DO-PDAML: Applied Python for Data Analysis and Machine Learning

This course is designed to provide participants with a solid foundation in Python for solving real-world problems using data analysis, statistical modeling, and machine learning techniques. The program progresses from essential programming concepts to advanced topics including web scraping, data wrangling, visualization, and predictive modeling. Each module integrates theory with hands-on practice using relevant datasets.

Participants will learn to:

• Write clean, efficient Python code for data tasks.
• Use libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn.
• Clean, transform, and visualize datasets effectively.
• Apply machine learning algorithms to solve business problems.
• Build and evaluate predictive models.
• Integrate data from multiple sources, including APIs and websites

Upon completion, participants will be able to:

• Confidently manipulate and analyze structured datasets.
• Create visualizations and dashboards for business insights.
• Develop machine learning models to predict outcomes.
• Scrape and integrate web data into their analysis pipelines.
• Use Python as a complete toolset for modern data analytics

• Data Analysts and Scientists
• Researchers and Academics
• Engineers and Technical Professionals
• IT and BI Specialists
• Anyone working with data and aiming to leverage Python for analytics and automation

Module 1: Introduction to Python and IDEs

• Setting up Python, Jupyter Notebooks, and IDEs
• Python syntax, variables, and comments
• Executing code interactively and through scripts

Module 2: Data Types, Operators, and Control Structures

• Numbers, strings, lists, tuples, sets, dictionaries
• Conditionals, loops, and comprehensions

Module 3: Functions and Modular Programming

• Defining functions, scope, arguments
• Lambda, map, filter, and custom modules

Module 4: File I/O and Working with Data

• Reading and writing text, CSV, Excel, JSON
• Introduction to APIs and file formats

Module 5: Error Handling and Debugging

• Try/except blocks
• Best practices for robust code
• DATA STRUCTURES AND LIBRARIES

Module 6: Working with NumPy for Arrays and Mathematics

• Creating arrays, broadcasting, indexing, slicing
• Mathematical and statistical operations

Module 7: Data Wrangling with Pandas

• Series and DataFrames
• Reading, filtering, joining, reshaping data
• Handling missing data

Module 8: Advanced Data Manipulation Techniques

• GroupBy, pivot tables, aggregation
• Merging datasets and time series basics
• VISUALIZATION AND REPORTING

Module 9: Data Visualization with Matplotlib and Seaborn

• Bar, line, scatter, histogram, boxplots
• Styling and subplots

Module 10: Interactive Visuals with Plotly and Dash

• Building dashboards
• Choropleth and geographic plotting

Module 11: Exploratory Data Analysis (EDA)

• Using visuals to detect patterns and outliers
• Summary statistics and feature exploration
• MACHINE LEARNING AND PREDICTIVE MODELING

Module 12: Introduction to Machine Learning with Scikit-learn

• Supervised vs unsupervised learning
• ML workflow and pipeline

Module 13: Regression and Classification Models

• Linear regression, logistic regression
• Decision trees, random forest, k-NN

Module 14: Model Evaluation and Metrics

• Accuracy, precision, recall, F1
• Cross-validation and ROC curves

Module 15: Clustering and Dimensionality Reduction

• K-means, DBSCAN
• PCA and t-SNE for visualization
• SPECIAL TOPICS AND APPLICATIONS

Module 16: Feature Engineering and Data Preparation

• Encoding categorical data
• Feature scaling, transformations

Module 17: Text Data and Natural Language Processing (NLP)

• Tokenization, text cleaning
• Word frequency, basic sentiment analysis

Module 18: Web Scraping and Automation with BeautifulSoup and Selenium

• Extracting data from websites
• Browser automation for dynamic content
• CAPSTONE AND INTEGRATION

Module 19: Working with Databases and Large Datasets

• SQL with Python
• Connecting to MySQL, PostgreSQL
• Reading from cloud sources (optional: BigQuery, S3)

Module 20: Capstone Project: Build and Deploy a Predictive Model

• Apply all learned concepts on a real dataset
• Build, validate, and present a machine learning solution
• Optional: Deployment with Streamlit or Flask