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CCDS
Core Concepts in Data Science

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المخرجات الرئيسية

Harvard University
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Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty are engaged with teaching and research to push the boundaries of human knowledge. The University has twelve degree-granting Schools in addition to the Radcliffe Institute for Advanced Study. Established in 1636, Harvard is the oldest institution of higher education in the United States. The University, which is based in Cambridge and Boston, Massachusetts, has an enrollment of over 20,000 degree candidates, including undergraduate, graduate, and professional students. Harvard has more than 360,000 alumni around the world.

محتوى الدورة التدريبية

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

على من يجب الحضور؟

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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