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The Complete Collection of Data Science Projects – Part 1

The first part covers the list of Programming, Web scraping, Data Analytics, SQL, Business Intelligence, and Time Series projects.

The Complete Collection of Data Science Projects – Part 1
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Editor's note: For the full scope of repositories included in this 2 part series, please see The Complete Collection of Data Science Projects – Part 2.




If you are new to data science, the programming projects will help you get used to syntax, debugging, and learning new tools. Python, R, and Julia are mostly used for data processing, data analysis, machine learning, and research projects.











Web Scraping


Web scraping is a core part of data engineering and data science, where you collect new data from multiple websites to build a data set for data analysis or machine learning tasks. In general, it is used to create real-time data systems.


Data Analytics


The analytics project will teach you new tools for data cleaning, processing, and visualization. You will learn to understand data and create a report with valuable insights. 




SQL is the most used tool for creating, managing, and streaming database systems. In most cases, you have run a few SQL scripts for analytical tasks, but integrating them into your project is hard to imagine. The list of projects will teach you how the scripts are used to create databases, store and retrieve the data, and how they are integrated with other tools. 


Business Intelligence


Learn to create interactive dashboards and analytical reports using BI tools. You will learn how small modules join together to create a dashboard and what value it brings to the business. 


Time Series


Learn to understand, process, and visualize time series data. You will learn to create anomaly detection systems, forecasting, and visualize multiple graphs for comparison. Time series is a whole new world within data science, so it will be quite valuable to add one of the projects to your portfolio. 




After taking a few courses, you should dive right into the projects. Working on projects will improve your understanding of the subject, and it will be contributing to your portfolio that you can add to your resume. Working on projects also makes you good at solving problems. You will be learning new tools and concepts as you dive into more complex problems. 

In this blog, we have learned about programming, web scraping, data analytics, SQL, business intelligence, and time series projects. You can learn about projects through source code, tutorials, or initial descriptions in ReadMe. The main thing is that you replicate the results. 

In the next part, we will cover:

  1. Machine Learning
  2. Deep Learning
  3. Computer Vision
  4. Natural Language Processing
  5. Data Engineering
  6. MLOps

This is the 5th edition in the collection series, check out:

  1. The Complete Collection of Data Science Cheat Sheets – Part 1 and Part 2
  2. The Complete Collection of Data Repositories – Part 1 and Part 2
  3. The Complete Collection of Data Science Books – Part 1 and Part 2
  4. The Complete Collection of Data Science Interviews – Part 1 and Part 2

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.