The Data Series
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.