Foundations of Quantitative Data Analysis

The course is aimed to develop and build analytical and data interpretation skills

Course Fee - ₹ 1000

Overview

The course is aimed to develop and build analytical and data interpretation skills of learners which is required for evidence based decision making and also enhances the employability in data driven roles. It covers key quantitative research approaches, fundamental statistical concepts and different methods of analyzing numerical data.

START DATE

Jul 01 2026

END DATE

Mar 31 2027

Delivery Mode

Online

Duration

15 Hours

Type

Theory

Course Fee

₹ 1000

Credentials and Certification

1 Credit
1 Digital Badge
1 Certificate

Course Category Micro-Credential (Advanced)

TARGET AUDIENCE CHRIST University students

COURSE DELIVERY

Action-based learning materials with guided assessments and faculty support.

Digital Credential

indicating verified micro-credential completion.

RECOMMENDED FOR

AUDIENCE 01

Students exploring cloud computing

AUDIENCE 02

Professionals planning cloud projects

AUDIENCE 03

Faculty building digital deployment literacy

COURSE DELIVERY

Action-based learning materials with guided assessments and faculty support.

Course Outcomes

Our curriculum matches modern standard practices to provide exceptional training milestones.

Quantitative Research Fundamentals

Understand the core principles of quantitative research and statistical methods used to collect, analyze, and interpret numerical data effectively.

CORE KNOWLEDGE

Statistical Concepts

Learn essential statistical concepts, including data types, measures, distributions, and probability, to support accurate quantitative analysis and interpretation.

STATISTICAL FOUNDATIONS

Data Analysis Techniques

Apply quantitative data analysis techniques to organize datasets, identify patterns, and generate meaningful insights for informed decision-making.

ANALYTICAL SKILLS

Interpreting Numerical Results

Develop the ability to interpret statistical outputs, evaluate findings, and communicate numerical results with clarity and confidence.

DATA INTERPRETATION

Evidence-Based Decision Making

Use quantitative evidence and statistical reasoning to evaluate alternatives, solve problems, and support informed business and research decisions.

DECISION MAKING

Practical Data Applications

Apply quantitative analysis methods to real-world datasets, strengthening analytical thinking and practical problem-solving across diverse disciplines.

PRACTICAL LEARNING

ELIGIBILITY CRITERIA

UG - 2nd and 3rd years and First Year PG

Faculty In Charge

Expert guidance from acclaimed industry professional leaders.

Dr. M. Martha Sucharitha

Associate Professor

Dr. M. Martha Sucharitha is an Associate Professor specializing in Finance, Predictive Analytics, Machine Learning, and Quantitative Research Methods. With extensive teaching and research experience, she is passionate about simplifying data analysis through practical learning. Her work empowers learners with analytical skills for research, business, and evidence-based decision-making.

Program Overview

Academic Curriculum

A meticulous, guided learning path engineered to transform your cloud engineering expertise.

Module 1

Introduction to Quantitative Data

Module 2

Methods of Data Visualization

Module 3

Descriptive Statistics

Module 4

Probability & Probability Distribution

Module 5

Inferential Statistics & Hypothesis Testing

Module 6

Foundations of Quantitative Data Analysis

Overview

Credentials and Certification

COURSE DELIVERY

Digital Credential

Students exploring cloud computing

Professionals planning cloud projects

Faculty building digital deployment literacy

Course Menu

COURSE DELIVERY

Course Outcomes

Quantitative Research Fundamentals

Statistical Concepts

Data Analysis Techniques

Interpreting Numerical Results

Evidence-Based Decision Making

Practical Data Applications

ELIGIBILITY CRITERIA

Faculty In Charge

Dr. M. Martha Sucharitha

Associate Professor

Academic Curriculum

Introduction to Quantitative Data

Methods of Data Visualization

Descriptive Statistics

Probability & Probability Distribution

Inferential Statistics & Hypothesis Testing

Correlation & Regression Analysis

Build skills that matter in the real world