This course provides a practical, progressive approach to applied data analytics and cloud web application development. The training is organized so participants focus on one primary programming language at a time while SQL serves as the common data layer.

The course begins with Python and SQL for data processing and analytics, continues with R and SQL for statistical analysis and modeling, and concludes with Python-based web application development, testing, integration, and cloud deployment.

 

Duration 10 Day – 70 hrs.

 

Objectives

• Use Python and SQL to retrieve, clean, transform, and analyze business datasets.
• Connect Python applications to relational databases and process query results using data analysis libraries.
• Use R and SQL to perform exploratory analysis, statistical analysis, and basic business modeling.
• Interpret statistical and model results, including assumptions and limitations.
• Create visualizations and communicate actionable business insights.
• Develop a functional Python web application connected to a relational database.
• Integrate analytical outputs and business calculations into a web application.
• Apply input validation, error handling, configuration management, and basic security controls.
• Test application functionality, database integration, and analytical calculations.
• Deploy and verify a functional web application in a cloud environment.

  

Target Audience 

• Data analysts and business analysts seeking programming and application development skills.
• Business intelligence professionals working with relational data and reporting.
• Software developers interested in integrating analytics into web applications.
• Database professionals expanding into Python and R analytics.
• IT professionals supporting data-driven applications and cloud services.
• Researchers and technical professionals who need to present statistical findings through web applications.
• Professionals with basic programming knowledge transitioning into applied data analytics or data application development.

 

Prerequisites 

• Basic programming knowledge, including variables, conditions, loops, and functions in any language.
• Familiarity with spreadsheets, tabular datasets, and common data operations.
• A basic understanding of database tables and relationships; prior SQL experience is helpful.
• Basic mathematical and statistical knowledge, including averages, percentages, and variability.
• Confidence installing software, managing files, and using a command-line interface.
• Prior experience with R, Python web frameworks, or cloud deployment is not required.
• Technical requirements: A computer with Python, R, an R development environment such as RStudio, a code editor, and access to a relational database. Participants also need internet access and a cloud environment with permission to deploy an application.

Course Outline

Day 1: Python Foundations for Data Analytics
Module 1: Python Programming Fundamentals

• Python environment and project setup
• Variables and data types
• Lists, tuples, sets, and dictionaries
• Conditions and loops
• Functions and reusable code
• Modules and packages
• Basic exception handling

 Module 2: Working with Data in Python

• Introduction to NumPy and Pandas
• Series and DataFrames
• Importing CSV and tabular datasets
• Inspecting datasets
• Filtering and sorting
• Selecting and transforming columns
• Basic data profiling

Hands-On Activity

Import and explore a business dataset using Python.

Day 2: SQL for Python Data Analytics

Module 3: SQL and Relational Data

• Tables, columns, and data types
• Primary and foreign keys
• Relationships
• SELECT, WHERE, and ORDER BY
• DISTINCT
• Working with NULL
• SQL functions and expressions

 Module 4: Business Data Queries

• COUNT, SUM, AVG, MIN, and MAX
• GROUP BY
• HAVING
• INNER and LEFT JOIN
• Subqueries
• Common Table Expressions
• Business metrics and KPIs

Hands-On Activity

Build SQL queries for sales, customers, products, and business performance.

Day 3: Python + SQL Data Preparation and Analysis

Module 5: Integrating Python and SQL

• Connecting Python to a relational database
• Executing SQL queries from Python
• Loading query results into Pandas
• Parameterized queries
• Combining database and Python processing

 Module 6: Data Cleaning and Exploratory Analysis

• Missing values
• Duplicate records
• Invalid values
• Data-type conversion
• Dates and text
• Derived variables
• Descriptive statistics
• Group comparisons
• Trends and relationships
• Basic Python visualizations

Mini-Project

SQL Database -> Python/Pandas -> Clean Dataset -> Business Analysis

 

Day 4: R Foundations and SQL Integration

Module 7: R Programming Fundamentals

• R and RStudio environment
• Variables and data types
• Vectors
• Lists
• Matrices
• Data frames
• Conditions
• Functions
• Packages

 Module 8: Working with SQL Data in R

• Connecting R to a database
• Running SQL queries
• Loading SQL results into R
• Inspecting data frames
• Preparing database data for analysis
• Reusable R data-processing workflows

Hands-On Activity

Retrieve the same business dataset through SQL and analyze it using R.

 

Day 5: Statistical Analysis and Visualization with R

Module 9: Exploratory and Statistical Analysis

• Descriptive statistics
• Populations and samples
• Sampling variability
• Probability
• Common distributions
• Estimation
• Confidence intervals
• Correlation
• Statistical hypotheses
• Basic hypothesis testing
• Statistical vs. practical significance

 Module 10: Visualization and Business Insights

 

• Selecting appropriate visualizations
• Bar and line charts
• Histograms
• Scatter plots
• Comparing groups
• Identifying trends
• Communicating uncertainty
• Translating analysis into business recommendations

Hands-On Activity

Create an R statistical analysis and visualization report based on SQL data.

 

Day 6: Statistical Modeling with R + SQL

Module 11: Business Modeling

• Defining target variables
• Explanatory variables
• Preparing model data using SQL
• Linear regression
• Formula-based business models
• Revenue and cost analysis
• Break-even analysis

 Module 12: Model Evaluation

• Training and test datasets
• Prediction errors
• Residual analysis
• Model assumptions
• Overfitting
• Data leakage
• What-if analysis
• Sensitivity analysis
• Model limitations

Mini-Project

SQL -> R -> Statistical Model -> Visualization -> Business Recommendation

Day 7: Web Application Foundations

Module 13: Web Application Architecture

• Client/server concepts
• HTTP fundamentals
• Request/response lifecycle
• Frontend vs. backend
• Application architecture
• Designing a data-driven application
• Mapping user input, processing, and output 

Module 14: Building a Python Web Application

• Creating the project
• Application structure
• Routes
• Templates
• Navigation
• Forms and user input
• Displaying results
• Dependency management

Hands-On Activity

Create the initial structure of a Python-based business web application.

 

Day 8: Database-Driven Web Applications

Module 15: Web Application + SQL

• Database configuration
• Connecting the application to SQL
• Retrieving records
• Parameterized queries
• Displaying database records
• Searching and filtering
• Creating and updating records
• Database error handling

Module 16: Integrating Analytics

• Reusing Python analytics functions
• Incorporating R-generated outputs where appropriate
• Loading model results
• Scenario calculations
• Displaying KPIs
• Tables and charts
• Presenting business explanations

Hands-On Activity

Build a database-driven web application that displays business data and analytical results.

 

Day 9: Application Reliability and Testing

Module 17: Application Reliability

• Input validation
• Exception handling
• Missing data
• Processing errors
• Database failures
• User-friendly error messages
• Configuration management
• Environment variables
• Secrets and credentials
• Basic access controls
• Logging 

Module 18: Application Testing

• Functional testing
• Database testing
• Data-transformation testing
• Model-calculation testing
• Invalid-input testing
• Failure scenarios
• Debugging
• Defect correction and verification

Hands-On Activity

Test the application against functional, database, analytics, and failure scenarios.

 

Day 10: Cloud Deployment and Capstone

Module 19: Cloud Deployment

• Cloud application architecture
• Hosting requirements
• Preparing dependencies
• Startup configuration
• Environment variables and secrets
• Database connectivity
• Deploying the application
• Logging and monitoring
• Troubleshooting deployment problems

 Module 20: Capstone Project

• Integrate the SQL database with Python data processing
• Incorporate R statistical analysis or model outputs
• Develop the Python web application interface
• Display business calculations, tables, and visualizations
• Implement validation and error handling
• Deploy the application to a cloud environment
• Verify application functionality and document the solution

Hands-On Activity

Final Workflow

• SQL Database -> Python Data Processing -> R Statistical Analysis/Model -> Python Web Application -> Cloud Deployment