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7 Top Rated Data Science Courses On Coursera To Become A Data Science Professional

7 Top Rated Data Science Courses On Coursera To Become A Data Science Professional

by Aratrika Dutta

September 11, 2021

Want to become a data science professional? Check out the list of the top rated data science courses on Coursera.

The field of data science is growing with increasing demand. Data science isn’t limited to consumer goods, technology, or healthcare. There is a strong demand for optimizing business processes using data science, banking, transportation to manufacturing. Organizations are now hiring data science professionals to deal with complex data. To become a data science expert, read the article and check out the list of top rated data science courses on Coursera.

Data science specialization

Johns Hopkins University

This specialization includes the concepts and tools that are needed throughout the data science channel, from asking the right types of questions to making inferences and publishing the results. In this course, you can apply the skills learned by developing a data product using real world data. At the end you will have a portfolio demonstrating your expertise in the material.

Data Science: Foundations Using the R Specialization

Johns Hopkins University

This specialization involves fundamental data science tools and techniques, including obtaining, cleaning and exploring data, programming in R, and performing reproducible research. Learners who complete this specialization will be ready to take the Statistics and Machine Learning specialization, in which they create a data product using real-world data. There are five courses in this specialization which are the same as those that make up the first half of the Data Science specialization.

Professional Certificate in Data Science


This professional certificate from IBM will help anyone interested in pursuing a career in data science or machine learning to develop skills and experience relevant to their career.

The program consists of 9 online courses that will provide you with the latest out-of-the-box tools and skills including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling and machine learning. algorithms. Students will learn data science through hands-on practice in the IBM cloud using real data science tools and real world datasets.

Python data structures

University of Michigan

This course will introduce the basic data structures of the Python programming language. We’ll move beyond the basics of procedural programming and explore how to use Python’s built-in data structures, such as lists, dictionaries, and tuples, to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the “Python for Everyone” manual. This course covers Python 3.

R programming

Johns Hopkins University

In this course, you will learn how to program in R and how to use R for efficient data analysis. You will learn to install and configure the software necessary for a statistical programming environment and to describe generic concepts of the programming language as implemented in a high level statistical language. The course covers practical statistical computational issues, including programming in R, reading data in R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and the commentary on the R code. The subjects of the statistical data analysis will provide working examples.

Applied Data Science with Python Specialization

University of Michigan

The 5 courses of this specialization at the University of Michigan introduce learners to data science via the python programming language. This skills-based specialization is intended for learners who have a basic background in Python or programming and who wish to apply techniques in statistics, machine learning, information visualization, text analysis and analysis. social media through popular python toolkits such as pandas, scikit-learn, nltk, and network to better understand their data.

Introduction to Data Science in Python (Course 1), Applied Tracing, Graphing and Representing Data in Python (Course 2), and Applied Machine Learning in Python (Course 3) should be completed in order and before any other courses in the specialisation. After completing them, courses 4 and 5 can be taken in any order. All 5 are required to obtain a certificate.

SQL for data science

University of california

This course is designed to give you an introduction to SQL fundamentals and working with data so that you can begin to analyze it for data science purposes. You will begin to ask the right questions and find the right answers to provide valuable information to your organization. This course starts with the basics and assumes you have no knowledge or skills in SQL. It will build on this foundation and gradually ask you to write simple and complex queries to help you select data from tables. You will start working with different types of data like strings and numbers and discuss methods to filter and refine your results.

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