An Introductory Guide to Data Science and Machine Learning Course Syllabus

An all-in-one bootcamp-style intro to data science and ML—combining essential theory, real-world tools, and project work in a concise and practical 6-hour interactive format. This course guides you through the full data science lifecycle, from data acquisition to model deployment, using modern Python libraries and hands-on coding exercises. You'll build foundational skills in statistics, machine learning, and big data tools, culminating in a capstone project that applies everything you've learned.

Module 1: Introduction to Data Science

Estimated time: 0.5 hours

• Differentiate data science vs. analysis and engineering
• Understand the phases of the data science lifecycle
• Explore common data structures used in data science
• Reflect on real-world industry use cases

Module 2: Applications of Data Science

Estimated time: 0.5 hours

• Examine applications in healthcare analytics
• Explore recommendation systems
• Analyze use cases in image analysis
• Build a miniature recommender system prototype

Module 3: Essential Libraries

Estimated time: 2 hours

• Perform web scraping using BeautifulSoup and Scrapy
• Manipulate arrays with NumPy
• Work with dataframes using Pandas
• Apply NLP basics with SpaCy
• Create visualizations using Seaborn

Module 4: Probability & Statistics

Estimated time: 2 hours

• Study probability distributions and descriptive statistics
• Apply Bayes’ theorem in practical scenarios
• Understand sampling methods and central tendency
• Conduct hypothesis testing including t-tests

Module 5: Machine Learning Essentials

Estimated time: 5.5 hours

• Implement regression models (linear, multivariate, SVM)
• Apply classification algorithms (SVM, Naive Bayes, ensembles)
• Perform feature engineering and scaling
• Evaluate models and tune hyperparameters
• Apply unsupervised learning: K-Means, DBSCAN, hierarchical clustering
• Use PCA for dimensionality reduction and Apriori for association rules

Module 6: Deep Learning Essentials & Big Data Tools

Estimated time: 2.5 hours

• Understand neural network basics and backpropagation
• Build CNNs for image tasks and RNNs/LSTMs for sequences
• Explore AutoML with PyCaret
• Accelerate workflows using RAPIDS on GPUs
• Get introduced to Hadoop and Apache Spark for big data processing

Module 7: Final Project

Estimated time: 1 hour

• Integrate data acquisition, cleaning, and visualization
• Apply supervised and unsupervised learning techniques
• Deliver a complete end-to-end data science solution

Prerequisites

• Familiarity with Python programming
• Basic understanding of mathematics and algebra
• No prior experience in data science or machine learning required

What You'll Be Able to Do After

• Explain the data science lifecycle and its components
• Scrape, clean, and analyze real-world datasets
• Apply statistical methods to interpret data
• Build, evaluate, and tune machine learning models
• Use deep learning and big data tools in practical scenarios