# How to Get a Data Science Education with Coursera Plus

With the recent launch of Coursera Plus, it’s now possible to receive a solid data science education in a year for about *$1.10* per day. This makes for a pretty attractive alternative to bootcamps, which cost upwards of *$7000*.

Essentially all of the courses and specializations mentioned in my top data science and machine learning course reviews are included in Plus, so setting up a highly-rated data science curriculum using only Coursera Plus is an easy way to save money.

Side note: partnerships with platforms like Coursera helps fund the comprehensive educational articles found for free in our publication. Coursera is generously providing commissions to LearnDataSci from purchases made through the links in this article. Thank you in advance to everyone that helps support our growing team.

## Curriculum

In this article, I’m going to outline an example education based on the knowledge and skills needed for data science work. In short, the following curriculum is easily achieved with Coursera Plus:

- Python programming
- Math Foundations
- Algebra
- Statistics & Probability
- Linear Algebra
- Some Calculus

- Data Wrangling and Cleaning
- Data Visualization
- Databases and SQL
- Machine Learning
- Big data - Spark/Hadoop
- Cloud Computing
- Focused Interest and Projects

Data science work is starting to require more and more knowledge of engineering, so it would be wise to include a few courses about utilizing some of the tools available. Furthermore, if you plan on creating your own product or service instead of working for a company, engineering skills will be essential.

Depending on your starting point, the amount of spare time at your disposal and which courses you want to take at once, this curriculum could be completed in about a year. Each specialization contains a varied amount of courses, and each course is around four weeks long.

## Courses

### Python Programming + Databases

#### Python for Everybody Specialization - University of Michigan

One of Coursera’s most popular specializations, and one of the best introductions to Python programming on the internet. Not only will you learn the basics of Python from the ground up, you’ll also pick up some essential data skills, like web scraping and working with **SQL databases**.

This should be everyone’s point of entry to a data science education.

*4.8 stars - 749k+ enrolled*

### Math Foundations

#### Data Science Math Skills — Duke University

If you need to cover some of the basics of **Algebra** and **Probability Theory** before anything else, then this is a good starting point for your data science education.

This course allows you to approach those foundational math skills from the data science perspective, which is much more fun and interesting than taking a generic course about these topics.

Taking this course along with the first course of *Python for Everybody* mentioned above would be a great first month of your learning path.

*4.5 stars - 193k+ enrolled*

#### Statistics with Python Specialization — University of Michigan

Unlike other courses on the same topic, this three part specialization on **Probability** and **Statistics** will also build on to your Python programming foundation.

Bear in mind that this is more of an introduction to these topics, so if you’ve already taken statistics and probability courses in the past, your time would be better spent learning more advanced concepts. In this case, skipping the first course might be a better idea since course two and three focus on Python’s statistical libraries and Jupyter notebooks.

*4.5 stars - 27k+ enrolled*

#### Matrix Algebra for Engineers — Hong Kong University of Science and Technology

Perhaps not immediately obvious to those new to data science and machine learning, but **Linear Algebra** is one of the underpinnings of many algorithms you’ll be working with, such as neural networks and dimensionality reduction. You’ll be very thankful for taking the time to understand the matrix algebra when you need to make sense of why algorithms work the way they do.

The presentation and straightforwardness of this course makes it an ideal addition to your curriculum.

*4.8 stars - 28k+ enrolled*

#### Introduction to Calculus — University of Sydney

While **Calculus** isn’t absolutely necessary to start doing data science and machine learning, I would recommend this be taken in conjunction with your other courses after the other foundational math courses have been completed. This is especially true when you want to dive into **Machine Learning** because many of the more interesting advances in the field heavily rely on calculus.

*4.8 stars - 79k+ enrolled*

### Data Wrangling, Cleaning, and Visualization + Machine Learning

#### Applied Data Science with Python Specialization — University of Michigan

This is sort of an all-in-one introduction to data science. The specialization gives you the basics of how to work with data, how to clean it, and how to visualize it all in Python. Then, by course three, you’ll start learning how to use your clean data and predict outcomes with Machine Learning.

Course four – text mining, NLP – and five – social network analysis – are more specific tracks you can go down for a **focused interest**. One of the benefits of Coursera Plus is that if you have other interests, like Deep Learning, there’s no need to continue with the specialization past course three if you don’t want to. You can easily hop into any other advanced topic or specialty you want without extra enrollment costs.

*4.5 stars - 218k+ enrolled*

### Big Data

#### Big Data Specialization — UC San Diego

This is where you start to hone some of those engineering skills. Pretty soon you’ll want to work with data that’s too large to fit into memory, or takes to long to clean and filter. This is where tools like Hadoop and Spark come in.

By learning how to use larger machines in the cloud and paradigms like MapReduce, you can start to really harness the vast swaths of data we’re generating every day.

*4.5 stars - 78k enrolled*

### Focused Interest and Projects

There’s many interesting paths you can go down now that you’ve built a solid foundation. Ultimately, the question is “What do you want to do with data science and machine learning?” At this point you might have an idea for what to do, and a simple search through Coursera should yield an interesting course on that topic.

For example, maybe you’re more interested in the engineering side of data science. In that case, there’s the Data Engineering with Google Cloud series. Or maybe you want to dive deeper into machine learning and learn about advanced techniques. There’s many great courses in that case, such as the Reinforcement Learning Specialization.

Lastly, while you’re learning, it’s extremely important to make sure you’re constantly working through projects. This will both solidify your knowledge and provide examples of your competency when applying for jobs. Coursera Plus also includes **Guided Projects**, many of which follow pretty closely to the curriculum laid out here.

There’s guided projects on almost any topic, but you can start with a simple search for data science guided projects here.

## Conclusion

Outlined above is an example data science curriculum that you could work through over a year’s time or more. Making sure you have a strong mathematical foundation is of the utmost importance because the intuition of data science and machine learning begins with the intuition of their math building blocks. If you aren’t starting with a strong math background, it may take longer than a year to actually get a good feeling for the concepts needed to model data and make good predictions. Either way, it’s an exciting and interesting road ahead, and I hope you go on to do something great with the knowledge and skills you’ve gained from learning data science.