MS Excel  20 HRS 
Python/R  20 HRS 
Machine Learning  20 HRS 
Relevance in industry & need of the hour 
Types of analytics – Marketing, Risk, Operations, etc 
Business & Technology drivers for analytics 
Future of analytics & critical requirement 
Types of problems and business objectives in various industries 
Different phases of Analytics Project 
Introduction to Excel 
Working with Formulas and functions 
Formating & Conditional Formating 
Filtering, sorting, paste special etc 
Functions (Logical & Text, Mathematical, Statistical etc) 
Data Manipulation & Data Aggregation 
Data Analysis using functions 
Analyzing Data using Pivots 
Descriptive Statistics 
Creating Charts & Graphics 
Data analytics tool (What if analysis, Goal seek, Data Table, Solver) 
Protecting Workbooks, worksheets and formulas 
Working with VBE (Visual Basic Editor) 
Introduction to Excel Object Model 
Understanding of Sub and Function Procedures 
Key Component of Programming Language 
Understanding 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 
Why do we need Python? 
Program structure in Python 
Interactive Shell 
Executable or script files. 
User Interface or IDE 
Numbers 
Strings 
List 
Tuple 
Dictionary 
Other Core Types 
Assignments, Expressions and prints 
If tests and Syntax Rules 
While and For Loops 
Iterations and Comprehensions 
Opening a file 
Using Files 
Other File tools 
Function definition and call 
Function Scope 
Arguments 
Function Objects 
Anonymous Functions 
Cleansing Data with Python 
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc) 
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc) 
Python Builtin Functions (Text, numeric, date, utility functions) 
Python User Defined Functions 
Stripping out extraneous information 
Normalizing data 
Formatting data 
Important Python Packages for data manipulation (Pandas, Numpy etc) 
Importing Data from various sources (Csv, txt, excel, access etc) 
Database Input (Connecting to database) 
Viewing Data objects  subsetting, methods 
Exporting Data to various formats 
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) 
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats 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/ttests (One sample, independent, paired), Anova, Correlation and Chisquare 
Introduction R/RStudio  GUI 
Concept of Packages  Useful Packages (Base & other packages) in R 
Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists) 
Importing Data from various sources 
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(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type converstions, renaming, formating etc) 
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc) 
R Builtin Functions (Text, numeric, date, utility functions) 
R User Defined Functions 
R Packages for data manipulation(base, dplyr, plyr, 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, gmodes, car, vcd, Hmisc, psych, doby etc) 
R Packages for Graphical Analysis (base, ggplot, lattice etc) 
What is machine learning? 
What are the use case of Machine learning? 
Statistical learning vs. Machine learning 
Iteration and evaluation 
Major Classes of Learning Algorithms Supervised vs Unsupervised Learning 
Different Phases of Predictive Modelling (Data Preprocessing, Sampling, Model Building, Validation) 
Concept of Overfitting and Under fitting (BiasVariance Trade off) & Performance Metrics 
Types of Cross validation(Train & Test, Bootstrapping, KFold validation etc) 
Introduction to CARET package 
Introduction to H2O package 
Linear Regression 
Logistic regression 
Generalization & Non Linearity 
Recursive Partitioning(Decision Trees) 
Ensemble Models(Random Forest, Bagging & Boosting(ada, gbm etc)) 
Artificial Neural Networks(ANN) 
Support Vector Machines(SVM) 
KNearest neighbours 
Naive Bayes 
Kmeans clustering 
Challenges of unsupervised learning and beyond Kmeans 
Market Basket Analysis 
Collaborative Filtering 
Social Media – Characteristics of Social Media 
Applications of Social Media Analytics 
Metrics(Measures Actions) in social media analytics Examples & Actionable Insights using Social Media Analytics 
Text Analytics – Sentiment Analysis using R 
Text Analytics – Word cloud analysis using R 
Taming big text, Unstructured vs. Semistructured Data; Fundamentals of information retrieval, Properties of words; Vector space models; Creating TermDocument (TxD);Matrices; Similarity measures, Lowlevel processes (Sentence Splitting; Tokenization; PartofSpeech Tagging; Stemming; Chunking) 
Handling big graphs 
The purpose of it all: Finding patterns in data 
Finding patterns in text: text mining, text as a graph 
Natural Language processing (NLP) 
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