Best Rated Data Science Courses on Coursera

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

To learn more about data science, read our blog – What is data science?

Criteria

These data science courses are picked basis the following criteria –

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

Top Data Science Courses on Coursera

Data Science as a Field

Course Description

Data Science as a Field is a specifically designed course for aspiring data scientists, content experts who work with data scientists, or anyone interested in learning about what Data Science is and what it’s used for.

Course Details

Rating – 4.9
Duration – 4 weeks
Skill Level – Intermediate

Syllabus

1. Introduction to Data Science: the Past, Present, and Future of a New Discipline
2. Data Science in Industry, Government, and Academia
3. Data Science Process and Pitfalls
4. Communicating Your Results

GIS, Mapping, and Spatial Analysis Specialization

Course Description

This specialization will help you gain a better understanding of GIS and Mapping. It also includes an applied learning capstone project.

Course Details

Rating – 4.9
Duration – 6 months
Skill Level – Beginner

Syllabus

Course 1 – Introduction to GIS Mapping
Course 2 – GIS Data Acquisition and Map Design
Course 3 – Spatial Analysis and Satellite Imagery in a GIS
Course 4 – GIS, Mapping, and Spatial Analysis Capstone

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
Duration – 6 Hours
Skill Level – Beginner

Syllabus

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

How to Win a Data Science Competition: Learn from Top Kagglers

Course Description

In this course, you will learn to gain practical experience, improve and harness your data modeling skills in various domains such as credit, insurance, marketing, natural language processing, sales forecasting, and computer vision. This is an advanced-level course.
Prerequisite
– Python: work with DataFrames in pandas, plot figures in matplotlib, import and train models from sci-kit-learn, XGBoost, LightGBM.
– Machine Learning: basic understanding of linear models, K-NN, random forest, gradient boosting, and neural networks.

Course Details

Duration – 5 weeks
Skill Level – Advanced
Rating – 4.7

Syllabus

• Introduction & Recap
• Exploratory Data Analysis
• Metrics Optimization
• Hyperparameter Optimization
• Competitions go through

Practical Data Science with MATLAB Specialization by MathWorks

Course Description

This specialization is among the best-rated data science courses on Coursera that includes 4 courses. This specialization covers the concepts of MATLAB and you be provided with free access to MATLAB for the duration of the specialization to complete your work.

Prerequisite

You should have some knowledge of basic statistics (histograms, averages, standard deviation, curve fitting, interpolation)

Course Details

Duration – 5 months
Skill Level – Beginner
Rating – 4.7

Syllabus

1. COURSE 1- Exploratory Data Analysis with MATLAB
2. COURSE 2 – Data Processing and Feature Engineering with MATLAB
3. COURSE 3 – Predictive Modeling and Machine Learning with MATLAB
4. COURSE 4 – Data Science Project: MATLAB for the Real World

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
Duration – 8 Hours
Skill Level – Beginner
Language – English

Syllabus

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

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

Rating – 4.7
Duration – 46 Hours
Skill Level – Beginner

Syllabus

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

Business Statistics and Analysis Specialization

Course Description

You will develop a basic understanding of business data analysis tools and techniques, and will master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling.

Course Details

Rating – 4.7
Duration – 5 months
Skill Level – Beginner

Syllabus

1. Introduction to Data Analysis Using Excel
2. Basic Data Descriptors, Statistical Distributions, and Application to Business Decisions
3. Business Applications of Hypothesis Testing and Confidence Interval Estimation
4. Linear Regression for Business Statistics
5. Business Statistics and Analysis Capstone

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

Rating – 4.6
Duration – 2 weeks
Skill Level – Beginner

Syllabus

1 – Welcome to the Big Data Specialization & Big Data: Why and Where

2 – Characteristics of Big Data and Dimensions of Scalability & Data Science: Getting Value out of Big Data

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 understanding of linear algebra and probability

Course Details

Rating – 4.6
Duration – 6 weeks
Skill Level – Advanced

Syllabus

1. Introduction to optimization
2. Introduction to neural networks
3. Deep Learning for images
4. Unsupervised representation learning
5. Deep learning for sequences
6. Final Project

Statistics for Data Science with Python

Course Description

This course will introduce you to the basic principles of statistical methods and procedures used for data analysis. You will learn about important statistics topics such as data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA, and regression and correlation analysis.

Course Details

• Rating – 4.6
• Duration – 6 weeks (32 Hours)
• Skill Level – Intermediate

Course prerequisites

Basic knowledge of Python programming language, Jupyter notebooks, and libraries.

Syllabus

1. Course Introduction and Python Basics and Introduction & Descriptive Statistics
2. Data Visualization
3. Introduction to Probability Distributions
4. Hypothesis testing
5. Regression Analysis
6. Project Case: Boston Housing Data

Data Science Fundamentals with Python and SQL Specialization by IBM

Course Description

With the help of this course, you can develop hands-on experience with Jupyter, Python, SQL. You will also learn how to perform Statistical Analysis on real data sets.

Course Details

Rating – 4.6
Duration – 6 months
Skill Level – Beginner

Syllabus

1. Tools for Data Science
2. Python for Data Science, AI & Development
3. Python Project for Data Science
4. Statistics for Data Science with Python
5. Databases and SQL for Data Science with Python

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
Skill Level – Beginner
Rating – 4.6

Syllabus

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

Google Cloud Platform Big Data and Machine Learning Fundamentals

Course Description

It is one of the top-rated data science courses on Coursera. The course will help you to learn about Big Data and Machine Learning capabilities of the Google Cloud Platform (GCP). Explore the Google Cloud Platform and dive deeper into the data processing capabilities.

Course Prerequisites

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

Syllabus

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

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 real-world data science project challenges.

Course Details 

Rating – 4.5
Course Duration – 2 months
Efforts – 4 hours per week

Syllabus

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

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

Syllabus

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

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
Duration – 7 Months
Skill Level – Beginner

Syllabus

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

If you have recently completed a professional course/certification, click here to submit a review.