Description

Course Description:

This  Data Science with R course introduces learners to data science fundamentals using R programming. Designed for beginners and professionals alike, it focuses on data wrangling, exploratory data analysis, statistical modeling, machine learning, and data visualization using the R ecosystem. By the end of the course, students will be capable of handling real-world data projects using R.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Data Science with R Certified Course:

• Data Analyst
• Data Scientist
• Business Intelligence Analyst
• Machine Learning Engineer
• Quantitative Analyst
• Research Scientist
• Data Engineer
• Marketing Analyst

Essential Skills you will Develop Data Science with R Certified Course:

• Statistical Analysis
• Data Manipulation
• Data Visualization
• Programming in R
• Machine Learning
• Exploratory Data Analysis

Tools Covered:

• tidy verse – A collection of packages
• caret – For building and evaluating machine learning models.
• table – High-performance data manipulation package.
• shiny – To build interactive web applications and dashboards.
• lubricate – For working with date-time data.
• stringer – For text data cleaning and manipulation.

Syllabus:

Module 1: Introduction to Data Science and R What is Data Science? Applications and Workflow of Data Science Installing R and Studio Basic R Programming: Variables, Data Types, and Operators.

Module 2: Data Structures in R Vectors, Lists, Matrices, Arrays, and Data Frames Indexing and Subletting Factors and Their Use in Data Analysis.

Module 3: Data Import and Export Reading and Writing CSV, Excel, JSON, and Text Files Connecting to Databases (e.g., MySQL, SQLite) Data Scraping and API Data Access.

Module 4: Data Cleaning and Preprocessing Handling Missing Values Data Transformation with String Manipulation using Date-Time Handling using.

Module 5: Exploratory Data Analysis (EDA) Summary Statistics and Distributions Univariate and Bivariate Analysis Outlier Detection and Handling Data Visualization.

Module 6: Statistical Analysis with R Probability Distributions Hypothesis Testing (t-test, chi-square test, ANOVA) Correlation and Covariance Linear and Logistic Regression.

Module 7: Machine Learning with R Supervised vs. Unsupervised Learning Model Building with Decision Trees, Random Forest Clustering: k-means and Hierarchical Clustering Model Evaluation (confusion matrix, ROC, AUC).

Module 8: Advanced Visualization and Reporting Interactive Dashboards with Advanced Plotting with Creating Reproducible Reports using.

Module 9: Time Series and Text Mining  Time Series Analysis with  Introduction to Natural Language Processing with Sentiment Analysis.

Module 10: Capstone Project and Industry Applications Real-world dataset analysis project Domain-specific use cases: Healthcare, Finance, Marketing Best Practices in Data Science Projects Resume & Interview Preparation for Data Science Roles.

Industry Projects:

• Customer Churn Prediction – Telecom Industry
• Sales Forecasting – Retail Sector
• Credit Risk Modeling – Banking

 Who is this program for?

• Students and Fresh Graduates
• Working Professionals
• Statisticians and Analysts

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com