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CCDS
Core Concepts in Data Science

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English
Intermediate
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Course Overview

The Core Concepts in Data Science is a five-day intensive program designed to build a strong foundation in data science. This course covers essential skills in R programming, data visualization, probability, statistical inference, and productivity tools. Participants will learn fundamental data science concepts and R programming basics, including data types, data frames, and essential programming constructs. Interactive exercises will help in effective data handling and manipulation using R.
The course emphasizes data visualization with ggplot2, teaching participants to create compelling visual representations and communicate data insights effectively. Key probability concepts and statistical inference techniques, such as hypothesis testing and regression analysis, are covered to help participants make data-driven decisions. Additionally, the course introduces productivity tools like version control with Git and GitHub, efficient project management with RStudio, and reproducible research using markdown. By the end of this course, participants will have a solid foundation in data science, ready for more advanced topics and projects.

Ard Z.
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Key Takeaways

1
Understand and explain the fundamental concepts and methodologies of data science, demonstrating a thorough grasp of the subject.
2
Apply R programming techniques to handle, manipulate, and analyze diverse data sets, ensuring data integrity
3
Create detailed data visualizations using ggplot2 in R, effectively communicating complex data insights to various audiences.
4
Analyze data using probability concepts and statistical inference techniques, making informed decisions through hypothesis testing and regression analysis
5
Utilize productivity tools like Git, GitHub, and RStudio to efficiently manage data science projects, ensuring reproducibility and collaboration.

Harvard University
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This program is from Harvard’s Office of the Vice Provost for Advances in Learning (VPAL), in association with HarvardX. It is offered in collaboration with GetSmarter, an edX partner.Gain a holistic perspective on cybersecurity risk and mitigation, and get recognised for your knowledge with a premier certificate from Harvard’s VPAL.

Course Outline

DAY 1 Introduction to Data Science and R Basics
→ Module 1: Welcome and Program Overview
→ Module 2: Introduction to Data Science
→ Module 3: Understanding Data Types in R
→ Module 4: Basic R Programming Exercises
→Module 5: Data Frames and Data Manipulation in R
→ Module 6: Programming Concepts: Functions, Loops, and Conditionals
→ Module 7: Data Handling Practical Session
→ Module 8: Q&A and Wrap-Up
DAY 2 Data Visualization with R
→ Module 1: Principles of Data Visualization
→ Module 2: Introduction to ggplot2 in R
→ Module 3: Creating Basic Plots (Histograms, Scatter Plots)
→ Module 4: Advanced Plotting Techniques
→ Module 5: Customizing Plots and Themes in ggplot2
→ Module 6: Hands-on Exercise: Creating Visualizations
→ Module 7: Q&A and Wrap-Up .
DAY 3 Probability and Its Applications
→ Module 1: Fundamental Concepts in Probability
→ Module 2: Random Variables and Probability Distributions
→ Module 3: Applying Probability in Data Science
Module 4: Statistical Inference and Probability
→ Module 5: Case Study: Real-World Applications
→ Module 6: Hands-on Exercise: Probability Problems in R
→ Module 7: Q&A and Wrap-Up
DAY 4 Inference and Modeling
→ Module 1: Introduction to Statistical Inference
→ Module 2: Building Statistical Models
→ Module 3: Hypothesis Testing
→ Module 4: Regression Analysis
→ Module 5: Practical Session: Inference and Modeling in R
→ Module 6: Model Evaluation Techniques
→ Module 7: Q&A and Wrap-Up.
DAY 5 Productivity Tools for Data Science
→ Module 1: Enhancing Productivity with Tools
→ Module 2: Version Control with Git and GitHub
→ Module 3: Using RStudio for Data Science Projects
→ Module 4: Reproducible Research with Markdown
→ Module 5: Hands-on Exercise: Productivity Tools
→ Module 6: Final Project Overview and Guidelines
→ Module 7: Q&A and Program Wrap-Up

Who Should Attend?

This highly practical and interactive course has been specifically designed for

→Business and Data Analysts: Professionals
who need to analyze and report on data to
inform business decisions.

→Project Managers: Those who manage
projects and require data to plan and
report progress.

→Finance Professionals: Analysts and
managers who need to handle complex
financial data and forecasts.

→IT Professionals: Those who support
data systems and need to understand
data flows and reporting.

→Operations Managers: Managers needing to
optimize operations through data analysis.

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