Business Analytics Course - Business Analytics 360!

Power-packed Business Analytics course for beginners who want start their career in Analytics & Data Science!

This business analytics training is for beginners who want to start from basics of Excel, VBA, Tableau moving to advanced tools like SAS, R business analytics and data science, including machine learning. Evolved from our most popular course SAS + Business Analytics training, this is the best  business analytics course in India curated for candidates who are looking for job oriented business analytics certification but have no prior knowledge of any business intelligence or data analytics tools. Most extensive business analytics course in Bangalore and Delhi NCR, with flexibility of also attending the live online training and self-paced video based mode as well. 


However, learning tools without techniques is half the job done in today's Analytics world. So to help candidates emerge as 'Industry Ready' professional, this Business Analytics course in Indida encompasses basic statistical concepts to advanced analytics and predictive modelling techniques, along with machine learning, using most widely used analytics tools, like Excel, Tableau, R, SAS (including Proc SQL). This analytics certification course is for all those aspirants who want to switch into the field of data science and begin their career as business analyst.


One of the best Business Analytics online course in India and is also available in Delhi NCR and Banaglore, with aim to provide you job oriented Data Science and Business Analytics skills.


Business Analytics training 'Business Analytics 360' Duration: 415 hours (At least 144 hours live training + 30 hours video based module + around 10 hrs of weekly self-study and practice)

Who Should do this course?

Beginner candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Business Management who are looking for Business Analytics training to start their career in the field of Analytics and Data Science.

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Course Duration 415 hours
Classes 50
Tools Excel, VBA, Tableau, SAS, R
Learning Mode Live/Video Based
Next Batch10th June, 2018 (Gurgaon)
27th May, 2018 (Bangalore)

Introduction to Excel

  • Introduction to Excel Environment
  • Explanation about data calculation in Excel
  • Use of Shortcuts
  • Formatting and Conditional Formatting
  • Working with Formulas - Logical and Text Functions
  • Understanding about Sorting, Filtering and Data Validation
  • Data Analysis using Pivot Tables

Introduction to Charts and Functions

  • Understaing of Mathematical, Statistical Functions
  • Worksheet and Workbook Protection and Security
  • Understanding of Name Ranges
  • Introduction of Charts
  • Introduction of Form Controls
  • Understanding of Data Tools Panel
  • Basics of Macro Recording

Dashboard Designing

  • Overview of Dashboards
  • Deciding on Dahsboards
  • Trends and Scenarios using charts
  • Advanced Charting Techniques using Thermometer, Doughnut, Pareto, Panel and Step Chart
  • Designing Sample Dashboard using Form Controls
  • Tips and Tricks to enhance dashboard designing

Introduction to VBA

  • Working with VBE (Visual Basic Editor)
  • Introduction to Excel Object Model
  • Understanding of Sub and Function Procedures
  • Key Component of Programming Language
  • Understaing of If, Select Case, With End With Statements
  • Looping with VBA
  • User Defined Function
  • Some Commonly Used Macro Examples
  • Error Handling
  • Object and Memory Management in VBA
  • User Form Controls
  • ActiveX Controls
  • Communicating with Database MS Access through ADO - Exporting/Importing Data

Tableau: Getting started with Tableau

  • What is Tableau? What does the Tableau product suite comprise of? How Does Tableau Work?
  • Tableau Architecture
  • What is My Tableau Repository?
  • Connecting to Data & Introduction to data source concepts
  • Understanding the Tableau workspace
  • Dimensions and Measures
  • Data Types & Default Properties
  • Tour of Shelves & Marks Card
  • Using Show Me!
  • Building basic views
  • Saving and Sharing your work-overview

Tableau: Building Views (Reports) – Basics

  • Date Aggregations and Date parts
  • Cross tab & Tabular charts
  • Totals & Subtotals
  • Bar Charts & Stacked Bars
  • Line Graphs with Date & Without Date
  • Tree maps
  • Scatter Plots
  • Individual Axes, Blended Axes, Dual Axes & Combination chart
  • Edit axis
  • Parts of Views
  • Sorting
  • Trend lines
  • Reference Lines
  • Forecasting
  • Filters
  • Context filters
  • Sets
    • In/Out Sets
    • Combined Sets
  • Grouping
  • Bins/Histograms
  • Drilling up/down – drill through
    • Hierarchies
    • View data
    • Actions (across sheets)

Tableau: Building Views (Reports) – Advanced Maps

  • Explain latitude and longitude
  • Default location/Edit locations
  • Symbol Map & Filled Map
  • Custom Geo Coding

Tableau: Calculated Fields

  • Working with aggregate versus disaggregate data
  • Explain - #Number of Rows
  • Basic Functions (String, Date, Numbers etc)
  • Usage of Logical conditions

Tableau: Table calculations

  • Explain scope and direction
  • Percent of Total, Running / Cumulative calculations

Tableau: Parameters

  • Create What-If analysis
  • Using Parameters in
    • Calculated fields
    • Bins
    • Reference Lines
    • Filters/Sets
  • Display Options (Dynamic Dimension/Measure Selection)

Tableau: Building Interactive Dashboards- (Building & Customizing)

  • Combining multiple visualizations into a dashboard (overview)
  • Making your worksheet interactive by using actions
    • Filter
    • URL
    • Highlight

Tableau: Working with Data

  • Multiple Table Join
  • Data Blending
  • Difference between joining and blending data, and when we should do each
  • Working with the Data Engine / Extracts
  • Toggle between to Direct Connection and Extracts

Introduction to the Analytics world and SAS role

Analytics World

  • Introduction to Analytics
  • ETL CONCEPT and role of SAS in ETL
  • SAS in advanced analytics
  • SAS Certification: Induction and walk through

Getting Started with SAS

  • SAS software installation
  • Introduction to SAS, GUI
  • Different components of SAS language
  • All SAS programming windows
  • Concept of SAS Libraries and Creating Libraries
  • Variable Attributes - (Name, Type, Length, Format, In format, Label)
  • Importing Data and Entering data manually

Understanding Datasets

  • Descriptor Portion of a Dataset (Proc Contents)
  • Data Portion of a SAS Dataset
  • Variable Names and Values
  • SAS Data Libraries

SAS: Accessing the Data

Understanding Data Step processing

  • Data Step and Proc Step
  • Data step execution
  • Compilation and execution phase
  • Input buffer and concept of PDV

Importing Raw Data files

  • Column Input and List Input and Formatted methods
  • Delimiters, Reading missing and non standard values
  • Reading one to many and many to one records
  • Reading Hierarchical files
  • Creating raw data files and put statement
  • Formats / Informat

Importing and Exporting Data (Fixed Format / Delimited)

  • Import Wizard
  • Proc Import / Delimited text files
  • Proc Export / Exporting Data from SAS
  • Datalines / Cards
  • Atypical importing cases (mixing different style of inputs)
    • Reading Multiple Records per Observation
    • Reading “Mixed Record Types”
    • Sub-setting from a Raw Data File
    • Multiple Observations per Record
    • Reading Hierarchical Files
  • Importing Tips

SAS: Data Understanding, Managing and Manipulation

Understanding and Exporing Data

  • Introduction to basic Procedures - Proc Contents, Proc Print
  • Operators and Operands
  • Conditional Statements (Where, If, If then Else, If then Do and select when)
  • Difference between WHERE and IF statements and limitation of WHERE statements
  • SAS Labels, Commenting
  • SAS System Options (OBS, FSTOBS, NOOBS etc…)

Data Manipulation

  • Proc Sort - with options / De-Duping
  • Accumulator variable and By-Group processing
  • Explicit Output Statements
  • Nesting Do loops
  • Do While and Do Until Statement
  • Array elements and Range

Combining Datasets (Appending and Merging)

  • Concatenation
  • Interleaving
  • Proc Append
  • One To One Merging
  • Match Merging
  • IN = Controlling SAS merge and Indicator

SAS: Functions

  • General form of SAS Functions
  • Arithmetic Functions
  • Date and Time Functions
  • Text Manipulation Functions
  • Nested Functions

SAS: Data Analysis and Reporting

  • Proc Freq
  • Proc Format for user defined formats
  • Proc Means
  • Proc Summary
  • Proc tabulate
  • Proc report
  • Concept of the Output Delivery System
  • Using ODS Statements to save data on external destination

Advance SAS - Data Mining with Proc SQL

  • Introduction to Proc SQL
  • Basics of General SQL language
  • Creating table and Inserting Values
  • Retrieve & Summarize data
  • Group, Sort & Filter
  • Using Joins (Full, Inner, Left, Right and Outer)
  • Reporting and summary analysis
  • Concept of Indexes and creating Indexes (simple and composite)
  • Connecting SAS to external Databases
  • Implicit and Explicit pass through methods

Advanced SAS - SAS Macros

  • Global and Local Variables
  • Macro Parameters and Variables
  • Different types of Macro Creation
  • Defining and calling a macro
  • Using call Symput and Symget
  • Macros options (mprint symbolgen mlogic merror serror)

Working smartly with SAS

Debugging SAS

  • How to read log file efficiently
  • Tips to debug code

Efficient SAS Programming

  • Code optimization and Efficient SAS Programming Techniques
  • Saving CPU Time, I/ O processing time
  • Disk Space Saving Measures
  • Memory saving tips

Introduction to MS Access (video-based)

  • What are Databases
  • Create and Modify Tables
  • Develop and Use of Queries
  • Working with Tables
  • Form Designing
  • Report Designing

DBMS - SQL (video-based)

  • SQL Basics
  • Getting Started with SQL Management Studio
  • Understanding of RDBMS concept
  • Data Manipulation - Reading, Manipulating and Functions
  • DDL,DML,DCL,DQL Commands
  • Data Aggregation & Summarization
  • Concept of Indexing
  • Partitioning, Decile creation, Reporting etc
  • Stored procedures
  • Work Optimization

Introduction to Data Science with R

  • What is analytics & Data Science?
  • Common Terms in Analytics
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • How leading companies are harnessing the power of analytics?
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem solving framework
  • List of steps in Analytics projects
  • Identify the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • Build Resource plan for analytics project
  • Why R for data science?

Introduction - Data Importing/Exporting

  • Introduction R/R-Studio - GUI
  • Concept of Packages - Useful Packages (Base & Other packages)
  • Data Structure & Data Types (Vectors, Matrices, factors, Data frames,  and Lists)
  • Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)
  • Database Input (Connecting to database)
  • Exporting Data to various formats)
  • Viewing Data (Viewing partial data and full data)
  • Variable & Value Labels –  Date Values

Data Manipulation

  • Data Manipulation steps
  • Creating New Variables (calculations & Binning)
  • Dummy variable creation
  • Applying transformations
  • Handling duplicates
  • Handling missings
  • Sorting and Filtering
  • Subsetting (Rows/Columns)
  • Appending (Row appending/column appending)
  • Merging/Joining (Left, right, inner, full, outer etc)
  • Data type conversions
  • Renaming
  • Formatting
  • Reshaping data
  • Sampling
  • Data manipulation tools
  • Operators
  • Functions
  • Packages
  • Control Structures (if, if else)
  • Loops (Conditional, iterative loops, apply functions)
  • Arrays
  • R Built-in Functions (Text, Numeric, Date, utility)
  • Numerical Functions
  • Text Functions
  • Date Functions
  • Utilities Functions
  • R User Defined Functions
  • R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)

Data Analysis - Visualization

  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
  • R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
  • R Packages for Graphical Analysis (base, ggplot, lattice,etc)

Introduction to Statistics

  • Basic Statistics - Measures of Central Tendencies and Variance
  • Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  • Inferential Statistics -Sampling - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

Introduction to Predictive Modeling

  • Concept of model in analytics and how it is used?
  • Common terminology used in analytics & modeling process
  • Popular modeling algorithms
  • Types of Business problems - Mapping of Techniques
  • Different Phases of Predictive Modeling

Data Exploration for modeling

  • Need for structured exploratory data
  • EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
  • Identify missing data
  • Identify outliers data
  • Visualize the data trends and patterns

Data Preparation

  • Need of Data preparation
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis

Segmentation: Solving segmentation problems

  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Behavioral Segmentation Techniques (K-Means Cluster Analysis)
  • Cluster evaluation and profiling - Identify cluster characteristics
  • Interpretation of results - Implementation on new data

Linear Regression: Solving regression problems

  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  • Assess the overall effectiveness of the model
  • Validation of Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  • Interpretation of Results - Business Validation - Implementation on new data

Logistic Regression: Solving classification problems

  • Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model (Binary Logistic Model)
  • Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
  • Validation of Logistic Regression Models (Re running Vs. Scoring)
  • Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
  • Interpretation of Results - Business Validation - Implementation on new data

Time Series Forecasting: Solving forecasting problems

  • Introduction - Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Classification of Techniques(Pattern based - Pattern less)
  • Basic Techniques - Averages, Smoothening, etc
  • Advanced Techniques - AR Models, ARIMA, etc
  • Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

Machine Learning -Predictive Modeling – Basics

  • Introduction to Machine Learning & Predictive Modeling
  • Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
  • Overfitting (Bias-Variance Trade off) & Performance Metrics
  • Feature engineering & dimension reduction
  • Concept of optimization & cost function
  • Overview of gradient descent algorithm
  • Overview of Cross validation(Bootstrapping, K-Fold validation etc)
  • Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Unsupervised Learning: Segmentation

  • What is segmentation & Role of ML in Segmentation?
  • Concept of Distance and related math background
  • K-Means Clustering
  • Expectation Maximization
  • Hierarchical Clustering
  • Spectral Clustering (DBSCAN)
  • Principle component Analysis (PCA)

Supervised Learning: Decision Trees

  • Decision Trees - Introduction - Applications
  • Types of Decision Tree Algorithms
  • Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
  • Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
  • Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
  • Decision Trees - Validation
  • Overfitting - Best Practices to avoid

Supervised Learning: Ensemble Learning

  • Concept of Ensembling
  • Manual Ensembling Vs. Automated Ensembling
  • Methods of Ensembling (Stacking, Mixture of Experts)
  • Bagging (Logic, Practical Applications)
  • Random forest (Logic, Practical Applications)
  • Boosting (Logic, Practical Applications)
  • Ada Boost
  • Gradient Boosting Machines (GBM)
  • XGBoost

Supervised Learning: Artificial Neural Networks (ANN)

  • Motivation for Neural Networks and Its Applications
  • Perceptron and Single Layer Neural Network, and Hand Calculations
  • Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
  • Neural Networks for Regression
  • Neural Networks for Classification
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating ANN models

Supervised Learning: Support Vector Machines

  • Motivation for Support Vector Machine & Applications
  • Support Vector Regression
  • Support vector classifier (Linear & Non-Linear)
  • Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
  • Interpretation of Outputs and Fine tune the models with hyper parameters
  • Validating SVM models

Supervised Learning: KNN

  • What is KNN & Applications?
  • KNN for missing treatment
  • KNN For solving regression problems
  • KNN for solving classification problems
  • Validating KNN model
  • Model fine tuning with hyper parameters

Supervised Learning: Naïve Bayes

  • Concept of Conditional Probability
  • Bayes Theorem and Its Applications
  • Naïve Bayes for classification
  • Applications of Naïve Bayes in Classifications

Text Mining & Analytics

  • Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
  • Finding patterns in text: text mining, text as a graph
  • Natural Language processing (NLP)
  • Text Analytics – Sentiment Analysis using R
  • Text Analytics – Word cloud analysis using R

Text Mining & Analytics

  • Text Analytics - Segmentation using K-Means/Hierarchical Clustering
  • Text Analytics - Classification (Spam/Not spam)
  • Applications of Social Media Analytics
  • Metrics(Measures Actions) in social media analytics
  • Examples & Actionable Insights using Social Media Analytics
  • Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
  • Fine tuning the models using Hyper parameters, grid search, piping etc.

Project - Consolidate Learnings:

Applying different algorithms to solve the business problems and bench mark the results

I took three months training in Big Data Analytics from AnalytixLabs. Before joining this course, I had so many questions and apprehension that will this course be worthy enough but later I found myself fortunate to join this program. No compromise in quality. Live examples and Case Studies really help in understanding the concept and hands on practice. All trainers are amazing and always ready to help. I can not think of any better institute for learning analytics than AnalytixLabs. Not only they help in quality in depth knowledge transfer but also tend to give right direction to your career. Thanks you AnalytixLabs!!

- Manish Jindal (Assistant Consultant, TCS)

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