Business Analytics Course Details

Statistical TechniquesDifferent types of data, Data summerization, Frequency table, Frequency Distributions,Histogram, Measures of central tendency and dispersion, Skewnesss and kurtosis, Basic Probability, Conditional Probability, Normal Distribution, Sampling methods, Point and Interval estimation, Central Limit Theorem, Nul and alternative hypothessis, Level of significance, P value, Types of errors, Hypothesis Testing
 Predictive Analytics: Linear and Multilinear RegressionSimple and Multiple Linear Regression, R2 and Adj R2, ANOVA, Interpretation of coefficients, Dummy Variables, Residual Analysis, Outliers, Logistic Regression, Assumptions, Logistic Function, Chi-Square, Hosmer Lemeshow test, Kolmogorov-Smirnov statistic and chart, Classification Table, Interpreting Coefficients, Dependent Variable Prediction
Predictive Analytics: Forecasting (Time Series)Principles of Forecasting, Time Series, Causal models, Types of Forecasting Methods and their characteristics, Moving Average, Exponential Smoothing, Trend, Seasonality, Cyclicity, Holt Winter's forecasting method.
Data Mining Techniques: Market Basket AnalysisBasic concepts, Frequent Itemset Mining Methods, Apriori, FPGrowth, Pattern Evaluation Methods: Lift, Chi –Square,
Data Mining Techniques: ClassificationClassification, Decision Tree Induction, Bayes Methods, Rule-Based Classification, Model Evaluation and Selection, Ensemble Approaches
Data Mining Techniques: ClusteringPartitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods, Evaluation of Clustering, Kmeans Method.
Excel ProficiencyFormatting of Excel Sheets, Use of Excel Formula Function , Advanced Modeling Techniques, Data Filter and Sort , Charts and Graphs, Table formula and Scenario building, lookups, pivot tables
Application of concepts using R and SASReading and writing data in R, Vectors, Frames and Subsets, Code Writing and R code Debugger, Managing and Manipulating Data in SAS, Creating Charts in SAS, Simple Linear Regression in SAS, Multiple Linear Regression in SAS, Data Mining in SAS
Orientation on Big Data and Hadoop

Awareness of Big Data and Hadoop, Why is it relevant? The four V’s, Is Big Data = Hadoop?, Big Data and Cloud Computing, Generators of Big Data, Applications of Big Data

Web Analytics and Mobile BIExposure to Web and Mobile Analytics with focus on: Text Analytics, Sentiment Analytics, Click Analytics, Google Analytics, Difference between Web and Mobile Analytics
Case StudiesPopulation census, Marketing, Banking, Retail, Industrial and Telecom domain case studies- Cleaning data, Mining patterns, Making models, Model selection and validation.
Business Analytics - NSE India (NCFM) Certification Exam
After completion of above topics students have to take the Business Analytics Certification Exam. After clearing the exam, Proschool will provide additional training (Online or Live Virtual Classroom) without any additional charges on the below topics (Maximum 3) depending upon the job requirements.

Base SAS

Overview, SAS statements, Comments, Data types, Data steps & Proc steps
Importing and exporting data, Data transformation and manipulation, 
Formats and Informats, 
Advanced data manipulation, Conversion of variables, 
SAS Macro, SAS SQL, Basic SAS procedures,
Statistical analysis in SAS – Regression, Time series, Clustering and Market Basket Analysis.
Tableau – 
(Data Visualization tool) 
Extracting data into Tableau, 
Data Preparation, Dimensions, 
Transformation of variables, 
Creating Views, 
Working with charts, 
Exporting visualizations, 
Project Work
Text Analytics (Application)
Difference between Structured & Unstructured Data,
Typical use cases of Text Analytics, Sentiment analysis,
Scrapping some data from the web, 
Working with a static dump of Movie review data, 
Cleaning the data, Handling the NA's and Stop words,
Using the sentiment package in r, 
Error handling, 
Classify sentiments, Classify polarity, 
Using gplot for visualization, 
Building the word cloud
Introduction to Databases 
Terminologies - Records, Fields, Tables 
Introduction to database normalisation 
Primary Key
How data is accessed
Introduction to SQL  
SQL Syntax
SQL data Types
SQL Operators
Table creation in SQL : Create, Insert, Drop , delete and updating 

Introduction to SQL  - Table access & Manipulation 
Select with Where Clause (In between, logical operators, wild cards, order, group by)
SQL constraints
Concepts of Join - Inner, Outer

Case study
Capstone Project
Benefits of Capstone Project:

At the end of this Capstone Project, you'll be able to make sense of the given data and gain insights on how to use Analytical techniques effectively to address the business challenge.

Once you've completed the project, you'll be better able to apply analytical techniques on a business case and accordingly prepare a detailed report.

In case you don't have any relevant experience in Analytics, this project will enable you to showcase your expertise in a job interview.