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)

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