Data Science With R Programming Course Syllabus

Introduction

With our data science with R programming course syllabus, you can easily learn how to use R for data science, from data manipulation and machine learning and thrive as a pro data scientist. Our course content will also help you gain the career-building R skills you need to succeed as a data professional. Our curriculum will also equip yourself with the basic as well as advanced concepts of data science along with necessary R skills to start your career as a data analyst. Our first class data science with R programming course syllabus along with expert training will definitely take your career to the next level. Our data science with R programming course in Chennai comes with 100% placement assistance and data science certification.

Data Science With R Programming Course Syllabus

Data Science – An Overview

• What do you know about Data Science?
• How is Machine Learning useful for Data Science?
• Is Deep Learning important?
• What is Data Analytics?
• Types of Data Analytics

An Overview of R Programming

• A Brief note about R
• How do we need to Install R?
• How do we need to install R Studio?
• Deep Dive on usage of R in the real world

Deep Dive on R

• What is Vectors?
• How to create Vectors?
• How to use [] brackets?
• Vectorized Operations
• Functions in R
• Packages in R

Matrices

• What do you know about Matrices?
• How to build your First Matrix?
• How to Name Dimensions?
• Colnames() and Rownames()
• Matrix Operations
• How to Visualize with Matplot()
• Create your first function

Data Frames

• Import Data using R
• Explore your Data Set
• How to use the $ Sign?
• Basic Operations with a Data Frame
• Filter a Data Frame
• An Overview of qplot
• How to Build Data Frames?
• How to Merge Data Frames?

Data Manipulation Techniques Using R Programming

• Data In R
• Reading And Writing Data
• R And Databases
• Dates
• Factors
• Subscribing
• Character Manipulation
• Data Aggregation
• Reshaping Data

Statistical Applications Using R Programming

• Basics
• The R Environment
• Probability And Distributions
• Descriptive Statistics And Graphics
• One- And Two-Sample Tests
• Regression And Correlation
• Analysis Of Variance And The Kruskal–Wallis Test
• Tabular Data
• Power And The Computation Of Sample Size
• Advanced Data Handling
• Multiple Regression
• Linear Models
• Logistic Regression
• Survival Analysis
• Rates And Poisson Regression
• Nonlinear Curve Fitting

Data Visualization Techniques

• Bubble Chart
• Sparklines
• Waterfall chart
• Box Plot
• Line Charts
• Frequency Chart
• Bimodal & Multimodal Histograms
• Histograms
• Scatter Plot
• Pie Chart
• Bar Graph
• Line Graph

Introduction to Machine Learning

• Overview & Terminologies
• What is Machine Learning?
• Why Learn?
• When is Learning required?
• Data Mining
• Application Areas and Roles
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement learning

 Machine Learning Concepts & Terminologies

Steps in developing a Machine Learning application

• Key tasks of Machine Learning
• Modelling Terminologies
• Learning a Class from Examples
• Probability and Inference
• PAC (Probably Approximately Correct) Learning
• Noise
• Complexity-Noise and Model
• Triple Trade-Off
• Association Rules
• Association Measures

Regression Techniques

• Concept of Regression
• Best Fitting line
• Simple Linear Regression
• Building regression models using excel
• Coefficient of determination (R- Squared)
• Multiple Linear Regression
• Assumptions of Linear Regression
• Variable transformation
• Reading coefficients in MLR
• Multicollinearity
• VIF
• Methods of building Linear regression model in R
• Model validation techniques
• Cooks Distance
• Q-Q Plot
• Durbin- Watson Test
• Kolmogorov-Smirnof Test
• Homoskedasticity of error terms
• Logistic Regression
• Applications of logistic regression
• Odds concept
• Concept of Odds Ratio
• Derivation of logistic regression equation
• Interpretation of logistic regression output
• Model building for logistic regression
• Model validations
• Confusion Matrix
• Concept of ROC/AOC Curve
• KS Test

Statistical Modelling in R

• Logical Regression
• Hierarchical Clustering PCA for Dimensionality Reduction

Conclusion

With the help of our data science with R programming course content, you will learn how to program with R in order to explore and extract data and create data visualizations. With our expert training, you will also be capable of working independently to resolve data management issues by creating your tailored and reusable programs in the data science area. With dedicated teaching practices and expert mentoring techniques, our cutting-edge course curriculum will help develop your career-ready skills. Simply join our data science with R programming training in Chennai and build expertise in the whole data science concepts. If you work hard with us and follow our course syllabus, you will master data skills and get ahead in your career.