Dv analytics detailed course contents

DV ANALYTICS COURSE CONTENTS

A Comprehensive Analytics Training Institute

DV Analytics

[email protected] 9591793303

Contents Excel : Course Contents ................................................................................................................................................... - 2 -

Access Course Contents .................................................................................................................................................. - 4 -

SQL Course Contents:...................................................................................................................................................... - 5 -

Base SAS Course Contents .............................................................................................................................................. - 7 -

Advanced SAS Course Contents ...................................................................................................................................... - 8 -

Qlikview Course Contents: .............................................................................................................................................. - 9 -

Analytics Course Contents ............................................................................................................................................ - 12 -

R Course Contents ......................................................................................................................................................... - 17 -

Tableau Course Contents .............................................................................................................................................. - 18 -

Introduction to BIG Data Analytics Course Contents .................................................................................................... - 21 -

Contact us

9591793303

[email protected]

DV Analytics

Krishnappa Garden,

Bhagmane techpark

CV Raman Nagar,

Bangalore-560093

mailto:[email protected]


Excel : Course Contents Section1:

Introduction to MS Excel

Navigation technique using excel ribbons (a journey to excel Home, Insert, Page Layout, Data, View and Developer)

Cell Reference, Range, Rows and Columns (Excel shortcut keys)

Excel manipulations objects

Format Paint, Border Style and Designing, Cell Merging, Conditional Formatting, Autosum, Sorting and filtering, Data Validation, Data consolidation

Exercise

Section2:

Data Importing and Exporting and Data normalization standardization process

o Data Import and Export from csv,.txt,.xlsx and ODBC connections

o Text to column data

o Remove duplicates

o Data sorting and customized sorting

o Data validation

o Data Consolidation

o Data connection properties

o What-if analysis

Exercise

Excel Charts

o Vbar chart

o Hbar chart

o Pie Chart

o Scatter chart

o Area Chart

o Line chart

o Snake chart

o Excel hit map

o Bubble chart

o Radar chart

Exercise

Excel Pivot

o Basic pivot (Understanding of pivot table and field objects for row label, column label, Value and report filter)

o Pivot slicers and slicer connection



o Pivot relational data model preparation

o Pivot data visualization designing techniques

o Pivot slicer dashboard

o Pivot options

o Pivot connection to other database

o Pivot calculations and values manipulation techniques

o Power pivot

o Live projects

Exercise

Section3:

Excel formula and functions

o Character Functions (Upper, Lower, Proper, Left, Right, Mid, Concatenate, Trim Istext , find , substitute and replace etc.)

o Numeric Functions (Ceiling, Flooring, Round, Round UP/DOWN, Int, Isnumber, Count if, Sum if etc.)

o Date Functions (Today,Now,Hour,Minute,Second,Datediff,Day,Month,QTR,Year,Networkingdays etc.)

o Excel Formulas and Functions like IF and Nested IF, Vlook-up, HLook-up, Sum,Sum IF,Match, Offset and Index etc.

Section4: Excel Advanced

Excel Dashboard using Form control and ActiveX control, Excel formula and functions

Excel VBA Programming

Excel automated dashboard using Excel VBA, formula and functions

Live Dashboard making practical’s

Exercise



Access Course Contents Introduction MS ACCESS

Navigation technique in ACCESS and Access Objects

Creating Database, Tables, Field Properties

Access Queries (Select, Make Table, Append, Update, Delete, Crosstab,

Union and Union All)

Data Import and Export in Access

Access Pivot Table, Chart

Access Join

Forms and Reports

Access Formulas and Functions

Access Modules using Access VBA

Access Data Manipulation technique using SQL queries



SQL Course Contents: Audience

This reference has been prepared for an analyst for understanding the basics and advanced use of SQL as relational

database engine.

Prerequisites

Before coming to access you need to have basic idea about database and RDBMS concept.

Section 1: Introduction to SQL What is SQL Why SQL SQL Process

SQL Commands

o DDL-Data Definition Language o DML-Data Manipulation Language o DCL-Data Control Language o DQL-Data Query Language

SQL RDBMS Concept

o Database o Table (Fields and Records) o SQL Constraints o Keys (Primary and Foreign)

SQL Syntax

o Create Database Statement o Drop Database Statement o Use Statement o Create Table Statement o Alter Table Statement o Insert Into Statement o Drop Table Statement o Delete Table Statement o Truncate Table Statement o Create Index/ Drop Index Statement o Select statement o Select Top Clause o Column alias o Distinct clause o Where Clause o And/or clause o In clause



o Between clause o Like clause o Group by clause o Order by clause o Count clause o Having clause o Create Table Statement o Update Statement o Delete Statement

SQL Data type

o Exact numeric Data Type o Approximate Numeric Data Type o Date and Time Data Type o Character Strings Data Type o Unicode Character strings data type o Binary Data Type o Misc Data Type

SQL Operator

o Arithmetic Operator o Comparison Operator o Logical Operator

SQL Join

o Inner Join o Outer Join

Left Left Null

Right Right Null

Full Unmatched Join

o Intersect o Except o Cross join/ Cartesian Join o Self-join

SQL Functions

o Character Functions o Numeric Functions o Datetime Functions

SQL Case and When statement SQL Unpivot and Pivot concept SQL Sub queries SQL Views SQL Store procedure

Practical problem solving and creating data model



Base SAS Course Contents Section1:

Introduction SAS PC and SAS EG My first programming in SAS using Cards and Datalines Nomenclatures in SAS Vs SQL Criteria to be followed for creating dataset name, variable name and variable values SAS Library and the criteria to be followed to create this How to see Descriptor and Data portion of a dataset and library SAS Programming steps Data Step and Proc Step Exercise

Section2:

How to Import and Export data from csv, excel and accessdb How to read data from text file to create dataset How to export sas dataset to a text file using file statement How to connect sas different database server How to create dataset from an existing dataset Exercise

Section3:

List report SAS options and formats Proc sort procedure Enhancing output using ODS Use of where statement Use of if statement User defined format vs system defined format Exercise

Section4:

Appending dataset Merging dataset SAS Merge Vs SAS SQL Join How to create multiple dataset from one dataset Exercise

Section5:

How to transpose dataset from row to column and column to rows Retain statement Difference between sum and addition Use of first. and last. SAS Function Exercise

Section6:

SAS Loops and Arrays SAS Summary Report SAS Graphs



Advanced SAS Course Contents Section1: SAS SQL

Introduction to SAS SQL Retrieving data using

• Select statement • Where statement • Group by statement • Order by statement • Having clause

SAS SQL Options How to create a new table from an existing table Altering table, creating index and views Use of case and when statement Update query, Updating a Table with Values from Another Table Delete query Appending Table SAS SQL Join, Except and Intersect Creating macro variable using sas sql Exercise

Section2: SAS Macro Introduction to Macro Facility Creating the first macro Understand the concept of macro statement, options and functions Creating Macro Variable Macro debugging options Conditional macro statement

• %if %then • %do %end

Macro Expressions Macro Quoting Macro Functions Storing and using of Macro Exercise



Qlikview Course Contents: Section 1: Introduction to Data Visualization

About Data Visualization Software lined up for Data Visualization support Why Qlikview A sample dashboard in Qlikview for an introduction to visualization techniques and benefits

Section 2: Introduction to Data Driven approach for dashboard preparation

Data Access

o Data Importing from Excel, CSV and Flat file

o Data Importing from ODBC and OLE DB

o Data Importing from QVD file

o Data Preparation using Inline

o Data storing into QVD

o How to save script into .QVS file

o Reload and Partial Reload (For Appending and Replacing data)

o Reduce data

Data Management

o Treating Null Value

o Mapping Table

o Concatenate and No concatenate

o Resident

o Adding field and Functions for data manipulation

o Creating Variables (Use of Set and Let)

o Qlikview Functions

Data Analysis

Data Presentation and Reporting

Section 3: An Introduction to Business Intelligence Architecture

Understanding Data Structure Creating Data Model Concept about OLAP, Fact Table and Dimension Table



Section 4: A journey through Qlikview User Interface

Navigation through Qlikview Menu commands and Toolbars and status bar o Starting with Qlikview o Getting Started wizard o Qlikview file o Menu commands o Toolbars and status bar o User preferences o Exporting and printing o Logic and selections o Bookmark o Reports o Alerts o Variable overview o Expression overview o Internal file

Section 5: Sheet and Sheet Objects

Sheet Properties New Sheet objects

o List Box and properties o Statistics Box and properties o Multi Box and properties o Table Box and properties o Current Selection Box and properties o Input Box and properties o Button and properties o Text Object and properties o Line and Arrow Object and properties o Slider and Calendar Object and properties o Bookmark Object and properties o Search Object and properties

Container Custom Object Server Object pane Layout theme

Section 6: Charts

Introduction to chart Bar Chart (Vbar and Hbar) Lines Chart



Pie Chart Combo Chart Radar Chart Scatter Chart Grid Chart Funnel Chart Block Chart Gauge Chart Mekko Chart Pivot Table Straight Table Chart Expressions

Section 7: Scripting and Security

Variables and fields Script dialogs Script syntax Script Expressions Data Structure/Data Model Evaluating the loaded data QVD files

Practical with live dashboard making



Analytics Course Contents Section 1: Introduction to Statistical Analysis

Ch1: What is Statistics?

Ch2: Basic Statistical Concepts in Business Analytics

1. Population 2. Sample 3. Variable 4. Variable Types in Predictive Modeling Context 5. Parameter 6. Statistic 7. Example Exercise

Ch3: Statistical Analysis Methods

1. Descriptive Statistics

2. Inferential Statistics

3. Predictive Statistics

Ch4: Solving a Problem Using Statistical Analysis

1. Setting Up Business Objective and Planning 2. The Data Preparation 3. Descriptive Analysis and Visualization 4. Predictive Modeling 5. Model Validation 6. Model Implementation

Ch5: An Example from the Real World: Credit Risk Life Cycle

1. Business Objective and Planning 2. Data Preparation 3. Descriptive Analysis and Visualization 4. Predictive Modeling 5. Model Validation 6. Model Implementation

Exercise

Section 2: Basic Descriptive Statistics and Reporting in SAS

Ch1: Rudimentary Forms of Data Analysis

1. Simply Print the Data 2. Print and Various Options of Print in SAS

Ch2: Summary Statistics



1. Central Tendencies 2. Calculating Central Tendencies in SAS 3. What Is Dispersion? 4. Calculating Dispersion Using SAS 5. Quantiles 6. Calculating Quantiles Using SAS 7. Box Plots 8. Creating Boxplots Using SAS

Ch3: Bivariate Analysis

Exercise

Section 3: Data Exploration, Validation, and Data Sanitization

Ch1: Data Exploration Steps in a Statistical Data Analysis Life Cycle

1. Example: Contact Center Call Volumes

Ch2: Need for Data Exploration and Validation

Ch3: Issues with the Real-World Data and How to Solve Them

1. Missing Values 2. The Outliers 3. Manual Inspection of the Dataset Is Not a Practical Solution 4. Removing Records Is Not Always the Right Way

Ch4: Understanding and Preparing the Data

1. Data Exploration 2. Data Validation 3. Data Cleaning

Ch5: Data Exploration, Validation, and Sanitization Case Study: Credit Risk Data

1. Importing the Data 2. Step 1: Data Exploration and Validation Using the PROC CONTENTS 3. Step 2: Data Exploration and Validation Using Data Snapshot 4. Step 3: Data Exploration and Validation Using Univariate Analysis 5. Step 4: Data Exploration and Validation Using Frequencies 6. Step 5: The Missing Value and Outlier Treatment

Exercise



Section 4: Testing of Hypothesis

Ch1: Testing: An Analogy from Everyday Life

Ch2: What Is the Process of Testing a Hypothesis?

1. State the Null Hypothesis on the Population: Null Hypothesis (H0) 2. Alternate Hypothesis (H1) 3. Sampling Distribution 4. Central Limit Theorem 5. Test Statistic 6. Inference 7. Critical Values and Critical Region 8. Confidence Interval

Ch3: Tests

1. T-test for Mean 2. Case Study: Testing for the Mean in SAS 3. Other Test Examples 4. Two-Tailed and Single-Tailed Tests

Exercise

Section 5: Correlations and Linear Regression

Ch1: What is Correlations?

1. Pearson’s Correlation Coefficient (r) 2. Variance and Covariance 3. Correlation Matrix 4. Calculating Correlation Coefficient Using SAS 5. Correlation Limits and Strength of Association 6. Properties and Limitations of Correlation Coefficient (r) 7. Some Examples on Limitations of Correlation 8. Correlation vs. Causation 9. Correlation Example 10. Correlation Summary

Ch2: Linear Regression

1. Correlation to Regression 2. Estimation Example

Ch3: Simple Linear Regression

1. Regression Line Fitting Using Least Squares 2. The Beta Coefficients: Example 1 3. How Good Is My Model? 4. Regression Assumptions

Ch4: When Linear Regression Can’t Be Applied



Ch5: Simple Regression: Example

Exercise

Section 6: Multiple Regression Analysis

Ch1: Multiple linear regression

1. Multiple Regression Line 2. Multiple Regression Line Fitting Using Least Squares 3. Multiple Linear Regression in SAS 4. Example: Smartphone Sales Estimation 5. Goodness of Fit 6. Three Main Measures from Regression Output 7. Multicollinearity Defined

Ch2: How to Analyze the Output: Linear Regression Final Check List

1. Double-Check for the Assumptions of Linear Regression 2. F-test 3. R-squared 4. Adjusted R-Squared 5. VIF 6. T-test for Each Variable 7. Analyzing the Regression Output: Final Check List Example

Exercise

Section 7: Logistic Regression

Ch1: Predicting Ice-Cream Sales: Example

Ch2: Nonlinear Regression

Ch3: Logistic Regression

Ch4: Logistic Regression Using SAS

Ch5: SAS Logistic Regression Output Explanation

1. Output Part 1: Response Variable Summary 2. Output Part 2: Model Fit Summary 3. Output Part 3: Test for Regression Coefficients 4. Output Part 4: The Beta Coefficients and Odds Ratio 5. Output Part 5: Validation Statistics

Ch6: Individual Impact of Independent Variables

Ch7: Goodness of Fit for Logistic Regression

1. Chi-square Test 2. Concordance

Ch8: Prediction Using Logistic Regression

Ch9: Multicollinearity in Logistic Regression



1. No VIF Option in PROC LOGISTIC Ch10: Logistic Regression Final Check List

Ch11: Loan Default Prediction Case Study

1. Background and Problem Statement 2. Objective 3. Data Set 4. Model Building 5. Final Model Equation and Prediction Using the Model

Exercise

Section 8: Time Series Analysis and Forecasting

Ch1: What Is a Time-Series Process?

Ch2: Main Phases of Time-Series Analysis

Ch3: Modeling Methodologies

Ch4: Box–Jenkins Approach

1. What Is ARIMA? 2. The AR Process 3. The MA Process 4. ARMA Process

Ch5: Understanding ARIMA Using an Eyesight Measurement Analogy

Ch6: Steps in the Box–Jenkins Approach

1. Step 1: Testing Whether the Time Series Is Stationary 2. Step 2: Identifying the Model 3. Step 3: Estimating the Parameters 4. Step 4: Forecasting Using the Model 5. Case Study: Time-Series Forecasting Using the SAS Example 6. Checking the Model Accuracy

Exercise

Section 9: Cluster Analysis

Ch1: What is cluster analysis

Ch2: Customer segmentation introduction

Ch3: What is distance matrix

Ch4: K-Means clustering algorithm

Ch5: Super market customer segmentation case study

Ch6: Employee performance segmentation case study



R Course Contents Section 1: Introduction to R

Ch1: R-Introduction

Ch2: R Data Type

1. Vectors 2. Matrices 3. Lists 4. Data frames

Ch3: Programming on R environment

1. Writing R code 2. R syntax 3. Debugging R Code

Ch4: Live data project on R

Section2: Data Manipulation in R

1. R-Data frames 2. Creation of new variable in datasets 3. Sub setting of data in R 4. Joining R datasets 5. Where and if conditions 6. Live data manipulations projects

Section3: Advanced Analytics Using R

1. Basic descriptive statistics in R 2. Data analysis using graphs in R 3. Correlation and regression in R 4. Multiple Regression in R 5. Logistic regression in R 6. Cluster analysis in R 7. Live data analytics projects



Tableau Course Contents Section 1: Introduction and Overview

Introduction to Tableau Tableau workspace and various options Navigating in tableau Exercise

Section 2: Connecting to data

Connecting to desktop data files Connecting to Access files Connecting to Excel files and Txt files Importing data from tableau extracts Connecting to database servers Connecting to MS Sql & Mysql Connecting to other database servers Exercise

Section 3: Building Basic Views

Various data related options Dimensions and Measures Quick graph show me option Simple graph creation Exercise

Section 4: Data Manipulation

Joining multiple tables Data Extracts Custom SQL Working with multiple connections in the same workbook Data update & its effects Exercise

Section 5: Data Visualizations Using Graph

Crating Cross tab & options Map & options Heat Map & options Scatter Plots & options



Pie Charts and Bar Charts & options Bubble chart and options Exercise

Section 6: Calculated fields

Creating a new field Working with String Functions Basic Arithmetic Calculations Working with dates Working with Totals Custom Aggregations Logic Statements Exercise

Section 7: Formatting the graphs

Titles and Captions Formatting the Visualization Working with Labels and Annotations Exercise

Section 8: Building Dashboards & formatting

Creating a dashboard Filters and parameters in the dashboard Formatting the dashboard Animations in the dashboard Building interactive dashboards Exercise

Section 9: Publishing the visualizations

Publish to Tableau Server and Sharing over the Web Other options of exporting the visualizations Exercise



Section 10: Advanced Data Options

Clipboard data Connecting two data sources Joining data sources Creating hierarchies Measure values and Measure names Exercise

Section 11: Advanced Graph Options

Sorting Groups Sets Actions Parameters Exercise

Section 12: Basic Statistics using Tableau

Mean, Median Quartiles Box plots Outlier Identification Exercise

Section 13: Visualization Mock Projects

Data Importing Data validation and sanitization Creating basic visualizations Exercise

Section 14: Data Visualization Final Projects

Data Importing Data validation and sanitization Creating basic visualizations Analysis and creating interactive dashboard



Introduction to BIG Data Analytics Course Contents

Introducing Big Data Analytics

Ch1: Traditional Data-Handling Tools

1. Walmart Customer Data 2. Facebook Data 3. Examples of the Growing Size of Data

Ch2: What Is Big Data?

1. The Three Main Components of Big Data 2. Applications of Big Data Analytics

Ch3: The Solution for Big Data Problems

Ch4: Distributed Computing

Ch5: What Is MapReduce?

1. Map Function 2. Reduce Function

Ch6: What Is Apache Hadoop?

1. Hadoop Distributed File System 2. MapReduce 3. Apache Hive 4. Apache Pig 5. Other Tools in the Hadoop Ecosystem 6. CompaniesThat Use Hadoop

Ch7: Big Data Analytics Example

1. Examining the Business Problem 2. Getting the Data Set 3. Starting Hadoop 4. Looking at the Hadoop Components 5. Moving Data from the Local System to Hadoop 6. Viewing the Data on HDFS 7. Starting Hive 8. Creating a Table Using Hive



9. Executing a Program Using Hive 10. Viewing the MapReduce Status 11. The Final Result



Contact us

9591793303

[email protected]

DV Analytics

Krishnappa Garden,

Bhagmane techpark

CV Raman Nagar,

Bangalore-560093

http://dvanalyticstraininginstitute.blogspot.in/


http://dvanalyticstraininginstitute.blogspot.in/

Data & Analytics

Dv analytics detailed course contents