Data-Science-RoadMap

What is Data Science ?

Data Science is an interdisciplinary field that focuses on extracting knowledge from data sets which are typically huge in amount. The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization.

Getting Started 🚀

In this roadmap I will recommend Python, although you may encounter R in more Data Analytics related jobs. Python mastery will come with time - learn enough basics to be able to read code and implement some simple projects with it.

Beginner Level 👇

Descriptive Stats :

To become a successful data scientist, you should have knowledge of Statistics. Statistics knowledge will give you the ability to decide which algorithm is good for a certain problem

• Statistics Fundamentals
• Udacity
• YouTupe playlist
• Head First Statistics Book

Excel :

• YouTupe

Python :

• A Python Data Analyst’s Toolkit Book
• Python for Data Analysis Book
• Udacity Basics
• OOP

Now let's dive into python's libraries :

Notes I strongly recommend to study these libraries from This Book , in case you aren't familiar with reading i will put some resources i hope it help!

Pandas

Pandas is used for data cleaning and data preprocessing

• YouTupe
• Kaggle

Numpy

NumPy is short for numerical Python and is one of Python’s most important libraries it used for matrix and multidimensional array operations in Python

• YouTupe
• Tutorial

Data Cleaning :

feel free to take a look at a buch of Notebooks of others through kaggle :)

• Blog
• Kaggle
• Playlist
• Pandas 100 tricks

EDA :

As a Data scientist, you have to showcase your findings in a visual form, so that stakeholders can understand them properly

• How to ask good questions?
• Practical Statistics for Data Scientists Book
• HowHow To choose the right chart
• Matplotlib Course 1
• Matplotlib Course 2
• Seaborn Course 1
• Seaborn Course 2

Dashboards :

• Power BI
• Youtube
• Fundamentals of Data Visualization with Power BI– edX

SQL and DB :

Note,All you need from week 1 to week 5

• SQL ITI

Data Science Tools

• Version Control with Git

Intermediate Level 👇

Quick Remember, going into ML is a lifelong learning commitment - you are going to learn new things time after time no matter how far you are in your career.

Getting start with machine learning the first thing is to study math as much as you can !

• Mathematics for Machine Learning Specialization
• The essence of calculus
• Essence of linear algebra
• Multivariable Calculus
• Intro to Inferential Statistics

Questions about Machine Learning?

• How do I choose which attributes of my data to include in the model?
• How do I choose which model to use?
• How do I optimize this model for best performance?
• How do I ensure that I'm building a model that will generalize to unseen data?
• Can I estimate how well my model is likely to perform on unseen data?

To learn theory:

• Machine Learning
• Machine Learning with Python

Hands On:

• Hands-On Machine Learning with Scikit-Learn Book
• Applied Machine Learning

Advanced statistics :

• Think Bayes
• Think Stats
• Practical Statistics for Data Scientists

last but not least everything you need to know about statistics feel free to go ahead to this Amazing YouTupe chanel BrandonFoltz

Time Series Analysis :

• fbprohet
• playlist
• Case-study

Feature Engineering :

• kaggle
• Article

Join Data Science Communities :

Instagram Pages :

• analytics_vidhya
• know_datascience
• a.i.india

Reddit communities :

• r/datascience
• r/learnmachinelearning
• r/MachineLearning

Twitter Accounts :

• Andrew Ng
• KDnuggets
• Yann LeCun

Useful Links :

• Kaggle Competitions
• The Top 15 Places to Find Datasets 📊
• Deploying Machine Learning Models in Production

Next:

In the upcomming years i will update the roadmap with the Advanced Level

Happy learning.