Data science course syllabus

This course provides an introduction to the field of data science, covering key concepts, techniques, and tools used to analyze and interpret large datasets. Topics include data wrangling, exploratory data analysis, statistical modeling, machine learning, and data visualization. Students will gain hands-on experience working with real-world datasets and tools such as Python, R, and popular libraries for data analysis.



Week 1: Introduction to Data Science

- Overview of data science and its applications

- Introduction to Python programming language and Jupyter Notebooks

- Basic data types and data structures in Python


Week 2: Data Wrangling

- Data cleaning techniques

- Handling missing data

- Data transformation and manipulation with pandas library


Week 3: Exploratory Data Analysis (EDA)

- Descriptive statistics

- Data visualization using Matplotlib and Seaborn

- Exploratory data analysis techniques


Week 4: Statistical Analysis

- Probability distributions and hypothesis testing

- Correlation and regression analysis

- Introduction to Bayesian statistics


Week 5: Machine Learning Basics

- Introduction to supervised and unsupervised learning

- Model evaluation and validation

- Linear regression and logistic regression


Week 6: Machine Learning Algorithms

- Decision trees and ensemble methods (Random Forests, Gradient Boosting)

- Introduction to clustering algorithms (K-Means, Hierarchical Clustering)


Week 7: Dimensionality Reduction

- Principal Component Analysis (PCA)

- t-Distributed Stochastic Neighbor Embedding (t-SNE)

- Applications of dimensionality reduction


Week 8: Natural Language Processing (NLP)

- Introduction to NLP and its applications

- Text preprocessing techniques

- Building NLP models with NLTK and spaCy


Week 9: Deep Learning Fundamentals

- Introduction to neural networks

- Building and training neural networks with TensorFlow/Keras

- Convolutional Neural Networks (CNNs) for image classification


**Week 10: Advanced Topics in Data Science**

- Time series analysis

- Reinforcement learning

- Ethics and privacy in data science


Week 11: Capstone Project

- Students will work on a data science project applying concepts learned throughout the course

- Project presentation and peer review


Week 12: Final Exam and Course Wrap-Up

- Review of key concepts

- Final exam covering material from the course

- Course evaluation and feedback

Prerequisites: Basic knowledge of programming (preferably Python) and statistics.


This is just a sample syllabus and can be customized based on the specific goals, audience, and duration of the course.

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