Roadmap

This roadmap is Divided into 3 levels:

1. Beginner: Learn the tools that will help you complete your journey.
2. Intermediate: Dive deeper into cleaning, visualizations, and other topics.
3. Advanced A: Start your ML and Math journey.
   Advanced B: Discover Deep Learning, Computer Vision, and NLP.

Beginner

1. Python

• Getting Started with Python

• Getting Started with Python

  Libraries:
  Pandas - Corey Schafer - Kaggle Tutorial

  Numpy One of them - Huawei Talent Numpy Tutorial - Numpy Tutorial For Beginners

  Python Regular Expression - Python Regular Expression Tutorial

2. Statistics

• Statquist(Recommended)
• Udacity
• Khan Academy

3. SQL

• ITI

• SQL for Data Analysis(Recommended- enough with iti course)

  DataCamp

  • introduction-to-sql
  • Introduction to Relational Databases in SQL

4. Git & GitHub

• Elzero Web School(Recommended)

Intermediate

At this level, it is highly recommended to practice. You can use Kaggle datasets, and after completing each topic, there will be some recommended datasets for you to work on.

1. Data Cleaning

One of the MOST important skills that you need to master to become a good data scientist

• Data camp course
  

• kaggle
  

• Medium
  

  Practices Datasets:

  • Shoe price
    
  • candy

2. Matplotlib

• Corey Schafer
  

  Practice Sataset:

  • Airline Passengers satification
    

3. Seaborn

• KimberlyFessel
  or
  

• introduction to Data Visualization(paid)

• Intermediate Data Visualization(paid)

  Practice Sataset:

  • Super market sales

4. EDA

It is highly recommended to watch analysis videos and observe both the actions and thought processes of the analysts

• Learn EDA in python

• Krish Naik
  

• Simplilearn
  

• Datacamp(paid)

  Practice Datsets: -Telecom Churn -Netfix

6. power bi

• Alex the Analyst
• ITI labs(Recommended)

7. Tableau

• Udacity
  

9. Web scraping

• Codzilla Try to scrape websites by yourself
  

*** The Nex Steps, I perefer select career Data science and AI or PowerBI ***

Advanced Data science and AI

Advanced A

More Mathematics

Studying these topics in parallel with Machine Learning is recommended

1.**Mathematics For ML **

• [Mathematics for Machine Learning]-Coursera
  - Linear Algebra-Coursera
  - Multivariate Calculus-Coursera
  - PCA-Coursera
  
• Harvard Statistics110: Probability-Coursera
  

2.Infrential Statistics

• Coursera: Inferential stats 1
• Coursera: Inferential stats 2
• Coursera: Bayesian Statistics 1
• Coursera: Bayesian Statistics 2
• Coursera: Mixture Models

ML Journey Begins from here:

1. ML

• Statquist(Recommended)
• Coursera: Machine Learning Specialization(Recommended)
• Coursera: Machine Learning with Python(Less Recommended)

Books:

• Hands On Machine Learning

3. Feature Engineering

• Tutorial-Kaggle
  
• Medium Blog-

4. Interpreting ML models more to be added here

• Docs-SHAP
  
• Course-Kaggle
  

After finishing this level apply to 2 or 3 good sized projects.

Advanced B

DL Journey Begins from here:

1. Deep Learning
   Course-Coursera
   Book
   Github of the previous book

2. Tensorflow
   Course-Coursera
   

3. Model Deployment More to be added here..
   Blog-Medium
   Course-Coursera
   Guided project
   

4. Probabilistic Graphical Models MORE yet to come in this section..
   Course-Coursera
   

More to come in this level...

Feel free to contact us:

• Menna Waleed