Attention all aspiring data scientists! Do you dream of a career where you can unlock the power of information and solve complex problems? Look no further than Harvard University! This prestigious institution is offering a treasure trove of completely free online courses designed to equip you with the foundational skills you need to thrive in the exciting field of Data Scientists
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Many courses could teach you the basics of Data Scientists, but Harvard University is undoubtedly at the top. Coming from an elite university, all their courses certainly provide you with the skills necessary to become a data scientist.
So, what are these free courses that you should know?
1. Course HarvardX: CS50’s Introduction to Programming with Python
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.
About this course
An introduction to programming using a language called Python. Learn how to read and write code as well as how to test and “debug” it. Designed for students with or without prior programming experience who’d like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs,
and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software is required except for a web browser, or you can write code on your own PC or Mac.
Course Link – HarvardX: CS50’s Introduction to Programming with Python
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What you’ll learn
- Functions, Variables
- Conditionals
- Loops
- Exceptions
- Libraries
- Unit Tests
- File I/O
- Regular Expressions
- Object-Oriented Programming
- Et Cetera
2. Course HarvardX: Fat Chance: Probability from the Ground Up
Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.
About this course
Created specifically for those who are new to the study of probability, or for those who are seeking an approachable review of core concepts prior to enrolling in a college-level statistics course, Fat Chance prioritizes the development of a mathematical mode of thought over rote memorization of terms and formulae. Through highly visual lessons and guided practice, this course explores the quantitative reasoning behind probability and the cumulative nature of mathematics by tracing probability and statistics back to a foundation in the principles of counting.
What you’ll learn
- An increased appreciation for, and reduced fear of, basic probability and statistics
- How to solve combinatorial counting problems
- How to solve problems using basic and advanced probability
- An introductory understanding of the normal distribution and its many statistical applications
- An ability to recognize common fallacies in probability, as well as some of the ways in which statistics are abused or simply misunderstood
Course Link – HarvardX: Fat Chance: Probability from the Ground Up
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3. Course HarvardX: Introduction to Data Scientists with Python
Learn the concepts and techniques that make up the foundation of data science and machine learning.
About this course
Every single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?
Syllabus
Course Outline:
- Linear Regression
- Multiple and Polynomial Regression
- Model Selection and Cross-Validation
- Bias, Variance, and Hyperparameters
- Classification and Logistic Regression
- Multi-logistic Regression and Missingness
- Bootstrap, Confidence Intervals, and Hypothesis Testing
- Capstone Project
Course Link – HarvardX: Introduction to Data Science with Python
4. Course HarvardX: Machine Learning and AI with Python
Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.
About this course
It’s time to make a decision: beach or mountains? When choosing where you want to go for vacation, it can be simple. The options may be a or b. From a decision-making standpoint, it’s easy for the brain to process this decision tree. But, what happens when you’re faced with more complex,
multifaceted decisions? You might make a comprehensive pro/con list, rank-ordering the most important considerations. But, that can take endless amounts of time that you might not have to spare. When parsing through thousands or millions of data points, you and your organization need to tap into a more sophisticated approach.
What you’ll learn
In this course, you will:
- Explore advanced data science challenges through sample Data Scientists sets, decision trees, random forests, and machine learning models
- Train your model to predict the most effective way to handle a problem
- Examine machine learning results, recognize data bias in machine learning, and avoid underfitting or overfitting data
- Build a foundation for the use of Python libraries in machine learning and artificial intelligence, preparing you for future Python study
- Build on your Python experience, preparing you for a career in advanced data science
Course Link – HarvardX: Machine Learning and AI with Python
