist needs to know more about data analysis and its types.

Rest assured, as time progresses; you will develop the necessary skills needed to collect data and produce reports based on your findings. You should also be able to:

  • Clean the web data
  • Execute exploratory analysis
  • Flush cluttered datasets
  • Visually communicate your results.

However, the most crucial part of becoming a skilled data scientist is working on various projects that focus on data scraping, exploratory analysis, and data visualization. So, let’s get started. Here are some of the project ideas that you will need to build up your job profile as a data scientist.

Data Scraping Project Ideas

1. Movie Data Collection

This beginner project will help you gain the necessary skills needed for a data scientist. Its primary aim is to collect and extract data for further analysis. For that purpose, you can use the IMDB website to gather information about popular movies, TV shows, actors, etc. The format for this website is relatively consistent and makes it easier to attain data for analysis. Besides, the project has great potential when it comes to data collection.

2. Job Websites

Nowadays, scraping data from job portals are used for training beginner data scientists. It is because these websites contain standard data types. You can also maximize your learning capabilities through different online tutorial sessions. The main objective is to collect data and information about job titles, companies, locations, skills, etc. This project has an excellent aptitude for further visualization enhancements, such as comparing and mapping out the difference between talents and companies.

3, Online Shopping Sites

Another way to improve your necessary data analytics skill set is to scrap product and cost data from online shopping sites. For example, you can collect data and information about the trending Bluetooth headsets on Flipkart. And the collected data is analyzed further for processing the information you need for the project. It is wiser to start experimenting and analyzing data that uses more straightforward algorithms first. And then, pave your way to getting comfortable with intricate data design.

4. Social Media Platforms

A beginner level data analyst is expected to scrape data from social media websites. For instance, you can collect data from unconventional sites like Reddit or Twitter. Searching for keywords, upvotes, user data, etc., is all possible in Reddit, giving you ample resources for further investigation.

The website has gained popularity over the past years for its straightforwardness and content creation. As a data analyst, you can compare and analyze popular keywords with upvoted content. You can also take it a step further with exploratory analysis to check for any correlation between them.

Exploratory Data Analysis Project Ideas

1. Global Suicide Scale

The next step in improving your data scientist skills is to carry out exploratory data analysis on the data structure, patterns, and characteristics. For example, analyze the datasets that cover the numbers of suicide cases happening in different countries.

Also, find information on almost everything you get your hands on, ranging from the year, gender, the age to population and GDP. After completing the data collection process, try to see if any patterns involve suicide rates. If you get better at analyzing data, you can evaluate the percentages based on the rise or fall in suicide rates.

2. UN World Happiness Report

Compared to the previous project, this assignment involves the World Happiness report. This particular report keeps track of six main factors that measure happiness around the world. The six factors are life expectancy, economy, social support, lack of corruption, freedom, and generosity. Multiple questions can pop into your mind based on the report, which is an excellent exercise to expand your data analyst skills.

The first step will be to collect and extract the data needed for your project. You can find the report to be well-organized and consistent, making it easier for analysis. The main focus here will be to observe the patterns and data structure used to design the world report. Probing for more information is the best way to perform a complete analysis.

Utilizing the right dataset will give you room to enhance your technical skills. If you find yourself drawing a blank when it comes to complex structures, try resetting the analysis to your advantage. Make it simple, clear and concise to extract the necessary information needed to achieve your project goals.

Related: Top Data Science Project Ideas

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Data Visualization Project Ideas

1. Covid-19 World Report

Apart from scraping, tiding, and analyzing the data, we have to find the means to communicate our results visually. In this case, we will be inspecting the Covid-19 health report. If you visit some famous sites like Kaggle, you get access to several thousands of Covid-19 datasets. The next step would be to collect data and scrap it. Tidy up the collected data for further investigation. Organized datasets make it easier for the analyst to visualize the results.

You can also perform various comparisons between different countries based on the number of active cases vs. the number of recovered patients. Producing charts and graphs are the critical elements needed for visualizing the results. And if you want to dive deeper, look for some online tutorials that can help you.  

2. Instagram

It does not matter whether you are interested in actors or brand culture. What matters is that Instagram has a unique set of data and information on various topics, making it a perfect instrument for visualization. The available options for analyzing this social media platform are boundless.

You can track the changes in the most followed accounts in real-time. Creating and developing bar charts based on the gathered information can help achieve your project goals. Advertising plays an essential role in this social media platform. Even comparing the company brands with popular brands will be an excellent exercise to amp up your tech skills.

Also Read: Top Data Analytics Project Ideas

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After mastering the necessary skills needed for data scraping, exploratory analysis, and data visualization, you can look forward to improving your data analyzing abilities further. You can start by taking up machine learning projects. Some of the projects include sentiment analysis, predictive analysis, and many more.

A vital element to take away from this post is that practice makes it perfect. So, try spending time on more straightforward projects at first to get comfortable with algorithms that are frequently used on datasets. Then, climb your way to taking up bugger projects that can help you grow in the industry.

If you are curious about learning data science to be in front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

What problems you might face while doing a Data mining project?

In addition to the broad range of project ideas, data analysts face a number of challenges while working on these projects.
1. One of the main issues you'll face when it comes to monitoring real-time environments is that there aren't many suitable solutions. You should familiarise yourself with the various technologies you'll need when working on a big data project.
2. One of the most common data analysis issues is how long it takes to process data after virtualization is completed. More commonly, latency issues occur because of high-level performance demands, and most of these tools require it.
3. Higher-level scripting may be required when continuing to work on big data analytics projects, particularly if you're encountering tools or problem situations that you haven't used before
4. Inadequate security leads to leaks of confidential data, which has disastrous consequences for both your project and your work. Of can happen, so you must always be cognizant of this.
5. End-to-end testing can't be done with just one tool. Make sure you determine which software will be required to accomplish a particular project.
6. Occasionally, you'll find a dataset too large for you to manage. Alternatively, you may need to validate more data to finish the project.

What are some Data Analysis Projects?

Some good data analysis projects are –
1. Classify 1994 Census Income Data.
2. Analyze Crime Rates in Chicago.
3. Health status prediction.
4. Anomaly detection in cloud servers.
5. Malicious user detection in Big Data collection.
6. Tourist behaviour analysis.
7. Credit Scoring.
8. Electricity price forecasting.

What are some good tools to manage big data?

To be successful in the Big Data industry, you must acquire these technologies.
1. The Apache Storm software is used for handling data streams in real-time. Java and Clojure are used, and integration with any computer language is possible.
2. MongoDB is indeed an open-source NoSQL database similar to modern databases.
3. Cassandra is used for managing massive quantities of data across several servers, with a distributed database management system.
4. In comparison to other Big Data technologies, Cloudera is among the fastest and most secure.
5. Refining data, converting it into different formats, and cleaning data are among the numerous applications for which OpenRefine is widely used.

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