Business Analytics Using SAS Training Course Online Course

Business Analytics using SAS Training Course

All levels
English

Course Description

Business Analytics Using SAS training course is for those who want to enhance their predictive and statistical modeling skills to drive data-informed business outcomes. If modeling data for business outcomes is relevant in your job role or industry, this certificate is a valuable indication of your proficiency. Business Analytics for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression. This course (or equivalent knowledge) is a prerequisite to many of the courses in the statistical analysis curriculum. A more advanced treatment of ANOVA and regression occurs in the Statistics ANOVA and Regression course. A more advanced treatment of logistic regression occurs in the Categorical Data Analysis Using Logistic Regression course and the Predictive Modeling Using Logistic Regression course.

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What you’ll learn

Live Class Practical Oriented Training
Timely Doubt Resolution
Dedicated Student Success Mentor
Certification & Job Assistance
Free Access to Workshop & Webinar
No Cost EMI Option
Use basic statistical concepts on multiple types of data to prepare reports.
Use data sampling techniques to select, manipulate and analyse different data points to identify patterns and trends.
Solve complex problems with R which is the most popular language for data science and an essential tool for Finance and...
Optimise business situations that involve whole numbers, take decisions that involve multiple input variables to predict...
Model decisions under a variety of future uncertain states, depending on the decision maker’s proneness or aversion to r...
Compute correlation between data points in a time series.
Test hypothesis for experiments involving different treatments and Identify the source of differences to pinpoint which...
Model continuous outcomes that depend on more than one input variable.

Covering Topics

Lecture-1 Introduction to Business Analytics, R programming and R Studio

Lecture-2 Understanding Data

Lecture-3 Introduction to R programming and R Studio

Lecture-4 Data Exploration

Lecture-5 Data Manipulation

Lecture-6 Data Import Techniques in R

Lecture-7 Exploratory Data Analysis

Lecture-8 Data Visualization

Lecture-9 Introduction to Statistics

Lecture-10 Machine Learning

Lecture-11 Linear Regression

Lecture-12 Logistic Regression

Lecture-13 Decision Trees and Random Forest

Lecture-14 Unsupervised Learning

Lecture-15 Association Rule Mining & Recommendation Engines

Lecture-16 Time Series Analysis

Lecture-17 Support Vector Machine (SVM)

Lecture-18 Naïve Bayes

Lecture-19 Text Mining

Case Studies

Curriculum

  Lecture-1 Introduction to Business Analytics, R programming and R Studio
Live Lecture 
·      Introduction to Business Intelligence

·      Introduction to Business Analytics

·      Introduction to Data

·      Introduction to Information

·      How information hierarchy can be improved/introduced

·      Understanding Business Analytics and R

·      Knowledge about the R language

·      Its community and ecosystem

·      Understand the use of 'R' in the industry

·      Compare R with other software in analytics

·      Install R and the packages useful for the course

·      Perform basic operations in R using command line

·      Learn the use of IDE R Studio and Various GUI

·      Use the ‘R help’ feature in R

·      Worldwide R community collaboration

·      Practical Exercise

  Lecture-2 Understanding Data
Live Lecture 
·      Importance of data in business analytics

·      Differences between data, information and knowledge

·      The various stages that an organization goes through in terms of data maturity

·      Business Analytics, Business Intelligence and Data Mining

·      Differences between Business Analytics and Business Intelligence

·      Describe the two major components within Business Analytics and Business Intelligence

·      Data Mining technique helps both Business Intelligence and Business Analytics

·      Analytical Decision-Making Process

·      Analysing Business Problems

·      Practical Exercise

  Lecture-3 Introduction to R programming and R Studio
Live Lecture 
·      Installation of rstudio

·      Implementing simple mathematical operations

·      Logic using R operators

·      Loops

·      If statements

·      Switch cases

·      Practical Exercise

  Lecture-4 Data Exploration
Live Lecture 
·      Introduction to data exploration

·      Importing and exporting data to/from external sources

·      What are data exploratory analysis and data importing?

·      Dataframes

·      Accessing individual elements

·      Vectors

·      Factors

·      Operators

·      In-built functions

·      Conditional Looping statements

·      User-defined functions

·      Data types

·      Practical Exercise

  Lecture-5 Data Manipulation
Live Lecture 
·      Need for data manipulation

·      Introduction to the dplyr package

·      Selecting one or more columns with select()

·      Filtering records on the basis of a condition with filter()

·      Adding new columns with mutate()

·      Sampling, and counting

·      Combining different functions with the pipe operator

·      Implementing SQL-like operations with sqldf

·      The various steps involved in Data Cleaning

·      Functions used in Data Inspection

·      Tackling the problems faced during Data Cleaning

·      Uses of the functions

·      Coerce the data

·      Uses of the apply() functions

·      Practical Exercise

  Lecture-6 Data Import Techniques in R
Live Lecture 
·      Import data from spreadsheets and text files into R

·      Import data from other statistical formats

·      Packages installation used for database import

·      Connect to RDBMS from R using ODBC

·      Basic SQL queries in R

·      Basics of Web Scraping

·      Practical Exercise

  Lecture-7 Exploratory Data Analysis
Live Lecture 
·      Understanding the Exploratory Data Analysis(EDA)

·      Implementation of EDA on various datasets

·      Boxplots

·      Whiskers of Boxplots

·      Understanding the cor() in R

·      EDA functions

·      Multiple packages in R for data analysis

·      The Fancy plots like the Segment plot

·      HC plot in R

·      Practical Exercise

  Lecture-8 Data Visualization
Live Lecture 
·      Introduction to visualization

·      Different types of graphs

·      The grammar of graphics

·      The ggplot2 package

·      Categorical distribution with geom_bar()

·      Numerical distribution with geom_hist()

·      Building frequency polygons with geom_freqpoly()

·      Making a scatterplot with geom_pont()

·      Multivariate analysis with geom_boxplot

·      Univariate analysis with barplot, histogram & density plot

·      Multivariate distribution

·      Creating barplots for categorical variables using geom_bar()

·      Adding themes with the theme() layer

·      Visualization with plotly

·      Frequency plots with geom_freqpoly()

·      Multivariate distribution with scatter plots and smooth lines

·      Continuous distribution vs categorical distribution with box-plots

·      Sub grouping plots

·      Co-ordinates and themes

·      Understanding plotly

·      Various plots

·      Visualization with ggvis

·      Geographic visualization with ggmap()

·      Building web applications with shinyr

·      Practical Exercise

  Lecture-9 Introduction to Statistics
Live Lecture 
·      Why do we need statistics?

·      Categories of statistics

·      Statistical terminology

·      Types of data

·      Measures of central tendency

·      Measures of spread

·      Correlation and covariance

·      Standardization and normalization

·      Probability and the types

·      Hypothesis testing

·      Chi-square testing

·      ANOVA

·      Normal distribution

·      Binary distribution

·      Practical Exercise

  Lecture-10 Machine Learning
Live Lecture 
·      Introduction to Machine Learning

·      Practical Exercise

  Lecture-11 Linear Regression
Live Lecture 
·      Introduction to linear regression

·      Predictive modeling

·      Simple linear regression vs multiple linear regression

·      Concepts

·      Formulas

·      Assumptions

·      Residuals in Linear Regression

·      Building a simple linear model

·      Predicting results

·      Finding the p-value

·      Practical Exercise

  Lecture-12 Logistic Regression
Live Lecture 
·      Introduction to logistic regression

·      Logistic regression concepts

·      Linear vs logistic regression

·      Math behind logistic regression

·      Detailed formulas

·      logit function and odds

·      Bivariate logistic regression

·      Poisson regression

·      Building a simple binomial model

·      Predicting the result

·      Making a confusion matrix for evaluating the accuracy

·      True positive rate

·      False positive rate

·      Threshold evaluation with ROCR

·      Finding out the right threshold by building the ROC plot

·      Cross validation

·      Multivariate logistic regression

·      Building logistic models with multiple independent variables

·      Real-life applications of logistic regression

·      An introduction to logistic regression

·      Comparing linear regression with logistics regression

·      Bivariate logistic regression with multivariate logistic regression

·      Understanding the fit of the model

·      Using qqnorm() and qqline()

·      Understanding the summary results with null hypothesis & F-statistic

·      Practical Exercise

  Lecture-13 Decision Trees and Random Forest
Live Lecture 
·      What is classification?

·      Different classification techniques

·      Introduction to decision trees

·      Algorithm for decision tree induction

·      Building a decision tree in R

·      Confusion matrix & regression trees vs classification trees

·      Introduction to bagging

·      Random forest and implementing it in R

·      Computing probabilities

·      Impurity function

·      Entropy

·      Gini index

·      Information gain for the right split of node

·      Overfitting

·      Pruning

·      Re-pruning

·      Post-pruning

·      Cost-complexity pruning

·      Pruning a decision tree and predicting values

·      Finding out the right number of trees

·      Evaluating performance metrics

·      Practical Exercise

  Lecture-14 Unsupervised Learning
Live Lecture 
·      What is Clustering?

·      Its use cases

·      What is k-means clustering?

·      What is canopy clustering?

·      What is hierarchical clustering?

·      Introduction to unsupervised learning

·      Feature extraction

·      Clustering algorithms

·      The k-means clustering algorithm

·      Theoretical aspects of k-means

·      K-means process flow

·      K-means in R

·      Implementing k-means

·      Finding out the right number of clusters using a screen plot

·      Dendograms

·      Understanding hierarchical clustering

·      Implementing it in R

·      Explanation of Principal Component Analysis (PCA)

·      Implementing PCA in R

·      Practical Exercise

  Lecture-15 Association Rule Mining & Recommendation Engines
Live Lecture 
·      Introduction to association rule mining and MBA

·      Measures of association rule mining

·      Introduction to recommendation engines

·      User-based collaborative filtering

·      Item-based collaborative filtering

·      Implementing a recommendation engine in R

·      Recommendation engine use cases

·      Practical Exercise

  Lecture-16 Time Series Analysis
Live Lecture 
·      What is a time series?

·      The techniques

·      Applications

·      Components of time series

·      Moving average

·      Smoothing techniques

·      Exponential smoothing

·      Univariate time series models

·      Multivariate time series analysis

·      ARIMA model

·      Time series in R

·      Sentiment analysis in R

·      Text analysis

·      Practical Exercise

  Lecture-17 Support Vector Machine (SVM)
Live Lecture 
·      Introduction to Support Vector Machine (SVM)

·      Data classification using SVM

·      SVM algorithms using separable and inseparable cases

·      Linear SVM for identifying margin hyperplane

·      Practical Exercise

  Lecture-18 Naïve Bayes
Live Lecture 
·      What is Naive Bayes?

·      What is the Bayes theorem?

·      What is Naïve Bayes Classifier?

·      Classification Workflow

·      How Naive Bayes classifier works

·      Classifier building in Scikit-Learn

·      Building a probabilistic classification model using Naïve Bayes

·      The zero probability problem

·      Practical Exercise

  Lecture-19 Text Mining
Live Lecture 
·      Introduction to the concepts of text mining

·      Text mining use cases

·      Understanding and manipulating the text with ‘tm’ and ‘stringr’

·      Text mining algorithms and the quantification of the text

·      TF-IDF and after TF-IDF

·      Practical Exercise

  Case Studies
Case Studies

Frequently Asked Questions

basic understanding of Computer Programming Languages.

The course offers a variety of online training options, including: Live Virtual Classroom Training: Participate in real-time interactive sessions with instructors and peers. 1:1 Doubt Resolution Sessions: Get personalized assistance and clarification on course-related queries. Recorded Live Lectures*: Access recorded sessions for review or to catch up on missed classes. Flexible Schedule: Enjoy the flexibility to learn at your own pace and according to your schedule.

Live Virtual Classroom Training allows you to attend instructor-led sessions in real-time through an online platform. You can interact with the instructor, ask questions, participate in discussions, and collaborate with fellow learners, simulating the experience of a traditional classroom setting from the comfort of your own space.

If you miss a live session, you can access recorded lectures* to review the content covered during the session. This allows you to catch up on any missed material at your own pace and ensures that you don't fall behind in your learning journey.

The course offers a flexible schedule, allowing you to learn at times that suit you best. Whether you have other commitments or prefer to study during specific hours, the course structure accommodates your needs, enabling you to balance your learning with other responsibilities effectively. *Note: Availability of recorded live lectures may vary depending on the course and training provider.