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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 the industry for a 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)
- Support Vector Machines
- Neural Nets
- Random Forests
- Clustering (K-Means, Mean-Shift, DBSCAN, EM-GMM, Agglomerative Hierarchical)
- Deep Learning (CNNs, RNNs, LSTMs)

**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)
- Exploratory Data Analysis
- Feature Engineering
- 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.