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About AimAxis:

AimAxis is a leading e-learning platform providing live instructor-led interactive online training. We cater to professionals and students across the globe in categories like Data Science, Artificial Intelligence (AI), R Language, Python, SEO, Digital Marketing, Photoshop, Google Cloud Platform (GCP), Amazon Web Services (AWS), DevOps, SAP-BO-XIR 4.1/R4.2, Data Warehousing, IBM Cognos BI, Informatica Power Center 9.6.1/10.1, OBIEE, MSBI, Power BI, Tableau, Big Data, Hadoop, Business Analytics, Mobile Technologies, System Engineering, Project Management, and Programming. We have an easy and affordable learning solution that is accessible to all of the learners. With our students spread across countries like the US, India, UK, Canada, Singapore, Australia, Middle East, Brazil, and many others, we have built a community of overall learners across the globe.

Introduction to R

  • What is R?
  • Why R?
  • Installing R
  • R environment
  • How to get help in R
  • R console and Editor
  • The understanding R data structure
  • Variables in R
  • Scalars
  • Vectors
  • Matrices
  • List
  • Data frames
  • Using c, Cbind, Rbind, attach and detach functions in R
  • Factors

Importing data

  • Reading Tabular Data files
  • Reading CSV files
  • Importing data from excel
  • Importing data from SAS
  • Accessing database
  • Saving in R data
  • Loading R data objects
  • Writing to files

Manipulating Data

  • Selecting rows/observations
  • Selecting columns/fields
  • Merging data
  • Relabeling the column names
  • Converting variable types
  • Data sorting
  • Data aggregation

Using functions in R

  • Commonly used Mathematical Functions
  • Commonly used Summary Functions
  • Commonly used String Functions
  • User-defined functions
  • local and global variable

R Programming

  • While loop
  • If loop
  • For loop
  • Arithmetic operations

Charts and Plots

  • Box plot
  • Histogram
  • Pareto charts
  • Pie graph
  • Line chart
  • Scatterplot
  • Developing graphs

Statistics for Data Science

  • Introduction to Hypothesis
  • Types of Hypothesis
  • Data Sampling
  • Confidence and Significance Levels
  • Hypothesis Test
  • Parametric Test
  • Non-Parametric Test
  • Hypothesis Tests about Population Means
  • Hypothesis Tests about Population Variance
  • Hypothesis Tests about Population Proportions

Regression Analysis

  • Introduction to Regression Analysis
  • Types of Regression Analysis Models
  • Linear Regression
  • Simple Linear Regression
  • Non-Linear Regression
  • Regression Analysis with Multiple Variables
  • Cross-Validation
  • Non-Linear to Linear Models
  • Principal Component Analysis

Classification

  • Classification and Its Types
  • Logistic Regression
  • Support Vector Machines
  • K-Nearest Neighbours
  • Naive Bayes Classifier
  • Decision Tree Classification
  • Random Forest Classification
  • Evaluating Classifier Models
  • K-Fold Cross Validation

Clustering

  • Introduction to Clustering
  • Clustering Methods
  • K-means Clustering
  • Hierarchical Clustering

Association

  • Association Rule
  • Apriori Algorithm

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