MOOCs and online courses have been getting huge popularity over the last few years. The year 2020 has been huge for MOOC providers. MOOCs saw exponential growth during the COVID-19 pandemic. Enrollment at Coursera – one of the most popular MOOC providers, has skyrocketed and was 640% higher from mid-March to mid-April than during the same period last year.

Even at Stoodnt (an aggregator of MOOCs), we saw a 120% increase in enrollments. 90% of the paid learners signed up for online courses from Coursera on Stoodnt in 2020. In this post, we will have a quick look at the best courses on Coursera for the year 2020.

12 Best Courses on Coursera 2020

12. AWS Fundamentals

Percentage of Paid Learners who bought this Course on Stoodnt.com: 1%

Appx. Time to Finish the Course: 11 Hours

This course will introduce you to Amazon Web Services (AWS) serverless architecture. Through demonstrations and hands-on exercises, you’ll learn skills in building and deploying serverless solutions.

Using real-world examples of a serverless website and chatbot, you’ll build upon your existing knowledge of the AWS cloud to take advantage of the benefits of modern architectures for greater agility, innovation, and lower total cost of ownership across a range of AWS services, including AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and Amazon Lex.

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11. Mathematics for Machine Learning Specialization (Imperial College London)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 2%

Appx. Time to Finish the Course: 4 Months (4 hours per week)

For a lot of higher-level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics – stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science.

This Specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. Additionally, it also includes one applied project.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization, you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

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10. Learn SQL Basics for Data Science (UC Davis)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 2%

Appx. Time to Finish the Course: 4 Months (5 hours per week)

This Specialization is intended for a learner with no previous coding experience seeking to develop SQL query fluency. Through four progressively more difficult SQL projects with data science applications, you will cover topics such as SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more.

These topics will prepare you to apply SQL creatively to analyze and explore data; demonstrate efficiency in writing queries; create data analysis datasets; conduct feature engineering, use SQL with other data analysis and machine learning toolsets; and use SQL with unstructured data sets.

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9. Deep Learning Specialization

Percentage of Paid Learners who bought this Course on Stoodnt.com: 3%

Appx. Time to Finish the Course: 4 Months (5 hours per week)

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects.

You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing.

You will master not only the theory but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which the instructors will teach.

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8. Genomic Data Science Specialization (Johns Hopkins University)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 3%

Appx. Time to Finish the Course: 10 Months (2 hours per week)

With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome.

This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, Bioconductor, and Galaxy.

This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work.

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7. Python 3 Programming Specialization (University of Michigan)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 5%

Appx. Time to Finish the Course: 5 Months (7 hours per week)

This specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance.

You will have lots of opportunities to practice. You will also learn ways to reason about program execution, so that it is no longer mysterious and you are able to debug programs when they don’t work.

By the end of the specialization, you’ll be writing programs that query Internet APIs for data and extract useful information from them. And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer.

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6. Applied Data Science with Python Specialization (University of Michigan)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 5%

Appx. Time to Finish the Course: 5 Months (7 hours per week)

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

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5. Python for Everybody (University of Michigan)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 9%

Appx. Time to Finish the Course: 8 Months (3 hours per week)

This Specialization builds on the success of the Python for Everybody course and will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In the Capstone Project, you’ll use the technologies learned throughout the Specialization to design and create your own applications for data retrieval, processing, and visualization.

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4. Data Science: Foundations using R (Johns Hopkins University)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 11%

Appx. Time to Finish the Course: 5 Months (8 hours per week)

This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data.

The five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization. This specialization is presented for learners who want to start and complete the foundational part of the curriculum first, before moving onto the more advanced topics in Data Science: Statistics and Machine Learning.

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3. Statistics with R Specialization (Duke University)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 11%

Appx. Time to Finish the Course: 7 Months (3 hours per week)

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.

You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.

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2. IBM Data Science Professional Certificate

Percentage of Paid Learners who bought this Course on Stoodnt.com: 16%

Appx. Time to Finish the Course: 10 Months (5 hours per week)

This program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open-source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.

Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.

In addition to earning a Professional Certificate from Coursera, you’ll also receive a digital badge from IBM recognizing your proficiency in data science.

This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you practical skills with applicability to real jobs, including:

• Tools: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
• Projects: random album generator, predict housing prices, best classifier model, battle of neighborhoods

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1. Data Science Specialization (Johns Hopkins University)

Percentage of Paid Learners who bought this Course on Stoodnt.com: 20%

Appx. Time to Finish the Course: 11 Months (7 hours per week)

This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

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Featured Image Source: Forbes

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