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

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