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Many tech gurus and course sellers will tell you that you can become a work-ready data scientist in as little as two weeks or two months. However, they often hide a lot of facts. While it is possible to become a professional data scientist in a short period, this usually assumes that you already have a solid foundation in data science fundamentals, such as statistics, probability, SQL and Python for data management and analysis, as well as various data analysis and manipulation techniques.
Before you embark on your journey into data science, I highly recommend that you take the time to learn these basics. The list of courses I have shared on this blog are from top universities and IBM, which offer high-quality education to help you build a solid foundation.
1. Introduction to Databases with SQL – Harvard
Introduction to Databases with SQL is a fantastic starting point for anyone who wants to understand the basic structure of data storage and manipulation. This course covers the essentials of SQL, the language used to communicate with databases. Through hands-on projects and real-world examples, you'll learn how to query databases, design schemas, optimize queries, and much more.
Link: Introduction to Databases with SQL (Harvard.edu) from CS50
2. Introduction to Data Science with Python – Harvard
Data Science with Python is perfect for those who want to get into data science using Python, one of the most popular programming languages for data science and machine learning. The course covers data manipulation, visualization, analysis, and modeling using libraries such as pandas, matplotlib, and scikit-learn. By the end of the course, you will be able to perform complex data analysis and build predictive models.
Link: Introduction to Data Science with Python | Harvard University
3. Statistical Learning with R – Stanford
The Statistical Learning with R course is a comprehensive introduction to key concepts and techniques used in data science and machine learning. This course covers statistical methods, linear regression, classification, resampling methods, tree-based methods, clustering, deep learning, and more. It is designed for those with a basic understanding of statistics and linear algebra. Course materials include lecture videos and exercises.
Link: Statistical Learning | Stanford Online
4. Data Science Mathematics Topics – MIT
The Mathematics of Data Science course delves into the mathematical foundations of data science. The course is designed for those with a keen interest in conducting research on the theoretical aspects of algorithms used to extract insights from data. Topics covered include principal component analysis, learning manifolds and diffusion maps, spectral clustering, group testing, clustering on random graphs, and more.
Link: Data Science Mathematics Topics | Mathematics | MIT OpenCourseWare
5. Introduction to data analysis – IBM
The Introduction to Data Analytics course, available on Coursera, offers a practical introduction to data analysis. This course covers the process of data analysis, from data cleaning and preparation to data visualization and interpretation. You will learn the basics through video tutorials, written content, exams, and final assignments.
Link: IBM Introduction to Data Analytics Course | Coursera
Conclusion
If you don't know where to start or if you don't know how to start a career in data science, I recommend starting with a free course on the fundamentals of data science. These courses are short and cover the basics of Python, SQL, statistics, and various data analysis techniques. After completing these courses, I recommend enrolling in a paid bootcamp to become a professional data scientist. The bootcamp will provide you with practical experience and prepare you for the modern workplace.
Abid Ali Awan (@1abidaliawan) is a certified data scientist who loves building machine learning models. Currently, he focuses 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 create an ai product using a graph neural network for students struggling with mental illness.
