MS Excel  20 HRS 
BASE & Advanced SAS  30 HRS 
Python/R  30 HRS 
SPSS  20 HRS 
Big Data & Hadoop  40 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 
Introduction to SAS, GUI 
Concepts of Libraries, PDV, data execution etc 
Building blocks of SAS (Data & Proc Steps  Statements & options) 
Debugging SAS Codes 
Importing different types of data & connecting to data bases 
Data Understanding(Meta data, variable attributes(format, informat, length, label etc)) 
SAS Procedures for data import /export / understanding(Proc import/Proc contents/Proc print/Proc means/Proc feq) 
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type converstions, renaming, formatting, etc) 
Data manipulation tools (Operators, Functions, Procedures, control structures, Loops, arrays etc) 
SAS Functions (Text, numeric, date, utility functions) 
SAS Procedures for data manipulation (Proc sort, proc format etc) 
SAS Options (System Level, procedure level) 
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) 
SAS Procedures for Data Analysis(proc freq/Proc means/proc summary/proc tabulate/Proc univariate etc) 
SAS Procedures for Graphical Analysis (Proc Sgplot, proc gplot etc) 
Introduction to Reporting 
SAS Reporting Procedures (Proc print, Proc Report, Proc Tabulate etc) 
Exporting data sets into different formats (Using proc export) 
Concept of ODS (output delivery system) 
ODS System  Exporting output into different formats 
Introduction to Advanced SAS  Proc SQL & Macros 
Understanding select statement (From, where, group by, having, order by etc) 
Proc SQL  Data creation/extraction 
Proc SQL  Data Manipulation steps 
Proc SQL  Summarizing Data 
Proc SQL  Concept of sub queries, indexes etc 
SAS Macros  Creating/defining macro variables 
SAS Macros  Defining/calling macros 
SAS Macros Concept of local/global variables 
Introduction of Statistics 
Descriptive and inferential statistics 
Explanatory Versus Predictive Modeling 
Population and samples 
Uses of variable independent and dependent 
Types of variables quantitative and categorical 
Descriptive Statistics Introduction 
Descriptive Statistics Introduction 
Histogram 
Measures of shape skewness 
Box Plots 
Univariante Procedure 
Statistical graphics procedures 
The SGPLOT Procedure 
ODS Graphics Output 
Using SAS to picture your data 
Confidence Intervals for the Mean Introduction 
Distribution of sample means 
Normality and the central limit theorem 
Calculation of 95% confidence interval 
Hypothesis Testing introduction 
Decision Making Process 
Steps in Hypothesis Testing 
Types of error and power 
The p value effect size and sample size 
Statistical Hypothesis Test 
the t statistic t distribution and two sided t test 
Using proc univariate to generate a t statistic 
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) 
The Research Process 
Initial Observation 
Generate Theory 
Generate Hypotheses 
Data collection to Test Theory 
What to measure 
How to Measure 
Analyze data 
Descriptive Statistics: Overview 
Central Tendency 
Measure of variation 
Coefficient of Variation 
Fitting Statistical Models 
Conclusion 
Types of statistical models 
Populations and samples 
Simple statistical models 
The mean as a model 
The variance and standard deviation 
Central Limit Theorem 
The standard error 
Confidence Intervals 
Test statistics 
Nonsignificant results and Significant results: 
One and twotailed tests 
Type I and Type II errors 
Effect Sizes 
Statistical power 
Accessing SPSS 
To explore the key windows in SPSS 
Data editor 
The viewer 
The syntax editor 
How to create variables 
Enter Data and adjust the properties of your variables 
How to Load Files and Save 
Opening Excel Files 
Recoding Variables 
Deleting/Inserting a Case or a Column 
Selecting Cases 
Using SPSS Help 
The art of presenting data 
The SPSS Chart Builder 
Histograms: a good way to spot obvious problems 
Boxplots (box–whisker diagrams) 
Graphing means: bar charts and error bars 
Simple bar charts for independent means 
Clustered bar charts for independent means 
Simple bar charts for related means 
Clustered bar charts for related means 
Clustered bar charts for ‘mixed’ designs 
Line charts 
Graphing relationships: the scatterplot 
Simple scatterplot 
Grouped scatterplot 
Simple and grouped D scatterplots 
Matrix scatterplot 
Simple dot plot or density plot 
Dropline graph 
Editing graphs 
What are assumptions? 
Assumptions of parametric data 
The assumption of normality 
Quantifying normality with numbers 
Exploring groups of data 
Testing whether a distribution is normal 
Kolmogorov–Smirnov test on SPSS 
Testing for homogeneity of variance 
Correcting problems in the data 
Looking at relationships 
Standardization and the correlation coefficient 
The significance of the correlation coefficient 
Confidence intervals for r 
Correlation in SPSS i. Bivariate correlation ii. Pearson’s correlation coefficient iii. Spearman’s correlation coefficient iv. Kendall’s tau (nonparametric) v. Biserial and point–biserial correlations vi. Partial correlation vii. The theory behind part and partial correlation viii. Partial correlation using SPSS ix. Semipartial (or part) correlations 
Comparing correlations 
Comparing independent rs 
dependent rs 
Calculating the effect size 
How to report correlation coefficients 
An introduction to regression 
Some important information about straight lines 
The method of least squares 
Assessing the goodness of fit: sums of squares, R and R2 
Doing simple regression on SPSS 
Multiple regression: the basics 
How to do multiple regression using SPSS 
Descriptive 
Checking assumptions 
Background to logistic regression 
What are the principles behind logistic regression? 
Assessing the model: the loglikelihood statistic 
Assessing the model: R and R2 
Methods of logistic regression 
Interpreting logistic regression 
How to report logistic regression 
Testing assumptions 
Predicting several categories: multinomial logistic regression 
Running multinomial logistic regression in SPSS 
Looking at differences 
The ttest 
Rationale for the ttest 
Reporting the dependent ttest 
Reporting the independent ttest 
Between groups or repeated measures? 
The ttest as a general linear model 
Comparing several means : ANOVA (GLM) 
The theory behind ANOVA 
Inflated error rates 
Interpreting ftest 
ANOVA as regression 
Assumptions of ANOVA 
Planned contrasts 
Post hoc procedure 
Introduction and relevance 
Uses of Big Data analytics in various industries like Telecom, E commerce, Finance and Insurance etc. 
Problems with Traditional LargeScale Systems 
Motivation for Hadoop 
Different types of projects by Apache 
Role of projects in the Hadoop Ecosystem 
Key technology foundations required for Big Data 
Limitations and Solutions of existing Data Analytics Architecture 
Comparison of traditional data management systems with Big Data management systems 
Evaluate key framework requirements for Big Data analytics 
Hadoop Ecosystem & Hadoop 2.x core components 
Explain the relevance of realtime data 
Explain how to use big and realtime data as a Business planning tool 
Hadoop MasterSlave Architecture 
The Hadoop Distributed File System  Concept of data storage 
Explain different types of cluster setups(Fully distributed/Pseudo etc) 
Hadoop cluster set up  Installation 
Hadoop 2.x Cluster Architecture 
A Typical enterprise cluster – Hadoop Cluster Modes 
Understanding cluster management tools like Cloudera manager/Apache ambari 
HDFS Overview & Data storage in HDFS 
Get the data into Hadoop from local machine(Data Loading Techniques)  vice versa 
Map Reduce Overview (Traditional way Vs. MapReduce way) 
Concept of Mapper & Reducer 
Understanding MapReduce program Framework 
Develop MapReduce Program using Java (Basic) 
Develop MapReduce program with streaming API) (Basic) 
Integrating Hadoop into an Existing Enterprise 
Loading Data from an RDBMS into HDFS by Using Sqoop 
Managing RealTime Data Using Flume 
Accessing HDFS from Legacy Systems 
Apache PIG  MapReduce Vs Pig, Pig Use Cases 
PIG’s Data Model 
PIG Streaming 
Pig Latin Program & Execution 
Pig Latin : Relational Operators, File Loaders, Group Operator, COGROUP Operator, Joins and COGROUP, Union, Diagnostic Operators, Pig UDF 
Writing JAVA UDF’s 
Embedded PIG in JAVA 
PIG Macros 
Parameter Substitution 
Use Pig to automate the design and implementation of MapReduce applications 
Use Pig to apply structure to unstructured Big Data 
Apache Hive  Hive Vs. PIG  Hive Use Cases 
Discuss the Hive data storage principle 
Explain the File formats and Records formats supported by the Hive environment 
Perform operations with data in Hive 
Hive QL: Joining Tables, Dynamic Partitioning, Custom Map/Reduce Scripts 
Hive Script, Hive UDF 
Hive Persistence formats 
Loading data in Hive  Methods 
Serialization & Deserialization 
Handling Text data using Hive 
Integrating external BI tools with Hadoop Hive 
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