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Many tech gurus and course vendors will tell you that you can become a job-ready data scientist in as little as two weeks or two months. However, they often hide many facts. While it is possible to become a professional data scientist in a tiny time, it is usually assumed 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 processing and analysis techniques.
Before you start your data science journey, I highly recommend you take the time to learn these fundamentals. The list of courses I have shared on this blog are from top universities and IBM that offer high-quality education that will support 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 basics of storing and processing data. This course covers the basics 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 more.
To combine: CS50 Introduction to Databases with SQL (harvard.edu)
2. Introduction to Data Science with Python – Harvard
Data Science with Python is ideal for those who want to delve into data science using Python, one of the most popular programming languages for data science and machine learning. The course covers data organization, 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 complicated data analysis and build predictive models.
To combine: Introduction to Data Science with Python | Harvard University
3. Statistical Learning with R – Stanford
Statistical Learning with R is a comprehensive introduction to key concepts and techniques used in data science and machine learning. The course covers statistical methods, linear regression, classification, resampling, tree-based methods, clustering, deep learning, and more. It is intended for those with a basic understanding of statistics and linear algebra. Course materials, including lecture videos and exercises.
To combine: Statistical Learning | Stanford Online
4. Topics in Data Science Mathematics – MIT
The Mathematics of Data Science course topics delve into the mathematical foundations of data science. The course is tailored for those who are keenly interested in conducting research on the theoretical aspects of algorithms that are used to extract information from data. Topics covered include principal component analysis, manifold learning and diffusion maps, spectral clustering, ensemble testing, clustering on random graphs, and many more.
To combine: Topics in Data Science Mathematics | Mathematics | MIT OpenCourseWare
5. Introduction to Data Analysis – IBM
The Introduction to Data Analysis course on Coursera provides a practical introduction to data analysis. This course covers the data analysis process, from cleaning and preparing data to visualizing and interpreting it. You’ll learn the fundamental concepts through video tutorials, written content, quizzes, and final assignments.
To combine: Introduction to IBM Data Analytics Course | Coursera
Application
If you don’t know how to start a career in data science or where to start, I recommend starting with a free data science foundation course. These courses are tiny and cover the basics of Python, SQL, statistics, and various data analysis techniques. After completing these courses, I highly recommend enrolling in a paid bootcamp to become a professional data scientist. A bootcamp will give you hands-on experience and prepare you for the newfangled workplace.
Abid Ali Awan (@1abidaliawan) is a certified data science professional who loves building machine learning models. He currently focuses on content creation and writing technical blogs on machine learning and data science technologies. Abid has a Masters in Technology Management and a Bachelors in Telecommunication Engineering. His vision is to build an AI product using Graph Neural Network for students struggling with mental illness.