Data Analytics Foundations

Course details

This foundational course introduces students to the critical concepts and techniques in data analytics, emphasizing hands-on experience with real-world data sets and tools. Students will acquire a deep understanding of the data analytics process, from data collection to insightful visualization.

Start Date

TBD

Target Audience

2nd & 4th semester undergraduates interested in data science, business, and related fields. No prior technical expertise required.

Duration

14-15 weeks

Learning Objectives

Grasp the significance of data in modern business and science.
Understand the complete data analytics process.
Gain hands-on experience with data collection, pre-processing, and analysis.
Develop foundational skills in using popular analytics tools and software.
Create meaningful visualizations to interpret complex data.

Key Features

Weekly lectures by international faculty.
Live interactive sessions for doubt clearance.
Hands-on tutorials focusing on real-world applications.
Assignments based on practical business scenarios.
Comprehensive Learning Management System (LMS) access.

Learning path

Unit 1: Introduction to Data Analytics (4)
Unit 2: The Role of Data in Business (6)
Unit 3: Data Pre-processing (4)
Unit 4: Inferential Analytics (4)
Unit 5: Predictive Analytics (4)
Unit 6: Advanced Analytical Techniques (4)
Unit 7: Tools and Software (2)

Topics covered

The Role of Data in Business
Basic Data Types and Sources
Introduction to Data Analytics Tools
Ethics in Data Handling and Analysis

Central Tendencies: Mean, Median, Mode
Measures of Dispersion: Variance, Standard Deviation
Data Summarization Techniques
Data Visualization Basics
Advanced Graphical Representations
Exploring Data Relationships: Correlation and Covariance

Handling Missing Data
Data Cleaning and Transformation
Feature Engineering and Selection
Dimensionality Reduction Techniques

Hypothesis Testing
Confidence Intervals
Chi-Square Test
Analysis of Variance (ANOVA)

Regression Analysis: Linear & Logistic
Time Series Analysis Basics
Decision Trees and Random Forests
Cluster Analysis

Neural Networks Basics
Introduction to Machine Learning in Data Analytics
Support Vector Machines
Ensemble Methods

Overview of Data Analytics Tools: R, Python, Tableau
Integrating Analytics in Business Decision Processes

Grading:

Participation: 10%
Assignments: 40% (Across all units)
Mid Term Exam: 25%
Final Exam: 25%

Prerequisites: Students should have a basic understanding of statistics

Reading Material: Will be available on LMS. Specific textbooks may be recommended by the faculty.

Faculty: Professor from a leading international university with expertise in Data Analytics.

Teaching Instructors: TBD