Time Period for Analytical Positions Recruitment.

Though having rough estimate of how much time each analytical position recruitment takes (i.e. due to experience in the filed); thought of giving a quantitative touch using a real sample. Herein I would like to thank 13 recruitment agencies that provided the requested details. Most of the sample belong to India recruitment only.

Below figure provides average time period i.e. number of months to fill a said position for analytics. Wherever possible we have provided domain (Retail/CPG & BFSI) specific time as our sample has around 30-34% of data for these domains. 

R (Web Server) Solutions - Amplifying Artichokes

Every month I see one or more new R based web server solutions coming into the market, sight seeing some of them thought of sharing one of my old architecture map manifested to the client long back in early 2009 (good to see quick spreading of scalable and customizable open source statistical computing tool in the market).

Please feel free to reach me at mavuluri.pradeep@gmail.com for more details.

Implementing some BootStrap stuff in R

“The bootstrap is a computer-based method of statistical inference that can answer many real statistical questions without formulas”

  (From "An Introduction to the Bootstrap" by Efron and Tibshirani, 1993)

Analytical Skills Development Spending.

Challenged with the acquisitiveness for adaptability and agility, analytical service organizations are turning to real-world work & emergent technologies/methods to develop next-generation analytical skills (talent). However, corporate bosses seems to playing it cautious when coming to their spending on analytical skills development.

According to our survey conducted by us across a good number of analytical services organizations in India, most expect to increase their current spending on analytical skills development (43.8%) in next six months. Around 27.2% organizations expect to spend aggressively in next six months. Only 6.7% expect spending to decrease in next six months. While 22.3% of organizations spending on analytical skills development continue to be frozen in next six months.

Key Non-ignorable Points for Agile Analytical Services.

Taking workforce to immediate and next levels of analytical requirements

In today’s world, where the demand for analytical skilled workers is greater than the supply, employers’ needs to provide critical trainings to their workforce for two very good reasons (short-term) that enable current key deliveries for them, and (long-term) innovative and career development oriented that helps workforce to stay with them.

 

Reach me at mavuluri.pradeep@gmail.com for more details on analytical trainings.

Step Towards Making Data Analysts More Productive in Shorter Time?

Today lots of reports have been published and expected more;  highlighting analytical skills shortage, however, rare of them address, how it can be achieved in short term as in long-term supply and demand factors will match. Below are few things, which I have implemented in the past which helped in short term stint of less than three months for data analysts to become more productive:

1)  Focusing on business problems and help analysts define them appropriately. It worked superbly, since addressing or questioning required analysis helps them to quickly grasp complexity.
2)  Moving away from process of ETL.
3)  Make them learn and comfortable with automating repetitive tasks during the analytical cycle.
4)  Make them map business questions to analytical solutions using several mind map games (can be in both class-room environment or offline/online group environment)
5) Leverage them against appropriate analytical tools. 

Please feel free to reach me at mavuluri.pradeep@gmail.com to know about process more.

Knowing your data is first step for insightful analysis:

Let what ever be modeling algorithms, knowing your data is first step for insightful analysis.

 

Given today's buzz of big data, where more than 90% of the data's usefulness is questionable, it is not bad to stick to basics of the data analysis, i.e. first step, understanding received data properly.

For instance, if you receive a social media data for a single day and if it has 50 million records, knowing existent values format and frequency makes your first step for selecting appropriate modeling algorithm.