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This blog has compiled a list of the 10 best data science Coursera courses for beginners. Therefore, if you are looking for some of the best data science courses for beginners, you should read this article until the end.
A career in Data Science is one of the most sought-after careers in the IT industry today. That’s the reason; data science knowledge can go a long way in deciding the employability factor.
The good news is that anyone at any stage of their life can pursue online courses from various online education portals available. This article highlights some of the best Data Science courses for beginners available on Coursera.
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10 Best Data Science Coursera Courses for Beginners
While picking the best data science courses for this list, we considered various factors such as instructors and their backgrounds; comprehensive course enrolls, and reviews. Most importantly, we also explored the course content to see if it would provide the best ROI to students.
Now, let’s explore these best data science beginners courses available on the Coursera platform without further ado.
1. IBM Data Science Professional Certificate
Anyone interested in pursuing a career in data science or machine learning will benefit from this IBM professional certificate, which will help them gain career-relevant skills and experience.
This program comprises nine online courses that will teach you how to use open-source resources and libraries, Python, databases, SQL, data visualization, data processing, statistical analysis, predictive modeling, and machine learning algorithms, among other things.
You will learn data science through hands-on experience in the IBM Cloud using real-world data sets and fundamental data science tools. Until you complete this course, you will have developed a portfolio of data science projects, giving you the courage to dive into an exciting career in data science. In addition to a Coursera Professional Certificate, you’ll obtain a digital Badge from IBM recognizing your data science expertise.
Offered by– IBM
Rating– 4.6
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2. Data Science: Foundations using R Specialization
This Specialization covers fundamental data science tools and techniques, such as exploring data, programming in R, and conducting repeatable research. Students who take up this course will be required to create a data product using real-world data.
This specialization is designed for students who want to finish the foundational portion of the curriculum before moving on to more advanced Data Science topics such as statistics and machine learning.
Offered by: Johns Hopkins University
Rating: 4.6
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3. Data Science Specialization
This one is a ten-course introduction to data science. This specialization covers the principles and resources you’ll need in the data science pipeline, from asking the right questions to making inferences and reporting findings.
You’ll also learn to clean, analyze, and visualize data using R. You will also learn to handle data science projects and use GitHub. You will use regression models for regression analysis, least squares, and inference.
In the Capstone Project, you’ll put your newfound skills to use by creating a data product based on real-world data. Once this course is completed, students will be ready with a portfolio demonstrating their mastery of the subject.
Offered by: Johns Hopkins University
Rating: 4.5
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4. Data Science Fundamentals with Python and SQL Specialization
This Data Science program consists of four self-paced online courses that will teach you the fundamentals of Data Science, such as open-source software and repositories, Python, Statistical Analysis, SQL, and relational databases.
You will learn these data science prerequisites through hands-on experience with genuine data science software and real-world data sets. Once you complete these courses, you will have the practical knowledge and expertise to dive further into Data Science and work on more advanced Data Science ventures.
Offered By: IBM
Rating: 4.6
5. Data Science Fundamentals Specialization
This specialization course debunks the myths about data science and introduces learners to core data science skills, strategies, and concepts. It starts with the fundamentals like the cross-industry standard process for data mining, data diagnostics, and analytics taxonomy.
It also compares data science to traditional statistical techniques. The course also covers the most popular data science techniques: data processing, mathematical modeling, data engineering, large-scale data manipulation, data mining algorithms, data quality, remediation, and accuracy operations.
Offered by: University of California Irvine
Rating: 4.3
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6. Applied Data Science Specialization
This exciting Specialization is designed for data science enthusiasts who want to learn how to solve real-world data problems. This curriculum allows you to pursue a career in data science and already have foundational skills or have completed the Introduction to Data Science Specialization.
This four-course specialization will give you the knowledge and skills to analyze data and make data-driven business decisions using computer science and statistical analysis.
You’ll learn Python without the need for any previous programming experience in it, as well as data analysis and visualization techniques. You’ll practice predictive modeling and model selection and learn how to tell a convincing story with data to guide decision-making using tools like Numpy and Pandas.
You’ll get hands-on experience solving fascinating data problems from start to finish through guided seminars, labs, and projects in the IBM Cloud.
Before moving deeper into big data, AI, and deep learning, take this specialization to solidify your Python and data science skills.
You’ll get a digital badge from IBM identifying you as an expert in applied data science and a Coursera specialization certificate.
Offered by: IBM
Rating: 4.6
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7. Executive Data Science Specialization
This course teaches you everything about starting a data science enterprise. This is like a crash course in data science, so you’ll be up to speed on the subject and know what your job as a leader entails.
You’ll also learn how to build a team of complementary skill sets and positions by recruiting, assembling, evaluating, and developing them. You’ll learn about the data science pipeline’s structure, the priorities of each level, and how to keep your team on track.
Finally, you’ll learn practical skills to help you solve the common issues that cause data science projects to fail.
Offered By: Johns Hopkins University
Rating: 4.5
Also Read: Introduction to Data Science and Analytics
8. Applied Data Science with Python Specialization
This specialization course will introduce the learners to data science using Python programming.
This skills-based specialization is for learners with a basic understanding of Python or programming who want to use common Python toolkits like pandas, matplotlib, scikit-learn, nltk, and networkx to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques to gain insight into their data.
You should take the first three courses in the specialization in that order. After that, you may take courses 4 and 5 in any order. To receive a certificate, you must complete all five courses.
Offered by: University of Michigan
Rating: 4.5
9. Statistics with Python Specialization
This program teaches primary and intermediate statistical analysis principles using Python programming. Learners can discover where data comes from, what forms of data can be gathered, design, organize, and conduct data discovery and visualization effectively.
They can use data to estimate and evaluate hypotheses, build confidence intervals, interpret inferential effects, and use more sophisticated statistical modeling procedures.
Finally, they can understand the significance of research questions and relate them to the statistical and data analysis methods taught.
Offered by: University of Michigan
Rating: 4.6
10. SQL for Data Science
This beginner-level course has four modules in total, and when you finish it, you will have learned skills like data science, data processing, Sqlite, and SQL.
In this course, you’ll learn how to identify a subset of data from a column or set of columns you need and write a SQL query to restrict the results to that subset.
Later in this course, you will learn how to manipulate strings, dates, and numeric data using functions to incorporate various sources into fields with the correct format for analysis.
Offered by: University of California, Davis
Rating: 4.6
Summary
With this, we summarize our list of the 10 best data science Coursera courses for beginners. This article will help you pick the right data science course and kickstart your journey to becoming a successful data scientist. Subscribe to our newsletter to receive such articles straight to your inbox.
