Prerequisites

• Basic computer literacy
• Fundamental mathematics knowledge
• Spreadsheet software familiarity
• Optional: Basic programming experience
• Computer with minimum 8GB RAM
• Python installation recommended

12-Week Data Analytics in Python Course Syllabus

Week 1: Python Programming Fundamentals

• Python installation and environment setup
• Basic syntax and data types
• Variables and data structures
• Control flow and functions
• Introduction to Jupyter Notebooks
• Basic programming concepts
• Version control with Git

Week 2: Data Manipulation and Pandas

• NumPy and Pandas fundamentals
• Data loading and importing techniques
• DataFrame manipulation
• Data cleaning strategies
• Handling missing values
• Data transformation methods
• Basic statistical analysis

Week 3: Exploratory Data Analysis

• Descriptive statistics
• Data visualization techniques
• Matplotlib and Seaborn
• Statistical inference
• Hypothesis testing
• Data distribution analysis
• Correlation and relationship exploration

Week 4: Advanced Data Preprocessing

• Feature engineering
• Data normalization
• Categorical variable handling
• Binning techniques
• Data scaling methods
• Outlier detection and management
• Data quality assessment

Week 5: Statistical Analysis

• Probability theory
• Confidence intervals
• Parametric and non-parametric tests
• Regression analysis
• Linear and logistic regression
• Statistical modeling techniques
• Hypothesis testing in Python

Week 6: Machine Learning Fundamentals

• Introduction to scikit-learn
• Supervised learning algorithms
• Classification techniques
• Model evaluation metrics
• Cross-validation
• Predictive analytics
• Decision trees and random forests

Week 7: Advanced Machine Learning

• Support vector machines
• Clustering techniques
• Ensemble methods
• Hyperparameter tuning
• Model optimization
• Advanced feature selection
• Practical machine learning projects

Week 8: Data Visualization and Reporting

• Advanced visualization techniques
• Interactive dashboards
• Seaborn and Plotly
• Data storytelling
• Professional reporting
• Visualization best practices
• Creating compelling data narratives

Week 9: Big Data and Cloud Technologies

• Introduction to big data concepts
• Apache Spark fundamentals
• Cloud computing basics
• AWS/Google Cloud Platform
• Distributed computing
• Big data processing techniques
• Scalable data solutions

Week 10: Database Management

• SQL advanced techniques
• Database design principles
• NoSQL databases
• ETL processes
• Query optimization
• Database management strategies
• Data warehousing concepts

Week 11: AI and Ethics in Data Analytics

• Artificial intelligence overview
• Machine learning ethics
• Algorithmic bias detection
• Responsible AI development
• Privacy considerations
• Ethical decision-making frameworks
• Case studies in data ethics

Week 12: Capstone Project and Career Preparation

• End-to-end data analytics project
• Portfolio development
• Industry best practices
• Interview preparation
• Professional networking
• Final project presentations
• Career guidance in data analytics

Learning Outcomes

• Proficiency in Python for data analytics
• Advanced data manipulation skills
• Statistical analysis expertise
• Machine learning model development
• Data visualization capabilities
• Ethical data science understanding
• Professional portfolio creation
• Industry-ready data analytics skills

Certification

Students receive a comprehensive data analytics certification upon successful course completion.

Note: Course content may be dynamically updated to reflect emerging industry trends and technological advancements.