My Data Analytics journey with Thinkful: Part 2
Part 2 (final) of my experience with Thinkful’s Data Analytics Flex program.
It’s been a while; for those that have followed me since my first post of my Data Analytics Journey with Thinkful, found here. Initially, I thought this would be at minimum a three part series. However, in the end I’m glad to be able to go through my experience in only two parts, so as to keep it as concise and easy to digest as possible. Without further adieu, let’s dive into it.
Disclaimer: As mentioned in the previous part, there will be little to no mention of specific modules, as I feel that would be a disservice to Thinkful and its staff.
What’s new since Part 1?
I’m glad you ask. Since posting the first part to my series, I’ve graduated (woo!) and have come to familiarize myself with the various skills/tools that a data analyst may utilize in a day-to-day basis. I say “may” because I’ve come to realize that it depends.
Depends on what?
Well, there are various analysts in the world of data analytics. To name a few: market(ing) analysts, financial analysts, HR analysts, and so on. With these various paths, you can imagine that there are variations to what a potential candidate might need as far as technical skills for each role.
With that said, I believe Thinkful’s data analytics program does a great job of getting you proficient with the foundational tools that analysts should be familiar with. These tools being Excel, SQL, Python, and Tableau. In the first part of the series, I was finishing up my lesson in Excel and was moving onto SQL. I figured that for the bulk of this second and final part of this blog series, I would rate my overall confidence in my “job-readiness” for each of these skills by the end of the program and explain why.
1. Excel: 8/10
The program does an excellent job with giving hands-on experience with each of the four data analytics tools — especially Excel. Concepts like pivot tables and VLOOKUPs are a must for any analyst and it’s something that is taught and taught well in the program. These specific concepts are the main requirements I have seen in various data analyst job descriptions in the Excel department, so I am content with the level of proficiency I ended up having when completing the program.
2. SQL: 6/10
I’m fairly content at the level of proficiency I was at by the end of the program in regards to SQL. However, any data analyst will tell you how important and quite frankly, vast, the world of SQL is as a language. That being said, an important foundational skill of any junior level data analyst is to be able to wrangle and aggregate data through SQL, and I believe that the program was able to equip me with the knowledge to perform those specific tasks with various queries and logical statements.
3. Tableau: 7/10
I came into the program with zero knowledge of Tableau or any other data visualization software. However, I left the program with a level of proficiency that most of my recent interviewers were surprised by. Not all analyst job descriptions require Tableau (some might require Power BI), however, knowing your way through Tableau is a huge plus to most companies. The program gives ample examples and datasets to work with to be able to create a multitude of charts and graphs necessary for various business solutions.
4. Python: 6/10
Python is a beast in and of itself. It has many capabilities — especially outside of data analytics. The program does an excellent job with providing various hands-on projects that allow us to gain experience on utilizing this amazing language in a data analytics capacity/environment. That said, I wouldn’t say I’m 100% comfortable hitting the ground running in an analyst position that requires this language. However, from what I’ve seen — and frankly, why I gave this as high a score as 6/10 despite what I just said — is that most junior level analyst positions have this language in the requirements as a “preferred” skill. So it’s definitely a great thing to have and be familiar with, but not something that is absolutely necessary when starting out (for most positions).
The big question. Do I recommend Thinkful’s Data Analytics program?
Short answer: Yes.
Long answer: Yes; but with some things in mind.
As I previously mentioned above, there are many paths that one can take as a data analyst. And again, with those paths, come specific tools and skills that is needed to be considered to be an analyst of that specific capacity.
Therefore, I strongly suggest that anyone that is considering a data analytics program with ANY online bootcamp to also consider learning outside of that specific program with other online resources. In other words, continue to learn concepts and dig deeper with specific topics that interest you in this field with other courses such as those from Udemy, Coursera, and many more. Supplement your learning with more learning.
That said, it’s important to keep a balance in your schedule and to not get burned out. The main priority is going through the program successfully. Learning extra outside of the curriculum is the cherry on top. It is not fully necessary; however, it is worth doing as I am finding that after graduating, I am taking these extra courses to gain some familiarity in something Google Analytics and the such, when it comes to prepping for certain analyst positions. Overall, it’s a program worth taking, as it leaves you with much more than just the knowledge and experience of the main concepts.
The consistent career coaching, Slack community-building, and being able to have the entire curriculum available even after graduating are huge pluses in my opinion. It is a program that definitely has a well-rounded picture of the process of taking the student from having no prior knowledge of data analysis, to not only having the abilities to perform the necessary duties, but to also have that confidence in networking and being able to learn the self-discipline of applying and reapplying those concepts daily.
Some note-worthy bullet points of my experience.
Here are a handful of personal anecdotes on various experiences/features within the program that I may or may not have mentioned above or in the previous post, that sum up some worthy points of Thinkful’s program.
- I thoroughly enjoyed the community of students, alumni and mentors on the Slack platform. It was and still is a great way to communicate and network with others that are in similar situations as you are.
- My mentor continued to be a great resource throughout the program. It’s important to be open and communicate any concerns or questions you may have to him/her.
- Having full access to the program even after graduating is a huge plus. I find myself having to go back and refresh on certain topics while prepping for job interviews.
- With 30 pause days, students have ample time to finish the course even if there are certain emergencies. I’ve had to utilize the whole month towards the end of the program, being that I needed more time on some of the harder concepts in Python. Therefore, don’t even think about utilizing pause days in the earlier parts of the program unless absolutely necessary.
- One of the strongest positives from this program is that you leave it with a very decent portfolio website via Github that show potential employers your various projects in Excel, Tableau, SQL and Python.
UPDATE: I landed a Full-Time HR Analytics Analyst position with The Home Depot two weeks after graduating this course. See my post here in regards to a breakdown of the process and some additional thoughts!
As always, thank you for taking the time to read my posts. I hope I was able to answer some questions you may have had about this program or coding bootcamps in general. If you are a potential student for Thinkful’s program(s) — or any online bootcamp for that matter — do not hesitate to connect with me on LinkedIn if you have further questions.
If you enjoy content on all things analytics, career advice, and/or motivation, consider following my blog here for more posts in regards to these topics. Have a great day!
