Data Science Using R+ Python + SQL + Data Visualization

The objective of this program is to provide participants with a strong foundation in end-to-end data science by combining the power of R, Python, SQL, and modern data visualization tools. The course focuses on data handling, statistical analysis, machine learning, database querying, and insight-driven storytelling. Participants will learn to work with multiple tools, build analytical workflows, and apply data science techniques to solve real-world business problems.

Category: Analytics

Description

1. Learning Outcomes

Understand the fundamentals of data science, data pipelines, and analytical methodologies.
Use Python and R for data cleaning, exploratory data analysis, and building machine learning models.
Write efficient SQL queries for data extraction, transformation, and aggregation.
Develop predictive and descriptive models using libraries like pandas, NumPy, scikit-learn.
Create interactive dashboards and visual insights using tools like Power BI / Tableau / R Shiny / Python libraries.
Build an end-to-end data science project integrating all tools (R, Python, SQL, Visualization).

2. Program Highlights & Key Features

Hands-on practice with real datasets across business, finance, marketing, and operations.
Training on Python, R programming, SQL fundamentals, and visualization best practices.
Step-by-step guidance on building machine learning models and evaluating performance.
Practical exercises in database querying, EDA, model building, and dashboard creation.
Coverage of modern trends such as automated ML, cloud-based analytics, and multi-tool integration workflows.

Module 1: Introduction to Data Science
Module 2: Data Analysis Using Python
Module 3: Data Analysis Using R
Module 4: Data Analysis Using SQL
Module 5: Data Visualization and Reporting
Module 6: Applied Data Science Use Cases

Introduction to Data Science

What is Data Science
Role of Data Science in Business Decision-Making
Data Science Lifecycle
Types of Data (Structured and Unstructured)
Tools Used in Data Science (R, Python, SQL, Visualization Tools)
Conclusion and QnA

Data Analysis Using Python

Python Basics for Data Analysis
Data Handling with Pandas and NumPy
Data Cleaning and Preprocessing
Exploratory Data Analysis (EDA)
Statistical Analysis Using Python
Data Visualization Using Python
Conclusion and QnA

Data Analysis Using R

Introduction to R for Data Science
Data Structures and Data Frames in R
Data Cleaning and Transformation
Exploratory Data Analysis Using R
Statistical Analysis Using R
Data Visualization Using R
Conclusion and QnA

Data Analysis Using SQL

Role of SQL in Data Science
Database Concepts and Tables
Data Retrieval Using SQL Queries
Joins, Aggregations, and Subqueries
Analyzing Business Data Using SQL
Conclusion and QnA

Data Visualization and Reporting

Importance of Data Visualization
Choosing the Right Charts
Visualization Tools (Power BI / Tableau / Excel)
Dashboard Creation Basics
Storytelling with Data
Conclusion and QnA

Applied Data Science Use Cases

Combining R, Python, SQL, and Visualization
End-to-End Data Analysis Workflow
Business Use Case Analysis
Insight Generation and Decision Support
Ethical Use of Data
Future Scope of Data Science
Conclusion and QnA

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