5 Free University Courses to Learn Data Science

5 Free University Courses to Learn Data Science :-

From Python data science libraries to the inner workings of machine learning algorithms, you can check out one more of these courses to find the best fit for you. The complete details about 5 Free University Courses to Learn Data Science are as follows.

1. Data Science: Machine Learning by Harvard University

Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.

In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

What you’ll learn:

• The basics of machine learning
• How to perform cross-validation to avoid overtraining
• Several popular machine learning algorithms
• How to build a recommendation system
• What is regularization and why it is useful

Course Link: Click Here To Apply

2. Statistical Learning with R Stanford University

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

Prerequisites: Introductory level understanding of core concepts in statistics, linear algebra, and computing.

Time Commitment: It will take approximately 3-5 hours per week to go through the materials and exercises in each section.

Course Link: Click Here To Apply

3. Topics in Mathematics of Data Science

This is a mostly self-contained research-oriented course designed for undergraduate students (but also extremely welcoming to graduate students) with an interest in doing research in theoretical aspects of algorithms that aim to extract information from data. These often lie in overlaps of two or more of the following: Mathematics, Applied Mathematics, Computer Science, Electrical Engineering, Statistics, and / or Operations Research.

Prerequisites: Working knowledge of 18.06SC Linear Algebra and 18.05 Introduction to Probability and Statistics is required. Some familiarity with the basics of optimization and algorithms is also recommended.

Course Link: Click Here To Apply

4. Introduction to Data Science with Python by Harvard University

Data science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI). 

Learners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.

What you’ll learn:

• Gain hands-on experience and practice using Python to solve real data science challenges
• Practice Python coding for modeling, statistics, and storytelling
• Utilize popular libraries such as Pandas, numPy, matplotlib, and SKLearn
• Run basic machine learning models using Python, evaluate how those models are performing, and apply those models to real-world problems
• Build a foundation for the use of Python in machine learning and artificial intelligence, preparing you for future Python study

Course Link: Click Here To Apply

5. Introduction to Computational Thinking and Data Science by MIT

6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course.

For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient’s body.

Topics covered include:

• Advanced programming in Python 3
• Knapsack problem, Graphs and graph optimization
• Dynamic programming
• Plotting with the pylab package
• Random walks
• Probability, Distributions
• Monte Carlo simulations
• Curve fitting
• Statistical fallacies

Course Link: Click Here To Apply

** From Python data science libraries to the inner workings of machine learning algorithms, you can check out one more of these courses to find the best fit for you.**