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A lot of tech gurus and course sellers will tell you that you can become a job-ready data scientist in just two weeks or two months. However, they often hide a lot of facts. While it’s possible to become a professional data scientist in a short period, this usually assumes you already have a strong foundation in data science fundamentals like statistics, probability, SQL, and Python for data management and analysis, as well as various data wrangling and analysis techniques.
Before you embark on your data science journey, I highly recommend that you take the time to learn these fundamentals. The list of courses I have shared in this blog are from top universities and IBM, offering 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 looking to understand the backbone 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 will learn how to query databases, design schemas, query optimization, and more.
Link: CS50’s Introduction to Databases with SQL (harvard.edu)
2. Introduction to Data Science with Python – Harvard
Data Science with Python is perfect for those who want to dive into data science using Python, one of the most popular programming languages for data science and machine learning. The course covers data wrangling, 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
Statistical Learning with R course is a comprehensive introduction to the 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. The course materials, including lecture videos and exercises.
Link: Statistical Learning | Stanford Online
4. Topics in Mathematics of Data Science – MIT
Topics in Mathematics of Data Science course dives into the mathematical foundations of data science. The course is tailored for those with a keen interest in conducting research in 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, group testing, clustering on random graphs and more.
Link: Topics in Mathematics of Data Science | Mathematics | MIT OpenCourseWare
5. Introduction to Data Analytics – IBM
Introduction to Data Analytics course, available on Coursera, provides a practical introduction to data analytics. This course covers the data analysis process, from data cleaning and preparation to visualization and interpretation. You will learn the basics concepts through video tutorials, written content, quizzes, and final assignments.
Link: Introduction to Data Analytics Course by IBM | Coursera
Conclusion
If you are confused about starting a career in data science or where to begin, I recommend starting with a free data science fundamentals course. These courses are short and cover the basics of Python, SQL, Statistics, and various data analysis techniques. After completing these courses, I highly recommend signing up for 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 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.