PROGRAM SYLLABUS
Data Science
3-Month Advanced Bootcamp
Coding Macaw Bootcamp
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12-week curriculum
Python and SQL foundations
Statistics and machine learning
Applied projects and model deployment
PROGRAM OVERVIEW
Program Structure
Coding Macaw Bootcamp
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Month 1 · Foundations
Python, pandas, SQL, statistics and exploratory analysis
Month 2 · Machine Learning
Regression, classification, feature engineering and model evaluation
Month 3 · Applied Data Science
NLP, neural network fundamentals, deployment and capstone
BEFORE YOU START
Learning Outcomes & Preparation
Coding Macaw Bootcamp
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By the end of the program
Clean and query data, investigate patterns and explain findings.
Build predictive models with appropriate validation.
Deploy a simple application and present a documented portfolio project.
Preparation
Basic computer skills and high-school mathematics.
A laptop, stable internet and time for weekly assignments.
Python starts with fundamentals. No prior machine learning required.
Month 1 · Week 1
Python for Data Science
Coding Macaw Bootcamp
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Python syntax, variables, data types and operators
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Lists, dictionaries, sets and tuples
03
Conditions, loops, functions and error handling
04
Jupyter notebooks, environments and Git basics
Lab: Analyze a small sales dataset with reusable Python functions.
Month 1 · Week 2
Data Wrangling with NumPy & pandas
Coding Macaw Bootcamp
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NumPy arrays, indexing and vectorized operations
02
DataFrames, filtering, grouping and aggregation
03
Missing values, duplicates and data type conversion
04
Merge datasets, reshape tables and process dates
Lab: Produce a clean customer dataset and a data quality report.
Month 1 · Week 3
SQL for Data Analysis
Coding Macaw Bootcamp
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SELECT, filtering, sorting and aggregate functions
02
Joins, subqueries and common table expressions
03
Window functions, ranking and running totals
04
Extract data from a relational database into pandas
Lab: Answer business questions using a retail database.
Month 1 · Week 4
Statistics & Exploratory Data Analysis
Coding Macaw Bootcamp
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Descriptive statistics, probability and distributions
02
Sampling, confidence intervals and hypothesis testing
03
Correlation, confounding and introductory A/B testing
04
Matplotlib and Seaborn charts for business insights
Project 1: Deliver an exploratory analysis notebook and findings presentation.
Month 2 · Week 5
Machine Learning Workflow & Regression
Coding Macaw Bootcamp
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Problem framing, targets and baseline models
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Train, validation and test splits with leakage prevention
03
Linear regression, regularization and feature scaling
04
MAE, RMSE and R² for regression evaluation
Lab: Build and evaluate a house price prediction model.
Month 2 · Week 6
Classification & Model Evaluation
Coding Macaw Bootcamp
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Logistic regression, decision trees and k-nearest neighbors
02
Confusion matrix, precision, recall and F1 score
03
ROC-AUC, PR-AUC and decision threshold selection
04
Class imbalance, stratified splits and cross-validation
Lab: Compare customer churn classifiers against a baseline.
Month 2 · Week 7
Feature Engineering & Ensemble Models
Coding Macaw Bootcamp
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Categorical encoding, imputation and feature selection
02
Random forests and gradient boosting concepts
03
Hyperparameter tuning and scikit-learn pipelines
04
Feature importance, error analysis and explainability
Project 2: Submit a reproducible churn prediction pipeline.
Month 2 · Week 8
Clustering, PCA & Time Series Basics
Coding Macaw Bootcamp
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K-means, DBSCAN and cluster quality evaluation
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Principal component analysis for dimensionality reduction
03
Trend, seasonality and lag features in time series
04
Chronological validation and naive forecasting baselines
Lab: Build customer segments and a simple sales forecast.
Month 3 · Week 9
Natural Language Processing
Coding Macaw Bootcamp
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Text cleaning, tokenization and normalization
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Bag-of-words, TF-IDF and text classification
03
Embeddings and transformer concepts
04
Evaluate sentiment models and inspect prediction errors
Lab: Create a product review sentiment classifier.
Month 3 · Week 10
Neural Networks & Responsible Data Science
Coding Macaw Bootcamp
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Neural network layers, activations and loss functions
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Training, regularization and overfitting control
03
Introductory neural network with a selected framework
04
Privacy, bias checks and transparent model limitations
Lab: Compare a small neural network with a conventional baseline.
Month 3 · Week 11
Model Deployment & Capstone Build
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Save models and reproduce dependency environments
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Build a Streamlit application or a FastAPI endpoint
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Input validation, inference tests and basic monitoring
04
Capstone data preparation, modeling and review
Deliverable: Working prediction demo and capstone progress review.
Month 3 · Week 12
Capstone Delivery & Technical Communication
Coding Macaw Bootcamp
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Finalize evaluation on an untouched test dataset
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Document assumptions, limitations and business impact
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Prepare GitHub README and reproducible instructions
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Demonstrate the solution and defend modeling decisions
Deliverable: Final project repository, application demo and presentation.
PRACTICAL APPLICATION
Projects & Capstone
Coding Macaw Bootcamp
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Project 1
Retail or customer exploratory analysis
Project 2
Churn prediction with a validated ML pipeline
Capstone
A complete solution for a selected business problem
Capstone requirements: data quality checks, baseline comparison, test-set evaluation, reproducible code, demo and a clear explanation of limitations.
LEARNING PRACTICE
Tools & Assessment
Coding Macaw Bootcamp
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Core tools
Python, Jupyter, NumPy, pandas, SQL, Matplotlib, Seaborn,
scikit-learn and Git/GitHub
Applied tools
Streamlit or FastAPI, plus one introductory neural network framework
Assessment
Weekly coding assignments, milestone projects and a final capstone review.
Evaluation focuses on code quality, sound analysis and clear communication.
PROGRAM GUIDANCE
Curriculum & Delivery Notes
Coding Macaw Bootcamp
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The curriculum follows a 12-week teaching plan across three months.
The final batch calendar determines holidays and session dates.
Coding Macaw management finalizes the syllabus. Topic coverage may
vary by approximately 20–30% based on trainer expertise and job trends.
Career services and mentorship depend on the selected enrollment plan.
This syllabus describes the academic curriculum.