Unlock the Power of Data with ENCODE-IT’s Data Science with R Course

In today’s data-driven world, data science has become a critical component for making informed
business decisions, driving innovation, and solving real-world problems. If you're looking to build a
solid foundation in data science, then R is the language of choice for many data scientists,
statisticians, and analysts due to its rich ecosystem and powerful capabilities for data manipulation,
visualization, and statistical analysis. ENCODE-IT’s Data Science with R course is designed to provide
you with hands-on experience in these areas, giving you the skills to handle complex datasets,
develop machine learning models, and generate insights from data.

This course offers a comprehensive understanding of data science using R, ranging from basic data
manipulation and visualization to advanced machine learning techniques and statistical analysis. By
the end of this course, you'll be equipped with the knowledge to perform end-to-end data science
tasks and use R to tackle challenges in real-world business applications.

Course Overview
Data Science with R is an in-depth course tailored to those who want to understand the core
concepts and techniques used in the field of data science using R. Throughout the course, you'll
learn how to handle and analyze large datasets, build predictive models, and visualize complex data
through intuitive and insightful graphics. This course integrates both theory and practical
applications, ensuring you not only understand the concepts but also gain hands-on experience that
will be useful in the workplace.
By the end of the course, you will be able to confidently apply R to clean and manipulate data,
perform statistical analysis, build machine learning models, and visualize data effectively for
decision-making.

Salary Scale in India
The demand for data scientists in India has surged in recent years, especially for professionals skilled
in programming languages such as R. Entry-level data scientists can expect to earn between ₹6 lakh
to ₹12 lakh per year. For mid-level professionals, salaries typically range from ₹12 lakh to ₹18 lakh,
while experienced data scientists and machine learning engineers can command salaries above ₹20
lakh annually. As data-driven decision-making continues to gain momentum, R-skilled professionals
will find themselves in high demand across industries such as healthcare, e-commerce, finance, and
technology.

Placement Assistance & Certification in India
Upon successful completion of the Data Science with R course, ENCODE-IT offers comprehensive
placement assistance to help you secure a position in the competitive data science field. The course
concludes with a Certificate of Completion, which is recognized by top employers and will enhance
your chances of landing your dream job. Our placement support includes personalized resume
building, interview preparation, and direct job referrals to leading companies in the data science and
analytics sectors.

Course Curriculum

1. Introduction to Data Science and R
o Overview of Data Science and its Importance
o Introduction to R and RStudio: Setup and Installation
o Basic R Syntax and Data Types
o Working with R Objects: Vectors, Lists, Matrices, and Data Frames
o Importing Data into R from Different Sources (CSV, Excel, SQL)
o Exploring R Packages: dplyr, ggplot2, caret, and more

2. Data Cleaning and Preprocessing
o Understanding the Data Cleaning Process
o Handling Missing Data: Imputation and Removal Techniques
o Data Transformation with R: Reshaping, Merging, and Aggregating Data
o Working with Dates and Times in R
o Outlier Detection and Handling
o Data Normalization and Scaling Techniques

3. Data Exploration and Visualization
o Introduction to Data Visualization with R
o Creating Basic Plots: Histograms, Box Plots, and Scatter Plots
o Advanced Visualization with ggplot2: Customizing Graphs and Plots
o Exploring Relationships Between Variables
o Visualizing Time Series and Geospatial Data
o Best Practices for Data Visualization: Making Data Insights Accessible

4. Statistical Analysis with R
o Introduction to Statistics for Data Science
o Descriptive Statistics: Mean, Median, Mode, Variance, and Standard Deviation
o Probability Distributions and Hypothesis Testing
o Correlation and Causation: Analyzing Relationships Between Variables
o Statistical Inference and Confidence Intervals
o Regression Analysis: Simple Linear and Multiple Regression

5. Machine Learning with R
o Introduction to Machine Learning Concepts

o Types of Machine Learning: Supervised vs. Unsupervised Learning
o Setting Up and Using the caret Package for Machine Learning
o Building Regression Models in R
o Classification Algorithms: Logistic Regression, Decision Trees, and Random Forests
o Model Evaluation and Performance Metrics (Accuracy, Precision, Recall, F1 Score)

6. Clustering and Dimensionality Reduction
o Introduction to Clustering Algorithms: K-Means, Hierarchical Clustering
o Choosing the Right Number of Clusters with the Elbow Method
o Visualizing Clusters in R
o Introduction to Principal Component Analysis (PCA) for Dimensionality Reduction
o Applying PCA for Feature Selection and Data Compression

7. Advanced Machine Learning in R
o Support Vector Machines (SVM) for Classification and Regression
o Neural Networks and Deep Learning in R
o Building Ensemble Models: Random Forests, Boosting, and Bagging
o Hyperparameter Tuning with Grid Search
o Cross-Validation Techniques for Model Selection
o Model Interpretation and Feature Importance

8. Time Series Analysis in R
o Introduction to Time Series Data and its Components
o Time Series Decomposition: Trend, Seasonal, and Residual Components
o Forecasting with ARIMA and Exponential Smoothing
o Evaluating Time Series Models: MAE, RMSE, AIC, and BIC
o Using R for Stock Market and Financial Data Analysis

9. Text Mining and Natural Language Processing (NLP) with R
o Introduction to Text Mining and NLP Concepts
o Preprocessing Text Data: Tokenization, Stopwords Removal, and Lemmatization
o Sentiment Analysis with R
o Word Clouds and Text Visualization in R
o Topic Modeling with Latent Dirichlet Allocation (LDA)
o Text Classification and Clustering with R

10. Real-World Data Science Projects
o Analyzing Customer Data and Building Predictive Models
o Time Series Forecasting for Business Trends
o Building a Recommendation System for E-commerce
o Performing Sentiment Analysis on Social Media Data
o Developing a Predictive Maintenance System for Manufacturing
o Implementing Machine Learning Models for Healthcare Data

11. Final Project and Certification Exam
o Final Project: End-to-End Data Science Project Using R
o Problem-Solving with Data Cleaning, Analysis, and Machine Learning
o Final Exam: Comprehensive Evaluation of Data Science with R Skills
o Certificate of Completion from ENCODE-IT and Job Placement Assistance

Key Features
 Tools & Platforms: R, RStudio, dplyr, ggplot2, caret, randomForest, ARIMA, and more.
 Real-World Projects: Hands-on experience in building predictive models, time series
forecasting, and text mining.
 Expert Instructors: Learn from professionals with vast experience in data science, machine
learning, and R programming.
 Certification & Placement Support: Industry-recognized certification and job assistance to
help you secure a data science role.
 Comprehensive Curriculum: Covering the essentials from data cleaning to advanced
machine learning and NLP techniques.
 Career Advancement: Equip yourself with the skills to land high-paying jobs in the rapidly
growing data science field.

With ENCODE-IT’s Data Science with R course, you will gain both theoretical knowledge and
practical skills to excel in the world of data science. Start your journey to becoming a proficient data
scientist today!