Is it too late to become a good Data Scientist?

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If you’re looking for career change, that’s never too late!

If you’re looking to learn something new, that’s never too late!

If you’re looking to continue learning and go deeper in data science, that’s never too late!

If you don’t like Software engineering and want to switch to something else, that’s never too late!

But if you are after the “Data Science” gold rush, then you did miss the first wave! You are late.

But seriously, you should apply first-principles thinking to your career strategy and ideally not jump to whatever’s “hot” because by the time you get on that train, it’s usually too late.

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[Resource] 8 Methods to calculate CLV:

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There are lot of ways to apply a CLV (customer lifetime value) model. But I hadn’t seen a single document that would summarize all of them — Until I saw this: http://srepho.github.io/CLV/CLV

If you are building a CLV model, one of first things that you might want to figure out is whether you have a contractual model or non-contractual model. And then figure out which methodology would work best for you. Here are 8 methods that were summarized in the link that I shared with you:

Contractual
  • Naive
  • Recency Frequency Monetary (RFM) Summaries
  • Markov Chains
  • Hazard Functions
  • Survival Regression
  • Supervised Machine Learning using Random Forest

Non-Contractual

  • Management Heuristics
  • Distribution Based Approaches

Hope that helps!

As a student preparing for data anaylst & science roles, should I generalize vs specialize?

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This question was posted on Springboard forum.

Here’s my answer:

It depends on your target industry & where they are in their life-cycle.

It has four stages: Startup, Growth, Maturity, Decline.

Industry lifecycle

Generalization is great in earlier stages. If you are targeting jobs at startups; generalize. You should know enough about lot of things.

T-shaped professionals are great for Growth stage. They specialize in something but still know enough about lot of things. E.g. Sr Growth/Marketing Analyst. Know enough about analytics & data science to be dangerous but specializes in marketing.

Specialization is great for mature industries. They know a lot about few things. E.g. Statisticians in an Insurance industry. They have made careers out of building risk models.

Any advice for moving into data science from business intelligence?

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This was asked on Reddit: Any advice for moving into data science from business intelligence?

Here’s my answer:

I come from “Business Intelligence” background and currently work as Sr. Data Scientist. I found that you need two things to transition into data science:

Data Culture: A company where the data culture is such that managers/executives ask big questions that need a data science approach to solve it. If your end-consumers are still asking bunch of “what” questions then your company might NOT be ready for data science. But if your CEO comes to you and says “hey, I got the customer list with the info I asked for but can you help me understand which of these customers might churn next quarter?” — then you have a data science problem at hand. So, try to find companies that have this culture.

Skills: And you need to upgrade your skills to be able to solve data science problems. BI is focused too much on technology and automation and so may need to unlearn few things. For example: Automation is not always important since you might work on problems where a model is needed to predict just a couple of times. Trying to automate wouldn’t be optimal in that case. Also, BI relies heavily on tools but in Data science, you’ll need deeper domain knowledge & problem-solving approach along with technical skills.

Also, I personally moved from BI (as a consultant) -> Analytics (as Analytics Manager) -> Data science (Sr Data Scientist) and this has been super helpful for me. I recommend to transition into Analytics first and then eventually breaking into data science.

Hope that helps!

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Is the R data science course from datacamp worth the money?

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DataCamp R Data Science

Question (on Quora) Is the R data science course from datacamp worth the money?

Answer:

It depends on your learning style.

If you like watching videos then coursera/udacity might be better.

If you like reading then a book/e-book might be better.

If you like hands-on then something like Data Camp is a great choice. I think they have monthly plans so it’s much cheaper to try them out. When I subscribed to it, it was like 30$/Month or so. I found it was worth it. Also, if you want to see if “hands-on” is how you learn best. Try this: swirl: Learn R, in R. — it’s free! Also, Data Camp has a free course on R too so you could try that as well.

Also, if you want to have free unlimited access for 2-days then try this link: https://www.datacamp.com/invite/G8yVkTrwR3Khn

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Data analytics vs. Data science vs. Business intelligence: what are the key differences/distinctions?

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They are used interchangeably since all of them involve working with data to find actionable insights. But I like to differentiate them based on the type of the question you’re asking:

  • What:

What are my sales number for this quarter?

What is the profit for this year to date?

What are my sales number over the past 6 months?

What did the sales look like same quarter last year?

All of these questions are used to report on facts and tools that help you build data models and reports can be classified as “Business Intelligence” tools.

  • Why:

Why is my sales number higher for this quarter compared to last quarter?

Why are we seeing increase in sales over the past 6 months?

Why are we seeing decrease in profit over the past 6 months?

Why does the profit this quarter less compared to same quarter last year?

All of these questions try to figure why something happened? A data analyst typically takes a stab at this. He might use existing Business Intelligence platform to pull data and/or also merge other data sets. He/she then applies data analysis techniques on the data to answer the “why” question and help business user get to the actionable insight.

  • What’s next:

What will be my sales forecast for next year?

What will be our profit next year for Scenario A, B & C?

Which customers will cancel/churn next quarter?

Which new customers will convert to a high-value customer?

All of these questions try to “predict” what will happen next (based on historical data/patterns). Sometimes, you don’t know the questions in the first place so there’s a lot of pro-active thinking going on and usually a “data scientist” are doing that. Sometimes you start with a high level business problem and form “hypothesis” to drive your analysis. All of these can be classified under “data science”.

Now, as you can see as we progressed from What -> Why -> What’s next, the level of sophistication needed to do the analysis also increased. So you need a combination of people, process and technology platform in an organization to go from having a Business Intelligence maturity all the way to achieving data science capabilities.

Here’s a related blog post that I wrote on this a while back: Business Analytics Continuum: – Insight Extractor – Blog

Data Science

..And you can check out other stuff I write about here: Insight Extractor – Blog – Paras Doshi’s Blog on Analytics, Data Science & Business Intelligence.

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Where can I find a data analyst mentor, be it in-person or online?

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Data Analyst MentorFind a mentor, where do I. Hmmmmmm….

There are few options. 1) Paid online courses with Mentoring 2) Free Options

#1, Paid online courses with mentoring.

I am a mentor for an ed-tech startup Springboard – Learn Data Science & UX Design online — it’s similar to what you are asking for. If you see value in that, you should check it out.

#2. Free options:

a. Quora: You could ask questions here and A2A — Build a network and someone may offer to mentor you offline

b. Mooc: You could join courses on MOOC’s like coursera and udacity — they have good forum support so you could use it for getting your questions answered

c. Cold email: There are lot of analytics/data-science professionals active in the community (linkedin groups, blogs, etc) and if you cold email them, you might find one!

d. local meetups: go to local meetups, meet people and find your mentor.

Stepping back, having a mentor helps and accelerates your progress – but not having one, shouldn’t stop you from achieving what you want.

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