After teaching data science for two years, Thinkful is adding a flexible, online Data Analytics Program. We asked Thinkful’s Data Science Program Director, Matt Shull, about the differences between data analytics and data science, what the curriculum covers, the demand for data analytics roles around the country, and what sort of companies are hiring!
What you need to know:
- Applicants still need to prep for the course, but it’s less selective than the Data Science Flex program
- Part-time, flexible program. Thinkful suggests 20 to 25 hours a week of work to complete the course within six months.
- Tuition Reimbursement Program applies to this new course – eligibility requirements here.
- Launched in November and currently enrolling!
What’s your job and what inspired you to work at Thinkful?
Today, I’m the Program Director for Thinkful’s Data Science and Data Analytics Career Tracks. Thinkful set the course for my own career way back in the day, so now I’ve come back to help do the same for other people. It's really fun.
For the last 13 years, I've been a Web Development Engineer and Product Manager, but I’ve always loved working with data, machine learning, and getting a sense of the story that data tells. Five years ago, I started mentoring part-time for Thinkful’s Web Development Track. Living in Arkansas, there were not a lot of opportunities like that. So I told myself that I would drop everything I was doing and work for Thinkful full-time if I ever got the opportunity. That opportunity came about in 2017, when I took a job as Thinkful’s Data Science Program Manager.
Thinkful has been teaching data science in the Data Science Flex program for a while now – why have you decided to launch a track dedicated to Data Analysis and Business Intelligence?
In the two years that we’ve run the Data Science Career Track, we noticed that there are even more Data Analyst, Business Intelligence, and Data Journalism roles out there. Unlike data science, those roles don’t require a background in programming or a background in statistics to be successful. We also noticed a number of data analysts coming into our data science program to further their own careers.
So we wanted to design a program to get students job ready for a Data Analyst role in six months. The Data Analytics Track is more accessible to a broader audience – not only because you don't need a programming or statistics background, but also because junior or mid-level data analytics jobs don’t always require professional experience.
So what types of students should take the Data Analytics Flex course?
In the Data Science Career track, we find that people with Python or programming experience and statistics experience who had a masters or a Ph.D. were typically very successful. But for the Data Analytics course, we realized that people who had a Bachelor's degree (like myself, I have a psychology degree), not necessarily a STEM-focused degree, who are looking to get into technology or get into the world or industry of data, have the right educational requirements to get into data analytics.
This program is for people who want to learn how to use tools like Tableau for visualization, or use Excel to do some modeling, organize data to present to stakeholders, and learn how to present that data so that they can tell the story of data.
It’s also for people who are starting from Square One and want to learn what Python is all about, how to work with databases using SQL, and how to scrape data so that they can collect data to analyze.
Is there anything in the Data Analytics Career Track curriculum that isn’t included in the Data Science Career Track curriculum?
Yeah, there are definitely subjects that we've not covered before. The course starts out with Excel, which is fairly new for us at Thinkful. We teach the foundations of Excel, how to do V-Lookups, modeling and charting within Excel, and analyze data.
Then we move on to SQL foundations. We teach SQL in our Data Science Career Track as well, but we’ll be more thorough in the Data Analysis Career Track. We’ll expect students to become intermediate or advanced at SQL. Then we learn Tableau, a visualization tool used by a lot of companies. And from there, we’ll learn Python and statistics.
Students will then use the skills that they have learned to build projects based on scenarios that we've built with different types of data. Mentors will grade those and make sure each student can present and articulate how they got to the results and tell the overarching story of the data.
Lastly, there a number of assessments and case study interviews, behavioral mock interviews, all with the goal of preparing students to confidently move into careers as a data analyst or business analysts.
How will the Data Analytics Capstone Projects compare to the Data Science Capstone Projects? Could you give us an example?
They're completely different. In data science, projects require machine learning, along with supervised and unsupervised learning – those concepts aren't in the data analytics program at all.
For the data analytics program, students will use Excel and SQL for their Capstone Projects. By the time they do their Capstone Project, students are able to find datasets on their own.
What is the learning style? Is this a synchronous or asynchronous course? Are students matched with mentors?
It’s the same kind of mentor model we use for all of our Thinkful programs. Students learn via our online learning platform, then have two, 45-minute mentoring sessions each week. Students can go to office hours where they meet with different mentors and get their questions answered for support.
We also have workshop lectures to break down topics, and mentors go through a presentation and dive a little bit deeper into certain things. Our mentors – many of whom are working data analysts – often share something in the field that they are passionate about. I think that's important because it’s validating for students to see a professional data analyst talk about how they work through problems.
And what kind of schedule should students expect?
This is a part-time, flexible program. We suggest 20 to 25 hours a week of work to complete the course within six months. Program Managers and Mentors for each student help keep them on track and keep them accountable to their graduation plan that's set at the beginning of their time together.
Will students have the opportunity to communicate with each other and work together?
Yes, definitely. Now that we have a physical presence in a number of cities across the US, we do monthly meetups. Students can meet each other there, go to office hours and workshops, talk to each other, and talk about their projects.
We also use Slack for our student community. We have channels for different locations and cities across the US. We've found that students organically coordinate with each other, get together at coffee shops, to talk about projects or pitch their presentations to each other for feedback. That's been really exciting to see students do that organically on their own.
What's the admissions process for the Data Analytics Flex program? Is it selective?
The admissions process is less strict than the Data Science admissions process, but still selective. It involves an application, and then each applicant works with an education advisor.
Thinkful’s admissions counselors and education advisors help each student prepare for and set expectations so that they clearly understand the commitments. We make sure students are willing to commit to working 20 to 25 hours a week for the next six months, and help them figure out their payment plan options. And if they're not ready at that exact moment, we work with them and follow up, so that when they are ready, they can start confidently.
Once a student enrolls, they meet with Thinkful’s Student Success team which can handle financial questions or something like taking a pause if you've got a vacation coming up. Then each student has a Program Manager who helps create a graduation plan and sets a pacing expectation. Then, of course, you meet with your mentor for your first session.
We found it’s important to set expectations and goals even before students enroll so that they feel they are using their time and money wisely, and to make sure they're ready to switch careers after graduating.
If a student is struggling to get through assessments or falling behind, how do you help them through? Can they repeat sections?
Definitely – students can absolutely go through the assessments again. If a mentor decides that a student is not quite ready to move on, they'll provide feedback, the student will talk about that feedback with their mentor, implement that feedback, and then take the assessment again.
So there are three layers of support to keep a student accountable. The Program Manager would follow up with a struggling student to rework their graduation plan. And the Student Success team can help pause their program if they fall too far behind.
Is there a Job Guarantee for this Data Analytics Career Track?
We do offer a tuition reimbursement guarantee. To qualify, students need to have a bachelor's degree and live in one of the cities in our eligibility requirements. If you don't find a job within six months, and you meet the criteria, we will reimburse your tuition.
Whether you qualify for our tuition reimbursement guarantee or not, you still get assigned a career coach after you graduate, and career services for six months after you graduate. The Career Services team actually starts meeting with students during the program so that when they graduate, they can hit the ground running and start aggressively applying for jobs in the area.
The career coach isn't just there to help students apply for jobs. They do mock behavioral interviews with students; teaching them how to perform well in these behavioral interviews. They help students until they sign that offer letter when they get their full-time job.
What are some examples of the jobs that students might be able to get after graduating?
Data Analysts, Business Analysts, Business Intelligence Analysts. Data Journalist is another role that is very popular. You may be called a Financial Analyst if you're working for a bank, or maybe even a Product Analyst if you're working at a startup. But data analysts and business analysts are the two major general roles.
Throughout the curriculum, we’ll cover industries like finance, marketing, and product, and allow students to do certain projects through the lens of those industries.
You mentioned banks and startups – what other kinds of companies would want to hire Data Analysts?
That's the exciting part – pretty much any company is interested in hiring data analysts. That means healthcare, finance, startups, and e-commerce. News and media organizations, government organizations, even HR departments are looking for data analysts to help understand data about their employees. It's a very broad field that touches a lot of different industries.
Businesses have been told for decades to “collect all the data that you can." And companies are now asking what they should do with all that data. That's where data analysts and data scientists come in – to help companies sift through and understand all the data, and to help tell the story of that data to stakeholders so that they can optimize their business processes.
What is your advice for students embarking on the Data Analytics Flex program? Do you have any tips for getting the most out of it, especially if they're trying to change careers?
If you’re on the fence about whether or not this is the right program for you, then talk to alumni who have already gone through the program. Once you’re in the program, here’s my advice:
- Talk to as many mentors as you can about what they do. Understand the landscape, and get a good bird's eye view of the different types of data analysts jobs that are out there. All of our mentors are doing incredible things at startups all across the world and for big companies like IBM and Google and Intel. Find what you’re most passionate about and then lean into that.
- As a student, start answering questions for other students who are behind you in the Slack community. That will help you keep topics fresh as you're digging into more complex material.