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Business Analytics Course - Business Analytics 360!

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

Undoubtedly one of the best Business Analytics courses in India and globally. Apt for beginners who want to start from basics of Excel, SQL, Tableau moving to advanced tools like R, Python data science and learn up to including machine learningAnalytixLabs pioneers in Business Analytics training and launched first version of Business Analytics Course in Delhi NCR in 2011! Evolved from our most popular course 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 e-learning 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 India encompasses basic statistical concepts to advanced analytics and predictive modelling techniques, along with machine learning, using most widely used analytics tools, like Excel, Tableau, SQL, Python and R. 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.


With aim to provide all the aspirants world class Data Science and Business Analytics skills irrespective of their location, we provide flexibility to attend this Business Analytics course online through fully interactive live and e-learning mode as well. Classroom and Bootcamp options are available for candidates looking for Business Analytics training in Bangalore and Delhi NCR. 


Business Analytics training 'Business Analytics 360' Duration: 450 hours (At least 150 hours live training + 48 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 450 hours
Classes 50
Tools Excel, VBA, Tableau, SQL,R, Python
Learning Mode Live/Video Based
Next Batch15th March, 2020 (Gurgaon)
29th March, 2020 (Bangalore)

Course Outline

  • Introduction to Excel Environment
  • Formatting and Conditional Formatting
  • Data Sorting, Filtering and Data Validation
  • Understanding Name Ranges

  • Descriptive functions: sum, count, min, max, average, counta, countblank
  • Logical functions: IF, and, or, not
  • Relational operators > >= < <= = !=
  • Nesting of functions
  • Date and Time functions: today, now, month, year, day, weekday, networkdays, weeknum, time, minute, hour
  • Text functions: left, right, mid, find, length, replace, substitute, trim, rank, rank.avg, upper, lower, proper
  • Array functions: sumif, sumifs, countif, countifs, sumproduct
  • Use and application of lookup functions in excel: Vlookup, Hlookup
  • Limitations of lookup functions
  • Using Index, Match, Offset, concept of reverse vlookup

  • Data Analysis using Pivot Tables - use of row and column shelf, values and filters
  • Difference between data layering and cross tabulation, summary reports, advantages and limitations
  • Change aggregation types and summarisation
  • Creating groups and bins in pivot data
  • Concept of calculated fields, usage and limitations
  • Changing report layouts - Outline, compact and tabular forms
  • Show and hide grand totals and subtotals
  • Creating summary reports using pivot tables

  • Overview of chart types - column and bar charts, line and area charts, pie charts, doughnut charts, scatter plots
  • How to select right chart for your data
  • Chart formatting
  • Creating and customizing advance charts - thermometer charts, waterfall charts, population pyramids

  • What is dashboard & Excel dashboard
  • Adding icons and images to dashboards
  • Making dashboards dynamic

  • Concept of pivot cache and its use in creating interactive dashboards in Excel
  • Pivot table design elements - concept of slicers and timelines
  • Designing sample dashboard using Pivot Controls
  • Design principles for including charts in dashboards - do's and don’t's

  • Complete Management Dashboard for Sales & Services
  • Best practices - Tips and Tricks to enhance dashboard designing

  • Schema – Meta Data – ER Diagram
  • Looking at an example of Database design
  • Data Integrity Constraints & types of Relationships (Primary and foreign key)
  • Basic concepts – Queries, Data types & NULL Values, Operators and Comments in SQL

  • What is SQL – A Quick Introduction
  • Installing MS SQL Server for windows OS
  • Introduction to SQL Server Management Studio
  • Understanding basic database concepts

  • Creating, Modifying & Deleting Databases and Tables
  • Drop & Truncate statements – Uses & Differences
  • Alter Table & Alter Column statements
  • Import and Export wizard to get the data in SQL server from excel files or delimited files

 

  • Insert, Update & Delete statements
  • Select statement – Subsetting, Filters, Sorting. Removing Duplicates, grouping and aggregations etc
  • Where, Group By, Order by & Having clauses
  • SQL Functions – Number, Text, Date, etc
  • SQL Keywords – Top, Distinct, Null, etc
  • SQL Operators -  Relational (single valued and multi valued), Logical (and, or, not), Use of wildcard operators and wildcard characters, etc

  • Append and JoinsUnion and Union All – Use & constraints
  • Intersect and Except statements
  • Table Joins - inner join, left join, right join, full join
  • Cross joins/cartesian products, self joins, natural joins etc
  • Inline views and sub-queries
  • Optimizing your work

 

  • What is Tableau? What does the Tableau product suite comprise of? How Does Tableau Work?
  • Tableau Architecture
  • 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
  • Saving and Sharing your work-overview

  • 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
  • Parts of Views
  • Sorting
  • Trend lines/ Forecasting
  • Reference Lines
  • Filters/Context filters
  • Sets
    • In/Out Sets
    • Combined Sets
  • Grouping
  • Bins/Histograms
  • Drilling up/down – drill through
  • Hierarchies
  • View data
  • Actions (across sheets)

  • Explain latitude and longitude
  • Default location/Edit locations
  • Building geographical maps
  • Using Map layers

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

  • Explain scope and direction
  • Percent of Total, Running / Cumulative calculations
  • Introduction to LOD (Level of Detail) Expressions
  • User applications of Table calculations

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

  • Combining multiple visualizations into a dashboard (overview)
  • Making your worksheet interactive by using actions
    • Filter
    • URL
    • Highlight
  • Complete Interactive Dashboard for Sales & Services

  • Options in Formatting your Visualization
  • Working with Labels and Annotations
  • Effective Use of Titles and Captions

  • Multiple Table Joins
  • Data Blending
  • Difference between joining and blending data, and when we should do each
  • Toggle between to Direct Connection and Extracts

 

  • Introducing VBA
  • What is Logic?
  • What Is VBA?
  • Introduction to Macro Recordings, IDE
  • How VBA Works with Excel 
  • Working In the Visual Basic Editor
  • Introducing the Excel Object Model
  • Using the Excel Macro Recorder
  • VBA Sub and Function Procedures
  • Key Components of  Programming  language
  • Essential VBA Language Elements
  • Keywords & Syntax
  • Programming statements
  • Variables & Data types
  • Comments
  • Operators
  • Working with Range Objects
  • A look at some commonly used code snippets
  • Programming constructs in VBA
  • Control Structures
  • Looping Structures
  • The With- End with Block
  • Functions & Procedures in VBA – Modularizing your programs
  • Worksheet & workbook functions
  • Automatic Procedures and Events
  • Arrays
  • Objects & Memory Management in VBA
  • The NEW and SET Key words
  • Destroying Objects – The Nothing Keyword
  • Error Handling
  • Controlling accessibility of your code – Access specifiers
  • Code Reusability – Adding references and components to your code
  • Communicating with Your Users 
  • Simple Dialog Boxes
  • User Form Basics
  • Using User Form Controls
  • Add-ins
  • Accessing Your Macros through the User Interface
  • Retrieve information through Excel from Access Database using VBA

  • 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 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 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
    • 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)

  • 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, car, Hmisc, psych, etc)
  • R Packages for Graphical Analysis (base, ggplot, etc)

  • 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

  • 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 

  • 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

  • Need of Data preparation
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction

  • 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

  • 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


Machine Learning -Predictive Modeling – Basics 

•Overview of Cross validation(Bootstrapping, K-Fold validation etc)

•Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )


Segmentation: Solving segmentation problems

•Introduction to Segmentation & Role of ML

•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


Unsupervised Learning: Segmentation

•Concept of Distance and related math background

•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)


Supervised Learning: Support Vector Machines

•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)


Text Mining & Analytics

•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 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

  • 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 Python for data science?

  • Overview of Python- Starting with Python
  • Why Python for data science?
  • Anaconda vs. python
  • Introduction to installation of Python
  • Introduction to Python Editors & IDE's(Jupyter,/Ipython)
  • Understand Jupyter notebook & Customize Settings
  • Concept of Packages - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.)
  • Installing & loading Packages & Name Spaces
  • Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations - Mathematical - string – date
  • Control flow & conditional statements
  • Debugging & Code profiling
  • Python Built-in Functions (Text, numeric, date, utility functions)
  • User defined functions – Lambda functions
  • Concept of apply functions 
  • Python – Objects – OOPs concepts
  • How to create class and modules?
  • How to call classes and modules?
  • Concept of pipelines in Python

  • What is NumPy?
  • Overview of functions & methods in NumPy
  • Data structures in NumPy
  • Creating arrays and initializing
  • Reading arrays from files
  • Special initializing functions
  • Slicing and indexing
  • Reshaping arrays
  • NumPy Maths
  • Combining arrays
  • Basic algebraic operations using NumPy arrays
  • Solving linear equations
  • Matrix inversions
  • Calculating Eigen vectors

  • What is pandas, its functions & methods
  • Pandas Data Structures (Series & Data Frames)
  • Creating Data Structures (Data import – reading into pandas)

  • Importing Data from various sources (Csv, txt, excel,etc.)
  • Database Input (Connecting to database)
  • Viewing Data objects -  sub setting, methods
  • Exporting Data to various  formats
  • Understanding of data
  • Important python modules: Pandas, NumPy

  • Understand the data
  • Sub Setting Data or Filtering Data or Slicing Data
    • Using [] brackets
    • Using indexing or referring with column names/rows
    • Using functions
  • Dropping rows & columns
  • Mutation of table (Adding/deleting columns) 
  • Binning data (Binning numerical variables in to categorical variables using cut() and qcut() functions)
  • Renaming columns or rows
  • Sorting 
    • By data/values, index
    • By one column or multiple columns
    • Ascending or Descending
  • Type conversions
  • Setting index 
  • Handling duplicates
  • Handling missing values – detect, filter, replace
  • Handling outliers 
  • Creating dummies from categorical data (using get_dummies())
  • Applying functions to all the variables in a data frame (broadcasting)
  • Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)
  • Important Python modules for data manipulation (Pandas, NumPy, re, math, string, datetime etc.)

  • Exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Uni-variate Analysis (Distribution of data & Graphical Analysis)
  • Bi - Variate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating different Graphs using multiple python packages- Bar/pie/line chart/histogram/stack chart/boxplot/ scatter/ density etc)
  • Important Packages for Visualization (graphical analysis) – Pandas, Matplotlib, Seaborn, Bokeh etc.)

  • Basic Statistics - Measures of Central Tendencies and Variance
  • What is probability distribution?
  • Important distributions (discrete & continuous distributions)
  • Deep dive of normal distributions and properties
  • Concept of sampling & types of sampling
  • Concept of standard error and central limit theorem
  • Inferential Statistics - Concept of Hypothesis Testing
  • Statistical Methods - Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square
  • Important modules for statistical methods: NumPy, SciPy, Pandas

  • Introduction to Predictive Modelling
  • Types of Business problems - Mapping of Techniques
  • Relevance in industry and need of the hour
  • Difference between jargons i.e. data science, data analysis, data analytics, data mining
  • What is Machine Learning?
  • What is the goal of Machine Learning?
  • Applications of ML (Marketing, Risk, Operations, etc.)
  • Key components of ML
  • Overall process of executing the ML project  (Data Pre-processing, Sampling, Model Building, Validation)
  • Common mistakes done in ML project and how to overcome
  • Different terms to know for ML

  • Major Classes of Learning Algorithms -Supervised vs. Unsupervised Learning vs. Semi supervised vs. Reinforcement Learning
  • Important   Consideration   like   fitment   of techniques
  • Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Concept of optimization - Gradient descent algorithm
  • Concept of feature engineering
  • Regularization (LASSO, LARS, Elastic net and Ridge regression)
  • Types of Cross validation(Train & Test, K-Fold validation etc.)
  • Cost & optimization functions

  • Introduction - Applications
  • Assumptions of Linear Regression
  • Building Linear Regression Model
  • Important steps in Model building
  • Need of Data preparation
  • Data Audit Report  and Its importance
  • Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
  • Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc.)
  • 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

  • Introduction - Applications
  • Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  • Building Logistic Regression Model
  • Important steps in model building
  • Understanding standard model metrics (Concordance, Variable significance, Gini, KS, Misclassification, 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, etc.)
  • Interpretation of Results - Business Validation 
  • Implementation on new data and Tracking the model

  • Over view of Decision Trees
  • Types of decision trees (Regression Trees, Classification trees, Oblique Decision Trees)
  • Types of decision tree algorithms (CART vs. CHAID vs. C50 etc.)
  • Concept of objective segmentation
  • How to use decision trees to solve regression, classification & segmentation problems)
  • Rule Based Knowledge: Logic of Rules, Evaluating Rules, Rule Induction and Association Rules 
  • Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; 
  • Splitting criteria: Entropy; Information Gain, Gini Index, Chi Square; ANOVA)
  • Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness 
  • Pruning decision tree
  • Cost as a consideration
  • Fine Tuning model using tuning parameters
  • Model validation
  • Over fitting - Best Practices to avoid
  • Implementation of Solution

  • What is concept of Ensemble learning (Stacking, Mixture of Experts)?
  • Types of ensemble models (homogeneous, heterogeneous)
  • Logic, Practical Applications
  • Ensemble learning techniques
  • Bagging
  • Random Forest
  • Boosting
  • AdaBoost
  • Gradient Boost
  • XGBoost
  • Fine tuning the model using tuning parameters

  • What is concept of Instance based learning?
  • What is KNN?
  • KNN method for regression & classification
  • KNN method for missing imputation
  • Computation of Distance Matrix
  • The Optimum K value
  • Model Building, validation & Evaluation of Model
  • Advantages & Disadvantages of KNN Models
  • Applications of KNN  in collaborative filtering, digit recognition
  • KNN in collaborative filtering, digit recognition

  • Fundamentals of Probability; Conditional and Marginal Probability;  Bayes Theorem and Its Applications
  • Probabilities - The Prior and Posterior Probabilities
  • Bayesian Belief nets, MAP, Naïve Rule and Naïve Bayes
  • Naïve Bayes for classification - Data Processing - Discretization of Features
  • Applications of Naïve Bayes in Text Mining, Spam Engines and Classifications
  • Model Building,  Validation and Evaluation of model
  • Pros/Cons of Naïve Bayes Models

  • What is Support vector machines?
  • Understanding SVM 
  • Concepts of Linearly separable vs. non separable data
  • Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
  • Train/Test/Tune the Model using SVM
  • Applications and Interpretation

  • Introduction to Segmentation
  • Types of Segmentation (Subjective Vs. Objective, Heuristic Vs. Statistical)
  • Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  • Concept of Distance and related math background
  • Segmentation Techniques 

  • K-Means/K-Medians Clustering
  • Density Based clustering (DBSCAN)
  • Identifying number of segments (Pseudo F-value, Silhouette score, elbow method etc.)
  • Cluster evaluation and profiling
  • Identifying the characteristics of segmentation
  • Interpretation of results - Implementation on new data
  • Overview of other unsupervised learning techniques (Factor analysis, Hidden Markov models, Gaussian mixture models etc.)

  • What is forecasting?
  • Applications of forecasting
  • Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Types of Seasonality (Hourly, daily, weekly, monthly, quarterly etc.)
  • Classification of Techniques(Pattern based - Pattern less)
  • Important terminology: lag, lead, Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
  • Classification of Time Series Techniques (Univariate & Multivariate)

Stationary Time Series Methods

  • Moving Averages
  • Weighted moving averages
  • Exponential Smoothening
  • Comparison between MA & ES


Trend Based Time Series

  • Linear Regression
  • Double exponential smoothening (Holt’s Method)
  • Comparison between Regression & DS


Seasonal Time Series

  • Decomposition - CMA Method


Advanced Techniques

  • Box Jenkins Methodology
  • AR, MA, ARMA Models
  • ARIMA/SARIMA
  • ARIMAX, SARIMAX

  • Understanding Forecasting Accuracy
  • Goodness Metrics: MSE, MAPE, RMSE, MAD

  • Motivation for Neural Networks and Its Applications
  • Understand Neural Networks
  • Structure of Networks
  • Perceptron and Single Layer Neural Network, and Hand Calculations
  • Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques 
  • The ANN Model
  • Types of Activate functions
  • Train/Test/Tune the ANN Model

  • Unstructured vs. Semi-structured Data
  • Text Mining - characteristics, trends
  • Domain presentation - discussion of various areas and their applications
  • Programming languages designated for working on Text Mining analysis
  • Data Scientist - a profession comprising mainly of working with Text Mining
  • Social Media – Characteristics of Social Media
  • Applications of Social Media Analytics
  • Examples & Actionable Insights using Social Media Analytics 

  • Text processing using string functions & methods   
  • Understanding regular expressions 
  • Identifying patterns in the text using regular expressions  

  • Getting Started with NLTK
  • Introduction to NLP & NLTK
  • Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)

  • Fundamentals of information retrieval
  • Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
  • Cleaning and normalization of data
  • Sentence Tokenize and Word Tokenize,  Removing insignificant words,(“stop words”), Removing special symbols, removing bullet points and digits, changing letters to lowercase, stemming /lemmatisation /chunking
  • Creating Term-Document matrix
  • Finding associations
  • Removing rare terms (Sparse terms)
  • Measurement of similarity between documents and terms
  • Visualization of term significance in the form of word clouds
  • Tagging text with parts of speech
  • Word Sense Disambiguation

  • Sentiment analysis
  • vocabulary approach, based on Bayesian probability methods
  • Name entity recognition (NER)
  • Methods of data visualization
  • word length counts plot
  • word frequency plots
  • word clouds
  • correlation plots
  • letter frequency plot
  • Heat map
  • Grouping texts using different methods

  • Data-centric methods
  • K-means
  • Classification Models (spam detection, topic modelling)
  • K Nearest Neighbours
  • SVM (Linear Support Vector Classifier)
  • Naive Bayes
  • Decision tree 
  • Semantic similarity between texts
  • Language Models and n-grams -- Statistical Models of Unseen Data (Smoothing)

  • Sentiment Analysis (Classification, weighted score etc) 
  • Word cloud analysis (Examples) 
  • Segmentation using K-Means/Hierarchical Clustering (Grouping the similar words) 
  • Classification (Spam/Not spam)
  • Topic Modeling (LDA, LSA, Louvain etc)
  • Text Summarization

FAQS

With rise of digitization, organizations are flooded with ever increasing flow of information. A business analyst or a data analyst uses a combination of skills to derive actionable insights which can be vital for making smart decisions. And in this scenario, businesses rely upon Data Analysis skills more than ever before to stay competitive. This is why business analytics demand has surged globally, creating a huge demand for business analysts. Analytics Industry in India is expected to grow 7 times in next 7 years (from 2019 to 2025), as per the finding of ‘Analytics Industry Study 2018’ conducted by AIM & AnalytixLabs. So, like all previous years the demand of Business Analyst will stay strong with promising opportunities across various domains. 

If we go with trends suggest by LinkedIn and Glassdoor some of the top organizations hiring for business analytics in India are Tata Consultancy Services (TCS), Accenture, Cognizant, Deloitte, American Express, Evalueserve, Ernst & Young, Amazon and Flipkart.

In terms of salary packages, Business Analysts are some of the highest paid professionals as compared to their peers. As per Glassdoor, the average beginner level salary for a business analyst in India is INR 6,44,857 per annum. However, post some experience analysts earn up to INR 12,00,000 per year, especially those who are adept at the latest techniques and tools. With higher experience and well established domain expertise, salaries can range anywhere between INR 25,00,000 to INR 40,00,000 annually.

 

No, coding skills are not mandatory for Business Analytics. Most of the analysis can be performed using the tools like Excel, Tableau, Power BI without coding, whereas while working on SQL one needs basic understanding of SQL commands. However, to leverage advanced analytics it is helpful to have working knowledge of specialized tools, like R and Python. But even in this case with right coaching, candidates with no-prior technical background can work effectively on these tools.

For most of the aspirants a professional certification can effectively help in up-skilling and acquiring job relevant skills in usually around 6 months (with part-time training).  Apart from this there are several post-graduate programs available, with duration of 1 or 2 years and average investment ranging from 6-12 lakhs. In short to medium term, part-time certification offer better ROI, but this may vary based on candidate’s overall profile and career stage. 

In best case scenario, an undergraduate degree in quantitative or technical stream, possible followed by an MBA and then a certified course in BA would ensure your access to the upper echelons of BA jobs. Going with an institute of certain repute is one of the best options, but candidates should consider some key aspects:

• Domain expertise: Given the popularity, plethora of institutes have jumped into this foray. But only handful of institutes bring domain expertise, deep intellectual property and significant experience to deliver industry relevant program. 

• Industry recognition: A top institute would have strong industry presence in the particular domain, with prestigious clientele. This certainly adds meaningful value to the certificate and industry recognition.

• Mentors’ experience: It is utmost important that faculty members/ mentors have vast industry experience and also strong hands-on themselves.

• Industry relevant curriculum: Curriculum should cover latest tools and techniques with enough practical components. Frequent curriculum upgradation is key in such a fast evolving field.

• Projects and case studies: Projects and assignments should have extensive coverage of real world problems from key industries, so that it inculcates confidence in candidates to face interviews and on job challenges.

• Post class support: It is imperative that candidates have ample support available outside class hours for doubts and problems they face while self-studying and working on projects. Institutes with only part time instructors usually lack in this important aspect. 

 

Among numerous institutes, AnalytixLabs is certainly one of the finest and well renowned option. AnalytixLabs has been a pioneer in Business Analytics courses since 2011 and is constantly rated among India’s top institute with repeated clientele of several prestigious MNCs. Considering the value for money, industry relevant programs and stellar track record no one can overlook the value proposition AnalytixLabs offers when it comes to Business Analytics certification. 

Following are the 2 most popular courses which caters to business analytics career track:

• Analytics Edge: Main focus of this course is to help beginners build basic to intermediate level analytics skills for descriptive and statistical analysis. This course covers most widely used tools for BI/ descriptive analytics, like MS-Excel, SQL, Tableau and VBA. A major focus on this program is data science and predictive modelling skills using R.

• Business Analytics 360: One of our most coveted course with dual specialization, with combination of Analytics Edge + Data Science & Machine Learning using Python. This is a very extensive 7 month program that covers basis to advanced analytics including Data Science and Machine Learning using Python & R. 

 

All of our business analytics courses are available in following 3 formats:

• Bootcamp/ Classroom contact: Offline classroom sessions with in-person training and mentor interactions and in-class practice.

• Interactive live online: Interactive live online format, which offers real-time interactive experience through in-built VoIP and chat with convenience of attending the class from anywhere globally.  

• Self-paced e-learning: Set of pre-recorded video lectures, with flexibility of completing course at your own pace. Candidates can periodically schedule doubt sessions in-person or remotely as per their convenience too. 

For all the three formats candidates get an access to online learning portal, to access study material and class recordings for future reference. 

 

Analytics job roles usually demand combination of skills and same is true when it comes to tools. To deliver a project effectively, one may need combination of different tools. We can categorize the most popular tools as following:

MIS Reporting and Descriptive Analytics – MS-Excel, Tableau, QlikView, Power BI and SQL

Advanced Analytics/ Statistical Modelling/ Machine Learning – SAS, R and Python

Over last few years new tools like Tableau, QlikView, Power BI with particular focus on data visualization have become very prominent, whereas incumbents like SAS have lost ground to open source tools like R & Python when it comes to advanced analytics.


A comprehensive Business Analytics certification program like Business Analytics 360, covers following keys aspects as part of the curriculum:

Analytics Methodologies: Develop conceptual understanding and learn practical application of various analytical methods, interpretation of different steps involved in end-to-end analytics projects, such as data handling, data extraction, descriptive & predictive analytics using statistical modelling and machine learning techniques. 

Analytics Tools: Build hands-on skills on most widely used tools like Excel, SQL, Tableau/ Power BI, R and Python to enable candidates to work effectively on real world problems and be job ready. 

Business problem solving: No tools and techniques are of use unless they don’t provide any real business value. An accomplished analyst must know how to define, structure and approach a given problem. With help of multiple case studies and projects spread across the course curriculum, candidates learn to effectively leverage combination of different tools and techniques to answer critical business questions and solve different type of problems. 

Some of the common designations offered to business analytics professional are:- Business Analyst, Data Analyst, Analyst, Analytics consultant, Analytics specialist. This depends on the organizational structure and candidates experience and education background. However, in the present world corporate structures are dynamic and designations can vary from organization to organization. One should mainly understand the nature of work and what type of responsibilities a given job role entails.

For a Business Analytics professional work primarily revolves around data, which could be gathered from varied resources, like market research, operations, internal CRM, ERP and BI systems. Business Analytics comprises of deciphering the past trends and/ or make predictions with combination of different analytics tools and methodologies. This ensures the right and most effective action is undertaken. The execution of these occurs by combining statistical software findings with business know-how while trying to reach ideal business solutions.  These solutions are shared with stakeholders who utilise this information to make operational and strategic decisions. A skilled Business Analytics professional gauges the quality of data, understand business objective, utilises relevant techniques to generate superior data-driven business decisions.

An experienced analytics professional is also expected to develop domain expertise over a period of time with deep business understanding of at least one industry, for example Retail, FMCG, e-Commerce, Insurance, Banking, Finance, Travel and Logistics etc.

Business Analytics provides a golden opportunity for young professionals to switch to the strategic side of the business. This is certainly a lucrative and sought after job, which involves a lot of hard work to internal and external client expectations.


Minimum qualification to pursue Business Analyst career track is graduation and for Analytics it helps to have graduate degree in quantitative streams, like engineering, eaths, statistics, econometrics, operation research. 

After graduation, one has choice of pursuing career in Business Analytics through a certification course. However, for some high paying jobs, which involves consulting and advisory roles, companies prefer candidates with Masters in Management (MBA) or other quantitative fields . Although this degree doesn’t essentially gets you in the field of Business Analytics, but it propels the career faster from base position with higher salary. 

Although it not a prerequisite, but good communication skill is important for a Business Analytics professional. As part of the work, a Business Analyst needs to understand the problem and ask the right questions to deliver the project effectively. As part of day to day responsibilities, Business analysts interacts with the client often, hence, it is important put forward different business and technical aspects articulately.

"Best Institute in Analytics and Data Science Thank you Analytics Labs for support and Guidance to right career path , Very Talented team which is always there to support you and mentor you with effective teaching methodology. Awesome content and mentors. a must Go Institute for people looking for career in Analytics field."


Uttkarsh Shukla
(Analyst, EXL)

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