In this section, we will be discussing the data warehousing concepts, concepts of Data Visualization, Big Data, Machine Learning, NoSQL, Introduction to R

This section will cover installing R and R Studio in Windows or Ubuntu, or Mac.

In this section, we will discuss Types of Data Types in R Programming such as Numeric, Integer, Logical, Raw, Data Frames, Matrix, etc., Operators such as Arithmetic, Logical, Relational, etc. Assignment and Strings such as String Lengths, Substring, Uppercase and Lowercase.

In this topic, we will be covering Date, Time, how to format Date and Time, Conversion of Date and Time, Extraction of Date Components and Time Zones in R Programming.

In this section, we will be discussing various control statements such as If, If Else, Else If, and Nested if. Furthermore, we will be discussing repeat, while and for loops in R Programming.

In this section, we will be discussing several Built-in Functions in R Programming, such as Sum, Min, Max, Count, Average, Sequence, Repetition, Range, etc.

This section will discuss how to write user-defined functions, functions with parameters, parts with default values, call functions by position and name, lazy evaluation, etc.

This section will discuss the creation of vectors, lists, and matrices. Furthermore, we will be deep diving into these concepts, such as sub-setting using index position or names and performing arithmetic operations.

In this section, we will be discussing data frames in detail. We will be covering topics such as data frame creation, structuring data frames, a summary of data frames, how to apply functions, filters, aggregation, joins, and many more.

In this section, we will be discussing what a regular expression, available regular expression patterns are, and apply regular expression patterns using built-in functions, such as Grep, Grepl, Regexpr, Gregexpr, Regexec.

In this section, we will discuss how to read and write CSV files, Excel Files, JSON files, text files, databases, Rest API, XML files, SAS, SPSS, STATA Data Sets, and Web Scrapping.

This section will discuss what R Packages are, various available R Packages such as SQLDF, PLYR, DPLYR, Data Tables, and how to install R packages.

In this section, we will be discussing how to produce Bar Charts, Pie Charts, Boxplots, Histograms, Line Graphs, Scatterplots, Google Visualization, and ggPlot2.

In this section, we will be discussing various statistical functions such as mean, median, mode, regression, distribution, etc.

In this section, candidates will be taught what data science is, how to get insights from the data, understand machine learning, how it works, and the role of a data scientist. Ideally, candidates will be getting a big picture about data science, machine learning and how to apply the machine learning concepts in data sets.

Before applying Machine Learning algorithms to a data set, it is mandatory to perform data cleansing, including fixing missing values, outliers, categorical data manipulation, and feature scaling. Furthermore, candidates will acquire knowledge and know-how in splitting data into train and tests.

In this section, candidates will learn what regression, when and where the regression algorithms can be applied is, and various regression algorithms such as simple linear regression, multiple regression, support vector regression, polynomial regression, random forest and decision tree. Candidates will learn how to choose predictor and dependent variables and know how to create a machine learning model on a data set. Furthermore, candidates will learn to evaluate machine learning models using machine learning error metrics.

In this section, candidates will learn what classification is, when and where the classification algorithms can be applied, various classification algorithms such as logistic regression, support vector machine classification, Naïve Bayes classification, and K-Nearest Neighbours (K-NN) algorithm, random forest and decision tree. Candidates will learn how to choose predictor and dependent variables and know how to create a machine learning model on a data set. Furthermore, candidates will learn to evaluate machine learning models using machine learning error metrics.

In this section, candidates will learn what clustering, when and where the clustering algorithms can be applied is, and various clustering algorithms such as K-means Clustering and Hierarchical Clustering. Candidates will learn how to choose cluster size. Furthermore, candidates will learn to evaluate machine learning models using machine learning error metrics.

In this section, candidates will learn how to build a market basket machine learning model. Furthermore, candidates will learn to evaluate machine learning models using machine learning error metrics.

This section will teach candidates how to build a machine learning model for scenarios with minimal data sets.

In this section, candidates will learn to gather features from the text files and create a sentiment analysis model.

In this section, candidates will learn to choose how to choose a suitable feature (variable) while building a machine learning model.

This section will teach candidates how to evaluate machine learning models using machine learning error metrics such as K-fold cross-validation and Grid Search techniques.

Once they complete the course, candidates will be provided with a project that they must complete applying the knowledge gathered during the training.