Data Analytics Mastery

Data Analytics Mastery: From Beginner to Pro

This course is designed to take students from beginner to advanced levels in data analysis. They will learn how to collect, clean, analyze, and visualize data using industry-standard tools. The course will emphasize real-world applications, providing students with the skills needed to enter the workforce as competent data analysts.

Course Duration

  • Total Duration: 12 weeks (flexible pacing)
  • Weekly Commitment: 6-8 hours
  • Prize: Free

Table of Contents

Module 1: Introduction to Data Analytics

  • Week 1: Understanding Data Analytics
    • What is Data Analytics?
      • Overview of data analytics and its importance in business
    • Types of Data Analytics
      • Descriptive, Diagnostic, Predictive, and Prescriptive analytics
    • Roles and Responsibilities of a Data Analyst
      • Core responsibilities and the typical workflow of a data analyst
  • Week 2: The Data Analytics Process
    • Data Collection
      • Methods of gathering data (surveys, databases, APIs)
    • Data Cleaning
      • Handling missing values, outliers, and inconsistent data
    • Data Exploration
      • Using basic statistical methods to understand data

Module 2: Tools and Technologies for Data Analysis

  • Week 3: Excel for Data Analysis
    • Data Handling in Excel
      • Sorting, filtering, and pivot tables
    • Basic Functions
      • VLOOKUP, INDEX-MATCH, and conditional formatting
    • Introduction to Macros
  • Week 4: SQL for Data Analysts
    • Introduction to Databases
      • Understanding relational databases
    • Basic SQL Queries
      • SELECT, JOIN, WHERE, and GROUP BY
    • Advanced SQL
      • Subqueries, CTEs, and window functions
  • Week 5: Python for Data Analysis
    • Getting Started with Python
      • Introduction to Python programming
    • Libraries for Data Analysis
      • Pandas, NumPy, and Matplotlib
    • Data Cleaning and Manipulation in Python

Module 3: Statistical Analysis

  • Week 6: Descriptive Statistics
    • Central Tendency and Dispersion
      • Mean, median, mode, variance, and standard deviation
    • Probability Distributions
      • Normal distribution, skewness, and kurtosis
    • Data Visualization
      • Histograms, box plots, and scatter plots
  • Week 7: Inferential Statistics
    • Hypothesis Testing
      • Null hypothesis, p-values, and t-tests
    • Correlation and Regression
      • Understanding relationships between variables
    • ANOVA and Chi-Square Tests
      • Analyzing differences across multiple groups

Module 4: Data Visualization

  • Week 8: Principles of Data Visualization
    • Designing Effective Visualizations
      • Choosing the right chart for your data
    • Introduction to Tableau/Power BI
      • Basic functionalities and dashboard creation
    • Storytelling with Data
      • How to craft compelling narratives with visuals
  • Week 9: Advanced Data Visualization
    • Interactive Dashboards
      • Creating dynamic visualizations with Tableau/Power BI
    • Data Visualization in Python
      • Using Matplotlib, Seaborn, and Plotly
    • Case Study
      • Creating a comprehensive data story using real-world data

Module 5: Applied Data Analytics

  • Week 10: Business Applications of Data Analytics
    • Marketing Analytics
      • Customer segmentation, A/B testing
    • Financial Analytics
      • Forecasting, budgeting, and risk management
    • Operational Analytics
      • Supply chain optimization, process improvement
  • Week 11: Data Ethics and Governance
    • Data Privacy
      • Understanding GDPR, CCPA, and other data regulations
    • Ethical Considerations
      • Bias in data, fairness, and transparency
    • Data Governance
      • Ensuring data quality and compliance
  • Week 12: Capstone Project
    • Project Selection
      • Choose a real-world dataset relevant to the student’s industry
    • Analysis and Reporting
      • Apply the skills learned to perform a full analysis
    • Presentation
      • Present findings in a report and visualization dashboard

Assessment and Certification

  • Weekly Quizzes and Assignments
    • Regular quizzes to reinforce learning
    • Assignments that apply concepts in practical scenarios
  • Final Capstone Project
    • A comprehensive project that demonstrates the student’s ability to analyze and present data effectively
  • Certification
    • Upon completion, students receive a certificate of achievement, validating their skills in data analytics

Course Materials and Resources

  • Textbooks and Articles
    • Suggested readings and online resources
  • Software Access
    • Instructions for accessing Excel, SQL databases, Python, Tableau/Power BI
  • Community and Support
    • Access to a discussion forum and regular Q&A sessions

Practical Tips for Course Development

  • Hands-On Practice: Ensure that each module includes hands-on exercises. Practical application is key to learning data analysis.
  • Industry-Relevant Examples: Use case studies and examples from various industries to illustrate how data analysis is used in the real world.
  • Flexible Learning Paths: Consider offering the course in both self-paced and instructor-led formats to cater to different learning styles.
  • Ongoing Support: Provide students with resources and support beyond the course, such as networking opportunities, job search tips, and advanced learning materials.
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