It's a multidisciplinary field that looks at raw and structured data sets and provides potentially actionable insights. The field of data science looks at ensuring that we are asking the right questions as opposed to finding exact answers. Data Scientists require skillsets centered on Computer Science, Mathematics, and Statistics. Data Scientists use several unique techniques to analyze data such as machine learning, trends, linear regressions, and predictive modeling. The tools that data scientists use to apply these techniques include Python and R.
- These are small differences between each job title:
- For more details about each job title, you can see this Arabic video or this other video.
The roadmap is divided into 4 main levels, each level will be divided into weeks and each week will have a set of tasks to be completed. We will try to provide task links one by one when it's finished. Each level is designed to be completed within 1-3 months on average, however, the time taken to complete the roadmap may vary depending on the individual.
- Entry: Good introduction to the field.
- Beginner: Data scientist toolkit and foundations.
- Intermediate: Dive deeper and solidly understand and work with data.
- Advanced A: Mathematics and Machine Learning.
- Advanced B: Deep Learning and specializing in a specific field.
It includes the following topics:
- Data Literacy
- Understanding Data Science
- Introduction to Statistics
- Python Basics
- OOP in Python
| π Phase | π Topics | π Resources | β Tasks |
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| Week 1 |
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| Week 2 |
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| Week 3 | π Python Basics |
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| Week 4 | π OOP in Python |
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It includes the following topics:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Power BI
- Git & GitHub
| π Phase | π Topics | π Resources | β Tasks |
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| Week 1 | π’ NumPy |
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| Week 2 | πΌ Pandas |
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| Week 3 | γ½οΈ Matplotlib |
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| Week 4 | π Seaborn |
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| Week 5 | π Power BI |
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| Week 6 | π Git & GitHub |
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It includes the following topics:
- Regular Expressions (RegEx)
- Data Cleaning
- Feature Engineering
- Exploratory Data Analysis
- Web Scraping
- Structured Query Language (SQL)
| π Phase | π Topics | π Resources | β Tasks |
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| Week 1 | π£ Regular Expressions (Regex) |
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| Week 2 | π§Ή Data Cleaning |
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| Week 3 | π Feature Engineering |
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| Week 4 | π Exploratory Data Analysis (EDA) |
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| Week 5 | πΈ Web Scraping |
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| Week 6 | π Structured Query Language (SQL) |
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It includes the following topics:
- Math required for Machine Learning:
- Linear Algebra
- Multi-variate Calculus
- Machine Learning Algorithms:
- Supervised Learning
- Unsupervised Learning
- Ensemble Learning
- Model Evaluation and Selection
- APIs
| π Phase | π Topics | π Resources | β Tasks |
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| Week 1 | π’ Linear Algebra |
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| Week 2 | π Multi-variate Calculus |
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In this stage you are ready to dive deep in the world of Machine Learning. The following resources are general and not divided into categories or weeks, you can follow them in parallel with the base resources in the weeks as a supplementary resource if you want.
- ΩStateQuest | Machine Learning
- Data School | Machine Learning
- Machine Learning from Scratch | YouTube playlist
- Machine Learning Mastery
- Udacity | Intro to Machine Learning
- Sentdex | Machine Learning with Python
- Hesham Asem | YouTube Arabic playlists
- Machine Learning in Arabic
Now we let's continue the roadmap into weeks.
| π Phase | π Topics | π Resources | β Tasks |
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| Weeks 3 - 4 | π΅οΈ Supervised Learning |
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| Weeks 5 - 14 | π Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition |
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| Weeks 15 - 16 |
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| Weeks 17 - 18 |
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| Week 20 | π APIs |
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In this stage, you now have strong basics about machine learning algorithms and how it works. Also, you learned about APIs and how to use them. Now you are ready to train models, practice on datasets, and make some projects involving the algorithms you learned. You may also make a machine learning algorithm from scratch; it would be great practice to understand the algorithms more.
In this stage, you will enter the Deep Learning and NLP World. It's divided into three phases:
- Phase 1: Basic concepts of Deep Learning (NN, CNN, RNN, Backpropagation, Optimizers, etc.)
- Phase 2: Transformers and LLMs.
- Phase 3: NLP fields.
| π Phase | π Topics | π Resources | β Tasks |
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| Weeks 1 - 3 | π§ Basic concepts of Deep Learning |
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| Weeks 3 - 4 |
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| Weeks 5 |
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| Weeks 6 - 8 | π Convolutional Neural Networks (CNN) |
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| Weeks 9 - 11 | β³ Recurrent Neural Networks (RNN) |
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| π Phase | π Topics | π Resources | β Tasks |
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| Weeks 12 - 13 | π€ Transformers |
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| Weeks 14 - 15 | Φ Large Language Models (LLMs) |
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- There are many sub-fields in this amazing field (NLP), one of them is RAG.
- At first you need to learn LangChain and LangGraph.
- Also we recommend to you Abu Bakr Soliman's course. In this course, you will learn a lot of concepts and tools to build a solid project like (fastapΩ, docker, MongoDB, and MVC Design pattern).
More to be added and we will try to update this roadmap with the latest resources.