Data Science Course Syllabus
Course Overview
Data Science Course Syllabus is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This course aims to introduce you to the core concepts of data science, including data analysis, visualization, machine learning, and deep learning. Through hands-on projects, you’ll learn to apply these concepts to real-world data and make data-driven decisions.
Duration
12 Weeks (3 hours per week)
Prerequisites
Basic understanding of Python programming and mathematics (algebra and basic calculus).
Course Objectives
By the end of this course, you will:
- Understand the data science workflow from data collection to model deployment.
- Be proficient in using Python for data analysis and visualization.
- Gain a solid understanding of statistical methods for data science.
- Learn to build, evaluate, and deploy machine learning models.
- Work on real-life data projects to solidify your learning.
Course Modules
Week 1-2: Introduction to Data Science
- Overview of Data Science
- Essential Python for Data Science
- Data Science Workflow: From Data Collection to Insights
Week 3-4: Data Manipulation and Visualization
- Data Manipulation with Pandas
- Data Visualization with Matplotlib and Seaborn
- Exploratory Data Analysis Techniques
Week 5-6: Statistical Foundations for Data Science
- Descriptive Statistics and Probability Theory
- Inferential Statistics and Hypothesis Testing
- Correlation and Regression Analysis
Week 7-8: Machine Learning Essentials
- Introduction to Machine Learning
- Supervised vs. Unsupervised Learning
- Building and Evaluating Machine Learning Models
Week 9-10: Advanced Machine Learning
- Introduction to Deep Learning and Neural Networks
- Natural Language Processing (NLP) Basics
- Time Series Analysis and Forecasting
Week 11-12: Real-world Data Science Projects
- Project 1: Predictive Modeling with Machine Learning
- Project 2: Text Analysis and Sentiment Classification
- Final Project: End-to-end Data Science Project
Learning Resources
This course will utilize a combination of textbooks, online resources, and interactive platforms:
- “Python for Data Analysis” by Wes McKinney
- “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
- Online platforms like Kaggle for datasets and competitions
- Jupyter Notebooks for interactive coding exercises
Assessment and Projects
Your understanding and skills will be assessed through quizzes, assignments, and projects. The real-world data science projects will serve as a capstone to apply what you’ve learned throughout the course.
