1. Course Syllabus

   Module 1: Introduction to Data Science & Python

   Description: Overview of the data science lifecycle and tools, plus Python environment setup.

   • Key Topics: What is data science (applications and career scope); stages of a data project (data collection, cleaning, analysis, modeling, visualization)
   • Tools Setup: Installing Anaconda/Python, using Jupyter Notebooks; introduction to Python IDEs and basic commands
   • Python Basics: Hello World, variables, types (strings, numbers, lists); simple arithmetic and print statements
   • Mini-Lab: Write and run a basic Python script; explore a small dataset (e.g. print summary statistics)

   Module 2: Python Programming Fundamentals

   Description: Core Python programming: control flow, functions, and data structures.

   • Control Flow: Conditional logic (if/else), looping (for, while), comprehension syntax for lists/dictionaries
   • Functions: Defining and calling functions, parameters and return values, scope
   • Data Structures: Lists, tuples, sets, dictionaries – creation, indexing, and common methods (append, pop, keys, etc.)
   • File I/O: Reading/writing text and CSV files in Python (e.g. open(), csv or pandas)
   • Mini-Lab: Process a text/CSV file (e.g. clean or aggregate data rows) using loops and functions

   Module 3: Data Manipulation (Pandas & NumPy)

   Description: Using powerful Python libraries to wrangle and transform data.

   • NumPy Arrays: Creation and operations on arrays, vectorized math and array indexing
   • Pandas DataFrames: Reading data into DataFrames (CSV, Excel, SQL); basic operations (filtering, sorting, grouping)
   • Data Cleaning: Handling missing values, merging/joining tables, converting types, dealing with outliers
   • Reshaping Data: Pivot tables, concatenating datasets, stacking/unstacking data for analysis
   • Mini-Lab: Import a real dataset (e.g. financial records or survey data) into Pandas, clean it (fill/drop NaNs), and compute summary stats

   Module 4: Data Visualization

   Description: Creating charts and plots to explore and communicate data.

   • Matplotlib Basics: Line plots, bar charts, histograms, scatter plots; customizing labels, legends, titles
   • Seaborn Enhancements: Statistical visualizations – boxplots, violin plots, pairplots, heatmaps for correlations
   • Plot Styling: Colors, styles, and saving figures; using subplots and annotations for clear storytelling
   • Dashboards (Intro): Brief intro to interactive plotting (e.g. Plotly basics) or Python dashboards (optional)
   • Mini-Lab: Generate visualizations for an EDA report (e.g. plot trends over time or category comparisons from a dataset)

   Module 5: Statistics & Exploratory Data Analysis (EDA)

   Description: Fundamental statistical concepts and techniques to summarize and understand data.

   • Descriptive Statistics: Mean, median, variance, standard deviation, percentiles, and distribution fitting
   • Probability Basics: Probability distributions (normal, binomial), sampling, Central Limit Theorem overview
   • Statistical Inference: Hypothesis testing (t-tests, chi-square), confidence intervals (conceptual overview)
   • EDA Techniques: Identifying trends, outliers, correlations; cross-tabulation and pivoting for insight
   • Mini-Lab: Perform EDA on a dataset (compute and interpret key stats; visualize distributions) to inform a hypothesis

   Module 6: Machine Learning Fundamentals

   Description: Introduction to supervised learning: building predictive models with scikit-learn.

   • Supervised Learning: Concepts of training vs. testing, overfitting/underfitting, cross-validation
   • Regression: Linear regression (ordinary least squares), interpreting coefficients and error metrics (RMSE)
   • Classification: Logistic regression and decision trees for binary outcomes; evaluating with accuracy, precision, recall, ROC AUC
   • Scikit-Learn Workflow: Using scikit-learn to build models – fit(), predict(), and evaluating on new data
   • Mini-Lab: Build a regression model (e.g. predict housing prices) and a classification model (e.g. predict loan default) using sample datasets

   Module 7: Advanced ML & AI Topics

   Description: Unsupervised learning and a primer on AI/deep learning concepts.

   • Unsupervised Learning: Clustering techniques (k-means clustering, DBSCAN) and dimensionality reduction (PCA)
   • Neural Networks (Intro): Overview of deep learning and neural networks; using Keras/TensorFlow at a high level
   • Natural Language Processing: Basics of text processing; tokenization, word embeddings, simple sentiment analysis (overview)
   • Model Evaluation: Confusion matrices, precision-recall curves, tuning model parameters (grid search concept)
   • Mini-Lab: Cluster an unlabeled dataset (e.g. customer segmentation) and experiment with a simple neural net on an image or text dataset

   Module 8: Domain Applications (Finance, Healthcare, AI)

   Description: Applying Python data science skills to real-world case studies in finance, healthcare, and AI.

   • Finance Case Studies: Time-series forecasting (stock price or sales trends), risk scoring or fraud detection modelsUse Pandas/NumPy on financial data; regression models for prediction.
   • Healthcare Case Studies: Analysis of patient or clinical data (e.g. predicting readmission, medical image features. Emphasize data-driven decision-making for care quality.
   • AI & Other Domains: Example of AI-driven application (e.g. recommendation engine or NLP on customer feedback) to show Python’s versatility.
   • Mini-Projects: Teams or individuals work on domain datasets (e.g. credit scoring in finance, patient analytics in health), consolidating techniques from earlier modules.

    

Key Features

  Duration: 40 hours total (e.g. 5 days of 8 h each)

  Delivery Mode: Instructor-led (online or classroom) and self-paced options, suitable for corporate training or individual learners

  Hands-On Labs: Extensive coding labs using Python, Jupyter notebooks, and libraries (Pandas, NumPy, etc.) with immediate practice

  Projects: Multiple mini-projects and case studies, including domain-specific tasks (see Modules)

  Capstone Project: End-to-end data science project applying all concepts (data collection, EDA, modeling, and presentation)

  Certification Prep: Content aligns with leading Data Science certificates (e.g. IBM Data Science Professional Certificate) and covers skills tested by industry exams

  Audience: Beginners to intermediate-level working professionals; no prior Python required, just basic comfort with computers

  Support & Materials: Course slides, code notebooks, sample datasets, and post-training Q&A forums or mentor support