Discover the Best Online Data Science Courses of 2021
Learning online is the easiest way to pick up any skill, be it academic or not. In these days of remote learning, it is even easier to use the Internet to teach yourself a new subject.
If you are into numbers and calculations, you might want to look into becoming a data scientist. Data science is the backbone of modern marketing and production cycles. It is evident that data scientists earn large salaries due to the immense value that they add to companies.
This guide surveys some of the best online data science courses to help you get started.
What Is Data Science?
Data science is a branch of mathematics that deals with data trends. Insights into these trends help businesses make decisions and improve their products.
Data science has been gaining popularity among businesses. It’s believed to boost marketing as well as product pipelines. This is one of the biggest reasons companies are keen on hiring data scientists and paying them well.
If you are well versed in high school mathematics and are keen to learn more, working in data science is a great career option for you.
Breakdown: The Top 10 Online Data Science Courses
Considering the market demand, you’d be well advised to take an online data science course. There are many great free and paid courses available online to introduce you to the world of data-driven development.
As you prepare to start your educational journey, check out our list of the best online data science courses in 2021. Keep in mind that the listed prices are subject to change.
|Provider and Course||Certificate||Length||Price|
|Coursera | Johns Hopkins University
Data Science Specialization
|Coursera | University of Michigan
Applied Data Science with Python
|edX | MIT
MicroMasters Program in
Statistics and Data Science
|edX | Harvard
in Data Science
Intro to Machine
Learning with PyTorch
Machine Learning A-Z
Python for Data Science
and Machine Learning Bootcamp
What Can I Learn in a Data Science Course?
Nearly all data science courses focus on both theory and practice. The theory sections teach you the origins of data science and the most popular techniques and algorithms used to draw out trends from large volumes of data.
Once you understand the concepts well, you learn how to utilize modern programming languages and libraries to apply these concepts to real-life data. This practical section helps you to get familiar with the real-life scenarios and requirements of a data science task.
Due to the diversity of tools used in data science, it is vital to choose your course carefully. Some courses teach the theoretical concepts well but use outdated methods. Other courses might omit the theoretical section and jump right into practical applications. Whichever course type resonates with you is the one you’ll get the most out of.
The Best Data Science Courses of 2021
To choose the right course for you, you need more than a title and a price tag. Below is a more detailed description of each of the online courses mentioned in the table above.
Data Science Specialization | Johns Hopkins University (Coursera)
Subjects Covered: R programming, data pre-processing, data analysis, regression, machine learning
This data science course is offered by John Hopkins University on Coursera, and it teaches R programming with the perfect mix of theory and application. One of the best things about this resource is that it includes a complete section on statistics, which most courses miss.
Having some prior programming experience and a good understanding of algebra will help you learn R in this course. Previous knowledge of linear algebra and calculus isn’t necessary, but it will help.
Key Takeaway: This specialization offers extensive insights into the various concepts that make data science the reliable technology it is today.
Applied Data Science with Python Specialization | University of Michigan (Coursera)
Subjects Covered: Data visualization, machine learning, text mining
Offered by the University of Michigan, this specialization focuses on the applied side of data science. It helps you get a strong introduction to the most commonly used Python data science libraries like matplotlib, pandas, NLTK, and scikit-learn.
Since this specialization focuses on applying data science in reality, it does not cover much of the statistics needed to understand data science. You don’t get to learn how the various machine learning algorithms are derived, but you do get to apply them to real data.
Key Takeaway: This is a great resource for those who are well versed in data science theory and are hoping to be trained on the applied front.
MicroMasters Program in Statistics and Data Science | MIT (edX)
Subjects Covered: Probability and statistics, data analysis, machine learning
With its inclusion of probability and statistics courses, this series from the Massachusetts Institute of Technology (MIT) is an all-around curriculum to understand data science intuitively. This series covers a lot more of the theory behind data science and hence is a great alternative if you want to understand how things work.
The series does not provide an introduction to Python or R programming, so before beginning with the machine learning segment, it’s a good idea to learn Python in an introductory course.
Key Takeaway: This is a relatively advanced course on data science. It is a great resource if you have had some experience with the math behind data science as well as Python programming.
Professional Certificate in Data Science | Harvard (edX)
Subjects Covered: Data visualization, data modeling, data wrangling, regression, machine learning
The HarvardX Data Science Certificate enables learners to pick up essential skills and knowledge for handling real-world data analysis challenges. The specialization covers multiple concepts, which include inference, machine learning, and regression.
Apart from that, you will also learn the techniques of implementing machine learning algorithms using superior tools. The program allows you to gain a more extensive knowledge of data science concepts via case studies.
Key Takeaway: The certificate course helps you pick up theoretical as well as practical understanding of data science using machine learning concepts.
CS109 Course | Harvard
Subjects Covered: Data reshaping, data cleanup, data analysis, SQL, statistical models, regression, classification
Offered by Harvard, this is a highly detailed course on data science for beginners. Although the learning platform is not highly interactive, the course makes up for it with its content. This course is meant solely for learning, and it does not offer any type of certification. Having said that, this course is absolutely worth investing your time and is available totally for free.
Key Takeaway: This is a great course for learning data science from scratch. It does not offer a certificate, but it offers some of the best beginner-friendly content on the Internet.
Introduction to Data Science | Metis
Subjects Covered: Statistics, data analysis, data visualization, data modeling
Introduction to Data Science is a six-week-long live course that covers nearly everything in the data science domain. Furthermore, apart from the completion certificate, you are also eligible to receive continuing education units, as this course is accredited.
You will join the instructor along with other students two nights per week, similar to an online college course. You will be able to ask questions, as well as interact with the instructor to ease your doubts.
Key Takeaway: As one of the top live resources on the topic, this course does a great job of teaching you math and its applications with a human touch.
Intro to Machine Learning with PyTorch | Udacity
Subjects Covered: Neural networks, model construction, PyTorch, deep learning
This Nanodegree program is an excellent option to improve your skills and knowledge in cleaning data, building supervised models, and understanding machine learning algorithms.
Additionally, learners will also explore other significant topics like deep learning. The program has been divided into different steps, each one helping learners test their skills through projects and exercises.
Key Takeaway: This specialization focuses on teaching learners neural networks and deep learning using PyTorch. It is a great resource to take up if you are looking to build your model construction skills using PyTorch.
Machine Learning A-Z | Udemy
Subjects Covered: Data processing, regression, classification, clustering, reinforcement learning
This course uses Python and the R programming languages to teach the various concepts of machine learning. It is a detailed course with ample content on all phases of a data analysis process, including processing, regression, classification, clustering, model construction, and more.
It also features a section on natural language processing that helps you to take your first steps into the world of language analysis.
Key Takeaway: It is a great course for those looking for descriptive content on machine learning using Python and R. The abundance of content makes for a good resource in all respects.
Python for Data Science and Machine Learning Bootcamp | Udemy
Subjects Covered: Python, machine learning, natural language processing, big data, neural networks
The course does an excellent job of explaining the Python language and the various roles it plays in the data science process. It also covers data visualization in detail, which helps beginners understand the impact that their code creates on the data.
An integral part of this course is the evenly distributed assignments. Learners can take them up on their own and can later check their answers against the instructor’s solutions.
Key Takeaway: This is a great course for understanding the language and visualization implemented in data science. Assignments help learners keep in sync with the course, as well as experience a college-like interaction.
Subjects Covered: Data handling, algorithms, TensorFlow, regression, matrix manipulation
On top of all that, you also get to learn how to create programs compatible with both Node.js and browser environments. The course also explains how to speed up matrix-based programs using the basics of linear algebra.
Professional Data Science Certifications
Professional certifications are a useful way to validate your skills and catch the attention of recruiters. Below is a short list of data science professional certifications that can get you hired faster.
Open Certified Data Scientist (Open CDS)
The Open CDS certification does not require the candidate to take any training courses or exams. It is rather an experience-based certification. Once you have it, it never expires.
Open CDS offers certifications for three levels of data science knowledge and expertise. After becoming a Certified Data Scientist, you can start working towards Master Certified Data Scientist and Distinguished Certified Data Scientist.
Google Professional Data Engineer Certification
This certification is meant for those with a foundational knowledge of the Google Cloud Platform (GCP). To earn this certification, you must complete an exam, which will test your ability to design, manage, and deploy machine learning models and data processing systems on GCP.
It is recommended that you have at least three years of professional experience. For best results, the candidate should have at least one year of experience using GCP.
Microsoft Certified: Azure Data Scientist Associate
This Microsoft certification emphasizes a candidate’s ability to deploy and manage machine learning models and applications using Azure, Microsoft’s cloud computing service. Candidates are tested on artificial intelligence, machine learning, natural language processing, computer vision, and data analytics.
Before applying, you should have some expertise in deploying and managing resources, as well as in implementing guidelines for using the resources properly.
How Much Can I Earn as a Data Scientist?
According to PayScale, data scientists are paid an average salary of about $96,501. Data science is a long-term career option, and senior data scientists are paid around $122,000 annually. These figures indicate that data scientists have a huge impact on a business’s growth, and thus they are compensated generously.
Should You Learn Data Science in 2021?
After going through a long list of data science courses, you should take a moment and assess your needs before jumping in. Data science is a high-paying field, but it requires a lot of dedication and preparation. If you are not into mathematics or statistics, you might find data science a little difficult to pick up.
It is always recommended to take a few introductory lessons on free platforms such as YouTube to see if you enjoy learning and working in this field. Once you are determined to dive into data science, come back here to find the best resources all in one place.