A Data Science Central Community

I have been following trends in Data Science since long, more from a research perspective ~ Trying to analyze what technique fits in where and what are the new developments in new fields and the bunch of new tools that have been cluttering the market.

However, I soon realized that every field had it's own modifications and techniques as well as jargon. I encountered Financial Analysts talking about Black-Scholes model as if it's something you learned in primary school. Having worked previously in Marketing and Operations, those were the areas I decided to narrow down upon. Marketing was easy since I had a Market Research background and Factor Analysis and Multi-dimensional scaling was something we Marketers learned in primary school. Supply Chain was a bit tricky since they had Operations Research intensive techniques such as multi- echelon inventory optimization.

Now with the advent of Big Data and Complex algorithms such as Random Forests and LASSO, there is a whole new clutter of jargon, tool and techniques. The boundaries that used to be defined with what technique to be in general used to solve what type of problem are fading. And, it is becoming impossible for one person or even one team to understand and know the gamut. It is just as the blind men trying to analyze the elephant and associating it with something they understood such as a rope, a pillar... On the flip side, I have also come across people discrediting each other's work analogous to the elephant story where one blind man thought it was a rope while another thought it was a pillar.

Lately, I have been taking the plunge into implementing some of these techniques trying to make some sense out of it. I've realized there might not be any value in just analyzing the trends anymore. While I am just another blind man trying to decipher the Data Science world in my own way, I'll try to document my journey in future blogs.

My take on the process is to put on a Mathematician's hat. Distill and Generalize the problems till the tools and techniques fade and only the equations remain. Once you understand the core aspects, associate it with whatever jargon you want to in whatever field.

© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge