Coursera Data Science Review
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You hear everywhere that IT jobs are very much in demand, and it sounds like a very lucrative career. But if you don’t have the necessary background knowledge, skills, and qualifications, you simply aren’t going to land that dream job.
Worst case scenario, you could instead end up in a job you despise. Just plodding through day to day, dreaming of what might have been, if you had only trained in a job that truly challenged you, that truly stimulated you, that truly was you.
But don’t worry, there’s a solution to hand. You can train in a challenging and stimulating role in the ever-growing field of data science.
Introduction
Working as a Data Scientist
Data Science is a growing field of study and work, and the role of data science was previously THE top job on Glassdoor’s annual list for a whopping 4 years in a row.
In 2020, the role dropped a mere two places to third, rivaled only by other IT jobs. So it’s a field of work that’s still very sought after to this day.
The average salary for a data scientist in the USA is currently about $111,200, so is very lucrative and would certainly appeal to anyone who felt they had what it takes.
Better yet, also according to Glassdoor, data scientists also reported an average job satisfaction rating of a very nice 4.25 stars out of a possible 5 stars. Not bad, huh?
Courses in Data Science
But to get ahead in the field you will need the right course behind you. How else would you be able to prove that you have the knowledge, the skills, and the ability?
There are a lot of courses out there in data science. But you need one that will adequately prepare you for the job and one that can persuade your next employer that you can do exactly what’s required of you.
That is why it’s important to sit down and take the time to read reviews of courses in data science to determine which is the right course for you, considering all things.
The Coursera Learning Platform
As you probably already know, there are several online learning platforms out there. And half the battle is picking the right one to meet your needs.
And you’re right to be looking into platforms such as Coursera. Their courses often come from top universities, such as Yale, Michigan, and Stanford. And from leading companies such as Google and IBM.
Coming Up
There are actually several different data science courses on the Coursera platform, but this article is going to concentrate on Coursera’s Data Science Specialization program, which is actually made up of ten separate courses in the field of Data Science.
We chose this one because it is one of the more thorough data science programs available on the platform.
In this article, we’ll begin by giving you an overview of the course, before introducing some of the main instructors. Then we’ll lay out the prerequisites for you, before presenting you with a breakdown of the program and its courses and modules.
We’ll also discuss the length of the course, how much it costs, and what students have said about it. Then, we’ll talk about the job market, before wrapping up with a discussion about whether it might be the right course for you.
About the Course
Coursera’s Data Science Specialization program has attracted a great many students, over 420,000 in fact, at the time this article was written.
It is provided by John Hopkins University and is taught predominantly by just 3 main instructors.
The program has been rated by students at 4.5 stars out of a possible 5 stars, which is impressive.
With its 10-course components and the length of time that it takes to complete the course, it tends to only attract those who are serious about getting into data science.
And we are pleased to report that a large percentage of students who’ve taken the course have had improved job prospects on completion of the course. Specifically, 38% of students started a new career, and 19% received a pay rise or promotion.
It’s 100% online and you will not have to attend any classes in person. There are graded assignments and quizzes, and you will always receive feedback on your work.
So, that’s the overview, now to get into the nitty-gritty.
How Coursera Specialization Programs Work
A Coursera Specialization program is a series of courses that helps you master a particular skill. In this case, data science.
As we mentioned previously, Coursera’s data science specialization is composed of 10 separate courses. You can choose which course you want to start with, and when you enroll in that course, you will automatically be enrolled in the specialization.
However, they do recommend that you take the Data Scientist’s Toolbox and Introduction to R Programming, to begin with.
If you decide you don’t want to take all 10 courses, that is fine, and you will still receive certificates for the courses you have completed.
However, you will need to receive a certificate in each of the preceding courses before you can be enrolled in the Capstone project at the end.
Every Specialization includes a hands-on project. And to receive the full Specialization certificate, you will have to successfully complete the project. This certificate can be shared with prospective employers and on LinkedIn.
Meet the Instructors
All of the main course instructors are professors or associate professors of biostatistics with the John Hopkins Bloomberg School of Public Health. But there’s more to them than that.
There’s Jeff Leek, Ph.D., who’s recognized for his contributions to genomic data analysis and statistical methods for personalized medicine. Some of his work has appeared in several top scientific and medical journals.
One of his courses won a teaching excellence award.
Then there’s Roger D. Peng, a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for environmental data.
He earned the Mortimer Spiegelman Award in 2016. His work has been published in major substantive and statistical journals, and he’s responsible for over a dozen software packages implementing statistical methods for environmental studies.
There’s also Brian Caffo Ph.D., who specializes in computational statistics and neuroinformatics. He has received the Bloomberg School of Public Health Golden Apple and AMTRA teaching awards. And is also known for co-creating the SMART working group.
What are the prerequisites?
While there isn’t officially a list of pre-requisites, there is, we would argue, substantial background knowledge required before you can start.
Programming experience is strongly recommended. You should have at least beginner-level experience in Python, and familiarity with regression is also very much recommended.
In terms of mathematical knowledge and ability, students should have a good grounding in algebra.
Course Breakdown (Modules)
There are 10 Courses in this Specialization. They are as follows:
- The Data Scientist’s Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Interference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- Data Science Capstone
Let’s look at each in a little more detail.
The Data Scientist’s Toolbox
The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. You will get a practical introduction to version control, markdown, git, GitHub, R, and RStudio.
R Programming
In this course, you will learn how to program in R and how to use R for effective data analysis.
You will also learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.
Getting and Cleaning Data
The course will cover obtaining data from the web, from APIs, from databases, and colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”.
Exploratory Data Analysis
This course covers the essential exploratory techniques for summarizing data.
Reproducible Research
This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner.
Statistical Interference
This course is about using statistical inference to draw conclusions about populations, or scientific truths from data. This course presents the fundamentals of inference in a practical approach to getting things done.
Regression Models
This course covers regression analysis, least squares, and inference using regression models.
Practical Machine Learning
This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications.
It will also cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
Developing Data Products
This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. It focuses on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.
Data Science Capstone
This final course is the icing on the cake. You will need to have a certificate in each of the preceding courses before taking on this one. Here students get to create a usable/public data product that can be used to demonstrate their skills to potential employers.
Projects are drawn from real-world problems and are conducted with industry, government, and academic partners.
How long does the course take?
As the course is very much self-paced, how long it takes to complete depends on how much time you have to devote to it each week.
Most learners can complete the Specialization in 3-6 months. However, you will not be made to rush, and some students, due to other commitments on their time sometimes take as longs as 11 months to complete the program.
Each course in the Specialization is offered monthly but is possible to work on two courses simultaneously.
What is the cost?
Although it is possible to enroll in the specialization for a free trial, if you want to receive the Specialization certificate at the end, you will have to pay the subscription fee, which is set at $49 per month.
You can, however, apply for financial aid if necessary.
What have other learners said?
The main thing that we’re hearing from students who’ve taken the course is that it’s not beginner-friendly.
Students are strongly recommended to get a good background in R programming before they start, and to have a good understanding of statistics and linear algebra.
Students have also said that while the first 3 courses in the specialization program are fairly straightforward and easy, the difficulty level goes up quite significantly after this point.
This is because many small steps in the programming tasks aren’t always covered in the course materials.
Students have also reported a disparity in the quality of the teaching and assessments in different courses within the program.
Another “issue” reported was that students have to come up with their own ideas for an app to make in their Capstone project in Course 10.
Students find the specialization comprehensive, rigorous, and informative. However, some students have felt that the courses lack structure.
Some students have found that they’ve had to revisit concepts over again to get their heads around them.
Other students have said that the title of the specialization is misleading and that the course should instead be called “R-Programming and functional cases”.
How is the job market?
Data Science is one of the most in-demand jobs for 2021 and beyond.
It’s no longer the number one job America as it once was, (for four years running) but it’s still way up there.
And according to the U.S. Bureau of Labor Statistics, 11.5 million new jobs will be created in the field of data science and analytics by the year 2026.
So it’s clearly an area worthy of becoming skilled, for the next 5 years at the very least. And we would argue, requiring skills that aren’t set to ever decline.
And with its lucrative average annual salary and job satisfaction rates, there’s no shortage of people turning to this field.
Which of course leads us to the question of just how competitive the job market is.
And we’re going to level with you here. Not everyone who studies data science goes on to become a data scientist.
In the real world, being a data scientist is not just about knowing how to code. Even though that’s a very big part of it. But, as with many other jobs out there, you also have to have soft skills too, such as communication skills.
Is this course for me?
You wouldn’t be reading this article if you weren’t already interested in computers, coding, and mathematics. But as to deciding whether you should take this course in data science, that’s a big question to answer.
Working your way through the specialization can be quite expensive. Even if you can take some of the courses in parallel. So it’s not a decision to take lightly.
That said, there are many much more expensive courses out there in this field and the figures show that very many students have decided the subscription field was worth it for them.
Many students have compared the cost of the course to that of a Masters in Data Science. At $49 per month, it is a far more affordable course. But unfortunately, of course, it just does not have the same level of recognition with employers.
As we mentioned earlier, students have generally found that the course is not beginner-friendly. And it takes some grit to make it all the way through to the end.
If you’re not 100% sure about taking the full Specialization program, you could take advantage of the option to enroll for free for the first 7 days. And this can help you to make up your mind.
Remember that if you don’t want to take all 10 courses in the specialization program, you will still receive certificates for the courses you do successfully complete. So all is not lost if you come to a stop part-way through.
That said, the 10th and final course of the specialization is quite the show stopper, and best demonstrates that you have acquired all the necessary knowledge and skills to excel in the field of data science.
As we mentioned earlier, a large percentage of students have had improved job prospects on completion of the course.
But this large percentage is not as high as we would like, and completion of the specialization does not guarantee that you will land your dream job.
But with the various assignments you’d have completed, you can create quite the portfolio to showcase your work to potential employers.
Or alternatively, you can use the specialization program as a stepping stone onto a Master’s Degree in Data Science. And by doing so, you can really improve your chances of getting work in data science.
Unfortunately, we can’t come to a conclusion on your behalf. Whether this specialization program is the right course for you is something you’ll have to mull over.
About the Author Jacob M.
Jacob has a background in finance and engineering. Outside of his day job, he is a lifelong learner, who enjoys reading, taking online courses, and writing about what he's learned.
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