Python for Data Science
This course provides a comprehensive introduction to Python for Data Science, covering data manipulation, visualization, statistical analysis, machine learning, and AI.
- Course fee
- Contact us
- Duration
- 5 days
- Group rate
- Ask an advisor
Course overview
This course provides a comprehensive introduction to Python for Data Science, covering data manipulation, visualization, statistical analysis, machine learning, and AI. Participants will learn how to use popular Python libraries like NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, and TensorFlow to analyze data, extract insights, and build predictive models. The course is highly practical, with hands-on exercises and a capstone project.
Training methodology
Hands-on Coding Labs – Practical exercises with real-world datasets
Live Demonstrations – Applying concepts to real case studies
Data Science Case Studies – Industry applications of Python
Collaborative Learning – Group projects and peer reviews
Capstone Project – Developing a complete data science solution
Target competencies
- Python Programming for Data Science
- Data Manipulation & Cleaning using Pandas & NumPy
- Data Visualization using Matplotlib & Seaborn
- Exploratory Data Analysis (EDA)
- Statistical Analysis & Hypothesis Testing
- Machine Learning & Predictive Modeling using Scikit-learn
- Introduction to Deep Learning & TensorFlow
Course objectives
By the end of the course, participants will be able to:
- Write Python scripts for data manipulation and analysis
- Use Pandas & NumPy for data handling and preprocessing
- Perform exploratory data analysis (EDA) and statistical analysis
- Create data visualizations using Matplotlib & Seaborn
- Apply Machine Learning techniques using Scikit-learn
- Implement basic Deep Learning models using TensorFlow
- Work on real-world data science problems
Course outline
- Overview of Data Science and Python’s Role
- Setting up the Python Environment (Jupyter Notebook, VS Code, Anaconda)
- Python Basics – Data Types, Functions, Loops, and OOP
- Using Python for Mathematical and Statistical Computations
- Understanding DataFrames & Series in Pandas
- Data Cleaning: Handling Missing Data, Duplicates, and Outliers
- Data Transformation & Aggregation
- Working with NumPy for Numerical Computing
- Reshaping Data: Pivot Tables, Melting, and Stacking
- Introduction to Data Visualization
- Creating Basic Charts (Line, Bar, Scatter, Pie, Histograms)
- Advanced Visualizations: Box Plots, Heatmaps, Pair Plots
- Customizing Plots and Styling Visualizations
- Best Practices for Data Storytelling
- What is EDA and Why is it Important?
- Descriptive Statistics & Summary Functions
- Identifying Trends, Patterns, and Anomalies in Data
- Feature Engineering for Machine Learning
- Automating EDA using Python Libraries
- Basics of Probability and Statistics
- Measures of Central Tendency & Variability
- Correlation & Regression Analysis
- Hypothesis Testing (T-tests, ANOVA, Chi-square)
- Identifying Statistical Significance
- Understanding Supervised vs. Unsupervised Learning
- Preparing Data for Machine Learning Models
- Training and Evaluating Models
- Overfitting & Underfitting Issues
- Cross-Validation Techniques
- Linear & Logistic Regression
- Decision Trees & Random Forest
- Support Vector Machines (SVM)
- Clustering Algorithms (K-Means, DBSCAN)
- Model Evaluation Metrics
- Understanding Neural Networks & Deep Learning
- Introduction to TensorFlow and Keras
- Building a Simple Neural Network
- Implementing Convolutional Neural Networks (CNNs) for Image Processing
- Introduction to Natural Language Processing (NLP) with Python
- Financial Data Analysis
- Customer Segmentation in Marketing
- Predictive Analytics in Healthcare
- Sentiment Analysis in Social Media
- Automating Data Science Workflows
Morning and evening classes are available. CompuBase Training Center, Abu Dhabi.
