Finding the Best Online Data Science Courses in 2021
Data science has applications in many different areas, including business, healthcare, cybersecurity, and finance. That’s why the demand for data science skills has skyrocketed over the past decade. The Bureau of Labor Statistics (BLS) predicts 15 percent job growth between 2019 and 2029, way above average.
The lucrative salary is another reason many people are now looking to pursuing a career in data science. According to PayScale, the average annual salary of a data scientist is $96,501. In this article, we will explore the best online data science courses out there to help you get started.
What Is Data Science?
Data science is a blend of data analysis, statistics, probability, machine learning, domain expertise, and computer science.
Data science is a form of scientific investigation to solve complex problems, all through data. It uncovers the hidden truth behind data and extracts valuable insights from it. The diversity of data types and techniques make it a challenging but exciting field to work in.
The Best Online Data Science Courses of 2021
Finding the best online data science course can be difficult. The Internet houses tons of different courses on data science, but they’re not all equally good. It’s challenging to filter out the best courses to get you started.
Here we’ll review the best online data science courses.
Applied Data Science With Python Specialization by Coursera
Length: 21 to 22 weeks
The University of Michigan offers this practical program through Coursera. The prerequisites are to learn Python before starting or have a programming background. It is a skill-based program involving hands-on experience with Python.
It features applied data analytics, machine learning, information visualization, and text analysis. All of these are functional domains and integral to the data science process. After this course, participants will be able to work with diverse datasets and extract useful information from them.
CS109 by Harvard
Length: 13 weeks
This course, which Harvard provides for free, offers a great mix of theory and application. It is highly effective for beginners because it covers every stage of the data science pipeline. It doesn’t award any certification, but it has an exceptionally well-rounded curriculum and covers a lot of ground.
Python’s libraries are used throughout this course. It offers an introduction to Python, but some prior familiarity is recommended to get the most out of it.
Data Science A-Z by Udemy
Length: Three to four weeks
The data science pipeline involves multiples stages, and this course focuses on four in particular. It first looks at data visualization, providing vital training in Tableau, a business intelligence tool. It then looks at modeling and data preparation. Finally, it touches on how best to communicate and present data science statistics.
Data Science for Business Leaders by Udacity
Length: Four to eight weeks
Data Science for Business Leaders is different from conventional data science courses because it adopts a business perspective. Businesses and organizations favor data-driven decisions these days, and this shift in trend requires business leaders and managers to acquire data science skills.
The prerequisites are a basic understanding of statistics and linear algebra. This course provides business leaders and managers with strategies for solving the human capital, technological, and management challenges of integrating data science into businesses.
Data Science MicroMasters by edX
Length: 43 weeks
This is a well-rounded course that involves learning a lot of mathematical concepts as well as the applied techniques and methodologies. In addition, participants will learn the fundamentals of statistics, probability, and machine learning using Python.
The applied techniques you’ll learn include acquiring and cleaning a dataset, using statistical and machine learning models to support decisions based on the data, and representing valuable insights through modern data visualization tools.
Data Science Specialization by Coursera
Length: 48 weeks
This course focuses on the data science pipeline, starting with data cleaning in the R programming language and building up to statistical inferences and data visualization. It provides a broader perspective of data science.
Another benefit of the course is that it is focused on industry-relevant technologies. For example, it touches upon Github, a code repository and version control tool. Github is popular in the software industry and almost every job requires Github knowledge.
Data Science: The Big Picture by PluralSight
Length: One week
This is an introductory course that gives you an overview of data science, its applications, and future trends. Like its name suggests, this course focuses on the big picture, and it is recommended for absolute beginners; it’s more of an introductory session than a full-fledged training course.
Beginners will gain insight into how to put big data into action. The course explores the basics of things like data analytics, the Internet of Things, and machine learning.
Elements of Data Science MicroBachelors by edX
Length: 26 weeks
This course is aimed at people who are looking to pursue a career in machine learning specifically. Participants learn signal processing and machine learning algorithms as well as tools to apply them to real-world problems.
It focuses heavily on the mathematical concepts that underlie signal processing and machine learning. The prerequisites for this course are thus calculus, linear algebra, and basic programming knowledge.
IBM Data Science by edX
Length: 56 weeks
The curriculum of this course is designed to teach the basics of data science by applying them to real problems. It covers many data science topics, including open-source libraries, Python, databases, and SQL.
A significant portion of the course is dedicated to data analytics, machine learning, and data visualization, the core building blocks of data science. In addition, participants get a professional certificate from IBM, which is a valuable addition to their resume.
Introduction to Data Science by Metis
Length: Six weeks
Python is well-known for its data science libraries. This course helps build a strong foundation in data science and trains participants to solve actual problems from a statistical perspective.
This course gives you an overview of the entire data science process, starting with introductory linear algebra and statistics all the way up to machine learning and advanced data modeling. This course requires you to have basic familiarity with Python, statistics, and some linear algebra.
Introduction to Data Science Using Python by Udemy
Length: One to two weeks
This course is specially designed for absolute beginners in data science. If you are interested in learning more about data science, this course will explain everything you need to know.
It doesn’t dive into intricate machine learning algorithms or complex mathematical questions. Instead, it focuses on the introduction and applications of data science. It could be a good starting point for beginners seeking a general overview of data science.
Introduction to Machine Learning for Data Science by Udemy
Length: Two to three Weeks
Machine learning is crucial for data science. That’s why a good grasp of machine learning algorithms, tools, and techniques is essential to excel in the field. This course provides a good introduction to machine learning and its applications. It is aimed at beginners, having no prerequisites other than high-school mathematics.
Learn Data Science With R by Udemy
Length: Two to three Weeks
This course gives an overview of the R programming language and its applications in data science. R provides a diverse set of data structures to store and process data, and learning these is crucial in data science. This course suits beginners with little programming knowledge but some familiarity with basic maths and stats.
Python for Data Science and Machine Learning by Udemy
Length: Two to three Weeks
This course is focused on Python and libraries like NumPy, pandas, scikit-learn, and Matplotlib. These libraries are useful for data analysis and data visualization. The course will also give you an introduction to machine learning, decision trees and random forests, and natural language processing.
The course is all about hands-on experience using real-world datasets, not really going into theoretical concepts. Knowing the fundamentals of Python is recommended to move forward in data science, and this course will help you grasp the basic but crucial concepts.
Statistics and Data Science MicroMasters by edX
Length: 56 weeks
This program consists of four courses and a virtual exam. It aims to teach you the fundamentals of data science and give you hands-on experience in data analytics and machine learning.
Familiarity with the basics of Python and calculus is recommended to excel in this course. This program provides participants with rigorous training in data science and helps them build strong core skills.
What Do Data Science Courses Cover?
Lengthier data science courses often include modules from all or some of the building blocks of the data science pipeline, including topics like machine learning, data analytics, and visualization. In contrast, specialized courses focus on one particular area to help you develop a deeper understanding of that subject. Because data science is such a diverse field, spanning many areas of expertise, courses can cover a wide variety of topics.
How to Choose the Best Data Science Course
There are hundreds of courses online, so choosing the right one can be a daunting task. There are many factors at play when deciding what the best option is for you.
Here we look at three key factors you should consider before taking a course.
Data science courses have different levels of difficulty and, therefore, different prerequisites. A typical data science course can require basic knowledge of mathematics, statistics, and programming; some introductory courses don’t have any prerequisites at all. That’s why we recommend always checking the requirements.
Different courses will use different programming languages. Python and R are well-known languages when it comes to data science. Both of these have powerful libraries to support data science processes.
For beginners, it’s always difficult to pick a programming language. Generally, you’ll find more data science courses using Python than R. It’s a good idea to choose a language before taking up a course.
The content is a crucial factor because it varies a lot from course to course. A good data science course tends to cover both mathematical concepts and applied techniques. If possible, pick a course that gives an overview of the whole data science pipeline and provides hands-on experience along with theoretical knowledge.
Data Science Certifications
Getting a professional certification indicates your expertise in a particular field, which will be attractive to potential employers. Certification is essentially a license that shows that you’re capable of doing a particular job. Here are some of the well-known professional data science certification programs.
Data Science Council of America (DASCA) Senior Data Scientist (SDS)
DASCA is designed for professionals with five-plus years of experience in research and data analytics. It requires knowledge of statistical analysis, SPSS/SAS, R, RDMS, and object-oriented programming. The program comprises five tracks, each with its own requirements.
Dell EMC Data Science Track (EMCDS)
EMCDS offers two tracks, the Data Science Associate v2 (DCS-DS) and the Data Science Specialist (DCS-DS) certification. The associate covers the fundamentals of big data and data analytics while the specialist level covers more advanced topics like natural language processing, Hadoop, Pig, and advanced analytics.
Data Science Council of America (DASCA) Principal Data Scientist (PDS)
DASCA-PDS is designed for professionals with ten-plus years of experience in big data. It spans advanced topics in data science like analytics, machine learning, and natural language processing. There are three tracks for completing the certification.
Next Steps After Finishing Your Course
After completing an online course, the next important step is practicing what you’ve learned. A good place to start practicing is Kaggle, a community for data scientists and machine learning practitioners. At Kaggle, you can find open-source code and datasets to work with.
If you’re motivated enough, you can find an internship or a training program, which will add a lot of value to your portfolio and job opportunities. Finally, when you’re confident enough to work on real-world problems, start looking for junior data science jobs and show them what you’re capable of.
Data science is an emerging field with great potential. Some would say it is one of the hottest jobs of the 21st century. The role of a data scientist spans many areas and industries, making it an exciting field full of opportunities. You could end up in areas like machine learning, data analytics, and visualization, or database administration.
Different organizations require different sets of skills when they’re looking for a data scientist. It can be daunting for beginners to wrap their heads around all the concepts and tools they need to learn. However, the fundamental goal behind all these concepts and tools is the same: deriving useful insights from data.