If you are looking for a well-paid career in an exciting and cutting-edge field of data science, then its high time that you choose the best course from the best platform. To help you in your search, we have handpicked some of the highest-rated data science courses from the leading online education platform, Coursera. The courses include extensive course content, online videos, quizzes, capstone projects at every level, and virtual classes by the best educators of the industry.

Criteria

These data science courses are picked basis the following criteria –

  • The course covers the desired data science process
  • The course uses popular open-source programming tools and libraries
  • The course has a good combination of theory and application
  • The course comprises of projects and case studies
  • The instructors are engaging and personable
  • The course has ratings, greater than or equal to 4.5/5

 

Courses

1. Executive Data Science Specialization by Johns Hopkins University

Course Description

This course can benefit managers and business leaders to develop their data science skills and be able to develop and work with a team. It will help the participants learn the structure of the data science pipeline, understand the goals, and develop the skills to overcome the real-world data science project challenges.

Course Duration – 2 months
Efforts – 4 hours per week
Rating – 4.5 stars (15,000 ratings)

Course Content

Course 1 – A Crash Course in Data Science
Course 2- Building a Data Science Team
Course 3 – Managing Data Analysis
Course 4 – Data Science in Real Life
Course 5 -Executive Data Science Capstone

2. The Data Scientist’s Toolbox by Johns Hopkins University

Course Description

It is a part of the Data Science Specialization program. As the name suggests, this course will give you an overview of the data, questions, and tools like version control, markdown, git, GitHub, R, and RStudio, data analysts and data scientists work with.

Course Details

Duration – 4 Weeks (Total – 13 Hours)
Skill Level – Beginner
Rating – 4.6 stars (25,297 ratings)

Course Content

Week 1 – Data Science Fundamentals
Week 2 – R and RStudio
Week 3 – Version Control and GitHub
Week 4 – R Markdown, Scientific Thinking, and Big Data

3. AI For Everyone by IBM

Course Description

The course gives detailed knowledge about common AI terminology, including neural networks, machine learning, deep learning, and data science, AI ethics, problem-solving in AI, building AI strategies, etc.

Course Details

Rating – 4.8 Stars (9459 ratings)
Duration – 6 Hours
Skill Level – Beginner

Course Content

Week 1 – What is AI?
Week 2 – Building AI Projects
Week 3 – Building AI in Your Company
Week 4 – AI and Society

4. Intelligence Tools for the Digital Age by Georgia Tech Masters

Course Description

This course explores new avenues of digital technologies through the usage of new tools like intelligence analysis, mental models, and practical frameworks developed by the US intelligence community. It will prepare the participants for the upcoming digital age and help them acquire sustainable business advantage through structured thinking.

Course Details

Rating – 4.7 Stars (205 ratings)
Duration – 8 Hours
Skill Level – Beginner
Language – English

Course Content

Week 1 – A Toolkit for the Digital Future: Intelligence Analysis for the Business Professional
Week 2 – The Intelligence Analyst’s Mindset
Week 3 – Intelligence Methods: Analysis, Part One (Macro Actors) The Case of Chinese Rare Earth Elements
Week 4 – Intelligence Methods Analysis, Part Two: Micro actors (understanding people/organizations)

5. IBM Applied AI Professional Certificate

Course Description

This is a professional certificate from IBM and follows IBM Watson AI services and APIs to create smart applications with minimal coding. The course also requires the participants to complete several projects to understand the application of AI and build AI-powered solutions.

Course Details

Rating – 4.5 Stars (19,488 ratings)
Duration – 7 Months
Skill Level – Beginner

Course Content

Course 1 – Introduction to Artificial Intelligence (AI)
Course 2 – Getting Started with AI using IBM Watson
Course 3 – Building AI-Powered Chatbots Without Programming
Course 4 – Python for Data Science and AI
Course 5- Building AI Applications with Watson APIs
Course 6 – Introduction to Computer Vision with Watson and OpenCV

6. Google Cloud Platform Big Data and Machine Learning Fundamentals

Course Description

The course will help you to learn about Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). Explore the Google Cloud Platform and dive deeper into the data processing capabilities.

Course Requisites

You must have prior knowledge of –

  • A common query language such as SQL
  • Extract, transform and load activities
  • Data modeling
  • Machine learning and/or statistics
  • Programming in Python

Course Details

Duration – 2 Weeks
Skill Level – Intermediate
Rating – 4.6 (10,650 ratings)

Course Content

Week – 1

  • Introduction to the Data and Machine Learning on Google Cloud Platform Specialization
  • Recommending Products using Cloud SQL and Spark
  • Predict Visitor Purchases with BigQuery ML

Week – 2

  • Create Streaming Data Pipelines with Cloud Pub/sub and Cloud Dataflow
  • Classify Images with Pre-Built Models using Vision API and Cloud AutoML

7. Applied Data Science with Python Specialization by University of Michigan

Course Description

This is a specialization program and is a collection of 5 courses that introduce learners to data science through the python programming language. This skills-based specialization will help you learn popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx.

Prerequisite

You should have a basic python or programming background.

Course Details

Duration – 5 months
Skill Level – Intermediate
Rating – 4.5 stars (32,163 ratings)

Course Content

Course 1 – Introduction to Data Science in Python
Course 2 – Applied Plotting, Charting & Data Representation in Python
Course 3 – Applied Machine Learning in Python
Course 4 – Applied Text Mining in Python
Course 5 – Applied Social Network Analysis in Python

8. Managing Big Data with MySQL by Duke University

Course Description

It is a beginner level course specifically designed for Big Data aspirants. The course introduces you to using relational databases in business analysis, and understand how data should be collected in business contexts.

Course Details

Duration – 46 Hours
Skill Level – Beginner
Rating – 4.7 (2,884 ratings)

Course Content

Week 1 – Understanding Relational Databases
Week 2 – Queries to Extract Data from Single Tables
Week 3 – Queries to Summarize Groups of Data from Multiple Tables
Week 4 – Queries to Address More Detailed Business Questions
Week 5 – Strengthen and Test Your Understanding

9. Introduction to Big Data by the University of California San Diego

Course Description

The course is a part of the Big Data Specialization program and covers the core concepts of Big Data. It introduces the participants to one of the most common frameworks, Hadoop.

Prerequisite

You must have the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments.

Course Details

Duration – 2 weeks (Total – 17 Hours)
Skill Level – Beginner
Rating – 4.6 (7200 ratings)

Course Content

Week 1 – Welcome to the Big Data Specialization & Big Data: Why and Where
Week 2 – Characteristics of Big Data and Dimensions of Scalability & Data Science: Getting Value out of Big Data

10. Introduction to Deep Learning by National Research University Higher School of Economics

Course Description

This course is a part of the Advanced Machine Learning Specialization. You will be introduced to the concepts of modern neural networks and their applications in computer vision and natural language processing.

Course prerequisites

  • Basic knowledge of Python
  • Basic knowledge of linear algebra and probability

Course Details

Duration – 6 weeks (32 Hours)
Skill Level – Advanced
Rating – 4.6 (1500 ratings)

Course Content

  • Introduction to optimization
  • Introduction to neural networks
  • Deep Learning for images
  • Unsupervised representation learning
  • Deep learning for sequences
  • Final Project

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