Data Science with Python Certification Course Syllabus

Overview: This comprehensive, hands-on course is designed to take you from Python basics to real-world data science applications in approximately 7 weeks. With a strong focus on practical skills, you'll progress through data analysis, visualization, statistics, machine learning, and text/time series analysis, culminating in a capstone project that simulates an end-to-end data science workflow. The course blends theory with project-based learning using real datasets and Jupyter Notebooks, preparing you for a career in data science, analytics, or machine learning.

Module 1: Python for Data Science

Estimated time: 10 hours

• Python basics and syntax
• Data types and structures
• Functions and control flow
• File handling and script execution

Module 2: Data Analysis & Visualization

Estimated time: 10 hours

• Data manipulation with Pandas and NumPy
• Data cleaning and transformation techniques
• Creating visualizations using Matplotlib and Seaborn
• Interpreting bar charts, histograms, and heatmaps

Module 3: Statistical Computing & Probability

Estimated time: 10 hours

• Descriptive statistics and data summarization
• Probability distributions and their applications
• Hypothesis testing fundamentals
• Performing t-tests and chi-square tests

Module 4: Machine Learning Algorithms

Estimated time: 20 hours

• Supervised vs. unsupervised learning concepts
• Linear and logistic regression models
• Support Vector Machines (SVM) and clustering algorithms
• Model evaluation and validation using Scikit-learn

Module 5: Time Series & Text Data Analysis

Estimated time: 10 hours

• Time series data preprocessing
• Forecasting with ARIMA models
• Introduction to Natural Language Processing (NLP)
• Sentiment analysis and text feature extraction

Module 6: Capstone Project & Interview Prep

Estimated time: 10 hours

• Solve a real-world business problem using end-to-end data science techniques
• Document and present findings in a professional report
• Prepare for data science interviews with Q&A practice and resume guidance

Prerequisites

• Basic computer literacy
• Familiarity with high school-level mathematics
• No prior programming experience required, but comfort with logical thinking is helpful

What You'll Be Able to Do After

• Apply Python programming to data science tasks
• Perform data wrangling, exploratory data analysis (EDA), and visualization
• Conduct statistical analysis and interpret results
• Build and evaluate machine learning models using real datasets
• Communicate data insights effectively and prepare for data science job roles