Data Science is one of the most in-demand and lucrative fields today. But, how do you get started if you don’t have the money to invest in traditional education or boot camps? The good news is, there are plenty of free online courses available to help you learn the skills you need to become a data scientist.
Here are some of the best free online courses to help you get started on your data science journey:
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1. Learn Python basics for data analysis
This course is designed to provide you with the basics of the Python programming language, specifically for data analysis purposes. We’ll look at how to organize and group information in your program by data type, use programming logic to make your program do what you want, and use and write functions to save time as you write programs.
How it works
Choose the right course for you and take the next step toward achieving your learning goals.
Track your own progress through the My Curriculum page.
Learn new skills with short video lessons, then test your knowledge with quizzes and activities.
2. Data Science Foundations
Data Science is a field that uses methods and algorithms to extract information from data, both structured and unstructured. Thanks to the growth in data and technological developments, the field of data science has experienced tremendous growth. This course provides you with a comprehensive introduction to the data science and analytics landscape. You will learn all the basics of data science and the data science life cycle.
How it works
Go to the course overview page, sign up for the course, and start studying in online classes.
Build skills by watching short coding tutorials and videos, taking quizzes, and doing hands-on exercises.
Upon successful completion of the course, you will receive a certificate from the Great Learning Academy.
3. Data Science with Python
This Data Science with Python program provides students with a thorough understanding of data analysis tools and techniques. Getting started with Python can help you learn about data analysis, visualization, NumPy, SciPy, web scraping, and natural language processing. This program is an ideal starting point for anyone looking to become a data scientist today.
How it works
Simplilearn offers 100% free online courses developed by industry experts in today’s most in-demand skill areas
Each course consists of video tutorials and self-tests to ensure the best learning experience. Get access to the latest e-books, salary guides, and insightful articles handpicked for you
After completing the course, receive a shareable electronic final statement. Add it to your resume for added benefits
4. Machine Learning Crash Course
This course teaches the fundamentals of machine learning through a series of lessons that include video lectures by Google researchers, texts written specifically for ML beginners, interactive visualizations of algorithms in action, and real-world case studies. As you learn new concepts, put them to practice quickly with coding exercises that walk you through deploying models in TensorFlow, an open-source machine intelligence library.
How it works
This course is designed for people who can code but know little or nothing about machine learning.
Learn the basics of machine learning by working on lessons ranging from text and video lectures to interactive visualizations and case studies.
Practice the concepts you learn with coding exercises.
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5. Machine Learning Specialization
The Machine Learning Major is a foundational online program developed in collaboration between DeepLearning.AI and Stanford Online. This beginner-friendly program will teach you the basics of machine learning and how to use this technique to create real-world AI applications.
This specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and pioneering work on Google Brain, Baidu, and Landing.AI advancing AI.
This 3-course specialization is the latest version of Andrew’s breakthrough Machine Learning course, which has a 4.9 out of 5 ratings and has been taken by over 4.8 million students since its launch in 2012.
Provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence innovation and machine learning (model evaluation and optimization, data-driven approaches to performance improvement, etc.)
WHAT YOU WILL LEARN
Build ML models with NumPy & sci-kit-learn, build and train supervised models for prediction tasks and binary classification (linear, logistic regression)
Build and train neural networks with TensorFlow to perform multiclass classification and to create and use decision trees and ensemble tree methods
Apply best practices for machine learning development and use unsupervised learning techniques for unsupervised learning, including clustering and anomaly detection
Build a recommender system with a collaborative filtering approach and a content-based deep learning methodology and build a deep reinforcement learning model