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Learning from free courses can be very beneficial for those looking to enter the field of data science. Free courses offer numerous advantages such as cost-effectiveness, flexibility, access to the latest tools and concepts, opportunities to learn from industry experts, community support, and hands-on learning experience rather than spoon-feeding them.
In this blog, I aim to help you improve your data science skills by providing you with a comprehensive list of free courses on various topics including Python, SQL, Data Analytics, Business Intelligence, Data Engineering, Machine Learning, Deep Learning, ai generative, and MLOps.
Most of these courses come from top universities and platforms such as Coursera, MIT, UC Davis, FreeCodeCamp, Google, Microsoft, IBM, Harvard, and Stanford. So, start your journey to becoming a professional data scientist today!
Note: Coursera courses are available to audit for free, and if that option is not available, you can complete the courses during the trial period or apply for financial aid.
Python is a programming language required for data science. You will learn it for data manipulation, analysis, visualization, and machine learning. It offers a wide range of libraries and frameworks that simplify complex tasks, making it a popular choice among data scientists.
SQL (Structured Query Language) is a query language used to manage and manipulate relational databases, which are crucial for data storage, retrieval, and analysis.
As you may know, data analytics is a crucial aspect of data science that helps businesses make informed decisions based on data-driven insights. This involves using a variety of tools and techniques to extract meaningful information from data.
General data science courses cover a wide range of topics, from data manipulation to time series analysis and data modeling.
You can use Business Intelligence tools like Power BI or Tableau to transform raw data into actionable information, which helps in decision making. There is no need to learn any other programming language apart from SQL.
Data engineering is the subfield of data science that deals with the design, construction, and maintenance of data infrastructures and pipelines.
Machine learning is a branch of artificial intelligence that involves creating algorithms capable of learning from data and making predictions. It is an essential skill for data scientists.
Deep learning is a subset of machine learning that focuses on neural networks with multiple layers. It is widely used in image and speech recognition, natural language processing and other complex tasks.
Generative ai refers to the process of creating new content, such as text, images, and audio, by analyzing patterns and structures learned from existing data. In your learning process, you will mainly focus on large language models and how to train, tune, and deploy them.
MLOps, short for Machine Learning Operations, is the process of automating and optimizing the deployment and management of machine learning models. Currently, it is one of the most in-demand professional fields in the data science industry.
- Python Basics for MLOps by Duke University
- MLOps for beginners by Udemy
- Specialization in Machine Learning Engineering for Production (MLOps) By DeepLearning.ai
- DataTalks.Club MLOps Boot Camp
- Made with machine learning by Goku Mohandas
You don't have to search Google to find high-quality data courses. All you have to do is bookmark this page and start your journey with Python and SQL. In a few months, you will be able to ingest, process, analyze and model data. After that, it will be a continuous learning journey. It is highly recommended to build your portfolio on GitHub or any other platform from the beginning if you want to get hired by top recruiters.
Check out the blog on “Five Free Platforms to Build a Strong Data Science Portfolio” to learn about other platforms and what they offer.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a master's degree in technology management and a bachelor's degree in telecommunications engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
