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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As a data scientist, are you dissatisfied with your career? Why?

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As a data scientist, I am not dissatisfied. I love what I do!

But I might have gotten lucky since I got into this for the right reasons. I was looking for a role that had a little bit of both tech & business and so few years back, Business Intelligence and Data Analysis seemed like a great place to start. So I did that for a while. Then industry evolved and the analytics maturity of the companies that I worked also evolved and so worked on building predictive models and became what they now call “Data scientist”.

It doesn’t mean that data science is the right role for everyone.

One of my friends feels that it’s not that “technical” and doesn’t like this role. He is more than happy with data engineer role where he gets to build stuff and dive deeper into technologies.

One of my other friends doesn’t like that you don’t own business/product outcomes and prefers a product manager role (even though he has worked as a data analyst for a while now and is working on transitioning away).

So, just based on the empirical data that I have, data science might not be an ideal path for everyone.

Hope that helps!

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How do you become a good data analyst?

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This was asked on quora and here’s my reply:

You can become a great data analyst by continuously improving the analytics maturity of the company/start-up that you work for:

[Go to my blog for more context on the picture above]

If you create bunch of reports and help answer what happened— then try to help business users with why it happened. [Example: Instead of just sending website traffic info, add why the traffic spikes (up/downs) are happening]

If you are working on building bunch of models that answer why questions then try to help build predictive models next [Example: You have been working on a model that helped you answer why customers churned. Now built upon that and predict which customers will churn next]

If you do analytics and data science well and are already answering what, why, what’s next questions and you’re killing it! Then figure out how can you help business owners take action. Or make it easier than ever before to take actions on your data/recommendations.


Other answers for questions are directly/indirectly covered if you do this:

  1. You will have to pick the right tool for the job
  2. You will have to continuously keep learning (by taking online courses and/or you-tube)
  3. Don’t just be a data analyst, be a thought partner to business owners and if possible, transition into role that help you own business outcomes.

Hope that helps!

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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!

How does rise of Power BI & Tableau affect SSRS?

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It does affect SSRS adoption but SSRS (sql server reporting service) still has a place as long as there’s need for printer-friendly reporting and self-service vendors don’t have a good solution to meet this need.

Also, SSRS is great for automating operational reports that sends out emails with raw data (list of customers, products, sales transaction etc).

I advocate an analytics strategy where we think about satisfying data needs using “self-service”-first (Power BI, tableau, qlik) but if thats not the optimal solution (for cases like need to print it, I just need you to send me raw data in excel, etc) then I’ll mark it as SSRS project. And this architecture is supported by a central data model (aka operational data store, data mart, data warehouse) which makes it much easier to swap in/out any reporting tools that we need and we are not locked in by one vendor.

About 10–20% data requests that I see are SSRS projects and if the self-service platforms start adding features that compete with SSRS, I know I would start using those capabilities and phase out SSRS. But if that doesn’t happen, I will continue using SSRS 🙂

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Let me know what you think in the comments section!

Paras Doshi

This post is sponsored by MockInterview.co, If you are looking for data science jobs, check out 75+ data science interview questions!

Journal of statistical software paper on tidying data:

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Data cleaning takes up a lot of time during a data science process; it’s not necessarily a bad thing and time spent on cleaning data is worthwhile in most cases; To that end, I was researching some framework that might help me make this process a little bit faster. As a part of my research, I found the Journal of statistical software paper written by Hadley Wickham which had a really good framework to “tidy” data — which is part of data cleaning process.

Author does a great job of defining tidy data:

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

And then applying it to 5 examples:

 1. Column headers are values, not variable names.
2. Multiple variables are stored in one column.
3. Variables are stored in both rows and columns.
4. Multiple types of observational units are stored in the same table.
5. A single observational unit is stored in multiple tables

It also contains some sample R code; You can read the paper here: http://vita.had.co.nz/papers/tidy-data.pdf

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.