This course is designed to provide students with a comprehensive understanding of data analytics, including data acquisition, preparation, analysis, and visualization. Students will learn how to use various data analytics tools and techniques to make data-driven decisions and solve real-world problems. The course will cover key concepts in data analytics, such as data wrangling, descriptive and inferential statistics, machine learning, and data visualization.
Course Outline:
Introduction to Data Analytics
- Definition of Data Analytics
- Key Drivers and Trends in Data Analytics
- The Role of Data in Business Decision Making
Data Acquisition and Preparation
- Data Sources and Types
- Data Cleaning and Transformation
- Data Integration and Aggregation
Descriptive Statistics and Data Visualization
- Data Exploration and Visualization
- Measures of Central Tendency and Variability
- Correlation and Regression Analysis
Inferential Statistics and Hypothesis Testing
- Sampling Techniques and Distributions
- Hypothesis Testing and Confidence Intervals
- ANOVA and Chi-Square Analysis
Machine Learning for Data Analytics
- Supervised and Unsupervised Learning
- Classification and Regression Models
- Clustering and Association Analysis
Big Data Analytics
- Introduction to Big Data and Hadoop
- Data Mining and Text Analytics
- Streaming Analytics and Real-time Processing
Data Visualization and Storytelling
- Data Visualization Principles and Best Practices
- Tools and Techniques for Data Visualization
- Effective Communication of Data Insights
Prerequisites:
- Basic knowledge of statistics and mathematics
- Familiarity with Excel or other data analysis tools
Recommended Textbook:
- “Data Analytics Made Accessible” by Anil Maheshwari.
Assessment:
- Midterm Exam (30%)
- Final Exam (40%)
- Group Project (30%)
Note: The course syllabus and assessment methods are subject to change based on instructor’s discretion.
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