Springboard Data Analytics for Business Office Hours

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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:

CLICK HERE

I answer following questions during the office hours:

  • What tools have I used in my career for Data Analytics & Data Science?
  • What are the different analysis/modeling that you do?
  • What are the biggest challenges that I found when I got in this Industry?
  • Being data-driven is not binary but it’s a scale — how do you do analyze what is their current level and how do you make a company more data-driven?
  • What is the challenge for newcomers in this industry? And what are the changes coming in next few years?
  • Which tools are widely used today? Which industry uses which tools heavily?
  • How do you verify “what’s next”? How do you verify that your forecast is good enough?

Related Post: $100 Discount Code For Springboard

What is the difference between courses offered by Springboard vs datacamp vs dataquest? Which is better?

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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:

  1. Strong Foundation
  2. Continuous learning

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!

Here’s a 2-day All-access pass for DataCamp: 2 Day Gift Card

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:

  1. A lot of people don’t have relevant college degrees OR
  2. They graduated a few years back and are looking to do a career transition now OR
  3. They are not willing to go back to do multi-year college programs focused on data science

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.

Here’s a $100 discount code for Springboard: Springboard: The best way to learn Data Science and UX Design skills online. (If you prefer to not use the referral link, just search for “springboard data science”)

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!

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Rumsfeld on Analytics:

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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!

Rumsfeld on Analytics

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.

-Paras

What are the must-know software skills for a career in data analytics after an MBA?

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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:

  1. Spreadsheet modeling
  2. Forecasting
  3. Customer Segmentation
  4. Root cause Analysis
  5. Data Visualization and Dash-boarding
  6. Customer Lifetime value
  7. A/B testing
  8. Web Analytics

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What are some of the most important resources a Data analyst needs to know about?

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This question was asked on Quora and here’s my answer:

I will list resources broken down by three categories.

  1. Business Knowledge: As a data analyst, you need to have at least basic knowledge of business areas that you are helping with. For example: if you are doing Marketing Analytics then you need to understand basic concepts in marketing and that will make you more effective. You can do so one of the three ways:
    • On-the-job: Pick up knowledge by interacting with business people and using internal knowledge bases.
    • Online resources: Pick up basics of marketing by taking a beginners course online on a platform like Coursera OR from resources like this: Business Concepts – Bootcamp | PrepLounge.com
    • College/University: If you are at a college/university then you can either audit a course or depending on your major/minor, core business courses might just be part of the curriculum
  2. Communication skills:
    • Public Speaking: Toastmaster’s is a great resource. if you don’t have access to a local Toastmasters club, you should be able to find a course online. Check out Coursera.
    • Data Storytelling: Just listening to someone like Hans Rosling can be very inspiring! The best stats you’ve ever seen . Also, If you search storytelling with data on YouTube, you will see few good talks: storytelling with data – YouTube
    • Problem structuring: If you are able to break down the problem into core components to identify root cause, you will not only increase your speed to insight but your structure will also help you communicate it more effectively. Learn to break down your problems and use that in communicating your data analysis approach. Imagine this list without the three high-level categories — wouldn’t it look like I am throwing random resources at you? By giving it a structure — Tech, Biz, Communication, I am not only able to structure it but also communicate it to you more effectively. More here: Structure your Thoughts – Bootcamp | PrepLounge.com
  3. Tech skills: Read Akash Dugam’s answer: Akash Dugam’s answer to What are some of the most important resources a Data analyst needs to know about? — it’s a nice list. Also, check this out: Learn #Data Analysis online – free curriculum

A great data analyst will focus on all areas and a good data analyst might just focus on tech. Hope that helps!

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“4W” framework for assessing your Analytics Maturity:

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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?

Analytics Framework What Why Whats Next HOW

Credit: Microsoft Data Insights Summit

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.

[VIDEO] Microsoft’s vision for “Advanced analytics” (presented at #sqlpass summit 2015)

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Presented at #sqlpass summit 2015.