Introduction to Data Science Specialization Course Syllabus

Overview: This specialization offers a beginner-friendly introduction to data science, designed to equip learners with foundational skills in Python, SQL, statistics, and machine learning. The course spans approximately 30-40 weeks of part-time study, featuring hands-on projects and real-world case studies. Learners will progress through core topics including data science fundamentals, data cleaning, exploratory data analysis, and machine learning basics, culminating in a comprehensive capstone project. Each module emphasizes practical experience with industry-standard tools such as Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and TensorFlow, preparing learners for entry-level data science roles.

Module 1: Foundations of Data Science

Estimated time: 20 hours

• Understand the core principles of data science and its applications across industries
• Explore different types of data, databases, and data collection methods
• Introduction to Python programming for data manipulation
• Introduction to SQL for data querying and manipulation

Module 2: Data Cleaning & Exploration

Estimated time: 30 hours

• Learn data wrangling techniques to clean and prepare messy data
• Work with Pandas and NumPy for data transformation
• Identify and handle missing values, outliers, and inconsistencies
• Perform basic data preprocessing for analysis

Module 3: Exploratory Data Analysis (EDA)

Estimated time: 30 hours

• Apply statistical methods to extract insights from data
• Use Matplotlib and Seaborn for data visualization
• Create histograms, scatter plots, and box plots
• Generate correlation heatmaps and interpret relationships

Module 4: Machine Learning Basics

Estimated time: 40 hours

• Introduction to supervised and unsupervised learning
• Build linear regression models for prediction
• Implement decision trees and clustering methods
• Apply machine learning models using Scikit-learn and TensorFlow

Module 5: Capstone Project

Estimated time: 50 hours

• Work on a real-world data science project from start to finish
• Use Python, SQL, and visualization tools to analyze data
• Present findings through reports and interactive dashboards

Prerequisites

• Beginner-friendly – no prior experience required
• Basic computer literacy
• Some familiarity with Python is beneficial but not required

What You'll Be Able to Do After

• Understand the fundamentals of data science, statistics, and machine learning
• Work with structured and unstructured data using industry-standard tools
• Perform data cleaning, transformation, and wrangling using Python and SQL
• Conduct exploratory data analysis and create meaningful visualizations
• Apply basic machine learning models to real-world datasets