Data Analytics Training Course

  Part-1 Data Analytics
Introduction to Data Analytics 
Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things.For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity.Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Topics Includes

Part 1 Advanced Excel

Part 2 Statistics & Probability

Part 3 Data Analytics with Python

Part 4 Data Analytics with R

Part 5 Data Analytics with SAS

Part 6 MS SQL

Part 7 Tableau

  Part-2 Advanced Excel
Lecture-1 Entering Data 
·     Introduction to Excel spreadsheet

·     Learning to enter data

·     Flling of series and custom fill list

·     Editing and deleting fields

·     Practical Exercise

Lecture-2 Referencing in Formulas 
·     Learning about relative and absolute referencing

·     The concept of relative formulae

·     The issues in relative formulae

·     Creating of absolute and mixed references

·     Practical Exercise

Lecture-3 Name Range 
·     Creating names range

·     Using names in new formulae

·     Working with the name box

·     Selecting range

·     Names from a selection

·     Pasting names in formulae

·     Selecting names

·     Working with Name Manager

·     Practical Exercise

Lecture-4 Understanding Logical Functions 
·     The various logical functions in Excel

·     The If function for calculating values and displaying text

·     Nested If functions

·     VLookUp and IFError functions

·     Practical Exercise

Lecture-5 Getting started with Conditional Formatting 
·     Learning about conditional formatting

·     The options for formatting cells

·     Various operations with icon sets

·     Data bars and colour scales

·     Creating and modifying Sparkline

·     Practical Exercise

Lecture-6 Advanced-level Validation 
·     Multi-level drop down validation

·     Restricting value from list only

·     Learning about error messages and cell drop down

·     Practical Exercise

Lecture-7 Important Formulas in Excel 
·     Introduction to the various formulae in Excel

·     Sum, Sum IF & Sum IFs

·     Count, Count A, Count IF and Count Blank

·     Network days, Network days International

·     Today & Now function

·     Trim (Eliminating undesirable spaces)

·     Concatenate (Consolidating columns)

·     Practical Exercise

·     Lecture-8 Working with Dynamic table

·     Introduction to dynamic table in Excel

·     Data conversion

·     Table conversion

·     Tables for charts

·     VLOOKUP

·     Practical Exercise

Lecture-8 Working with Dynamic table 
·     Introduction to dynamic table in Excel

·     Data conversion

·     Table conversion

·     Tables for charts

·     VLOOKUP

·     Practical Exercise

Lecture-9 Data Sorting 
·     Sorting in Excel

·     Various types of sorting

·     Alphabetical

·     Numerical

·     Row

·     Multiple column

·     Working with paste special

·     Hyperlinking

·     Using subtotal

·     Practical Exercise

Lecture-10 Data Filtering 
·     The concept of data filtering

·     Understanding compound filter and its creation

·     Removing of filter

·     Using custom filter and multiple value filters

·     Working with wildcards

·     Practical Exercise

Lecture-11 Chart Creation 
·     Creation of Charts in Excel

·     Performing operations in embedded chart

·     Modifying

·     Resizing

·     Dragging of chart

·     Practical Exercise

Lecture-12 Various Techniques of Charting 
·     Introduction to the various types of charting techniques

·     Creating titles for charts

·     Axes

·     Learning about data labels

·     Displaying data tables

·     Modifying axes

·     Displaying gridlines and inserting trendlines

·     Textbox insertion in a chart

·     Creating a 2-axis chart

·     Creating combination chart

·     Practical Exercise

Lecture-13 Pivot Tables in Excel 
·     The concept of Pivot tables in Excel

·     Report filtering

·     Shell creation

·     Working with Pivot for calculations

·     Formatting of reports

·     Dynamic range assigning

·     The slicers

·     Creating of slicers

·     Practical Exercise

Lecture-14 Ensuring Data and File Security 
·     Data and file security in Excel

·     Protecting row, column, and cell

·     Different safeguarding techniques

·     Practical Exercise

Lecture-15 Getting started with VBA Macros 
·     Learning about VBA macros in Excel

·     Executing macros in Excel

·     The macro shortcuts

·     Applications

·     The concept of relative reference in macros

·     Practical Exercise

Lecture-16 Core concepts of VBA 
·     In-depth understanding of Visual Basic for Applications

·     The VBA Editor

·     Module insertion and deletion

·     Performing action with Sub

·     Ending Sub if condition not met

·     Practical Exercise

Lecture-17 Ranges and Worksheet in VBA 
·     Learning about the concepts of workbooks & worksheets in Excel Protection of macro codes

·     Range coding

·     Declaring a variable

·     The concept of Pivot Table in VBA

·     Introduction to arrays

·     User forms

·     Getting to know how to work with databases within Excel

·     Practical Exercise

Lecture-18 IF condition 
·     Learning how the If condition works

·     How to apply it in various scenarios

·     Working with multiple Ifs in Macro

·     Practical Exercise

Lecture-19 Loops in VBA 
·     Understanding the concept of looping

·     Deploying looping in VBA Macros

·     Practical Exercise

Lecture-20 Debugging in VBA 
·     Studying about debugging in VBA

·     The various steps of debugging

·     Understanding breakpoints and way to mark it

·     The code for debugging and code commenting

·     Practical Exercise

Lecture-21 Messaging in VBA 
·     The concept of message box in VBA

·     Learning to create the message box

·     Various types of message boxes

·     The IF condition as related to message boxes

·     Practical Exercise

Lecture-22 Practical Projects in VBA 
·     Mastering the various tasks and functions using VBA

·     Understanding data separation

·     Auto filtering

·     Formatting of report

·     Combining multiple sheets into one

·     Merging multiple files together

·     Practical Exercise

Lecture-23 Best Practices of Dashboards Visualization 
·     Introduction to powerful data visualization with Excel Dashboard

·     Loading the data

·     Managing data and linking the data to tables and charts

·     Creating Reports using dashboard features

·     Practical Exercise

Lecture-24 Principles of Charting 
·     Learning to create charts in Excel

·     The various charts available

·     The steps to successfully build a chart

·     Personalization of charts

·     Formatting and updating features

·     Various special charts for Excel dashboards

·     Understanding how to choose the right chart for the right data

·     Practical Exercise

Lecture-25 Getting started with Pivot Tables 
·     Creation of Pivot Tables in Excel

·     Learning to change the Pivot Table layout

·     Generating Reports

·     The methodology of grouping and ungrouping of data

·     Practical Exercise

Lecture-26 Creating Dashboards 
·     Learning to create Dashboards

·     The various rules to follow while creating Dashboards

·     Creation of dynamic dashboards

·     Knowing what is data layout

·     Introduction to thermometer chart and its creation

·     How to use alerts in the Dashboard setup

·     Practical Exercise

Lecture-27 Creation of Interactive Components 
·     How to insert a Scroll bar to a data window

·     Concept of Option buttons in a chart

·     Use of combo box drop-down

·     List box control Usage

·     How to use Checkbox Control

·     Practical Exercise

Lecture-28 Data Analysis 
·     Understanding data quality issues in Excel

·     Linking of data

·     Consolidating and merging data

·     Working with dashboards for Excel Pivot Tables

·     Practical Exercise

  Part-3 Statistics & Probability
Lecture -1 Information of Statistics 
·     What is statistics?

·     How is this useful

·     What is this course for

·     Practical Exercise

Lecture -2 Data Conversion 
·     Converting data into useful information

·     Collecting the data

·     Understand the data

·     Finding useful information in the data

·     Interpreting the data

·     Visualizing the data

·     Practical Exercise

Lecture -3 Terms of Statistics 
·     Descriptive statistics

·     Let us understand some terms in statistics

·     Variable

·     Practical Exercise

Lecture -4 Plots 
·     Dot Plots

·     Histogram

·     Stemplots

·     Box and whisker plots

·     Outlier detection from box plots and Box and whisker plots

·     Practical Exercise

Lecture -5 Statistics & Probability 
·     What is probability?

·     Set & rules of probability

·     Bayes Theorem

·     Practical Exercise

Lecture -6 Distributions 
·     Probability Distributions

·     Few Examples

·     Student T- Distribution

·     Sampling Distribution

·     Student t- Distribution

·     Poison distribution

·     Practical Exercise

Lecture -7 Sampling 
·     Stratified Sampling

·     Proportionate Sampling

·     Systematic Sampling

·     P – Value

·     Stratified Sampling

·     Practical Exercise

Lecture -8 Tables & Analysis 
·     Cross Tables

·     Bivariate Analysis

·     Multi variant Analysis

·     Dependence and Independence tests ( Chi-Square )

·     Analysis of Variance

·     Correlation between Nominal variables

·     Practical Exercise

  Part-4 Data Analytics with Python
Lecture-1 Introduction to data science 
·     Different Sectors Using Data Science

·     The Purpose and Components of Python

·     The Data Analytics Process

·     Exploratory the Data Analysis (EDA)

·     EDA-Quantitative Technique

·     EDA - Graphical Technique

·     The Data Analytics Conclusion or Predictions

·     The Data Analytics Communication

·     The Data Types for Plotting

·     Practical Exercise

Lecture-2 Statistical Analysis and Business Applications 
·     Introduction to the Statistics

·     About Statistical and Non-statistical Analysis

·     The Major Categories of Statistics

·     About the Statistical Analysis Considerations

·     The Population and Sample

·     What is the Statistical Analysis Process?

·     The Data Distribution

·     Dispersion

·     Practical Exercise

Lecture-3 Introduction of Python Programming 
·     The Companies using Python

·     Different Applications where Python is used

·     Discuss Python Scripts on UNIX/Windows

·     Values, Types, Variables

·     Operands and Expressions

·     Conditional Statements

·     Loops

·     Command Line Arguments

·     Writing to the screen

·     Practical Exercise

Lecture-4 Sequences and File Operations 
·     Python files I/O Functions

·     Numbers

·     Strings and related operations

·     Tuples and related operations

·     Lists and related operations

·     Dictionaries and related operations

·     Sets and related operations

·     Practical Exercise

Lecture-5 Functions, Oops, Errors & Exceptions 
·     Functions

·     Function Parameters

·     Global Variables

·     Variable Scope and Returning Values

·     Lambda Functions

·     Object-Oriented Concepts

·     Standard Libraries

·     Modules Used in Python

·     The Import Statements

·     Module Search Path

·     Package Installation Ways

·     Errors and Exception Handling

·     Handling Multiple Exceptions

·     Practical Exercise

Lecture-6 Introduction To Statistics 
·     Introduction To Statistic

·     Distributions and Hypothesis Tests

·     Distributions of One Variable

·     Hypothesis Testing

·     Typical Analysis Procedure

·     Data Screening and Outliers

·     Normality Check

·     Hypothesis Concept

·     Errors

·     p-Value

·     Sample Size

·     Chi Square

·     ANOVA

·     Practical Exercise

Lecture-7 Pandas 
·     Pandas Introduction

·     Series

·     Data Frames

·     CSVs

·     Data from URLs

·     Describing Data with Pandas

·     Selecting and Viewing Data with Pandas

·     Manipulating Data

·     Practical Exercise

Lecture-8 Mathematical Computing with Python( NumPy ) 
·     NumPy Introduction

·     NumPy Data Types and Attributes

·     Creating NumPy Arrays

·     NumPy Random Seed

·     Viewing Arrays and Matrices

·     Manipulating Arrays

·     Standard Deviation and Variance

·     Reshape and Transpose

·     Dot Product vs Element Wise

·     Store Sales

·     Comparison Operators

·     Sorting Arrays

·     Turn Images Into NumPy Arrays

·     Practical Exercise

Lecture-9 The Scientific computing with Python (SciPy) 
·     Introduction to the SciPy

·     About the SciPy Sub Package - Integration and Optimization

·     What is SciPy sub package?

·     Know About the SciPy Sub Package - Statistics, Weave & IO

·     Practical Exercise

Lecture-10 Introduction to Machine Learning 
·     What Is Machine Learning?

·     AI/Machine Learning/Data Science

·     Practical Exercise

Lecture-11 Machine Learning & Data Science Framework 
·     Machine Learning Framework

·     Types of Machine Learning

·     Types of Data

·     Types of Evaluation

·     Features In Data

·     Modelling - Splitting Data

·     Modelling - Picking the Model

·     Modelling - Tuning

·     Modelling – Comparison

·     Practical Exercise

Lecture-12 Data Science Environment Setup 
·     Introducing Our Tools

·     Windows Environment Setup

·     Linux Environment Setup

·     Jupiter Notebook Walkthrough

·     Practical Exercise

Lecture-13 Matplotlib + Seaborne: Plotting and Data Visualization 
·     Matplotlib Introduction

·     Importing And Using Matplotlib

·     Anatomy Of A Matplotlib Figure

·     Scatter Plot And Bar Plot

·     Histograms And Subplots

·     Subplots Option 2

·     Plotting From Pandas DataFrames

·     Customizing Your Plots

·     Saving And Sharing Your Plots

·     Practical Exercise

Lecture-14 Scikit-learn: Creating Machine Learning Models 
·     Scikit-learn Introduction

·     Scikit-learn Cheat sheet

·     Typical Scikit-learn Workflow

·     Debugging Warnings In Jupiter

·     Splitting Your Data

·     Clean, Transform, Reduce

·     Convert Data To Numbers

·     Handling Missing Values With Pandas

·     Handling Missing Values With Scikit-learn

·     Choosing The Right Model For Your Data

·     Practical Exercise

Lecture-15 Scikit-learn - Regression Model 
·     Types of Regression Algorithms

·     Simple Linear Regression

·     Multiple Linear Regression

·     Logistic Regression,

·     Polynomial Regression

·     Support Vector Regression

·     Ridge Regression

·     Lasso Regression

·     ElasticNet Regression

·     Bayesian Regression

·     Decision Tree Regression

·     Random Forest Regression

·     Practical Exercise

Lecture-16 Scikit-learn – Classification 
·     Types of Classification Algorithms

·     Logistic Regression/Classification

·     K-Nearest Neighbours

·     Support Vector Machines

·     Kernel Support Vector Machines

·     Naive Bayes

·     Decision Tree Classification

·     Random Forest Classification

·     Practical Exercise

Lecture-17 K Means Clustering 
·     K-Means Clustering

·     A Simple Example of Clustering

·     Clustering Categorical Data

·     How to Choose the Number of Clusters

·     Pros and Cons of K-Means Clustering

·     To Standardize or not to Standardize

·     Relationship between Clustering and Regression

·     Market Segmentation with Cluster Analysis

·     Species Segmentation with Cluster Analysis

·     Advanced Statistical Methods - Other Types of Clustering

·     Types of Clustering

·     Dendrogram

·     Heatmaps

·     Practical Exercise

Lecture-18 Text Mining 
·     The concepts of text-mining

·     Use cases

·     Text Mining Algorithms

·     Quantifying text

·     TF-IDF

·     Beyond TF-IDF

·     Practical Exercise

Lecture-19 Basic Time Series Forecasting 
·     What is time series?

·     Techniques and applications

·     Time series components

·     Moving average

·     Smoothing techniques

·     Exponential smoothing

·     Univariate time series models

·     Multivariate time series analysis

·     Sentiment analysis in Python (Twitter sentiment analysis)

·     Text analysis

·     Rolling Mean For Detecting Temporal Variation

·     Simple Exponential Smoothing (SES)

·     Holt extended simple exponential smoothing

·     Holt Winters

·     Auto Regression Model (AR): Consider Previous Time Steps

·     Implement a Basic ARIMA Model

·     Automated ARIMA & Account for Seasonality (SARIMA)

·     Practical Exercise

Lecture-20 Natural Language Processing and Text Mining 
·     Introduction to Natural Language Processing (NLP)

·     Introduction to text mining

·     Importance and applications of text mining

·     How NLP works with text mining

·     Writing and reading to word files

·     OS modules

·     Natural Language Toolkit (NLTK) environment & text mining

·     Principal Component Analysis (PCA)

·     Practical Exercise

  Part-5 Data Analytics with R
Lecture-1 Introduction to Data Analytics and R 
·     Business Intelligence,

·     Business Analytics,

·     understanding Business Analytics and R

·     History of R

·     The R environment

·     What is Statistical Programming?

·     Practical Exercise

Lecture-2 Basic features of R 
·     Calculating with R

·     Named storage

·     Functions

·     Exact or approximate?

·     R is case-sensitive

·     Listing the objects in the workspace

·     Vectors

·     Vector arithmetic

·     Missing values and other special values

·     Character vectors

·     Factors

·     Matrices and arrays

·     Data frames

·     Dates and times

·     Practical Exercise

Lecture-3 Logical vectors and relational operators 
·     Boolean algebra

·     Logical operations in R

·     Relational operators

·     Data input and output

·     Changing directories

·     dump() and source()

·     Redirecting R output

·     Saving and retrieving image files

·     Practical Exercise

Lecture-4 Programming statistical graphics 
·     High-level plots

·     Choosing a high-level graphic

·     Low-level graphics functions

·     Practical Exercise

Lecture-5 Programming with R 
·     Flow control

·     Managing complexity through functions

·     Miscellaneous programming tips

·     Some general programming guidelines

·     Debugging and maintenance

·     Efficient programming

·     Practical Exercise

Lecture-6 Simulation 
·     Monte Carlo simulation

·     Generation of pseudorandom numbers

·     Simulation of other random variables

·     Monte Carlo integration

·     Advanced simulation methods

·     Practical Exercise

Lecture-7 Computational linear algebra 
·     Vectors and matrices in R

·     Matrix arithmetic

·     Eigenvalues and eigenvectors

·     Practical Exercise

Lecture-8 Numerical optimization 
·     The golden Section search method

·     Newton–Raphson

·     The Nelder–Mead simplex method

·     Built-in functions

·     Linear programming

·     Practical Exercise

Lecture-9 Data in R 
·     Modes and Classes

·     Data Storage in R

·     Testing for Modes and Classes

·     Structure of  R Objects

·     Conversion of Objects

·     Missing Values

·     Working with Missing Values

·     Practical Exercise

Lecture-10 Reading and Writing Data 
·     Reading Vectors and Matrices

·     Data Frames: read. Table

·     Comma- and Tab-Delimited Input Files

·     Fixed-Width Input Files

·     Extracting Data from R Objects

·     Connections

·     Reading Large Data Files

·     Generating Data

·     Working with Sequences

·     Spreadsheets

·     Saving and Loading R Data Objects

·     Working with Binary Files

·     Practical Exercise

Lecture-11 R and Databases 
·     A Brief Guide to SQL

·     ODBC

·     Using the RODBC Package

·     The DBI Package

·     Accessing a MySQL Database

·     Performing Queries

·     Normalized Tables

·     Getting Data into MySQL

·     More Complex Aggregations

·     Practical Exercise

Lecture-12 Dates 
·     Date

·     The chron Package

·     POSIX Classes

·     Working with Dates

·     Time Intervals

·     Time Sequences

·     Practical Exercise

Lecture-13 Factors 
·     Using Factors

·     Numeric Factors

·     Manipulating Factors

·     Creating Factors from Continuous Variables

·     Factors Based on Dates and Times

·     Interactions

·     Practical Exercise

Lecture-14 Sub scripting 
·     Basics of Subscripting

·     Numeric Subscripts

·     Character Subscripts

·     Logical Subscripts

·     Subscripting Matrices and Arrays

·     Practical Exercise

Lecture-15 Character Manipulation 
·     Basics of Character Data

·     Displaying and Concatenating Character

·     Working with Parts of Character Values

·     Regular Expressions in R

·     Basics of Regular Expressions

·     Breaking Apart Character Values

·     Using Regular Expressions in R

·     Substitutions and Tagging

·     Practical Exercise

Lecture-16 Data Aggregation 
·     Road Map for Aggregation

·     Mapping a Function to a Vector or List

·     Mapping a function to a matrix or array

·     Mapping a Function Based on Groups

·     Practical Exercise

Lecture-17 Reshaping Data 
·     Modifying Data Frame Variables

·     Recoding Variables

·     The recode Function

·     Reshaping Data Frames

·     Practical Exercise

Lecture-18 Probability and distributions 
·     Random sampling

·     Probability calculations and combinatory

·     Discrete distributions

·     Continuous distributions

·     The built-in distributions in R

·     Practical Exercise

Lecture-19 Descriptive statistics and graphics 
·     Summary statistics for a single group

·     Graphical display of distributions

·     Histograms

·     Empirical cumulative distribution

·     Q–Q plots

·     Boxplots

·     Summary statistics by groups

·     Graphics for grouped data

·     Histograms

·     Bar-plots

·     Dot-charts

·     Pie-charts

·     Practical Exercise

Lecture-20 One- and two-sample tests 
·     One-sample t test

·     Wilcoxon signed-rank test

·     Two-sample t test

·     Comparison of variances

·     Two-sample Wilcoxon test

·     The paired t test

·     Practical Exercise

Lecture-21 Regression and correlation 
·     Simple linear regression

·     Residuals and fitted values

·     Prediction and confidence bands

·     Correlation

·     Pearson correlation

·     Spearman’s ρ

·     Kendall’s τ

·     Practical Exercise

Lecture-22 Multiple Regression 
·     Plotting multivariate data

·     Model specification and output

·     Model search

·     Practical Exercise

Lecture-23 Linear models 
·     Polynomial regression

·     Regression through the origin

·     Design matrices and dummy variables

·     Linearity over groups

·     Interactions

·     Two-way ANOVA with replication

·     Practical Exercise

Lecture-24 Analysis of variance and the Kruskal–Wallis test 
·     One-way analysis of variance

·     Pairwise comparisons and multiple testing

·     Relaxing the variance assumption

·     Graphical presentation

·     Bartlett’s test

·     Kruskal–Wallis test

·     Two-way analysis of variance

·     Graphics for repeated measurements

·     The Friedman test

·     The ANOVA table in regression analysis

·     Practical Exercise

Lecture-25 Classification and Tabular data 
·     Single proportions

·     Two independent proportions

·     k proportions, test for trend

·     Practical Exercise

Lecture-26 Power and the computation of sample size 
·     The principles of power calculations

·     Power of one-sample and paired t tests

·     Power of two-sample t test

·     Practical Exercise

Lecture-27 Advanced data handling 
·     Recoding variables

·     The cut function

·     Manipulating factor levels

·     Working with dates

·     Text Mining

·     Recoding multiple variables

·     Per-group and per-case procedures

·     Time splitting

·     Practical Exercise

Lecture-28 Logistic regression 
·     Generalized linear models

·     Logistic regression on tabular data

·     The analysis of deviance table

·     Connection to test for trend

·     Likelihood profiling

·     Presentation as odds-ratio estimates

·     Practical Exercise

Lecture-29 Survival analysis 
·     Essential concepts

·     Survival objects

·     Kaplan–Meier estimates

·     Practical Exercise

Lecture-30 Rates and Poisson regression 
·     Basic ideas

·     The Poisson distribution

·     Survival analysis with constant hazard

·     Models with piecewise constant intensities

·     Practical Exercise

Lecture-31 Nonlinear curve fitting 
·     Basic usage

·     Finding starting values

·     Case Studies

·     Practical Exercise

  Part-6 Data Analytics Using SAS
Module-1 SAS Base Programming
Part 1: Access and Create Data Structures 
·   Lecture-1 SAS Programs

·   Introduction to SAS programs

·   Submitting a SAS program

·   Working with SAS program syntax         

·   Practical Exercise

·   Lecture-2 Create temporary and permanent SAS data sets.

·   Use a DATA step to create a SAS data set from an existing SAS data set.

·   Example: Data Mylib.NewData;

·   Set Mylib.OldData;

·   <other SAS statements>

·   Run;

·   Practical Exercise

·   Lecture-3 Investigate SAS data libraries using base SAS utility procedures.

·   Use a LIBNAME statement to assign a library reference name to a SAS library.

·   Investigate a library programmatically using the CONTENTS procedure.

·   Practical Exercise

·   Lecture-4 Combine SAS data sets.

·   Concatenate data sets.

·   Merge data sets one-to-one.

·   Merge data sets one-to-many.

·   Practical Exercise

·   Lecture-5 Access an Excel workbook.

·   Use the SAS/ACCESS XLSX engine to read an .xlsx file.

·   Practical Exercise

·   Lecture-6 Create and manipulate SAS date values.

·   Explain how SAS stores date and time values.

·   Use SAS date and time formats to specify how the values are displayed.

·   Practical Exercise

·   Lecture-7 Export data to create standard and comma-delimited raw data files.

·   Create a simple raw data file by using the EXPORT procedure as an alternative to the DATA step.

·   Practical Exercise

·   Lecture-8 Control which observations and variables in a SAS data set are processed and output.

·   Use the WHERE statement in the DATA step to select observations to be processed.

·   Subset variables to be output by using the DROP and KEEP statements.

·   Use the DROP= and KEEP= data set options to specify columns to be processed and/or output.

·   Practical Exercise

Part 2: Manage Data 
·   Lecture-9 Sort observations in a SAS data set.

·   Use the SORT Procedure to re-order observations in place or output to a new dataset.

·   Practical Exercise

·   Lecture-10 Conditionally execute SAS statements.

·   Use IF-THEN/ELSE statements to process data conditionally.

·   Use DO and END statements to execute multiple statements conditionally.

·   Practical Exercise

·   Lecture-11 Use assignment statements in the DATA step.

·   Create new variables and assign a value.

·   Assign a new value to an existing variable.

·   Assign the value of an expression to a variable.

·   Assign a constant date value to a variable.

·   Practical Exercise

·   Lecture-12 Modify variable attributes using options and statements in the DATA step.

·   Change the names of variables by using the RENAME= data set option.

·   Use LABEL and FORMAT statements to modify attributes in a DATA step.

·   Define the length of a variable using the LENGTH statement.

·   Practical Exercise

·   Lecture-13 Accumulate sub-totals and totals using DATA step statements.

·   Use the BY statement to aggregate by subgroups.

·   Practical Exercise

·   Lecture-14 Use SAS functions to manipulate character data, numeric data, and SAS date values.

·   Use SAS functions such as SCAN, SUBSTR, TRIM, UPCASE, and LOWCASE to perform tasks such as the tasks shown below.

·   Replace the contents of a character value.

·   Trim trailing blanks from a character value.

·   Search a character value and extract a portion of the value.

·   Convert a character value to upper or lowercase.

·   Use SAS arithmetic, financial, and probability functions to create or modify numeric values by using the INT and ROUND functions.

·   Create SAS date values by using the functions MDY, TODAY,DATE, and TIME.

·   Extract the month, year, and interval from a SAS date value by using the functions YEAR, QTR, MONTH, and DAY.

·   Perform calculations with date and datetime values and time intervals by using the functions INTCK, INTNX, DATDIF and YRDIF.

·   Practical Exercise

·   Lecture-15 Use SAS functions to convert character data to numeric and vice versa.

·   Explain the automatic conversion that SAS uses to convert values between data types.

·   Use the INPUT function to explicitly convert character data values to numeric values.

·   Practical Exercise

·   Lecture-16 Process data using DO LOOPS.

·   Explain how iterative DO loops function.

·   Use DO loops to eliminate redundant code and to perform repetitive calculations.

·   Use conditional DO loops.

·   Use nested DO loops.

·   Practical Exercise

·   Lecture-17 Validate and clean data.

·   Use PROC FREQ to list unique values, with the nlevel option to show the number of distinct values, with the order=freq to check for duplicate or missing values.

·   Use PROC PRINT with the WHERE statement to display observations with invalid values.

·   Use PROC MEAN to validate the range of numeric variables.

·   Use PROC UNIVARIATE to display extreme observations and missing values and with the ID statement to display the value of identifying variable

·   Practical Exercise

Part 3: Generate Reports 
·   Lecture-18 Generate list reports using the PRINT procedure.

·   Modify the default behavior of PROC PRINT by adding statements and options such as

·   Use the VAR statement to select and order variables.

·   Calculate totals with a SUM statement.

·   Select observations with a WHERE statement.

·   Use the ID statement to identify observations.

·   Use the BY statement to process groups.

·   Practical Exercise

·   Lecture-19 Generate summary reports and frequency tables using base SAS procedures.

·   Produce one-way and two-way frequency tables with the FREQ procedure.

·   Enhance frequency tables with options.

·   Use PROC FREQ to validate data in a SAS data set.

·   Calculate summary statistics and multilevel summaries using the MEANS procedure

·   Enhance summary tables with options.

·   Identify extreme and missing values with the UNIVARIATE procedure.

·   Practical Exercise

·   Lecture-20 Enhance reports through the use of user-defined formats, titles, footnotes and SAS System reporting.

·   Use the LABEL statement to define descriptive column headings.

·   Control the use of column headings with the LABEL and SPLIT=options in Proc Print output.

·   Practical Exercise

·   Lecture-21 Generate reports using ODS statements.

·   Identify the Output Delivery System destinations.

·   Create HTML, PDF, RTF, and files with ODS statements.

·   Use the STYLE=option to specify a style template.

·   Create files that can be viewed in Microsoft Excel.

·   Practical Exercise

Part 4: Error Handling 
·   Lecture-22 Identify and resolve programming logic errors.

·   Use the PUTLOG Statement in the Data Step to help identify logic errors.

·   Use PUTLOG to write the value of a variable, formatted values, or to write values of all variables.

·   Use PUTLOG with Conditional logic.

·   Use temporary variables N and ERROR to debug a DATA step.

·   Practical Exercise

·   Lecture-23 Recognize and correct syntax errors.

·   Identify the characteristics of SAS statements.

·   Define SAS syntax rules including the typical types of syntax errors such as misspelled keywords, unmatched quotation marks, missing semicolons, and invalid options.

·   Use the log to help diagnose syntax errors in a given program.

·   Practical Exercise

·   Lecture-24 Examine and resolve data errors.

·   Given a SAS program, use the log to determine the reason for a data error.

·   Practical Exercise

Module 2: SAS Advance Programming
Part-1 Accessing Data Using SQL 
·   Lecture-1 Generate detail reports by working with a single table, joining tables, or using set operators in SQL

·   Use PROC SQL to perform SQL queries

·   Select columns in a table with a SELECT statement and FROM clause

·   Create a table from a query result set

·   Create new calculated columns.

·   Assign an alias with the AS keyword.

·   Use case logic to select values for a column.

·   Retrieve rows that satisfy a condition with a WHERE clause.

·   Subset data by calculated columns.

·   Join tables - inner joins, full joins (coalesce function), right joins, left joins.

·   Combine tables using set operators - union, outer union, except, intersect.

·   Sort data with an ORDER BY clause.

·   Assign labels and formats to columns.

·   Practical Exercise

·   Lecture-2 Generate summary reports by working with a single table, joining tables, or using set operators in the SQL.

·   Summarize data across and down columns using summary functions (AVG, COUNT, MAX, MIN, SUM).

·   Group data using GROUP BY clause.

·   Filter grouped data using HAVING clause.

·   Eliminate duplicate values with the DISTINCT keyword.

·   Practical Exercise

·   Lecture-3 Construct sub-queries and in-line views within an SQL procedure step.

·   Subset data by using non-correlated subqueries.

·   Reference an in-line view with other views or tables (multiple tables).

·   Practical Exercise

·   Lecture-4 Use SAS SQL procedure enhancements.

·   Use SAS data set options with PROC SQL (KEEP=, DROP=, RENAME=, OBS=).

·   Use PROC SQL invocation options (INOBS=, OUTOBS=. NOPRINT, NUMBER)

·   Use SAS functions (SCAN, SUBSTR, LENGTH).

·   Access SAS system information by using DICTIONARY tables (members, tables, columns)

·   Use the CALCULATED keyword.

·   Practical Exercise

Part-2 Macro Processing 
·   Lecture-5 Create and use user-defined and automatic macro variables within the SAS Macro Language.

·   Define and use macro variables.

·   Use macro variable name delimiter. (.)

·   Use INTO clause of the SELECT statement in SQL to create a single variable or a list of variables.

·   Use the SYMPUTX routine in a DATA Step to create a single variable or a list of variables.

·   Control variable scope

·   Practical Exercise

·   Lecture-6 Automate programs by defining and calling macros using the SAS Macro Language.

·   Define a macro using the %MACRO and %MEND statements.

·   Calling a macro with and without parameters.

·   Document macro functionality with comments

·   Generate SAS Code conditionally by using the %IF-%THEN-%ELSE macro statements or iterative %DO statements.

·   Use the SAS AUTOCALL facility to permanently store and call macros.

·   Practical Exercise

·   Lecture-7 Use macro functions.

·   Use macro functions. (%SCAN, %SUBSTR, %UPCASE)

·   Use macro quoting functions. (%NRSTR, %STR)

·   Use macro evaluation functions. (%SYSEVALF)

·   Use %SYSFUNC to execute DATA step functions within the SAS Macro Language.

·   Practical Exercise

·   Lecture-8 Debug macros.

·   Trace the flow of execution with the MLOGIC option.

·   Examine the generated SAS statements with the MPRINT option.

·   Examine macro variable resolution with the SYMBOLGEN option.

·   Use the %PUT statement to print information to the log.

·   Practical Exercise

·   Lecture-9 Create data-driven programs using SAS Macro Language.

·   Create a series of macro variables.

·   Use indirect reference to macro variables. (&&, etc.)

·   Incorporate DICTONARY tables in data driven macros.

·   Generate repetitive macro calls.

·   Practical Exercise

Part-3 Advanced Techniques 
·   Lecture-10 Process data using 1 and 2 dimensional arrays.

·   Define and use character arrays.

·   Define and use numeric arrays.

·   Create variables with arrays.

·   Reference arrays within a DO loop.

·   Specify the array dimension with the DIM function.

·   Define arrays as temporary arrays.

·   Load initial values for an array from a SAS data set.

·   Practical Exercise

·   Lecture-11 Process data using hash objects.

·   Declare hash and hash iterator objects

·   Dataset argument

·   Ordered argument

·   Multidata argument

·   Use hash object methods

·   definekey()

·   definedata()

·   definedone()

·   find()

·   add()

·   output()

·   Use hash iterator object methods

·   first()

·   next()

·   last()

·   prev()

·   Use hash objects as lookup tables.

·   Use hash objects to create sorted data sets.

·   Use hash iterator objects to access data in forward or reverse key order

·   Practical Exercise

·   Lecture-12 Use SAS utility procedures.

·   Specify a template using the PICTURE statement within the FORMAT Procedure*

·   Specify templates for date, time, and datetime values using directives.

·   Specify templates for numeric values using digit selectors.

·   PICTURE statement options: round, default, datatype, multiplier, prefix

·   Create custom functions with the FCMP procedure

·   Create character and numeric custom functions with single or multiple arguments.

·   Create custom functions based on conditional processing.

·   Use custom functions with the global option CMPLIB=.

·   Practical Exercise

·   Lecture-13 Use advanced functions.

·   Finding strings or words with the FINDC/FINDW functions.

·   Counting strings or words with the COUNT/COUNTC/COUNTW functions.

·   Retrieve previous values with the LAG function.

·   Regular expression pattern matching with PRX functions

·   Practical Exercise

Module 3: SAS/ETS Programming Training
Part 1: Overview of Time Series 
·   Lecture-1 Introduction

·   Lecture-2 Analysis Methods and SAS/ETS Software

·   Options

·   How SAS/ETS Software Procedures Interrelate

·   Practical Exercise

·   Lecture-3 Simple Models: Regression

·   Linear Regression

·   Highly Regular Seasonality

·   Regression with Transformed Data

·   Practical Exercise

Part 2: Simple Models: Autogression 
·   Lecture-4 Introduction

·   Terminology and Notation

·   Statistical Background

·   Practical Exercise

·   Lecture-5 Forecasting

·   Forecasting with PROC ARIMA

·   Backshift Notation B for Time Series

·   Yule-Walker Equations for Covariances

·   Fitting an AR Model in PROC REG

·   Practical Exercise

Part 3: General ARIMA Model 
·   Lecture-6 Introduction

·   Statistical Background

·   Terminology and Notation

·   Practical Exercise

·   Lecture-7 Prediction

·   One-Step-Ahead Predictions

·   Future Predictions

·   Practical Exercise

·   Lecture-8 Model Identification

·   Stationarity and Invertibility

·   Time Series Identification

·   Chi-Squared Check of Residuals

·   Summary of Model Identification

·   Practical Exercise

·   Lecture-9 Examples and Instructions

·   IDENTIFY Statement for Series

·   Example: Iron and Steel Export Analysis

·   Estimation Methods Used in PROC ARIMA

·   ESTIMATE Statement for Series

·   Nonstationary Series

·   Effect of Differencing on Forecasts

·   Examples: Forecasting IBM Series and Silver Series

·   Models for Nonstationary Data

·   Differencing to Remove a Linear Trend

·   Other Identification Techniques

·   Practical Exercise

Part 4: ARIMA Model: Introductory Applications 
·   Lecture-10 Seasonal Time Series

·   Introduction to Seasonal Modeling

·   Model Identification

·   Practical Exercise

·   Lecture-11 Models with Explanatory Variables

·   Case 1: Regression with Time Series Errors

·   Case 1A: Intervention

·   Case 2: Simple Transfer Function

·   Case 3: General Transfer Function

·   Case 3A: Leading Indicators

·   Case 3B: Intervention

·   Practical Exercise

·   Lecture-12 Methodology and Example

·   Case 1: Regression with Time Series Errors

·   Case 2: Simple Transfer Functions

·   Case 3: General Transfer Functions

·   Case 3B: Intervention

·   Practical Exercise

·   Lecture-13 Further Examples

·   North Carolina Retail Sales

·   Construction Series Revisited

·   Milk Scare (Intervention)

·   Terrorist Attack

·   Practical Exercise

Part 5: ARIMA Model: Special Applications 
·   Lecture-14 Regression with Time Series Errors and Unequal Variances

·   Autoregressive Errors

·   Example: Energy Demand at a University

·   Unequal Variances

·   ARCH, GARCH, and IGARCH for Unequal Variances

·   Practical Exercise

·   Lecture-15 Cointegration

·   Introduction

·   Cointegration and Eigenvalues

·   Impulse Response Function

·   Roots in Higher-Order Models

·   Cointegration and Unit Roots

·   An Illustrative Example

·   Estimating the Cointegrating Vector

·   Intercepts and More Lags

·   PROC VARMAX

·   Interpreting the Estimates

·   Diagnostics and Forecasts

·   Practical Exercise

Part 6: State Space Modeling 
·   Lecture-16 Introduction

·   Some Simple Univariate Examples

·   A Simple Multivariate Example

·   Equivalence of Statespace and Vector ARMA Models

·   Practical Exercise

·   Lecture-17 More Examples

·   Some Univariate Examples

·   ARMA (1, 1) of Dimension

·   Practical Exercise

·   Lecture-18 PROC STATESPACE

·   State Vectors Determined from Covariances

·   Canonical Correlations

·   Simulated Example

·   Practical Exercise

Part 7: Spectral Analysis 
·   Lecture-19 Periodic Data: Introduction

·   Example: Plant Enzyme Activity

·   PROC SPECTRA Introduced

·   Testing for White Noise

·   Harmonic Frequencies

·   Extremely Fast Fluctuations and Aliasing

·   The Spectral Density

·   Some Mathematical Detail (Optional Reading)

·   Estimating the Spectrum: The Smoothed Periodogram

·   Cross Spectral Analysis

·   Interpreting Cross-Spectral Quantities

·   Interpreting Cross-Amplitude and Phase Spectra

·   PROC SPECTRA Statements

·   Cross-Spectral Analysis of the Neuse River Data

·   Details on Gain, Phase, and Pure Delay

·   Practical Exercise

Part 8: Data Mining and Forecasting 
·   Lecture-20 Introduction Data Mining and Forecasting

·   Forecasting Data Model

·   Time Series Forecasting System

·   HPF Procedure

·   Scorecard Development

·   Business Goal Performance Metrics

·   Graphical Displays

·   Goal-Seeking Model Development

·   Practical Exercise

  Part-7 MS SQL
Lecture-1 Introduction to SQL 
·     Various types of databases

·     Introduction to Structured Query Language

·     Distinction between client server and file server databases

·     Understanding SQL Server Management Studio

·     SQL Table basics

·     Data types and functions

·     Transaction-SQL

·     Authentication for Windows

·     Data control language

·     The identification of the keywords in T-SQL, such as Drop Table

·     Practical Exercise

Lecture-2 Database Normalization and Entity Relationship Model 
·     Data Anomalies

·     Update Anomalies

·     Insertion Anomalies

·     Deletion Anomalies

·     Types of Dependencies

·     Functional Dependency

·     Fully functional dependency

·     Partial functional dependency

·     Transitive functional dependency

·     Multi-valued functional dependency

·     Decomposition of tables

·     Lossy decomposition

·     Lossless decomposition

·     What is Normalization?

·     First Normal Form

·     Second Normal Form

·     Third Normal Form

·     Boyce-Codd Normal Form(BCNF)

·     Fourth Normal Form

·     Entity-Relationship Model

·     Entity and Entity Set

·     Attributes and types of Attributes

·     Entity Sets

·     Relationship Sets

·     Degree of Relationship

·     Mapping Cardinalities, One-to-One, One-to-Many, Many-to-one, Many-to-many

·     Symbols used in E-R Notation

·     Practical Exercise

Lecture-3 SQL Operators 
·     Introduction to relational databases

·     Fundamental concepts of relational rows, tables, and columns

·     Several operators (such as logical and relational), constraints, domains, indexes, stored procedures, primary and foreign keys

·     Understanding group functions

·     The unique key

·     Practical Exercise

Lecture-4 Working with SQL: Join, Tables, and Variables 
·     Advanced concepts of SQL tables

·     SQL functions

·     Operators & queries

·     Table creation

·     Data retrieval from tables

·     Combining rows from tables using inner, outer, cross, and self joins

·     Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’

·     Temporary table creation

·     Set operator rules

·     Table variables

·     Practical Exercise

Lecture-5 Deep Dive into SQL Functions 
·     Understanding SQL functions – what do they do?

·     Scalar functions

·     Aggregate functions

·     Functions that can be used on different datasets, such as numbers, characters, strings, and dates

·     Inline SQL functions

·     General functions

·     Duplicate functions

·     Practical Exercise

Lecture-6 Working with Sub queries 
·     Understanding SQL sub queries, their rules

·     Statements and operators with which sub queries can be used

·     Using the set clause to modify sub queries

·     Understanding different types of sub queries, such as where, select, insert, update, delete, etc.

·     Methods to create and view sub queries

·     Practical Exercise

Lecture-7 SQL Views, Functions, and Stored Procedures 
·     Learning SQL views

·     Methods of creating, using, altering, renaming, dropping, and modifying views

·     Understanding stored procedures and their key benefits

·     Working with stored procedures

·     Studying user-defined functions

·     Error handling

·     Practical Exercise

Lecture-8 Deep Dive into User-defined Functions 
·     User-defined functions

·     Types of UDFs, such as scalar

·     Inline table value

·     Multi-statement table

·     Stored procedures and when to deploy them

·     What is rank function?

·     Triggers, and when to execute triggers?

·     Practical Exercise

Lecture-9 SQL Optimization and Performance 
·     SQL Server Management Studio

·     Using pivot in MS Excel and MS SQL Server

·     Differentiating between Char, Varchar, and NVarchar

·     XL path, indexes and their creation

·     Records grouping, advantages, searching, sorting, modifying data

·     Clustered indexes creation

·     Use of indexes to cover queries

·     Common table expressions

·     Index guidelines

·     Practical Exercise

Lecture-10 Managing Data with Transact-SQL 
·     Creating Transact-SQL queries

·     Querying multiple tables using joins

·     Implementing functions and aggregating data

·     Modifying data

·     Determining the results of DDL statements on supplied tables and data

·     Constructing DML statements using the output statement

·     Practical Exercise

Lecture-11 Querying Data with Advanced Transact-SQL Components 
·     Querying data using sub queries and APPLY

·     Querying data using table expressions

·     Grouping and pivoting data using queries

·     Querying temporal data and non-relational data

·     Constructing recursive table expressions to meet business requirements

·     Using windowing functions to group

·     Rank the results of a query

·     Practical Exercise

Lecture-12 Programming Databases Using Transact-SQL 
·     Creating database programmability objects by using T-SQL

·     Implementing error handling and transactions

·     Implementing transaction control in conjunction with error handling in stored procedures

·     Implementing data types and NULL

·     Practical Exercise

Lecture-13 Designing and Implementing Database Objects 
·     Designing and implementing relational database schema

·     Designing and implementing indexes

·     Learning to compare between indexed and included columns

·     Implementing clustered index

·     Designing and deploying views

·     Column store views

·     Practical Exercise

Lecture-14 Implementing Programmability Objects 
·     Explaining foreign key constraints

·     Using T-SQL statements

·     Usage of Data Manipulation Language (DML)

·     Designing the components of stored procedures

·     Implementing input and output parameters

·     Applying error handling

·     Executing control logic in stored procedures

·     Designing trigger logic, DDL triggers

·     Practical Exercise

Lecture-15 Managing Database Concurrency 
·     Applying transactions

·     Using the transaction behaviour to identify DML statements

·     Learning about implicit and explicit transactions

·     Isolation levels management

·     Understanding concurrency and locking behaviour

·     Using memory-optimized tables

·     Practical Exercise

Lecture-15 Optimizing Database Objects 
·     Accuracy of statistics

·     Formulating statistics maintenance tasks

·     Dynamic management objects management

·     Identifying missing indexes

·     Examining and troubleshooting query plans

·     Consolidating the overlapping indexes

·     The performance management of database instances

·     SQL server performance monitoring

·     Practical Exercise

Lecture-16 Advanced Topics 
·     Correlated Sub query, Grouping Sets, Rollup, Cube

·     Implementing Correlated Sub queries

·     Using EXISTS with a Correlated sub query

·     Using Union Query

·     Using Grouping Set Query

·     Using Rollup

·     Using CUBE to generate four grouping sets

·     Perform a partial CUBE

·     Practical Exercise

Lecture-17 Microsoft Courses: Study Material 
·     Performance Tuning and Optimizing SQL Databases

·     Querying Data with Transact-SQL

·     Practical Exercise

  Part-8 Tableau
Lecture-1 Data Visualization and Power of Tableau 
·     What is data visualization?

·     Comparison and benefits against reading raw numbers

·     Real use cases from various business domains

·     Some quick and powerful examples using Tableau without going into the technical details of Tableau

·     Installing Tableau

·     Tableau interface

·     Connecting to DataSource

·     Tableau data types

·     Data preparation

·     Practical Exercise

Lecture-2 Tableau Architecture 
·     Installation of Tableau Desktop

·     Architecture of Tableau

·     Tableau Layout

·     Tableau Toolbars

·     Tableau Data Pane

·     Tableau Analytics Pane

·     How to start with Tableau

·     The ways to share and export the work done in Tableau

·     Practical Exercise

Lecture-3 Tableau Metadata and Data Blending 
·     Connection to Excel

·     Cubes and PDFs

·     Management of metadata and extracts

·     Data preparation

·     Joins and Union

·     Dealing with NULL values

·     Cross-database joining

·     Data extraction

·     Data blending

·     Refresh extraction

·     Incremental extraction

·     How to build extract

·     Practical Exercise

Lecture-4 Creation of Sets and Using Filters 
·     Mark

·     Highlight

·     Sort

·     Group, and use sets

·     Creating and editing sets

·     IN/OUT

·     Sets in hierarchies

·     Constant sets

·     Computed sets

·     Bins

·     Filters

·     Filtering continuous dates

·     Dimensions, and measures

·     Interactive filters

·     Marks card

·     Hierarchies

·     How to create folders in Tableau

·     Sorting in Tableau

·     Types of sorting

·     Filtering in Tableau

·     Types of filters

·     Filtering the order of operations

·     Practical Exercise

Lecture-5 Organizing Data and Visual Analytics 
·     Using Formatting Pane to work with menu, fonts, alignments, settings, and copy-paste

·     Formatting data using labels and tooltips

·     Edit axes and annotations

·     K-means cluster analysis

·     Trend and reference lines

·     Visual analytics in Tableau

·     Forecasting

·     Confidence interval

·     Reference lines

·     Bands

·     Practical Exercise

Lecture-6 Working with Mapping, Calculations, Expressions and Parameters 
·     Working on coordinate points

·     Plotting longitude and latitude

·     Editing unrecognized locations

·     Customizing geocoding, polygon maps

·     WMS: web mapping services

·     Working on the background image, including add image

·     Plotting points on images and generating coordinates from them

·     Map visualization

·     Custom territories

·     Map box

·     WMS map

·     How to create map projects in Tableau

·     Creating dual axes maps and editing locations

·     Calculation syntax and functions in Tableau

·     Various types of calculations, including Table, String, Date, Aggregate, Logic, and Number

·     LOD expressions, including concept and syntax

·     Aggregation and replication with LOD expressions

·     Nested LOD expressions

·     Fixed level

·     Lower level

·     Higher level

·     Quick table calculations

·     The creation of calculated fields

·     Predefined calculations

·     How to validate

·     Creating parameters

·     Parameters in calculations

·     Using parameters with filters

·     Column selection parameters

·     Chart selection parameters

·     How to use parameters in the filter session

·     How to use parameters in calculated fields

·     How to use parameters in the reference line

·     Practical Exercise

Lecture-7 Introduction of Charts, Graphs, Dashboards and Stories 
·     Dual axes graphs

·     Histograms

·     Single and dual axes

·     Box plot

·     Motion Charts

·     Pareto Charts

·     Funnel Charts

·     Pie Charts

·     Bar Charts

·     Line Charts

·     Bubble Charts

·     Bullet Charts

·     Scatter Charts

·     Waterfall charts

·     Tree Maps

·     Heat Maps

·     Market basket analysis (MBA)

·     Using Show me

·     Text table and highlighted table

·     Building and formatting a dashboard using size, objects, views, filters, and legends

·     Best practices for making creative as well as interactive dashboards using the actions

·     Creating stories, including the intro of story points

·     Creating as well as updating the story points

·     Adding catchy visuals in stories

·     Adding annotations with descriptions; dashboards and stories

·     What is dashboard?

·     Highlight actions, URL actions, and filter actions

·     Selecting and clearing values

·     Best practices to create dashboards

·     Dashboard examples; using Tableau workspace and Tableau interface

·     Learning about Tableau joins

·     Types of joins

·     Tableau field types

·     Saving as well as publishing data source

·     Live vs extract connection

·     Various file types

·     Practical Exercise

Lecture-8 Tableau Prep 
·     Introduction to Tableau Prep

·     How Tableau Prep helps quickly combine join, shape, and clean data for analysis

·     Creation of smart examples with Tableau Prep

·     Getting deeper insights into the data with great visual experience

·     Making data preparation simpler and accessible

·     Integrating Tableau Prep with Tableau analytical workflow

·     Understanding the seamless process from data preparation to analysis with Tableau Prep

·     Practical Exercise

  Part-9 Casestudies

  Choose Any Two Specialization