MS Excel  20 HRS 
BASE & Advanced SAS  30 HRS 
Python/R  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 
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) 
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