Data Science with Python
Artificial Intelligence (AI)
COURSE OVERVIEW
This intensive six-day training program is designed to equip participants with practical and in-demand data science skills using Python. The course covers the full data science lifecycle—from data handling and visualization to statistical analysis, machine learning, and model deployment. Participants will gain hands-on experience working with real datasets and building predictive models that can be applied in business, research, and technology domains.
By the end of the program, learners will be able to independently develop end-to-end data science solutions using Python.
COURSE OBJECTIVES
By the end of this course, participants will be able to:
- Use Python for data analysis and manipulation using NumPy and Pandas.
- Clean, transform, and prepare raw datasets for analysis.
- Perform exploratory data analysis (EDA) using visualization techniques.
- Apply statistical methods to interpret and analyze data effectively.
- Build machine learning models for regression, classification, and clustering.
- Optimize model performance using feature engineering techniques.
- Understand basic model deployment concepts.
- Develop and present a complete data science project.
Duration: 6 Days / 48 Hours
Delivery Method: Classroom-based, Virtual Instructor Led Training
COURSE OUTLINE
Day 1: Python for Data Science Foundations
Focus: Building core Python skills for data handling
- Introduction to Python for data science
- Python syntax, variables, and data structures
- Functions, loops, and conditional statements
- Introduction to NumPy (arrays, operations, broadcasting)
- Introduction to Pandas (Series, DataFrames, indexing)
- Data loading and handling (CSV, Excel, JSON files)
- Hands-on: Basic data manipulation using Pandas
Day 2: Data Cleaning and Visualization
Focus: Preparing and visualizing datasets
- Understanding data quality issues (missing values, duplicates, outliers)
- Data cleaning techniques using Pandas
- Data transformation and preprocessing
- Exploratory Data Analysis (EDA) techniques
- Data visualization using Matplotlib and Seaborn
- Identifying patterns and trends in data
- Hands-on: Cleaning and visualizing a real-world dataset
Day 3: Statistics and Data Analytics
Focus: Understanding data through statistical methods
- Introduction to descriptive and inferential statistics
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (variance, standard deviation)
- Probability fundamentals for data science
- Correlation and covariance analysis
- Hypothesis testing basics
- Hands-on: Statistical analysis using Python libraries
Day 4: Machine Learning with Python
Focus: Building predictive models
- Introduction to Machine Learning concepts
- Supervised vs Unsupervised learning
- Regression models (Linear Regression, Multiple Regression)
- Classification models (Logistic Regression, Decision Trees, KNN)
- Clustering techniques (K-Means clustering)
- Model training and evaluation metrics
- Hands-on: Building basic ML models using Scikit-learn
Day 5: Advanced Data Science Techniques
Focus: Improving and optimizing ML models
- Feature engineering and feature selection techniques
- Handling categorical and numerical features
- Model evaluation and performance tuning
- Overfitting and underfitting concepts
- Cross-validation techniques
- Introduction to model deployment concepts
- Hands-on: Improving model accuracy through optimization
Day 6: Final Project and Assessment
Focus: End-to-end implementation and presentation
- End-to-end data science workflow (problem to solution)
- Dataset selection and problem framing
- Data preprocessing and model building
- Model evaluation and interpretation
- Project deployment overview (basic level)
- Final project presentation
- Assessment and feedback session
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