Applied Data Science Course (Arabic)
Applied DataScience Course: Solving Real-world Problems Through Exploring Data and Making Decisions Using Practical Tool
Description
The Applied Data Science Course offers a comprehensive exploration of real-world problem-solving within the context of data science. Throughout the course, participants delve into diverse domains, applying their newfound skills to tackle challenges such as Titanic survival prediction, credit card fraud detection, house price prediction, advertising sales prediction, air quality prediction in India, and customer segmentation.
The course leverages the collaborative power of Google Colab and Kaggle, providing a dynamic learning environment. Google Colab, with its cloud-based Jupyter notebooks, allows participants to seamlessly write and execute code, fostering a collaborative atmosphere. Kaggle, a renowned platform for data science competitions, serves as a practical playground for implementing solutions to the identified problems. Participants engage with real datasets, gaining hands-on experience in preprocessing, exploratory data analysis, and model development.
The Titanic survival prediction task involves analyzing historical data to predict passenger survival, while credit card fraud detection requires participants to develop algorithms that identify fraudulent transactions. House price prediction challenges participants to build regression models for property valuation, and advertising sales prediction focuses on optimizing marketing strategies. The air quality prediction task in India necessitates understanding environmental factors impacting air quality, and customer segmentation involves clustering techniques to identify distinct consumer groups.
By intertwining these real-world challenges with the powerful tools of Google Colab and Kaggle, participants emerge from the course equipped with a robust skill set in data science, ready to tackle complex problems and contribute meaningfully to the data-driven landscape.
What You Will Learn!
- Data Science
- Data analysis
- scikit-learn
- Matplotlib
- Model Building
- Model Evaluation
- Data Cleaning and Preprocessing
- Seaborn
- Data Visualization
- Prepare Data
- Practical Data Science
- Python for data science
- Data Exploration and Feature Engineering
- Data Analysis and Interpretation
- Regression analysis
- Classification
- Autoregressive integrated moving average ARIMA
- XGBoost
Who Should Attend!
- Beginner Python developers curious about data science