Data Science With R Training

Dev Technology Solutions provides the best data science with R training in Chennai. Taught by experienced professionals, our course is structured with the latest topics. We use real time implementation techniques and practical exercises to help develop a better understanding.

R is an important tool for data scientists. You can become an expert in the field of data collection and analysis with our training. An ever-growing field, data science with R can open up a field of job opportunities for you.

Data Science With R Training Syllabus:

• Introduction to Data Science
• What is Data Science?
• Discuss about of Data Science
• Life cycle of Data Science
• Introduction to Business Intelligence
• Discuss about Data Analysis, Data Mining and Machine Learning
• Explain about Big Data and Machine Learning concepts
• Overview of Statistics
• Overview of Statistics
• Types of Data
• Explain about Correlation, Covariance & Causation
• Standardization & Normalization
• Discuss about Probability & Types of Probability
• Understanding of Probability Distributions
• Discuss about Central Limit Theorem
• Analysis of Variance (ANOVA)
• Exploratory Data Analysis(EDA) and Data Visualization
• Overview of EDA
• Discuss about Data Collection, Descriptive statistics and Data Mining
• Working with Data Import and Data Frames
• Conditional and looping statements
• User-defined functions, matrix, list and array
• Understanding of Variables, Transformation
• Discuss about Standardization and Normalization
• Understanding of Validation and Interpretation
• Introduction to Distributions
• Graphs and Bar Charts
• Box-whisker plot and Scatter plot
• Pie Charts and Bubble Charts
• R Programming Language
• Introduction to R
• Installation of R-studio
• Discuss about Data Types, Operators and Directories
• Understanding of Loops-For and While
• Explain about If-else statements and Nested statements
• Working with Objects, and Vectors
• Discuss about Strings, Arrays, Lists, Factors
• Implementation of Data Frames and Pipe Operator
• Working with Predefined and User defined functions
• List of R functions(apply, l-apply, s-apply, m-apply, t-apply, v-apply Subset/filter, which, sample, match, sort, mutate, grep, summary,gsub, select, groupby, gather, separate and Posixct)
• Discuss about Joins (Inner, Outer, Left, Right, Semi, Anti) in Data Frames
• Analysis of Univariate Analysis and Dplyr, Lubridate, Tibble
• Data manipulation language in SQL
• Overview of SQL and Databases
• Overview of SQL developer tools and usage
• Discuss about SQL Data types and Operators
• Create and Drop database
• Explain about DDL,DML, DCL ,TCL and Sorting commands
• Understanding of SQL-Wild cards, Constraints, Joins, Unions, Index, Views and Sub queries
• Machine Learning using R
• Overview of Machine Learning
• Supervised Learning and Algorithms
• Steps of Supervised learning
• Determine the co-relation, coefficient and Analysis
• Introduction to Regression & Classification
• Discuss about Predictive modeling with Regression
• Concepts and Formulas with Linear Regression
• Example of simple linear model
• Implementation of predicting results and finding pvalue
• Overview of logistic regression
• Explain about bivariate & multi-variate regression
• Evaluation of threshold with ROCR
• Understanding the model Prediction
• Summary results and p-value & F-statistic