BUSINESS ANALYTICS

BUSINESS ANALYTICS TRAINING

Excel Basics and Excel for Data Analysis

  •  Introduction to MS Excel
  •  Why MS Excel
  •  Functionalities for a Data Scientist
  •  Using Excel
  •  Data Analysis in Excel
  •  Basic Data Manipulation Functions
  •  Mean, Maximum, Round, Sum etc.
  •  Statical Functions
  •  Filter, Sort, Lookup in Excel
  •  Using Pivots in Excel
  •  Creating Pivot Tables and Charts
  •  Usage of Slicers
  •  Plotting in Excel – Usage of Visualization Capabilities

R Basics and R for Data Analysis

  •  Introduction to Programming
  •  Introduction to R and RStudio
  •  What is R?
  •  What is Open Source?
  •  Capabilities of R
  •  GUI for R
  •  R IDE -   RStudio
  •  Installation and Functioning of R and Rstudio
  •  Using R
  •  R interface
  •  R Session
  •  R Console
  •  Getting Help
  •  Entering and Running Commands/Programs
  •  Programming in R
  •  Data Types
  •  Operators in R
  •  Data Input and Output
  •  R Data Frames
  •  R statistics – Mean, Median, Mode etc.
  •  Data Manipulation in R – Counting, Merging, Append, Sort, Subset, Filter, New Variable Creation etc.
  •  R Logical Statements - If/ else, Loops etc.
  •  Plotting- Graphs and Charts
  •  Packages in R- Details of the most commonly used packages
  •  Functions in R (High Level)
  •  R- Best Practices

Statistics

  •  What is Statistics
  •  Data Types
  •  Qualitative vs. Quantitative
  •  Basic Operations Based on Data Type
  •  Variables
  •  Measurement Scales
  •  Measures of Variance
  •  Measures of Central Tendency
  •  Correlation vs. Causation (Correlational vs. Experimental Research
  •  Sampling – Usage of Sampling
  •  Distributions
  •  Normal Distribution
  •  Why the "Normal distribution" is Important
  •  Illustration of How the Normal Distribution is Used in Statistical Reasoning
  •  Characteristics
  •  Standard Normal Distribution
  •  Central Limit Theorem
  •  Hypothesis Testing
  •  What is Hypothesis Testing?
  •  The magic of Hypothesis Testing
  •  Null and Alternate Hypothesis
  •  P Value.
  •  Usage of Hypothesis Testing in Business Problems
  •  Explanation of Hypothesis Testing Using Real World Example
  •  Types of Hypothesis Testing
  •  Z test
  •  T test
  •  Chi Square test
  •  Introduction to ANOVA and Basics of Regression/Classification

Linear Regression Topic Details

  •  Introduction to Simple Linear Regression
  •  Graphical Understanding of Regression (Scatter Plot, Box Plot, Density Plot)
  •  Example Problem and Mathematics behind Regression
  •  Assumptions for Linear Regression
  •  Correlation (Linear and Non Linear
  •  Introduction to Multiple Linear Regression.
  •  Building A Regression Model (Steps to Establish a Regression
  •  Data Preparation – Data Audit, Missing Value and Outliers
  •  Building the model
  •  Linear Regression – Interpretation of  Output and Diagnostics
  •  Assessing the Coefficients
  •  P Value - Checking for Statistical Significance
  •  R-Square and Adjusted R Squared
  •  Standard Error and F-Statistic
  •  How to Know if the Model is Best Fit for Your Data?
  •  Using Linear Model for Predictions
  •  Checking Accuracy and Error Rates
  •  Heteroskadisticity
  •  Model Improvement
  •  Over-fitting and Cross Validation
  •  Multicollinearity and VIF
  •  Do it Yourself Case
  •  Flavor of Advanced Regression Models

Logistic Regression

  •  Why Logistic Regression
  •  Introduction to Classification and Challenges with Linear Regression
  •  Event Rate and Class Bias
  •  Example Problem (Some real world examples of Binary Classification problems),Mechanics and Mathematics behind Logistic Regression
  •  Assumptions for Logistic Regression
  •  Building a Logistic Regression Model.
  •  Data Preparation – Data Audit, Missing Value and Outliers.
  •  Variable Importance and Feature Extraction
  •  Create WOE for Categorical Variables
  •  Compute Information Value
  •  Compute Information Value.
  •  OMulticollinearity (VIF)
  •  Building Logit Models
  •  Predictions
  •  Logistic  Regression – Interpretation of  Output
  •  Coefficients
  •  Variable Importance
  •  Model Diagnostics
  •  Misclassification Error and Confusion Matrix
  •  ROC Curve
  •  Accuracy
  •  Specificity, Sensitivity and F Score/li>
  •  Lift/Gain Charts and KS Curve
  •  Model Improvement
  •  Over-fitting and Cross Validation
  •  Flavor of Advanced Classification Concepts – Classification of Unstructured Data
  •  Do it Yourself Case

Time Series Modeling

  •  Introduction to Time Series
  •  Difference between Time Series, Cross-Sectional and Pooled Data
  •  Example Problem (Some real world examples of Time Series Problems), Mechanics and Fundamental of Mathematics behind  Time series Analysis
  •  Assumptions for Time Series analysis
  •  Understanding Time Series Data
  •  Visualizing Time Series Data
  •  Stationary vs. No Stationary Data.
  •  Trend vs Seasonality vs White Noise
  •  Decomposing Time Series Data
  •  Decomposing Non-Seasonal Data
  •  Decomposing Seasonal Data
  •  Seasonally Adjusting
  •  Forecasts using Exponential Smoothing
  •  Simple Exponential Smoothing
  •  Holt’s Exponential Smoothing
  •  Holt-Winters Exponential Smoothing
  •  ARIMA Models
  •  Concept of Auto-Correlation and Partial Auto Correlation
  •  Differencing a Time Series
  •  Selecting a Candidate ARIMA Model
  •  Forecasting Using an ARIMA Model
  •  Predictions and Diagnostics
  •  Advanced Time Series Concepts
  •  Do it Yourself Case

Market Basket Analysis

  •  Supervised, Unsupervised and Semi-supervised Algorithms
  •  Concept of a Recommendation Engine
  •  Example Problem (Real world examples of MBA applications
  •  MBA Hyper Parameters
  •  Lift
  •  Confidance
  •  Support
  •  Generating output using Association rules
  •  Filtration of Rules
  •  Removal of Redundant Rules
  •  Control the Rules
  •  Finding rules for Particular Entity
  •  Visualizing Rules
  •  Challenges with Association Rules and Ways to Overcome
  •  Advanced Recommendation Engine Concepts
  •  Do it Yourself Case

Decision Trees

  •  Type of Classification Algorithms
  •  Fundamentals of Tree bases Systems
  •  Decision Boundary of Tree based Algorithms
  •  Types of Tree Algorithms
  •  C4.5
  •  CHAID
  •  CART
  •  Concept of Impurity Measure
  •  GINI
  •  Chi Square
  •  Building a Decision Tree Model
  •  Data Preparation – Data Audit, Missing Value and Outliers
  •  Creating Test and Training Samples
  •  Variable Importance and Feature Extraction
  •  Prediction using Decision Trees
  •  Over fitting and Cross Validation
  •  Flavor of Advanced Concepts in Trees (Random Forests)

K-Means Clustering

  •  Unsupervised Algorithms and Introduction to Clustering
  •  Intro to Distance based Algorithms
  •  Example Problem (Some real world examples of Clustering Applications
  •  Assumptions for Clustering
  •  Mechanics of Clustering
  •  Type of Measure- Euclidean, Manhattan, Jaccard
  •  How are Clusters Generated
  •  Creating Clusters
  •  Standardization of Inputs
  •  Deciding the Number of Clusters – Elbow Curve and Silhouette Distance
  •  Understanding the Output
  •  Cluster Diagnosis
  •  Profiling
  •  Advanced Clustering Concepts

Soft Skill Training

  •  Interpersonal Communication
  •  Introduction of Interpersonal Relations
  •  Johari Windows
  •  Listening skills  
  •  Closure
  •  Presentation skills I and II
  •  Different Stages of Making a Presentation –  Analysis, Thinking, Data, Creativity Needed at Each Stage
  •  Plan before Execution
  •  Plan Speech – Structures of Presentation, Numerical Prsentation, Visual Aids
  •  Requirements for Impactful Delivery
  •  Email and virtual communication
  •  Intent in Virtual Communication – NTPL Activity
  • Basics of Email Etiquette + Case Sudies 
  •  Digital Empathy
  •  Closure
  •  Assertiveness & Communication
  •  Introduction to Assertiveness
  •  Assertive, Non-assertive, and Aggressive (Inner Dynamics and Outer Behavior)
  •  Reflection Sheet 
  •  Assertiveness Technique and Role Plays 
  •  Recap 
  •  Interview Skills

Trainer Profile of Business Analytics 

  • Our Trainers provide complete freedom to the students, to explore the subject and learn based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates are free to ask any questions at any time.
  •  More than 7+ Years of Experience.
  •  Strong Theoretical & Practical Knowledge.
  •  Certified Professionals with High Grade.
  •  Expert level Subject Knowledge and fully up-to-date on real-world industry applications
  •  Our Trainers are working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies etc