DATA SCIENCE ROADMAP :pirate_flag: 2024
Data Science Roadmap for anyone interested in how to break into the field!
This repository is intended to provide a free Self-Learning Roadmap to learn the field of Data Science. I provide some of the best free resources.
Our Previous Roadmap ♥️
:warning: Before we start, :warning:
If you Dont know What`s Data Science or Projects Life Cycle (starting from Business Understanding to Deployment) or Which Programming Language you should go for or Job Descriptions or the required Soft & Hard Skills needed for this field or Data Science Applications or the Most Common Mistakes, then
:pushpin:This Video is for you (Highly Recommended :heavy_check_mark:)
Data Science vs Data Analytics vs Data Engineering - What's the Difference?
These terms are wrongly used interchangeably among people. There are distinct differences:
:small_orange_diamond: Data Science | :small_orange_diamond: Data Analytics | :small_orange_diamond: Data Engineering |
---|---|---|
Is a multidisciplinary field that focuses on looking at raw and structured data sets and providing potential actionable insights. The field of Data Science looks at ensuring we are asking the right questions as opposed to finding exact answers. Data Scientist require skillsets that are centered on Computer Science, Mathematics, and Statistics. Data Scientist use several unique techniques to analyze data such as machine learning, trends, linear regressions, and predictive modeling. The tools Data Scientist use to apply these techniques include Python and R. | Focuses on looking at existing data sets and creating solutions to capture data, process data, and finally organize data to draw actionable insights. This field looks at finding general process, business, and engineering improvements we can make based on questions we don't know the answers to. Data Analytics require skillsets that are centered on Statistics, Mathematics, and high level understanding of Computer Science. It involves data cleaning, data visualization, and simple modeling. Common Data Analytic tools used include Microsoft Power Bi, Tableau, and SQL. | Focuses on creating the correct infrastructure and tools required to support the business. Data Engineers look at what are the optimal ways to store and extract data and involves writing scripts and building data warehouses. Data Engineering require skillsets that are centered on Software Engineering, Computer Science and high level Data Science. The tools Data Engineers utilize are mainly Python, Java, Scala, Hadoop, and Spark. |
Prepare your workspace
Tip :one: : Pick one and stick to it. (:file_folder:Click)
Anaconda: It’s a tool kit that fulfills all your necessities in writing and running code. From Powershell prompt to Jupyter Notebook and PyCharm, even R Studio (if interested to try R)
Atom: A more advanced Python interface, highly recommended by experts.
Google Colab: It’s like a Jupyter Notebook but in the cloud. You don’t need to install anything locally. All the important libraries are already installed. For example NumPy, Pandas, Matplotlib, and Sci-kit Learn
PyCharm: PyCharm is another excellent IDE that enables you to integrate with libraries such as NumPy and Matplotlib, allowing you to work with array viewers and interactive plots.
Thonny: Thonny is an IDE for teaching and learning programming. Thonny is equipped with a debugger, and supports code completion, and highlights syntax errors.
Tip :two: : Focus on one course at least.
Tip :three: : Don’t chase certifications.
Tip :four: : Don’t rush for ML without having a good background in programming & maths.
This track is divided into 3 phases :arrow_down: :
1. Beginner: you get a basic understanding of data analysis, tools and techniques.
2. Intermediate: dive deeper in more complex topics of ML, Math and data engineering.
3. Advanced: where we learn more advanced Math, DL and Deployment.
:bell: For Data Camp courses, github student pack gives 3 free months. Google how to get it.
if you already used it, do not hesitate to contact us to have an account with free access.:hibiscus:
Legend
- :video_camera: Video Content
- :closed_book: Online Article Content / Book
💡 Roadmap Explanation ▶️ Youtube Video :movie_camera:
🔰 Beginner 🔰
Algorithms Book Every piece of code could be called an algorithm, but this book covers the
more interesting bits.
Specializations (data structures-algorithms)
1. Descriptive Statistics
📹 Intro to descriptive statistics | Same Course on YouTube
📹 Statistics Fundamentals - StatQuest - Youtube
📕 Online statistics education
📕 Intro to descriptive statistics Article1 & Article2
📹 Arabic Course
📹 Intro to Inferential Statistics++
📕 Practical Statistics for Data Scientists
2. Probability
📹 Khan Academy
📹 Arabic Course
📕 Introduction to Probability
3. Programming Languages
🔹R - good tool for visualization and statistical analysis.
📹 Introduction to R (Datacamp)
📹 Data Science Specialization - coursera
📕 An Introduction to R
📕 R for Data Science
🔹Python:100:
📹 Introduction to Python Programming
📹 OOP
📹 Arabic - Hassouna | Elzero
📹 Python Full Course - FreeCodeCamp on YouTube
📕 Intro to Python for CS and Data Science
more in OOP
4. Pandas
📹 Corey Schafer-Youtube
📕 Kaggle
📕 Docs
📹 Data School-Youtube
📹 Arabic Course
📹 PandasAI🐼1 - 2 Enhances the capabilities of Pandas by integrating Generative AI functionalities into it.
5. Numpy
📕 Kaggle
📹 Arabic Course
📕 Tutorial
📕 Docs
6. Scipy
📕 Tutorial
📕 Docs
7. Data Cleaning: One of the MOST important skills that you need to master to become a good data scientist, you need to practice on many datasets to master it.
Read this
📹 Course 1
📕 Notebook1
📕 Notebook2
📕 Notebook3
📕 Kaggle Data cleaning
8. Data Visualization :bar_chart:
📹 Introduction to Data Visualization with Matplotlib or
📹 Corey Schafer - Playlist on Youtube or
📹 sentdex - Playlist on YouTube
📕 Kaggle to Data Visualization with Seaborn
📹