Think of “continuum” as something you start and you never stop improving upon. In my mind, Business Analytics Continuum is continuous investment of resources to take business analytics capabilities to next level. So what are these levels?
Here are the visual representation of the concept:
End of the beginning by Benedict Evans.
Two great posts from Andrew Chen. Links below:
These posts were perfectly timed for me as we started thinking about Annual Planning for Alexa Voice Shopping org (Amazon) this week. As a part of my research of which metrics to use to measure things that our business cares most about and then setting the right benchmarks/goals for the org, the posts below were super helpful. So if you are in tech and if you care about 1) measuring frequency of usage 2) measuring the most engaged cohort then you should take some time to read these posts.
DAU/MAU is an important metric to measure engagement, but here’s where it fails
Cheers!
I was invited to lead the office hours for the Springboard’s Data Analytics for Business course and I wanted to share the recording with you all:
I answer following questions during the office hours:
Related Post: $100 Discount Code For Springboard
I am a data-camp subscriber + mentor w/ springboard + completed free-content on data-quest so familiar w/ all three products in some way.
You need two things to have a successful career:
Let’s talk about Continuous Learning first:
In a field that’s as dynamic as data science, you should always be learning! It could be through your projects at your work, side-projects or online resources.
I would categorize both data-camp and data-quest under this and are great platforms for continuous learning. I am a subscriber on DataCamp and it’s a great platform to just dive in, do some hands-on exercises and learn something new. I love it! I have heard equally positive things about DataQuest so if you are already working in the Industry as a Data Scientist and just want to get deeper technically, then go for these platforms!
Now Let’s talk about Strong Foundation:
You need a strong foundation to get hired as Data Scientist. You would do that by typically having a relevant college degree. But:
If that’s the case then there’s a new approach in the market where you attend these “boot camps” — you still need some foundation skills like for example: math/programming/statistics to be eligible for Data science boot camps and if you have those basic skills then you can go through these boot camps. There’s a bunch of them out there. Just search for “data science boot camps”. Springboard is one of them and I have heard nothing but positive things about them — just like I have about DataCamp & DataQuest. I have personally mentored 6 students so far and all them were looking for a career transition and had nothing but positive things to say! That’s just my empirical data though, you should do a trial w/ them and/or check out their job guarantee through their career track if that is important to you. But either ways, it’s a “Bootcamp” offering so it has regular mentor calls/check-in’s, projects, career-coaches, non-technical material like resume tips to give you a structured approach to everything that you might need to get hired as a data scientist — You can expect intense guided learning over a short period of time. The Bootcamp approach is different than self-learning and self-paced approach by DataCamp & DataQuest.
PS: I mentor for Springboard and I have a $750 OFF discount code to share if you decide to enroll. Please contact me through to get the code: Let’s Connect! – Insight Extractor – Blog (If you prefer to not use the referral link, just search for “springboard” and sign up there)
The World is not binary!
I am not saying that you can’t break into Data Science with just DataCamp and DataQuest — you would need to complement it w/ other resources and put more effort to cover everything that you may need. With enough motivation, it could be done for sure! Depending on how fast you want to break into data science + how much time you can invest in figuring out the right resources are two of the biggest factor to determine if you need to go through a Bootcamp.
Conclusion:
If you are already working as a data scientist, DataCamp and DataQuest are great for continuous learning! If you are new to this and don’t have a relevant education background then boot camps like Springboard are a great choice.
Hope that helps!
I loved the “Donald Rumsfeld on Analytics” framework shared by Avinash Kaushik in his strata talk. Even though the talk was from 5 years back, this is still relevant today! As a data analyst/data science professional, we should strive to automate the fact-checking and reporting as much as we can, so that we can focus on the good stuff: validating (or invalidating) intuition and exploring unknowns!
And if you like frameworks to structure your thoughts, you might also like the What-why-What’s-Next (4W) framework to test your analytics maturity here — this is important because if your organization is not mature, you might get stuck in data puking (reporting/fact-checking) and never get to the good stuff that Avinash talks about in the framework above. So figure out the analytics maturity of your organization and then take steps to help your organization improve.
SQL, Excel & Tableau-like tools are good enough to start. Then add something like R eventually. And then there are tools that are specific to the industry – example: Google Analytics for the tech industry.
Other than that, you should know what do with these tools. You need to know following concepts and continuously build upon that as the industry use-cases and needs evolve:
This question was asked on Quora and here’s my answer:
I will list resources broken down by three categories.
A great data analyst will focus on all areas and a good data analyst might just focus on tech. Hope that helps!
Most organizations could benefit from Analytics but before you set the Analytics road-map for your organization, it’s important to figure out your current stage and then build the road-map to achieve your vision. So how do we figure out the analytics maturity of an organization? Let me share a framework to think about this:
I have blogged about “Business Analytics Continuum” before — it’s a great framework to think about Analytics maturity in an organization — BUT the issue is that it’s harder for business people to remember the stages: Descriptive -> Diagnostics -> Predictive -> Prescriptive — And so there’s a simpler (but equally effective) framework that I have been using over past few months (What -> Why -> What’s next aka “3W” framework). And recently at a Microsoft Analytics conference, I saw this framework with an extra “W” which makes total sense that I liked a lot! So i thought I will share that with you all. So here you go — 4W framework:
Stage 1: What Happened?
Stage 2: Why did it happen?
Stage 3: What will happen?
Stage 4: What should I do?
I hope the framework as you think about your organization’s analytics vision/road-map and stages that you need to go through to help your org succeed with data!
Recommendations:
Building data driven companies — 3 P’s framework.
Presented at #sqlpass summit 2015.