- ankitrathi

# Data Science Digest

Data Science Digest

Data Science is an amalgamation of many other fields like mathematics, technology & domain; it has its own concepts, process & tools. It’s really tough to know each and everything related to the subject unless you have really worked on complex data science problems in industry for couple of years.

In this post, I have tried to aggregate & organize all the data science related topics from Quora (generic definitions), Medium (in-depth working) & GitHub (code). This post is organized in these sections of data science area:

Introduction

Prerequisites

Concepts

Algorithms

Process

Tools

**Data Science Introduction**

In this section, you can get introduced to data science world. What is data science? Why it is important? What is the difference between Artificial Intelligence, Data Science, Machine Learning & Deep Learning?

**Data Science Prerequisites**

Before diving deep into data science, one needs to cover a lot of ground like decent understanding of linear algebra, statistics, probability & data engineering.

**Data Science Concepts**

In this section, you can learn the data science concepts like types of learning and when to use which kind of learning algorithms?

**Data Science Algorithms**

This section covers various (mostly used) data science algorithms in detail. Which kind of problems these algorithms solve & what are the pros & cons of using these algorithms?

Classification (

__k-Nearest Neighbors__,__Logistic Regression__,__Decision Trees__,__Naive Bayes__)Regression (

__Linear, Polynomial, Ridge, Lasso, ElasticNet__)Clustering (

__K-Means, Mean-Shift, DBSCAN, EM-GMM, Agglomerative Hierarchical__)

**Data Science Process**

In this section, you will get to know data science as a process; once you have a problem, what approach will you take? How will you collect & clean data? Which evaluation and tuning technique will you use to optimize your data science algorithm.

Data Science Process (

__Data Collection, Data Cleaning, Modeling, Model Evaluation, Model Tuning, Prediction__)__Ensembling__(Bagging, Boosting & Stacking)

**Data Science Tools**

This section covers the tools being used in data science field like R, python, SQL or machine learning platforms provided by Azure & Amazon.

*Thank you for reading my post. I regularly write about Data & Technology on *__LinkedIn__* & *__Medium__*. If you would like to read my future posts then simply ‘Connect’ or ‘Follow’. Also feel free to connect on *__Slideshare__*.*