Data Science Foundation with Python / R

Executive Overview

Data Science is transforming how businesses operate, make decisions, and create value. This 5-day corporate training program provides a strong foundation in data science using Python or R — two of the most powerful languages for statistical computing and machine learning. Participants will gain a practical understanding of data manipulation, statistical analysis, and visualization, along with exposure to key libraries and frameworks used in enterprise data workflows. Through a blend of theory, real-world case studies, and hands-on exercises, this program equips professionals to build data-driven insights and prepare for advanced analytics and AI initiatives.

Objectives of the Training

Understand the core concepts of data science and its applications across industries.
Learn Python or R programming for data manipulation, analysis, and visualization.
Master foundational statistics and probability essential for data interpretation.
Gain proficiency in libraries such as pandas, NumPy, Matplotlib (Python) or tidyverse, ggplot2 (R).
Apply data wrangling, cleaning, and exploratory data analysis techniques.
Develop end-to-end understanding of a basic machine learning workflow.

Prerequisites

Basic understanding of mathematics and logical reasoning.
No prior programming experience required, though familiarity with Excel or basic coding is helpful.
A laptop with Python (Anaconda) or RStudio installed.

What You Will Learn

Data science lifecycle, processes, and business relevance.
Python/R programming for analytics — syntax, data types, and libraries.
Statistical concepts: mean, variance, correlation, regression, and hypothesis testing.
Exploratory Data Analysis (EDA) and data visualization.
Practical case studies in business analytics and prediction.
Building reproducible data workflows and preparing for machine learning.

Target Audience

This program is designed for Data Analysts, Business Intelligence Professionals, Software Developers, and Managers transitioning into data-driven roles. It is also ideal for professionals looking to build a foundation before advancing into machine learning or AI.

Detailed 5-Day Curriculum

Day 1 – Introduction to Data Science and Python/R Basics (6 Hours)

Session 1: Understanding Data Science – Definitions, Roles, and Real-World Applications.
Session 2: Setting Up the Environment – Anaconda, Jupyter, or RStudio.
Session 3: Core Syntax and Data Structures – Variables, Lists, Arrays, Data Frames.
Hands-on: Writing Basic Scripts, Reading/Writing Data, and Exploring Datasets.

Day 2 – Data Manipulation and Cleaning (6 Hours)

Session 1: Working with Libraries – pandas/NumPy in Python or dplyr/tidyverse in R.
Session 2: Handling Missing Data, Outliers, and Data Transformation Techniques.
Session 3: Combining and Reshaping Datasets for Analysis.
Workshop: Cleaning and Preparing Real-World Business Data for Analytics.

Day 3 – Exploratory Data Analysis and Visualization (6 Hours)

Session 1: Understanding Distributions, Correlations, and Relationships.
Session 2: Data Visualization Techniques – Matplotlib, Seaborn, ggplot2.
Session 3: Visual Storytelling – Creating Dashboards and Reports.
Hands-on: Building Interactive Visualizations and Insights from Data.

Day 4 – Statistics for Data Science (6 Hours)

Session 1: Descriptive Statistics and Probability Fundamentals.
Session 2: Inferential Statistics – Sampling, Confidence Intervals, and Hypothesis Testing.
Session 3: Correlation, Regression, and Statistical Modeling Basics.
Workshop: Analyzing Business Scenarios using Statistical Models.

Day 5 – Introduction to Machine Learning and Capstone Project (6 Hours)

Session 1: Understanding Machine Learning – Supervised vs. Unsupervised Learning.
Session 2: Building a Simple Predictive Model (Linear Regression or Decision Tree).
Session 3: Capstone Project – Applying the Complete Data Science Workflow on a Real Dataset.
Panel Discussion: Future of Data Science – AI, MLOps, and Data Democratization in Enterprises.

Capstone Project

Participants will perform an end-to-end data science project using Python or R — starting from data cleaning, analysis, and visualization, to building and evaluating a simple predictive model. The project will focus on a real-world business case, such as sales forecasting, customer churn prediction, or operational analytics.

Future Trends in Data Science Foundations and Enterprise Analytics

The data science ecosystem is rapidly evolving with automation, augmented analytics, and AI-driven decision systems. Low-code platforms, AutoML, and explainable AI (XAI) are reshaping how data professionals operate. Enterprises are increasingly blending data science and business strategy to drive innovation, sustainability, and customer-centric transformation. Mastering the foundations of Python/R-based data science today is the first step toward building future-ready analytical capabilities.