This Data Science curriculum, inspired by Holberton School's Software Engineering program, is designed to provide a comprehensive, hands-on learning experience. It combines foundational knowledge, practical skills, and advanced topics, ensuring students are well-equipped to tackle real-world data challenges. The curriculum is structured over three years, with each year focusing on different aspects of Data Science.
| Module | Course | Content | Projects | Resources/Links | Duration | Effort |
|---|---|---|---|---|---|---|
| Introduction to Programming and Computer Science | Introduction to Python | Basics of Python, control structures, functions, and data structures | Simple Python programs, basic data manipulation tasks | Automate the Boring Stuff with Python | 4 weeks | 10 hours/week |
| Introduction to Computer Science | Basic algorithms, complexity, data structures | Implementing basic algorithms, data structures in Python | CS50, Introduction to Algorithms | 4 weeks | 10 hours/week | |
| Mathematics for Data Science | Linear Algebra | Vectors, matrices, transformations, eigenvalues, and eigenvectors | Implementing linear algebra operations using NumPy | Linear Algebra and Its Applications, MIT OCW | 6 weeks | 8 hours/week |
| Probability and Statistics | Descriptive statistics, probability theory, distributions, inferential statistics | Statistical analysis on datasets using Python | Statistics for Business and Economics, Khan Academy | 6 weeks | 8 hours/week | |
| Data Analysis and Visualization | Data Wrangling with Python | Data cleaning, manipulation, and transformation using Pandas | Cleaning and analyzing real-world datasets | Python for Data Analysis | 4 weeks | 10 hours/week |
| Data Visualization | Visualization principles, tools (Matplotlib, Seaborn, Plotly) | Creating visualizations for datasets, storytelling with data | Storytelling with Data | 4 weeks | 10 hours/week |
| Module | Course | Content | Projects | Resources/Links | Duration | Effort |
|---|---|---|---|---|---|---|
| Machine Learning | Introduction to Machine Learning | Supervised learning, regression, classification, model evaluation | Implementing ML algorithms from scratch, using scikit-learn | Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow | 6 weeks | 12 hours/week |
| Advanced Machine Learning | Unsupervised learning, clustering, dimensionality reduction | Advanced ML projects, real-world applications | Pattern Recognition and Machine Learning | 6 weeks | 12 hours/week | |
| Big Data and Cloud Computing | Big Data Technologies | Hadoop, Spark, data pipelines | Processing large datasets using Hadoop and Spark | Big Data: Principles and best practices of scalable real-time data systems | 6 weeks | 10 hours/week |
| Cloud Computing for Data Science | Cloud platforms (AWS, Azure, GCP), data storage, and processing | Deploying data science projects on the cloud | Cloud Computing for Science and Engineering | 4 weeks | 10 hours/week | |
| Specialized Topics | Natural Language Processing (NLP) | Text processing, NLP models, sentiment analysis | Implementing NLP projects, text classification | Speech and Language Processing | 6 weeks | 12 hours/week |
| Deep Learning | Neural networks, CNNs, RNNs, deep learning frameworks | Implementing deep learning models using TensorFlow and PyTorch | Deep Learning | 6 weeks | 12 hours/week |
| Module | Course | Content | Projects | Resources/Links | Duration | Effort |
|---|---|---|---|---|---|---|
| Advanced Analytics and Optimization | Advanced Statistical Methods | Bayesian analysis, advanced regression techniques | Statistical modeling on real-world datasets | Bayesian Data Analysis | 6 weeks | 12 hours/week |
| Optimization Techniques | Linear programming, convex optimization, metaheuristics | Optimization problems in data science | Convex Optimization | 6 weeks | 12 hours/week | |
| Industry Applications | Data Science in Business | Case studies, business analytics, decision science | Business case projects, developing data-driven solutions | Data Science for Business | 4 weeks | 10 hours/week |
| Ethical and Responsible Data Science | Ethics in data science, privacy, fairness, and accountability | Analyzing ethical implications in data science projects | Weapons of Math Destruction | 4 weeks | 8 hours/week | |
| Capstone Project | End-to-end data science project, from problem formulation to deployment | Real-world data science problems, collaboration with industry partners or academic mentors | Capstone project guidelines, mentorship from industry experts | 12 weeks | 20 hours/week |
This structure ensures a comprehensive and practical approach to learning Data Science, mirroring the hands-on, intensive learning environment of Holberton School.