Blog Archives

November Thanksgiving – Data Science Style!

November 4, 2019
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November Thanksgiving – Data Science Style!

Hello All, November is the month of Thanksgiving, and vacations and of course deals galore! As part of saying thanks to my loyal readers, here are some deals specific to data science professionals and students, that you should definitely not miss on. Book deals: If you are exploring Data Science careers or preparing for interviews The post November Thanksgiving...

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Social Network Visualization with R

September 12, 2019
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Social Network Visualization with R

In this month’s we are going to look at data analysis and visualization of social networks using R programming. Friendster Networks Mapping Friendster was a yesteryear social media network, something akin to Facebook. I’ve never used it but it is one of those easily available datasets where you have a list of users and all The post Social Network...

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DataScience Portfolio Ideas for Students & Beginners

July 3, 2019
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DataScience Portfolio Ideas for Students & Beginners

A lot has been written on the importance of a portfolio if you are looking for a DataScience role. Ideally, you should document your learning journey so that you can reuse code, write well-documented code and also improve your data storytelling skills. However, most students and beginners get stumped on what to include in their

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Mapping Anthony Bourdain’s Travels

June 22, 2019
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Mapping Anthony Bourdain’s Travels

Anthony Bourdain was an amazing personality – chef, author, world traveler, TV showhost. I loved his shows as much for the exotic locations as for the yummilicious local cuisine. So I was delighted to find a dataset that included all travel location data, from all episodes of his 3 hit TV shows. Dataset attributed to

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Email Automation for Google Trends

June 4, 2019
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Email Automation for Google Trends

This blogpost will teach you set up automated email reports to view how search volumes i.e. Google Trends vary over time. The email report will also include important search terms that are “rising” or near a “breakout point”. This can be really useful as the breakout keywords indicate users across the globe have recently started

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How to Become a Data Scientist

May 30, 2019
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How to Become a Data Scientist

This question and its variations are the most searched topics on Google. As a practicing datascience professional, and manager to boot, dozens of people ask me this question every week. This post is my honest and detailed answer. Step 1 – Coding & ML skills You need to master programming in either R or Python.

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Data Science Job in 90 days – Book Review

May 25, 2019
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Data Science Job in 90 days – Book Review

Are you an R-programmer or Datascience enthusiast looking for a break in the datascience field? If so, my latest book “Data Science Jobs – land a lucrative job in 90 days” will help you find one quickly.

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India vs US – Kaggle Users & Data Scientists

November 4, 2018
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India vs US – Kaggle Users & Data Scientists

Introduction This is an analysis of the Kaggle 2018 survey dataset. In my analysis I am trying to understand the similarities and differences between men and women users from US and India, since these are the two biggest segments of the respondent population. The number of respondents who chose someting other than Male/Female is quite

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Automated Email Reports with R

November 1, 2018
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Automated Email Reports with R

R is an amazing tool to perform advanced statistical analysis and create stunning visualizations. However, data scientists and analytics practitioners do not work in silos, so these analysis have to be copied and emailed to senior managers and partners teams. Cut-copy-paste sounds great, but if it  is a daily or periodic task, it is more

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How to raise money on Kickstarter – extensive EDA and prediction tutorial

February 3, 2018
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How to raise money on Kickstarter – extensive EDA and prediction tutorial

In this tutorial, we will explore the characterisitcs of projects on Kickstarter and try to understand what separates the winners from the projects that failed to reach their funding goals. Qs for Exploratory Analysis: We will start our analysis with the aim of answering the following questions: How many projects were successful on Kickstarter, by

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