I like frameworks — it helps structure your thoughts. One of the most basic questions that I have asked looking at a company/org is to figure out how to evaluate the whether it’s good or great? And more importantly, how to help drive it to greatness? There’s a list of things that I could rattle off but it was not complete and also, I didn’t really have a structure. That is where the book “Good to Great” by Jim Collins comes into picture. It’s a great book that shares a “framework of ideas” for steering a company from good to great by sharing six key learning’s wrapped in a continual process he calls “flywheel”:
I encourage you to read the book if you can. But if you don’t have time, here’s a good overview:
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!
VIEW QUESTION ON QUORA
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.
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.
Someone asked on Quora: What analytics data gives you the most actionable advice to improve your blog? so here’s my answer:
I have been blogging about Analytics for past few years and this question is at the intersection of both so let me give it a shot:
It depends on two things: 1) Your goal for running the blog 2) Age of the blog
#1: Your goal
First let’s talk about your goal for running the blog. It’s important to define this as this would help set the metrics that you will monitor and take actions to improve it.
Let’s say that the goal of your blog is to earn is to monetize using ads. So your key performance indicator (KPI) will be monthly ad revenue. In that case you can improve by one of the three things: Number of People visiting the blog x % of visitors clicking on ads x average revenue per ad click. You can work on marketing your blog to increase number of people visiting the blog. Then you can work on ad placement on your blog to increase % of visitors clicking the ad and then you can work on trying different ad networks to see which one pays you the most per click.
let’s take one more example. Like me if your goal is to use your blog for “exposure” which helps me build credibility in the field that I work in. In this case, the KPI i look at is Monthly New Visitors. I drill down further to see which marketing channels are driving that change. That helps me identify channels that I can double down on and reduce investments in other areas. For example: I found that Social is not performing that great but Search has been working great — I started investing time in following SEO principles and spent less time on posting on social.
So first step: Define your goal and your KPI needs to align with that.
#2: Age of your blog:
- Early: Now at this stage, you will need to explore whether you can achieve what you set out to using blogging. So let’s say you wanted to earn money online. In first few weeks/months, you need to figure out if it’s possible. Can you get enough traffic to earn what you wanted? yes? Great! If not, blogging might not be the answer and eventually all your energy is being wasted. Figure this out sooner rather than later — and take first few weeks/months to make sure blogging helps you achieve your goal.
- Mid: By this stage, you should know how blogging is helping you achieve your goal. So it’s time to pick one metric that matters! So if your goal was to earn money using ads then go for Monthly ad revenue and set up systems to track this. Google Analytics will be a great starting point. Also, at this stage, you should be asking for qualitative feedback. Ask your friends, ask on social, get comments, do guest blogging on popular platforms and see if you get engagement — basically focus on qualitative feedback since you won’t have enough visitors that you can analyze quantitative data.
- Late: In this stage, you have the data and the blog is starting to get momentum. Don’t stop qualitative feedback loops but now start looking at quantitative data too. Figure out the underlying driving forces that move the needle on your KPI. Focus on improving those!
TL;DR: Define your “why” and then pick a metric— then use combination of qualitative and quantitative data to improve the underlying driving factors to improve the metric.
VIEW ON QUORA
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:
- Marketing Resource Allocation
- Metrics for Measuring Brand Assets
- Customer Lifetime Value
- Regression Basics
- 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!
A new milestone for this blog — 500+ posts! To commemorate this, I decided to change the domain name from ParasDoshi.com to InsightExtractor.com — my goal was two-fold:1) provide an easy to remember name 2) representative of the work that we all do: we help extract insights from data.
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I was talking with a database administrator about different categories that SQL Commands fall into — and I thought it would be great to document here. So here you go:
||Data Manipulation Language: SQL Statements that affect records in a table.
||SELECT, INSERT, UPDATE, DELETE
||Data Definition Language: SQL Statements that create/alter a table structure
||CREATE, ALTER, DROP
||Data Control Language: SQL Statements that control the level of access that users have on database objects
||Transaction Control Language: SQL Statements that help you maintain the integrity of data by allowing control over transactions
BONUS (Advance) QUESTION:
Is Truncate SQL command a DDL or DML? Please use comment section!
Author: Paras Doshi