Data Analytics and Data Driven Decision Making

Course Overview/Objective

Course Objective: The Data Analytics and Data-Driven Decision Making course is designed to equip learners with the knowledge and practical skills required to collect, process, analyze, and interpret data effectively to support strategic and operational decision-making. The course emphasizes understanding core concepts such as data collection methods, statistical analysis, data visualization, and the use of modern tools and technologies for data analytics. It also explores how data can reveal insights into trends, patterns, and opportunities that help organizations make informed decisions in real-time environments.

In addition to technical skills, the course fosters a critical thinking mindset and data literacy that are essential for making sound, evidence-based decisions. Learners will develop the ability to evaluate data sources, question assumptions, and communicate findings effectively to stakeholders. By integrating case studies and hands-on projects, the course aims to bridge the gap between data analysis and real-world business decisions, preparing participants to become data-informed professionals in various industries.

 

Module Code

Module Name

Duration (Hrs)

01

Introduction to Data Analytics

3

02

Data Collection & Cleaning

6

03

Data Exploration & Visualization

6

04

Statistical Analysis & Interpretation

6

05

Predictive Analytics & Modeling

6

06

Data-Driven Decision Making Framework

6

07

Tools for Data Analytics

6

08

Capstone Project / Case Study

6

Total

45 Hrs

 

 

 

Requirements

Graduate from any Discipline

Course Project
-
Used Tools
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Course Outline

Module 1: Introduction to Data Analytics

  • Importance of data in modern organizations
  • Types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive
  • Role of data in decision making

Module 2: Data Collection & Cleaning

  • Data sources (internal/external, structured/unstructured)
  • Data collection tools (surveys, web scraping, APIs)
  • Data quality issues
  • Data cleaning techniques (missing data, outliers, formatting)

Module 3: Data Exploration & Visualization

  • Exploratory Data Analysis (EDA)
  • Using tools like Excel / Power BI / Tableau
  • Charts, graphs, dashboards
  • Storytelling with data

Module 4: Statistical Analysis & Interpretation

  • Basic statistical concepts (mean, median, standard deviation, correlation)
  • Hypothesis testing, confidence intervals
  • Regression analysis
  • Making sense of statistical output

Module 5: Predictive Analytics & Modeling

  • Introduction to Machine Learning
  • Supervised vs. unsupervised learning
  • Simple models: linear regression, decision trees
  • Evaluation metrics

Module 6: Data-Driven Decision Making Framework

  • Decision-making process using data
  • Biases in decision making
  • Frameworks: OODA loop, PDCA, A/B testing
  • Case studies of data-driven decisions

Module 7: Tools for Data Analytics

  • Overview of Excel, Power BI, SQL, Python
  • Data manipulation using Excel / SQL basics
  • Integration of tools for end-to-end analytics

Module 8: Capstone Project / Case Study

  • Real-world data problem
  • Team analysis and presentation
  • Instructor feedback and reflection