Career Road Map for Artificial Intelligence & Data Science

The Data Science Skills Venn Diagram




What you saw in the previous Venn Diagram

Machine Learning = Statistics + Computer Science

It could roughly be interpreted as: 

“Machine Learning is doing statistics on a computer”.

It is not entirely wrong as there are a lot of Machine Learning models that have come directly from Statistics field such as:

# Linear Regression
# Decision Trees
# Naïve Bayes’ Classification Model

More than that the first step in doing Machine Learning on a data set involves doing:

Exploratory Data Analysis on the data.

This is roughly equal to doing: Descriptive Statistics and Inferential Statistics on the data.

The next two equations we could write for intersections of fields are:

Traditional Software = Computer Science + Business Expertise

This roughly means that you are: 
Doing the business via a computer.

And:

Traditional Research = Statistics + Business Expertise

This roughly means that you are: 
Using Statistics to understand, explain and grow your business.

And the last one:

Data Science = Machine Learning + Traditional Research + Traditional Software

The Artificial Intelligence Venn Diagram




The Definitions From The Previous Slide
Artificial Intelligence: A program that can sense, reason, act and adapt.

Machine Learning: Algorithms who performance improve as they are exposed to more data over time.

Deep Learning: Subset of Machine Learning in which multilayered neural networks learn from vast amounts of data.

And these definitions are not very different from what experts think of these fields:







The ‘Data Scientist vs Data Analyst vs ML Engineer vs Data Engineer’ Venn Diagram




The way to differentiate between ML Engineer and Data Analyst is that both of know the Math but Analyst knows more of Statistics and lesser of Programming while the Engineer knows more of Programming and lesser of Statistics.

Data Scientist

A data scientist is responsible for pulling insights from data. It is the data scientist’s job to pull data, create models, create data products, and tell a story. A data scientist should typically have interactions with customers and/or executives. A data scientist should love scrubbing a dataset for more and more understanding.
The main goal of a data scientist is to produce data products and tell the stories of the data. A data scientist would typically have stronger statistics and presentation skills than a data engineer.

Data Engineer

Data Engineering is more focused on the systems that store and retrieve data. A data engineer will be responsible for building and deploying storage systems that can adequately handle the needs. Sometimes the needs are fast real-time incoming data streams. Other times the needs are massive amounts of large video files. Still other times the needs are many reads of the data.In other words, a data engineer needs to build systems that can handle the 4 Vs of Big Data (Volume, Velocity, Variety and Veracity). 
The main goal of data engineer is to make sure the data is properly stored and available to the data scientist and others that need access. A data engineer would typically have stronger software engineering and programming skills than a data scientist.

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