9 Project Ideas For Your Data Analytics Portfolio
It can be hard to find projects for your data analysis portfolio, especially if it’s your first time. It might seem that data projects must be complex or elaborate, but this is not true. It is important to show your skills using data that interests you. The good news is that data is everywhere. The good news is that data is all around you. All you have to do to get it is to know how to use it.
This post will highlight the essential elements your data analytics portfolio must demonstrate. Then, we’ll share nine ideas for projects that can help you create your portfolio, with a focus on three areas: Data scraping and exploratory analysis.
1. What should you include?
Data analytics is about discovering insights that can help you make better decisions. However, that’s not the only goal. Every data analyst knows that consumers see insights only after a lot of work. About 80% of all data analysis tasks involve data preparation. This is logical, as our insights can only be as good as the data they are stored in.
Your portfolio must show that you are capable of performing different types data analysis. However, your portfolio should also show that data can be collected, cleaned, and presented in a visually appealing manner. As you gain more skills, your portfolio will get more complex. But, you must show your ability as a beginner.
– Use the web to scrape data
– To conduct exploratory analysis
– Clean untidy datasets
Visualizations can be used to communicate your results
It’s a good idea to create a mini-project for beginners if you don’t have the necessary skills. This allows you to practice your individual skills in an organized manner. We’ll keep this simple and give you some ideas and tools to help you on your way.
2. Ideas for data scraping projects to improve your portfolio
What is data-scraping?
Data scraping forms the foundation of any data analytics program. This involves retrieving data (mostly from the web) then compiling it to a usable form. Although there are many excellent data repositories online, it is possible to scrape and clean data by yourself.
Tools such as Octoparse, ScraperAPI and Octoparse can be used to automate the web scraping process. Developers can also use libraries like Beautiful Soup. No matter what tool you use to scrape web pages, it is important that you demonstrate your knowledge and ability to use it.
Be sure to have the permission to scrape a website before you do. You can search Kaggle for data if you aren’t sure. It’s likely that the dataset is already available on Kaggle. If not, you can always search for it directly and extract it yourself. If you’re looking for complex or dynamic websites, data scraping may be difficult. Start with something simple, a static site. These are just a few ideas to help you get started.
Ideas for data scraping projects
The IMDB provides information about films and actors.
IMDb data is a good starting point. It is possible to find information about TV shows, movies reviews, trivia, heights, and weights of actors. IMDb data is consistent across all pages. This makes it easier to do your research. This data can be further analysed.
– Job portals
Many job portals contain standard data types, so it is a popular choice for beginners. Many tutorials online can help you learn how to do it. Keep it fun by focusing on your local area. Track job titles and company information, as well as salaries, location data, and skills needed. This data can be used for visualization later, such as to graph salaries against them.
– E-commerce websites
Another popular option is to extract pricing and product information from e-commerce websites. You could extract information from Amazon about Bluetooth speakers, or gather reviews and prices for various tablets and laptops. Again, it’s easy and scalable. This means you can start out with a product which has only a few reviews. Once you’re more comfortable with the algorithm, you can move up.
– Reddit is a social media platform where people can connect, share stories, and interact with one another.
Scrape a Reddit site for something more unusual. Searches could be made for keywords, user data and upvotes. Reddit has a static website which makes it easy to do this task. You can also do exploratory analyses to find correlations between posts and keywords later. We now move on to the next section.
3. Project ideas for exploratory analysis of data
What is exploratory analysis?
Data analysts should be able to perform exploratory data analyses (EDA) as the next step. EDA analyzes the data structure to identify patterns and characteristics. They can also clean up your data. These tools allow you to extract vital variables, identify outliers, and analyze your underlying assumptions.
This can be a tedious process for data analysts, but can be rewarding. Later modeling concentrates on answering specific queries. EDAs are a great way to get started with the exciting part of modeling: generating answers to specific questions.
These tasks are often carried out using languages like R or Python. There are many algorithms already in place that can be used to do the job. Your project presentation and the results are where the real skill lies. You can do it however you like, but Jupyter Notebook is a popular interactive documentation tool. This allows you to combine code and explanation text into one place. These are some examples of portfolio ideas.
Project ideas for exploratory analysis of data:
– Global suicide rates
This global data set on suicide rates covers a variety of countries. The dataset also includes additional data like year, genders, years, population and GDP. Do you notice any patterns in your EDA? Is suicide rate increasing or decreasing in different countries? Which variables (such gender and age) might be associated with suicide rates?
The World Happiness Report is an annual study about the well-being of individuals around the globe.
The World Happiness Report measures happiness in six areas. It tracks the life expectancy, wealth, economics and social support. Which country is most content? On which landmass? Which factor has the largest (or least) effect on a country’s happiness? Is happiness on the rise or falling?
Apart from the above mentioned ideas, you can also use your data. You can also use data you already have if they are available. What are the most well-paid jobs in a given area or region, for example, if you’ve scraped a job site? Which ones are the lowest-paid? Why is that? E-commerce data can be used to identify the most value for money products and prices.
Whatever dataset you use, it should grab attention. You’ll quickly tire if the data is too complicated or not interesting you. You should also consider what additional probing you could do to find interesting patterns or trends and extract the insights that you require.
4. Ideas for data visualization projects
What is data visualisation?
One thing is to scrape, clean up, and analyze data. Communicating your findings takes another. While we don’t love numbers and graphs, our brains do enjoy visuals. Effective data visualizations are essential. Good visualizations-whether static or interactive-make a great addition to any data analytics portfolio. Employers will appreciate your ability to create effective visualizations.
Google Charts, Canva graph maker (free), or Tableau Public are some examples of free visualization tools. If you’re looking to showcase your programming skills, Seaborn has a Python library. Shiny lets you flex your R skills. You have many options. The type of work you are trying to accomplish will determine which tool you use. Here are some ideas…
Ideas for data visualization projects:
– Covid-19
Portfolios that include topical matter look great. The pandemic is no exception! Kaggle, for example, already has thousands upon thousands of Covid-19 datasets. How do you present the data? To show the areas in which cases have increased, and those that are decreasing, could you create a global heatmap? You could make two bar charts that overlap to show the difference between predicted and known infections. Here is a tutorial that will show you how to visualize Covid-19 data in R, Shiny, or Plotly.
– Instagram’s Most Followed Users
This data set of Instagram’s most followed people is great for visualizing social media trends. A bar chart with interactive data that tracks changes over time could be created. You could also explore which celebrity or brand accounts are more successful at influencer marketing. A visualization can also be created using another social media data set. Greg Rafferty created this map of America that shows the geographic source for Instagram’s top trends.
– Travel data
Transport data is another topic suited for visualization. Chen Chen has created a fantastic project on Github that uses Python to visualize top tourist destinations around the world and the relationship between inbound/outbound tourists and gross domestic product (GDP).
5. What’s next?
This post will discuss the skills that every data analyst should have.
No matter what dataset you use, you should be capable of demonstrating the following capabilities:
– Web scraping using tools such as Parsehub or Beautiful Soup to extract data from web pages (remember that static sites are much easier!)
– Exploratory data analysis and data cleaning-manipulating data with tools like R and Python, before drawing some initial insights.
– Data visualization-utilizing tools like Tableau, Shiny, or Plotly to create crisp, compelling dashboards, and visualizations.
Once you are comfortable with the basics, you can move on to more challenging data analytics projects. Why not try machine learning projects? It is important to keep it simple, and not to be too flashy about data analytics.
Online courses are a great way to further your skills. Take our five-day data analysis short course for free to get started.
You can also learn more about data analysts and how to build your portfolio by visiting the following:
How to build an analytics portfolio
– The most popular data analytics certification programs available today
These are the top data analytics interview question.
