Introduction
This roadmap explains data science in simple stages. The goal is to help beginners learn Python, statistics, data cleaning, machine learning, SQL, and deployment. Next, you follow each step with practical actions.
هاد ال roadmap غادي تعاونك تبدا ف data science ب خطوات واضحين. غادي تمشي من Python حتى ML و SQL و deployment.
1. Learn Python
Python drives data science. Write scripts. Handle files. Work with core data structures.
Key Skills
- Lists and dictionaries
- Functions
- File operations
- String processing
- Error handling
تعلم Python basics. خدم ب lists o dicts ودير سكريبتات ديال data loading.
2. Learn Math and Statistics
Math supports every analysis step. Focus on linear algebra, probability, and statistics.
Main Topics
- Vectors and matrices
- Derivatives
- Distributions
- Correlation and covariance
- Hypothesis testing
الرياضيات ضرورية. تعلم matrices و distributions و hypothesis testing.
3. Learn Data Handling
Work with real datasets. Clean and inspect data. Handle missing values. For a deeper guide, check our Data Cleaning Guide.
Tools to Use
- Pandas
- NumPy
- CSV tools
Skills to Build
- Missing values
- Duplicates
- Filtering
- Grouping
- Merging
- EDA
تعلم Pandas و NumPy. نقي data. حيد duplicates. دير groupby.
4. Learn Data Visualization
Visuals help explain patterns. Create simple charts and interpret results.
Libraries
- Matplotlib
- Seaborn
- Plotly
Charts to Practice
- Line charts
- Bar charts
- Histograms
- Scatter plots
- Heatmaps
دير charts بسيطة باش تفهم data. Line chart o bar chart مفيدين بزاف.
5. Learn Machine Learning Basics
Start with classical algorithms. Train small models. See our Machine Learning Basics guide for more.
Important Algorithms
- Linear regression
- Logistic regression
- KNN
- Decision trees
- Random forest
- K means
تعلم regression, classification و clustering بحال K means.
6. Learn Model Evaluation
Evaluate performance clearly. Use metrics that match each task.
Metrics
- Accuracy
- Precision
- Recall
- F1 score
- RMSE
- MAE
قيم الموديل ب accuracy ولا recall ولا RMSE حسب المهمة.
7. Learn Feature Engineering
Transform data to improve model performance.
Core Steps
- Scaling
- Encoding
- Normalization
- Feature selection
دير scaling و encoding و اختار features مهمين.
8. Learn SQL
Data science uses SQL daily. Extract, filter, and group data from databases.
Focus Points
- Select queries
- Joins
- Filtering
- Aggregation
تعلم SELECT و JOIN و GROUP BY باش تطلع data صحيحة.
9. Learn Big Data Tools
Large projects need distributed systems.
Technologies
- Spark
- Hadoop basics
- Databricks
تعلم Spark و كيفاش تخدم مع data كبيرة.
10. Build Real Projects
Projects build skill and confidence. Use public datasets and document results.
Project Ideas
- Sales forecasting
- Customer segmentation
- Fraud detection
- Churn prediction
- Housing price prediction
دير مشاريع بحال forecasting ولا churn prediction باش تولي محترف.
11. Learn Deployment
Deploy models with simple tools. Test inference performance.
Tools
- Flask
- FastAPI
- Streamlit
- ONNX
تعلم deployment ب Flask ولا FastAPI او Streamlit.
Syntax or Model Structure Example
Below is a simple example for loading and inspecting a CSV with Pandas.
import pandas as pd
data = pd.read_csv("data.csv")
print(data.head())
print(data.info())
هادا مثال باش تشوف data وتعرف الأعمدة.
Exercises
- Load a CSV file and print the first ten rows.
- Calculate correlation between two numeric columns.
- Plot a histogram for any feature.
- Clean missing values in a dataset.
- Train a linear regression model.
- Train a decision tree for classification.
- Create a SQL query with a JOIN.
- Write a grouping operation with Pandas.
- Do feature scaling with StandardScaler.
- Build a simple API that returns model predictions.
Conclusion
Follow the roadmap and practice daily. Data science grows with clean data, consistent learning, and real projects.
تبع الخطوات و خدم على data بزاف باش تزيد المهارات ديالك.