Data Analysis and Visualization

Learning Path

Module 1

What is Data Analytics and its role in modern organizations
Python installation and working with IDEs
Python variables and data types
Control flow in Python (conditions and loops)
Functions and modules
Introduction to Pandas Series

Module 2

Working with Pandas DataFrames
• Data selection and indexing techniques
• Cleaning data and handling missing values
• Transforming and filtering data
• Grouping and aggregating data

Module 3

• Understanding summary statistics
• Visualizing data distributions
• Detecting outliers and anomalies
• Understanding correlation and covariance
• Visualizing relationships with pairplots and heatmaps

Module 4

• Principles of effective data visualization
• Creating bar charts and line charts
• Creating histograms and boxplots
• Creating scatter plots and heatmaps
• Customizing plots with labels, colors, and annotations
• Working with subplots and multiple axes
• Storytelling with data
• Creating dashboards using Python

Module 5

• Capstone kickoff: problem selection and dataset scoping
• Data collection and preprocessing
• Data analysis and visualization
• Dashboard creation and storytelling
• Project demo recording and LMS submission

Electives

Basic understanding of computers and programming concepts
Familiarity with any programming language is helpful but not mandatory
Interest in working with data and analytical problem solving

Eligibility

The following candidates are eligible to join the program:

Current students of Cochin University of Science and Technology (CUSAT).
BTech. students who are currently pursuing their degree.
MTech. students who are currently pursuing their degree.
Applicants who are interested in learning data analysis, Python programming, and visualization techniques as part of their skill development.

Note: Candidates who have already graduated are not eligible to enroll in this program.

Highlights

Industry-Relevant Curriculum aligned with modern data analytics workflows
Hands-on Learning with Python (Pandas, NumPy, Matplotlib, Seaborn)
Self-Paced Learning with structured and guided modules
Practical Data Cleaning & Preprocessing techniques
Exploratory Data Analysis (EDA) for pattern and trend discovery
Data Visualization for effective insight communication
Introduction to Data Storytelling & Dashboard Creation
Capstone Project using real-world datasets
Strong Foundation for Data Careers (Data Analyst, Business Analyst, Junior Data Scientist)