Data Science with R Course Syllabus
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Our Data Science with R Training in Chennai provides extensive training in R programming, data manipulation, statistical analysis, and practical application development. Gain expertise in exploring and visualizing data using R libraries such as ggplot2 and dplyr, and constructing predictive models to derive valuable business insights. Hands-on projects and industry-focused training ensure you acquire the practical skills necessary for real-world scenarios. With Data Science with R Course with 100% Placement Support, you’ll be well-prepared for a rewarding career in data science. This training not only enhances job readiness but also offers placement assistance, ensuring you can secure promising opportunities in this dynamic and expanding field of data analytics.
Course Syllabus
Download SyllabusIntroduction to Data Science
- What is Data Science?
- What is Machine Learning?
- What is Deep Learning?
- What is AI?
- Data Analytics & it’s types
Introduction to R
- What is R?
- Why R?
- Installing R
- R environment
- How to get help in R
- R Studio Overview
R Basics
- Environment setup
- Data Types
- Variables Vectors
- Lists
- Matrix
- Array
- Factors
- Data Frames
- Loops
- Packages
- Functions
- In-Built Data sets
R Packages
- DMwR
- Dplyr/plyr
- Caret
- Lubridate
- E1071
- Cluster/FPC
- Data.table
- Stats/utils
- ggplot/ggplot2
- Glmnet
Importing Data
- Reading CSV files
- Saving in Python data
- Loading Python data objects
- Writing data to CSV file
Manipulating Data
- Selecting rows/observations
- Rounding Number
- Selecting columns/fields
- Merging data
- Data aggregation
- Data munging techniques
Statistics Basics
- Central Tendency
- Mean
- Median
- Mode
- Skewness
- Normal Distribution
Statistics Basics
- What does it mean by probability?
- Types of Probability
- ODDS Ratio?
- Data deviation & distribution
- Variance
- Underfitting
- Overfitting
- Euclidean Distance
- Manhattan Distance
- What is an Outlier?
- Inter Quartile Range
- Box & whisker plot
- Upper Whisker
- Lower Whisker
- Scatter plot
- Cook’s Distance
- What is an NA?
- Central Imputation
- KNN imputation
- Dummification
- Pearson correlation
- Positive & Negative correlation
Error Metrics
- Classification
- Confusion Matrix
- Precision
- Recall
- Specificity
- F1 Score
- Regression
- MSE
- RMSE
- MAPE
Machine Learning
Supervised Learning
- Linear Regression
- Linear Equation
- Slope
- Intercept
- R square value
- Logistic regression
- ODDS ratio
- Probability of success
- Probability of failure
- ROC curve
- Bias Variance Tradeoff
Unsupervised Learning
- K-Means
- K-Means ++
- Hierarchical Clustering
Machine Learning using R
- Linear Regression
- Logistic Regression
- K-Means
- K-Means++
- Hierarchical Clustering – Agglomerative
- CART
- 5.0
- Random forest
- Naïve Bayes
