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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:

  1. Introduction

  2. Prerequisites

  3. Concepts

  4. Algorithms

  5. Process

  6. 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?

  1. What is Data Science?

  2. Why Data Science is important?

  3. Artificial Intelligence Vs Data Science Vs Machine Learning Vs 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.

  1. Linear Algebra

  2. Statistics

  3. Probability Theory

  4. 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?

  1. Supervised Learning (Regression, Classification)

  2. Unsupervised Learning (Clustering, Anomaly Detection)

  3. Reinforcement Learning

  4. Deep Learning (Artificial Neural Networks)

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?

  1. Classification (k-Nearest Neighbors, Logistic Regression, Decision Trees, Naive Bayes)

  2. Regression (Linear, Polynomial, Ridge, Lasso, ElasticNet)

  3. Support Vector Machines

  4. Neural Nets

  5. Random Forests

  6. Clustering (K-Means, Mean-Shift, DBSCAN, EM-GMM, Agglomerative Hierarchical)

  7. 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.

  1. Data Science Process (Data Collection, Data Cleaning, Modeling, Model Evaluation, Model Tuning, Prediction)

  2. Exploratory Data Analysis

  3. Feature Engineering

  4. 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.

  1. R

  2. Python (TensorFlow, Keras)

  3. SQL

  4. Azure Machine Learning

  5. Amazon Machine Learning

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.

#DataScience #DeepLearning #Python #MachineLearning #R

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