Career-Focused Data Analytics Program in Kollam with Govt.Certification – Designed for Working Professionals

Learning Objectives

By the end of this course, students will be able to:

• Write Python code for data processing and analysis
• Clean and manipulate datasets using pandas
• Create professional data visualizations
• Perform statistical analysis and generate insights
• Build basic predictive models
• Present data-driven findings effectively

Module 1: Python Essentials for Data Analytics

Learning Goals: Master minimum viable Python for data work

• Python setup (Google Colab for immediate start)
• Python IDE (VS CODE)
• Variables, data types, and basic operators
• String Functions
• Lists, Tuple,Set and dictionaries (focus on data structures)
• Control flow essentials (if statements, for loops)
• Operartors
• Functions for code organization

Activities:

• Basic data calculations
• Simple data filtering with lists and dictionaries

Working with Data Files

Learning Goals: Handle data input/output efficiently

• Reading CSV and Excel files
• JSON data handling
• Basic file operations
• Error handling for data operation
• Modules & Packages

Activities:

• Loading real datasets from different file formats
• Basic data exploration without pandas

Module 2: NumPy and Pandas Fundamentals

NumPy for Data Analytics

Learning Goals: Efficient numerical computations

• Installation of jupyter note book
• Combaining and splitting arrays
• Array operations and mathematical functions
• Basic statistics with NumPy
• Array indexing and slicing for data selection

Activities:

• Financial calculations with arrays
• Data aggregation exercises

Pandas Core Skills

Learning Goals: Master the primary data analytics tool

• Creation of Dataframes
• Data loading from various sources (CSV, Excel)
• Handling Duplicate Values
• Data selection, filtering, and indexing
• Basic data cleaning (handling missing values, duplicates)
• Groupby operations and aggregations

Activities:

• Customer data analysis project
• Sales data exploration and summarization
• Data quality assessment exercise

Module 3: Advanced Data Manipulation

Data Wrangling Mastery

Learning Goals: Handle complex data transformation tasks

• Advanced filtering with multiple conditions
• Data merging and joining datasets
• Pivot tables and cross-tabulations
• Data reshaping (melt, stack, unstack)
• String manipulation for text data
• Date/time data processing

Activities:

• Multi-dataset integration project
• Time series data preparation
• Text data cleaning exercise

Data Cleaning and Preprocessing

Learning Goals: Prepare real-world messy data for analysis

• Missing data strategies (imputation, removal)
• Outlier detection and treatment
• Data type optimization
• Feature engineering basics
• Data validation techniques

Activities:

• Cleaning a messy real-world dataset
• Preparing data for analysis pipeline

Module 4: Data Visualization

Essential Data Visualization

Learning Goals: Create impactful visualizations quickly

• Matplotlib basics for custom plots
• Key plot types: line, bar, scatter, histogram, box plots
• Customizing plots (labels, colors, legends)
• Saving and exporting visualizations

Activities:

• Business dashboard creation
• Trend analysis visualization

Statistical Visualization with Seaborn

Learning Goals: Professional statistical graphics

• Distribution plots and categorical visualizations
• Correlation heatmaps and pair plots
• Regression plots for relationship analysis
• Multi-panel figures and styling
• Interactive basics with plotly (brief introduction)

Activities:

• Comprehensive exploratory data analysis
• Statistical relationship visualization project

Module 5: Statistical Analysis and Insights

Applied Statistics for Analytics

Learning Goals: Extract statistical insights from data

• Descriptive statistics and data summarization
• Hypothesis testing essentials (t-tests, chi-square)
• Confidence intervals and statistical significance
• Correlation analysis and interpretation

Activities:

• A/B testing analysis
• Customer behavior statistical analysis

Business Analytics Applications

Learning Goals: Apply analytics to business problems

• Cohort analysis and customer segmentation
• Time series analysis basics
• Performance metrics and KPI calculation
• Statistical reporting and interpretation

Activities:

• Customer lifetime value analysis
• Marketing campaign effectiveness study

Module 6: Introduction to Predictive Analytics

Machine Learning Essentials

Learning Goals: Build basic predictive models

• Introduction to scikit-learn
• Linear regression for prediction
• Model evaluation basics (train/test split, metrics)
• Classification fundamentals
• Clustering for customer segmentation

Activities:

• Sales forecasting model
• Customer segmentation with clustering

Final Integration Project

Analytics Project

Learning Goals: Demonstrate end-to-end analytics skills

• Complete analytics workflow implementation
• Professional presentation of findings
• Actionable insights generation

Project Options:

1. E-commerce performance analysis
2. Customer churn prediction
3. Marketing campaign optimization
4. Financial performance analysis