5 Data Analytics Projects For Beginners
You may have encountered an old dilemma when you are looking to start a new career in data analysis. If you’re searching for your first job in data analysis, you will need to have experience.
Your portfolio is your best tool. Portfolios show your experience and skills, even if you haven’t worked in data analytics before. Building confidence can be a big step in building your job-related skills and experience.
This article will cover five types that data analytics professionals should include in their portfolio. We’ll show you some real examples of these projects and provide a list to assist with completing your projects.
Tip: Think of your first project as a “mini project” when you are just starting out. Portfolio projects don’t have to include a full analysis. Instead, you can complete smaller projects that focus on the individual steps or data analytics skills of each project.
Ideas for data analysis projects
A portfolio of key skills is essential for an aspirant data analyst. These data analysis project ideas represent the fundamental tasks that are often required of data analysts.
1. Gathering data from websites using automated tools
There will be plenty of high-quality (and often free) data available on the web. However, you may want to demonstrate to potential employers that your abilities to locate and retrieve your own data. Additionally, you will be able to search for and use data sets relevant to your interests by knowing how web data is scraped.
You can use Beautiful Soup to learn Python and Scrapy for web crawling to find interesting data. You don’t need to know code. Many tools, such as Octoparse and ParseHub, can be used to automate the process.
Here are some useful websites that can help you find inspiration for your next project if there is no place to look.
– Reddit is a popular online discussion forum that allows users to discuss a variety of topics.
– According to Wikipedia…
– Job portals
Tip: Always respect the terms of service on each website you visit when scraping data. Limit your scraping activities to avoid overloading a company’s server. Cite your sources whenever you present data in your portfolio.
Example web scraping project by Todd W. Schneider at Wedding Crunchers. He scanned approximately 60,000 New York Times Wedding Announcements from 1981 through 2016, to calculate the frequency and specific phrases.
2. Data scrubbing
Data cleaning is an important part your role as data analyst. Data cleaning is also known data scrubbing. This is the process where duplicates and incorrect data are removed, holes are managed, and data formatting is consistent.
When searching for data sets to clean, make sure you have multiple files from different sources. You can find data sets that are “dirty” on these sites:
– CDC Wonder
– Data.gov
The World Bank is an international financial institution.
– Data.world
– /r/datasets
3. Exploratory Data Analysis (EDA).
Data analysis is the process of answering data-related questions. EDA, short for exploratory analysis of data, is a way to discover what questions you should ask. This can be done separately or together with data cleaning. This can be done in any way you choose.
Ask lots of questions.
– Find the structure underneath the data.
– Look out for patterns, trends, and anomalies within the data.
– Validate hypotheses and test hypotheses.
– Consider the problems that you might solve using data.
Example exploratory data analysis project
10 Free Public Datasets Available for EDA
EDA projects offer a wonderful opportunity to access the vast array of publicly available datasets online. These are 10 free and fun datasets that will help you get started with your explorations.
1. National Centers for Environmental Information: Explore the largest source of climate and weather data in the world.
2. World Happiness Report2021: What is it that makes countries the most happy in the world?
3. NASA: NASA makes it easy to find thousands of datasets available for free.
4. US Census: The latest census data for 2020 provides more information about the US economy and its people.
5.
FBI Crime Data Explorer CDE: Find crime data from over 18,000 law enforcement agencies.
6. World Health Organization COVID-19 Database: Follow the latest coronavirus statistics by country and WHO region.
7. Latest Netflix Data: This Kaggle data (updated April 2021), includes movie data divided into 26 attributes.
8. Google Books Ngram – Download the raw data for the Google Books Ngram in order to discover phrase trends within books published between 1960-1915
9. NYC Open Data. Discover New York City by using the many publicly-available datasets.
10. Yelp Open Dataset. Explore this collection to find out what Yelp users have to say, check ins and other business attributes.
4. Analysis of sentiment
Sentiment analysis can be performed on textual data. It is an NLP technique that determines whether data is neutral, negative, or positive. A lexicon is a collection of words and the corresponding emotions that can be used to identify a specific emotion.
This analysis is well-suited for public review sites and social media platforms where people can offer their opinions on various topics.
You can use sites such as:
Amazon (product review)
– Rotten Tomato (movie reviews)
– Social media platform, Facebook
– Social media platform Twitter
– Sites containing news
Example of sentiment analysis project
5. Data visualization
Visual creatures are a part of human nature. Data visualization is a powerful tool that transforms data into compelling stories that inspire action. They are fun to make and can be used to enhance your portfolio.
Example Data visualization project: Hannah Yan Han (data analyst) visualizes the skill requirements for 60 different sport to see which one is the most difficult.
Five data visualization tools for free
Visualization software is not required to create amazing visuals. These are just three of the many visualization tools that you can free to tell stories with data.
1. Tableau Public: Tableau is a popular visualization tool. The free version can be used to convert files or spreadsheets into interactive visualizations. Here are some examples, starting April 2021.
2. Google Charts: You can embed visualizations into your portfolio with HTML and JavaScript code using this gallery of interactive charts and data visualisation tools. The creation process is aided by a robust Guides section.
3. Datawrapper: You can copy and paste data directly from a spreadsheet. Or upload a CSV to create maps, charts, or tables. You can create unlimited visualizations for export to PNG files in the free version.
4. D3 (Data Driven Documents) – This JavaScript library is easy to use with a little technical knowhow.
5. RAW graphs: This open-source web app allows you to easily convert CSV or spreadsheet files into a wide range of chart types. You can even download sample data sets to try out the app.
Bonus: End to end project
Mini projects that highlight your individual skills are a good way to populate your portfolio. If you’ve scoured the internet for data, you may also want to use that data to complete an entire project. You can do this by taking the data you’ve scraped, and then applying the steps of data analytics to it-cleane, analyze, interpret.
This will show potential employers that you are not only a skilled data analyst, but also that you understand how they work together.
You can now complete three data analysis projects
There’s lots of data and plenty you can do. It can be difficult to know where to begin. You can get some direction with these Data Analysis Guided Projects on Coursera. These projects take just two hours. You don’t need to have any special software or download any additional software.
1. Exploratory Data Analysing with Python and Pandas. Use Python to apply EDA methods to any table using Python.
2. Twitter Sentiment analysis Tutorial: Use thousands of tweets to determine whether a customer’s happy.
3. COVID19 data visualization using Python: Visualize COVID19’s global spread with Python, Plotly, or a real dataset.
Next steps: Data analysis.
An online course that is project-based can be a great way of building portfolio-ready projects.
Coursera offers the Google Data Analytics Professional Certificate. It allows you to do hands-on work and share a case with potential employers.
