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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!

This business analytics training is for beginners who want to start from basics of Excel, SQL, Tableau moving to advanced tools like R, Python data science, including machine learning. 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 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 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.


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: 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 Batch13th October, 2019 (Gurgaon)
03rd November, 2019 (Bangalore)

Introduction to Excel data handling

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

Data manipulation using functions

  • 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 and reporting

  • 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

Data Visualization in Excel

  • 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

Overview of Dashboards

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

Create dashboards in Excel - Using Pivot Controls

  • 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

Business Dashboard Creation

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

SQL: Understanding RDBMS

  • 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

SQL: Utilising the Object Explorer

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

SQL: Data based objects creation (DDL Commands)

  • 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

 

SQL: Data manipulation (DML Commands)

  • 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

SQL: Accessing data from Multiple Tables using SELECT

  • 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

 

Tableau: Getting started

  • 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

Tableau: Data handling & summaries

  • 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)

Tableau: Building Advanced Reports/ Maps

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

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
  • Introduction to LOD (Level of Detail) Expressions
  • User applications of Table calculations

Tableau: Parameters

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

Tableau: Building Interactive Dashboards

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

Tableau: Formatting

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

Tableau: Working with Data

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

 

MS VBA (video-based)

  • 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

R: Exploratory Data Analysis - Introduction to Data Science

  • 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?

R: Exploratory Data Analysis - 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

R: Exploratory Data Analysis - 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
    • 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)

R: Exploratory Data Analysis - Data 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, car, Hmisc, psych, etc)
  • R Packages for Graphical Analysis (base, ggplot, etc)

R: Predictive Modeling - 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

R: Predictive Modeling - 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 

R: 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

R: Predictive Modeling - Data Preparation

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

R: Predictive Modeling - 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

R: Predictive Modeling - 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

R: Advance Analytics & Machine Learning (video-based)

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

Python: Introduction to Data Science with Python

  • 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?

Python Foundation: Essentials (Core)

  • 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

Python Foundation: Operations with NumPy (Numerical 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

Python Foundation: Overview of Pandas

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

Python Foundation: Accessing/Importing and Exporting Data using python modules

  • 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

Python Foundation: Cleansing data with Python

  • 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.)

Python Foundation: Visualization using Python

  • 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.)

Python Foundation: Basic statistics & implementation of stats methods in Python

  • 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

Python Machine Learning: Introduction to Machine Learning

  • 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

Python Machine Learning: Learning algorithms

  • 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

Python Machine Learning: Supervised Learning - Regression problems using Linear Regression

  • 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

Python Machine Learning: Supervised Learning:  Classification Problems using Logistic Regression

  • 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

Python Machine Learning: Supervised Learning:  Classification & Regression Problems using Decision Trees

  • 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

Python Machine Learning: Supervised Learning: Classification & Regression Problems using Ensemble Learning

  • 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

Python Machine Learning: Supervised Learning: Classification & Regression Problems using KNN

  • 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

Python Machine Learning: Supervised Learning: Classification & Regression Problems using Bayesian Techniques

  • 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

Python Machine Learning: Supervised Learning: Regression & Classification problems using Support Vector Machines

  • 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

Python Machine Learning: UnSupervised Learning: Segmentation problems using Cluster analysis

  • 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 

Python Machine Learning: UnSupervised Learning: Segmentation problems using Cluster analysis

  • 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.)

Python Machine Learning: Forecasting Overview and Basics of Time Series

  • 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)

Python Machine Learning: Supervised Learning: Forecasting problems using Time Series Analysis

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

Python Machine Learning: Evaluation of Forecasting

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

Python Machine Learning: Supervised Learning: Regression & Classification problems using Neural Networks

  • 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

Python Text Mining NLP/NLG: Introduction to Text Mining

  • 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 

Python Text Mining NLP/NLG: Text Processing using Base Python & Pandas, Regular Expressions

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

Python Text Mining NLP/NLG: Text Processing with specialized modules like NLTK, sklearn etc

  • 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)

Python Text Mining NLP/NLG: Initial data processing and simple statistical tools

  • 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

Python Text Mining NLP/NLG: Advanced data processing and visualization

  • 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

Python Text Mining NLP/NLG: Advanced data processing and visualisation

  • 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)

Python Text Mining NLP/NLG: Final Projects

  • 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

I did my DSSR course at AnalytixLabs and I can confidently say that i made the right choice, I was not from Analytics background and was looking for a job in Data Science domain, after a lot of research online i decided to study here, the course is very well complemented by lots of assignments and case studies which ensure that we get practical experience rather than just theoretical knowledge. I am eternally grateful to Sumeet Sir and Ankita Mam, I am strong in SAS and cleared the SAS certification easily because of the strong foundation given by Ankita Mam, and special thanks to Sumeet Sir who not only taught exceptionally well but also played a great role in guiding me in the right direction, when i came to him , i did not have a job, i was totally hopeless, but he was always supportive and more than me he was confident in my competency that i will get a job eventually. I finally got a job and i can't thank both of you enough for all the guidance,help and support.


Binoy Nair
(Data Scientist)

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