As a prospective Data Analyst intern, how do I answer the most challenging data analysis I have done so far?

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My answer on Quora for: As a prospective Data Analyst intern, how do I answer the most challenging data analysis I have done so far?

https://www.quora.com/As-a-prospective-Data-Analyst-intern-how-do-I-answer-the-most-challenging-data-analysis-I-have-done-so-far/answer/Paras-Doshi?srid=uWIN

When I hire for Data Analyst (Jr. or Intern) positions, I look for three things:

1) Analytical mindset:

I would do this by sharing a hypothetical case study and seeing how you go about solving this. I would look for things like: a) Approach: How do you break down the problem? b) Effectiveness: How effectively can go about solving the case. I am NOT looking for the “Right” answer but just want to see how you go about solving the case.

(Search for “Management consulting case studies” — I usually pick a simple case)

2) Communication skills:

This is pretty standard across many roles but it’s important for data analysts to be able to communicate their recommendations/findings to stakeholders.

3) Basic hard/tech skills + Willingness to learn new tech skills:

I would ask you basic tech questions around SQL, Excel OR other “tech skills” that you might have mentioned in your resume. I am not looking for expert-level knowledge but just want to make sure you know things that you have listed on your resume or things that you studied. Also, I would ask you questions that would help me figure out whether you are open to learning new tech skills.

So now that I have shared the framework with you, let me try and answer your question: How do I answer the most challenging data analysis project that I have done?

a. If you had a good approach for your project then It would mean that you know how to break down data analysis problems and solve them. So solving a basic case study shouldn’t be difficult for you and I could check box #1!

b. If you can communicate the “complexity” of the project effectively then I think I would check the box #2: communication skills!

c. Since you solved a challenging project, I assume that you picked up some tech skills (Bonus points if you picked up new tech skills while solving this problem). Just let me know what tech you used to solve the problem so that I can ask questions around that — if you are able to answer them then I would check box #3!

It’s NOT about the challenging project but your learning/takeaways from that project that will be help you the most!

Now, assuming that the interview team think you are a good “culture fit” plus you came out on top compared to other candidates then you will get an offer to join the team as a Data Analyst!

Hope that helps and may the force be with you!

Are Dashboards dead?… #Analytics

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Let’s think through:

Are Dashboards Dead?

With lot of vendors pushing for democratizing analytics (a.k.a self-service), it may seem that the dashboards would soon be dead!

You need two things to make a org data driven. 1) Push 2) Pull.

“Pull”

…is where most of the analytics vendors are focused right more — it’s set of technologies that the business users want.┬áThe big idea here is to enable business users to pull whatever data they want, whenever they want without having to wait for Analytics/IT. Note that the business users are doing the heavy-lifting in analysis (of course you need a data platform to enable this but still it’s the business users using the platform and doing their analysis)

“Push”

…is where there are dashboards which are built by central IT/Analytics and are ready to be consumed by the business users. This should be a governed environment where a lot of effort has been invested by Analytics/IT to make that the metrics are standardized & accurate. This is key to making this work — if the metrics on the dashboard are accurate and metrics are standardized then business users would trust these dashboards more than the self-service dashboards. This would also be their one-place to go view all key performance indicators for their org/department and then if they see something “interesting” (or better yet — get an alert!) then they can dive into the self-service environment and do their thing. You see, “Push” strategy is really great at getting the data to all business users and then “pushing” them to do use the self-service analytics platform.

[BTW: Putting bunch of reports in a grid layout is not what I am talking about here. I am limiting my definition of dashboard which have KPI’s and directs users to where they should be focusing on]

(Again, two things to do here to make sure the push strategy succeeds. 1) Having standardized & accurate data = earn trust! 2) Having KPI’s that align with the strategic plan of the org/dept)

Dashboards Push Pull Analytics Strategy

So now having understood what these strategies are let’s take a minute to put them to use to answer the question:

Are Dashboards Dead Yet?

So let’s imagine a scenario where a org does not a Push Strategy. They have implemented a self-service platform and are focused on evolving that. Now there are two problems that they will run into:

  1. For “casual” users — How do they get them the training they need? OR support that they need?
  2. For “power” users — Once they start creating their own calculated metrics then how do they make sure that they are standardized across what other power users are doing? (also, how do they validate if what they are analyzing is accurate?)

You see both of those problems can be partially (if not completely) solved by having Dashboards:

  1. Dashboards are a great way for casual users to look at their KPI and then they can figure out where they would focus on
  2. Also, Dashboards are a great way to provide standardized & accurate metrics so everyone could trust the number that they are looking at
  3. Note that it shouldn’t require you to start from zero! You should be using the data modeling layer built for your self service platform for the dashboards as well

And that’s why I think Dashboards are not dead yet.

PS: You might see some vendors that are pushing for a different approach where the platform would auto-magically go through the data and get you the “insights” — I think it’s a great approach. Usually they would target dashboards but I would argue that they compete more with “Pull” strategies rather than “Push” because now the business user won’t have to explore so many different variables but the platform could do that heavy-lifting and get them quick insights.

Webinar: Learn how to build a Machine Learning model to predict customer churn

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UPDATE: WEBINAR HAS ENDED. RECORDING CAN BE FOUND HERE:

https://www.youtube.com/user/PASSBAVC


Next week, on Mar 15th, 2016, We at Business Analytics VC are hosting a webinar to help you dive a little bit deeper with azure machine learning and learn about building a model to predict customer churn. Even if you don’t use Azure, I think you can still benefit from learning about the use-cases and the framework to solve this problem. You can register using this URL:

http://bit.ly/PASSBAVC031516

see you there!

PS: if you like to learn about how to build a recommend-er system in Azure ML then you can see last month’s presentation here: http://bavc.sqlpass.org/Home.aspx?EventID=4514

Completed Marketing Analytics Course from Coursera:

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I just successfully completed the Marketing Analytics course from coursera. The certificate was issued by coursera and university of virginia — it was great to brush up some of my existing skills and then build upon it by learning some new techniques/frameworks.

The course covered:

  1. Marketing Resource Allocation
  2. Metrics for Measuring Brand Assets
  3. Customer Lifetime Value
  4. Regression Basics
  5. Marketing Experiments

If you haven’t checked out courses on coursera yet then I would recommend to check those out! There’s a ton out there for data professionals!

Coursera Marketing Analytics Certificate

 

Machine Learning Algorithm Cheat Sheet:

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If you’re getting started with Data Science & Machine Learning then I think this would be a great resource for you. This “cheat sheet” helps you select the “algorithm” to test depending on the problem you are trying to solve and the data-set that you have.

Download link: http://aka.ms/MLCheatSheet (Courtesy: Azure Machine Learning)

Also, even though the cheat sheet was created to help you with “Azure Machine learning” product, it’s still valid if you use other machine learning tools.

Azure Machine Learning Algorithm Cheat Sheet

 

Building data driven companies — 3 P’s framework.

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Data Driven Comapnies need Process Platform People

Data Driven Companies — 3 P’s framework

People:

To build data-driven organization, you need decision makers to use data instead of anything else. So you need to help built a culture where data-driven decision-making thrives — usually this is most efficient if you have executive buy-in. Example: A CEO who is a stats-junkie! Of course, not every company would have this. It could be that you find yourself in an organization where the CEO is known to make huge bets just using “gut” — in cases like this, an organization could have some of the best platform and processes but unfortunately, it won’t do any good.

Now just having people who make data driven decisions is not enough — you (as a data professional) need to deliver “data” to them. To do that you need 1) Processes 2) Platform. So let’s talk about them:

Platform:

A platform in this context is the data and analytics platform used by the organization to get the data they need, when they need it. If the organization is small (e.g. less than 15 or so) then the platform could be excel and engineers/analyst writing ad-hoc queries but as you grow (= team size expands) then you need better platform to serve the data needs of the organization. Some tools are better than others and you would usually wind up using multiple vendors in your analytics stack — but remember that jut having a great analytics platform is not enough. You need the “people” and the “processes” to go with that. So, with that let’s talk about process:

Process:

Process is everything between Platform and People. Let me expand on this. Here are few things where having a defined process is key for building data-driven organizations.

  1. How to prioritize the analytics request? It will be great to have a process where you/team will work on projects that closely align with the strategic objective of the company
  2. What does the analytics org-structure look like? Do you have analyst embedded in each team or do you have a centralized team or do you go for a hybrid approach?
  3. What is the process to justify investment in analytics?
  4. Which is the “right” metric definition? (There needs to be a process that keeps the metric definition standardized in an organization)
  5. What is the process to clean data? (Maintaining data integrity is key. You could put this on “Platform” bucket as well)
  6. How do users get “help”? (Is there a ticketing system that they should use? Is it just another “IT” ticket? Who responds to tickets? What’s the SLA around analytics queue tickets? etc)
  7. Who owns “analytics”? There needs to be someone on the team owns analytics like analytics manager, VP of analytics and he/she should be reporting to someone on management team (CIO, CFO, COO, Chief of Staff, CEO) who is held responsible as well.

The list goes on…but I hope you get the point. Having a well-defined processes in an organization is important — usually, this stuff gets less attention and org’s/teams tend to focus just on “platform” which might not be the best thing to do.

Having shared the 3 P’s, let me share few tips on

How to go about implementing the framework:

Three tips:

  1. Identify the “P” that has the best ROI
  2. It’s an iterative process!
  3. Refine as needed

On #1. To help you identify the “P” that has the best ROI, your first step could be to create a matrix to help you evaluate where your organizations falls. I have shown an example below:

Building Data Driven companies 3 Ps framework matrix

If you want to build analytics from scratch then you would love working at early stage startups (bottom-right) but if you like advanced stuff (data-science) then Top-right corner is great! Also, For org’s in Top Left where you have the platform and processes but lack data-driven people — it would be wise to crank up your efforts to drive adoption. (since you already have the right platform and process than any additional investment here would yield little to no ROI).

On #2. Understand that it’s an iterative process. You are never done optimizing any of these P’s! It’s a journey and not a destination.

Continuos Improvement Process People Platform

On #3: Just like with other frameworks, you’ll need to refine and adjust this based on your needs. You may have noticed that I focused on “Org-wide” framework but you could be heading up an analytics function for a department and in that case, not all of the things here would help. “People”, “Process” and “Platform” would still apply on a high level but it might just be that you don’t have “control” over the platform. So, you may need to refine/adjust this as needed.

I hope the framework is a great tool for you to think about building data driven companies!

Best,
Paras Doshi

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Doing Data Science at Twitter — A great read!

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Doing Data science at Twitter

Doing Data science at Twitter

Why is “Doing Data Science at Twitter” a great read?

This is an insider’s perspective from someone who is working at a company that I classify as having the highest level of analytics maturity — In other words, Twitter is known to apply knowledge gained from data science into their products and business processes.

It’s also important to recognize that every company is different and the analytics/data-science tools/techniques/processes that would be implemented would also vary based on the analytics maturity — I love that this was one of the key insights shared in this article.

Also, the article talks about two types of data scientists…I thought it was great way to classify them because there’s a lot of confusion in the industry around what a Data scientist does. With that, Here’s the URL:

My two-year journey as a data scientist at twitter

Best,
Paras Doshi

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