There are a lot of online course platforms nowadays; from Coursera, Udemy, edX, Udacity, to other online bootcamps, like Springboard. But as of 2018, I've found Thinkful to be the best online learning platform for data science I've joined.

Upon joining, a few of the things stood out immediately:

• They have an extreme dedication for getting you career-ready
• There's a very thorough and targeted curriculum
• 1-on-1 mentoring multiple times per week
• Live Q&A sessions with data scientists
• They have a job guarantee

It also doesn't matter if you're brand new with little programming and math experience, or someone that's been learning already and hasn't filled in all the gaps, Thinkful is willing to work with you. When I joined they offered to tailor the mentoring and projects to my current interests and skills.

Thinkful invests a lot into creating an all-encompassing curriculum that evolves from student and employer feedback and the changing data science landscape.

What stands out the most to me is the job guarantee. If they fail to get you ready for a data science position, then you'll get your tuition back. It could be just a good marketing tactic, but it does require the platform, the curriculum, the mentors, the staff, and everything in-between to have a vested interest in making sure each student succeeds. Imagine if this was how colleges operated.

Note: In order to qualify for the job guarantee you need to hold a bachelor's degree, live near one of the designated cities, and a few other things. See here for the full breakdown: https://tf-assets-prod.s3.amazonaws.com/shoebill/PDFs/thinkful-job-guarantee.pdf

Below is my overall experience with Thinkful over the past few months. Hopefully this review will help you get a sense of what's inside to gauge whether or not Thinkful is a good fit for your learning style.

Disclaimer: I received complimentary access to Thinkful to write this review, and enrollments through links on this page may result in commissions, which help us produce free, in-depth tutorials and articles for everyone. Thank you in advance to anyone that decides to go through us -- it really means a lot.

As soon as I signed up, I was immediately set up for calls to chat with the program manager and my mentor. Within a couple of hours, I was sent a custom spreadsheet detailing my individual learning plan based on my goals and previous experience with data science and machine learning.

The spreadsheet is your custom curriculum. You’re given dates and goals for when you should be at certain points in your learning. One of the biggest challenges with learning a sporadic subject like data science online is determining how much you know, when you’re ready to move on to new topics, and what you’re knowledge gaps are, so it was great to be handed a complete track that I could follow without having to think about it.

There’s also a really nice goal progression on my homepage that gamifies the learning experience. Overall, the platform feels very robust, well-thought-out, and refined. I’ve also gotten more attention and help from the instructors, mentors, and other students than most in-person college courses I’ve taken.

Pros

• All custom learning material
• Assigned mentor with twice-weekly sessions
• Daily live video Q&As with a data scientist
• Very active Slack channel to talk with students, mentors, and instructors
• Engaging projects and capstones
• Interactive Python shells built-in

Cons

• No video lectures or video tutorials
• Cost prohibitive for some

Thinkful has a solid curriculum that’s always evolving to meet company needs.

Everything begins with a foundational mini bootcamp, which is geared towards teaching you the necessary skills for a strong understanding of the fundamentals. This is followed by the advanced bootcamp content, where you’ll learn all of the analytics, visualization, and machine learning you’re being hired for.

After many of the units you’ll be given assignments that are positioned to gauge your understanding, help refine your communication skills, as well as help build a portfolio employers will be looking for. In addition to frequent assignments, there’s also a major capstone project that you’ll develop and present to an online audience upon completion. The presentation and communication skills you’ll hone are pivotal since it’s one of the most desirable skills in data science positions.

All material is taught with Python, which is my favorite language for data science and machine learning. If you’re looking for R, this wouldn’t be a good fit for you. There’s a good discussion on Python vs R if you’re on the fence or wondering about switching.

The actual learning material consists of several units of in-browser, interactive Jupyter notebooks that walk you through all of the concepts you need to know.

There’s three parts to the curriculum:

1. Data Science Prep Course
2. The Data Science Bootcamp
3. Career Services

Here’s the breakdown of each and some of my thoughts:

Gets almost everyone up to speed on the prerequisites for the data science training. Consists of the following units:

1. Programming Fundamentals in Python
   Complete intro to Python. Touches on all the things, like lists, dictionaries, loops, objects, classes, etc.
2. Introduction to the Data Science Toolkit
   Intro to the most important Python packages for data science: pandas, numpy, and matplotlib. So you’ll see how to work a little bit with data and how to do some basic visualizations.
3. Statistics for Data Science
   A very nice, and often much needed breakdown of statistics and probability for a solid foundation. A lot of great stuff I remember looking up on my own back when I learned online before Thinkful.
4. Career Planning and Capstone Report
   A quick unit that gets you in the job search mindset from the beginning. Has a nice capstone that solidifies everything you learned in the prep course.

The core offering. This is where you’ll learn how to extract insights from data, produce predictive models, and where you’ll be able to pick a specialization. There’s a total of seven units:

1. Data and Analysis - Data Science for Investigations
   Starts off with an excellent introduction that sets expectations of what data science is and what it can do. Additionally, there’s a lessons on using SQL, more visualization, and Experimental Design (A/B tests and things).
   
   
2. Supervised Learning
   This unit is where a lot of really interesting things start to happen because now you are going to start building predictive models. The first lesson builds up to the actual modeling by walking you through data exploration, feature engineering, and a bit of cleaning. After that, you’re going to work on a Naive Bayes classifier and Linear Regression model, then learn how to evaluate their performance.
   
   What I like about this unit is that you learn a quick bit about the mathematics behind the algorithm then immediately apply it using pandas, sklearn, and matplotlib/seaborn. For me, the closer the theory is to the application the better.
   
   I think it would be good to pair this part with a book like ISLR(free) for a deeper understanding on the techniques. It always helps me get an intuitive feeling for the algorithm when I know exactly how it works, but it’s not exactly necessary to just be able to use it. That’s why I think Thinkful’s approach is really well done.
   
   Additionally, throughout this (and all) lessons you’ll get to chat with your mentor, Slack group, or live Q&A session if you need help. Definitely capitalize on that.
   
   
3. Deeper Dive into Supervised Learning
   A lot of other useful, and more advanced topics are found in this unit. KNNs, decision trees, random forests, more advanced regression, SVMs, and boosting are taught here. This and the previous unit provide the bulk of the models data scientists use today.
   
   Capstone – At the end of this unit you’ll be tasked with building out a full project using what you’ve learned and then presenting your findings to a group. What I like about this is that you’re basically thrown in to choose and figure out anything you want instead of a directed capstone like on Coursera.
   
   
4. Unsupervised Learning - Venturing into the Unknown
   Clustering and Neural Networks are now brought into the picture to round off the rest of the algorithms you need to know. There’s also a big lesson on Natural Language Processing that touches on both supervised and unsupervised cases. Since NLP is one of my favorite topics, I’m glad they added that in here.
   
   Capstone – Again, you’ll be in charge of using what you’ve learned to perform some analysis and present it to a group. This time, you’re given a little direction and will be testing your new NLP skillset.
   
   
5. Other Topics in Data Science
   A unit that seems designed to fill in some other gaps. Here you’ll learn some more advanced programming, scraping, a bit of distributed computing, survey design and advanced experimentation.
   
   
6. Specializations
   As of this writing there are eight specializations that you can choose from, and the one you choose will be the basis of your final capstone project. You will have access to all eight, but your goal is to focus on one or two (max) based on your career interest.
   
   The eight options are:
   
   1. Time Series
   2. Network Analysis
   3. Data Science for Economics
   4. Advanced NLP
   5. Data Science for the Social Sciences
   6. TensorFlow and Keras
   7. Biostatistics
   8. Big Data with Spark
      Each specialization has 5-7 lessons, all of which do an excellent job of providing the exact foundation you need for the topic.
      
      
7. Final Capstone
   Based off everything you’ve learned up to this point, you’ll need to propose, develop, and present a full data science project. The best part about doing a final capstone on Thinkful is that the project you propose is totally up to you and your interests, and no matter what you choose, your mentor will help you with any questions and provide much needed feedback.
   
   Once your capstone is completed, you’ll present your findings on a public video conference where other students can join in, watch your presentation, and ask questions.

In addition to your mentor performing mock interviews with you, there’s also 15 lessons in the curriculum that teaches you tactics for networking, branding yourself, and finding your ideal job.

Overall, it’s nice knowing that your mentor and everyone at Thinkful has a vested interest in getting you into a data science position as soon as possible after graduation.

Having 1-on-1 sessions over video chat each week with someone actually doing what you want to do is such a game-changer for online learning. It doesn’t really matter you’re at in your learning either. There’s mentors for every level and most specializations.

Thinkful allows you to switch and choose a mentor that fits your style and topic of interest. Since I’m really interested in NLP, I specifically requested someone that’s currently working in this area. I was connected with Lauren Washington, a lead data scientist and machine learning developer at a startup, called smartQED.

Lauren Washington, Lead Data Scientist, Machine Learning Developer, Previously worked at Google.

Being able to connect with Lauren was such a privilege. She has an incredible passion for teaching others, and even has created several workbooks for data science books to help online learners solidify their understanding.

Lauren was an ideal match for me because she is currently doing exactly what I’m interested in and would actually enjoy doing as a career. At smartQED, Lauren is leading a team that helps companies determine root causes and possible solutions to problems that arise in their infrastructure by analyzing text data from past case files, logs, and forums like StackOverflow.

Since I’m in an area that doesn’t have many data scientists, it was really beneficial and motivating to be able to just ask Lauren questions, like “How did you get started?”, “Where did you find your first job?”, and “What’s it like working there?” along with the other technical questions that comes with learning DS and ML.

Overall, I think the real value is in the mentorship and it would have made a huge impact on my learning early on.

Thinkful is by far the best online platform I’ve had the chance to explore, and I think it’s completely worth the price if you plan on actually getting into a data science career. The price to join should easily be offset by your new salary in one year.

I recall spending the equivalent amount on a couple of college courses, and although they had their benefits, I certainly didn’t receive a private tutor and there wasn’t any sort of job guarantee.

If Thinkful sounds like it would be a good fit for you, apply to join here.