Data Science Foundation: 7-Day Bootcamp
Build your foundation in Data Science with R and unlock the power of data-driven insights.
Mode: Online
Duration: 7 Days (Daily 1.5 - 2 hrs sessions)
For: Undergraduates, Students
Certificate Provided | Hands-on Practice | Live Q&A
Training Schedule
7 Days | Daily 1.5 - 2 hours sessions | Online Mode
| Day | Topics |
| Day 1 Introduction |
Introduction to Data Science and R Overview of Data Science: What is data science? Its applications across domains Introduction to R: Installation, RStudio interface, R scripts vs console Basic R operations: Arithmetic, objects, vectors Hands-on: Writing your first R code, simple data manipulations |
| Day 2 Data Structures |
Data Structures in R Core structures: Vectors, matrices, data frames, lists, factors Practical examples with student-friendly datasets Hands-on: Creating and managing data frames, indexing, subsetting |
| Day 3 Data Handling |
Data Import, Cleaning, and Visualization Importing data: CSV, Excel, text files Data wrangling: Handling missing values, renaming, recoding Basic visualization: plot(), hist(), boxplot(), ggplot2 intro Hands-on: Cleaning and exploring a real-world dataset |
| Day 4 EDA |
Descriptive Statistics and Exploratory Data Analysis Descriptive statistics: Mean, median, variance, SD, skewness, kurtosis EDA: Patterns, outliers, correlations Hands-on: Summary statistics with sample datasets |
| Day 5 Statistics |
Statistical Inference Concepts: Population vs sample, hypothesis testing, t-tests, chi-square Confidence intervals: Meaning and interpretation Hands-on: Hypothesis testing on real-life datasets |
| Day 6 Modeling |
Regression and Prediction Simple linear regression: Concept, interpretation, model fitting in R Multiple regression: Adding more predictors Hands-on: Predicting outcomes based on multiple factors Intro to model diagnostics: Residual plots, R² |
| Day 7 AI & Future |
Introduction to AI & LLMs What are LLMs?: Basics of large language models, how they are trained Applications: Chatbots, literature summarization, automating data cleaning Demo: Using AI tools for research and analysis Hands-on: Intro to ellmer and ragnar packages in R Wrap-up: Review of course, Q&A, feedback |
What You'll Learn
R Programming Fundamentals
Master the basics of R programming, from installation to advanced data structures and operations.
Data Management Skills
Learn to import, clean, and transform datasets for meaningful analysis.
Data Visualization
Create compelling visualizations to communicate your findings effectively using ggplot2 and base R.
Statistical Analysis
Understand and apply descriptive statistics, hypothesis testing, and exploratory data analysis.
Predictive Modeling
Build regression models to predict outcomes and understand model diagnostics.
AI & Data Science
Explore the role of AI and large language models in modern data science and analytics.
Frequently Asked Questions
Do I need prior programming experience?
No programming experience required! We start from the very basics and build up gradually.
What software do I need?
You'll need R and RStudio (both free). We'll guide you through the installation process on Day 1.
Will I get hands-on practice?
Yes! Each session includes practical exercises with real-world datasets.
Can I ask questions during the training?
Absolutely! Each session includes live Q&A time for your questions and clarifications.
Will I receive a certificate?
Yes, you'll receive a certificate of completion for this Data Science Foundation training.
What materials will be provided?
All datasets, R scripts, slides, and reference materials will be shared with participants.