# The Data Series

## The Data Series | Episode 1

# Series overview

Welcome to the first article of many taking you through the wonderful world of **Data Science**. Whether you are complete beginner or expert in the field of Data science, wish to become a Data scientist, or just want to know more about the field — this series is for you.

The series will be split into **three** sections:

**1. The Basics**

Answering common questions such as:

What is Data science?

Why is it Useful?

How do i become a Data scientist?

What does the Average Data scientist look like?

**2. Machine Learning**

I) Answering common questions such as:

What is Machine learning?

How is it related to Data Science?

II) Teaching how the following Machine learning algorithms work **step_by_step **accompanied with with illustrations, videos and real life applications.

**Linear Regression****Logistic Regression****Neural Networks**- Single-Layer Neural Networks

- Multi-Layer Neural Networks [ Deep learning — Multilayer Perceptron ]

- Convolutional Neural Networks

**Do not worry if you have not heard of any of these — we will cover each and every one in a lot of depth so get your note taking devices ready!**

**3. How to implement Machine Learning Models**

*This will be the lengthiest topic area as there will be a lot to cover and i plan to cover each aspect in a lot of depth.*

This series of articles will contain **step-by-step guides** to implementing your own machine learning models from scratch on real life data using Python. I plan to accompany this with videos from my Youtube channel.

The structure of this section will cover three important topics in Data Science:

**Preprocessing Data****Implementing Models****Evaluating and Improving Models**

These are the general steps followed by Data scientists when given a new project and are the key areas top employers look for competency in.