SAS Training Institute in Delhi

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Data Analytics Certification Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.9 (65479)

View Details Course Curriculum

Data Ananyst is a powerful analytics platform to make discoveries. By using different aspects of computer science, data visualisations, data analytics, statistics, R and Python Programming in data science, you may convert voluminous data into meaningful contents.

Introduction To Python

Installation and Working with Python

Understanding Python variables

Python basic Operators

Understanding the Python blocks.

Python Keyword and Identiers

Python Comments, Multiline Comments.

Python Indentation

Understating the concepts of Operators

Arithmetic
Relational
Logical
Assignment
Membership
Identity

Introduction To Variables

Variables, expression condition and function

Global and Local Variables in Python

Packing and Unpacking Arguments

Type Casting in Python

Byte objects vs. string in Python

Variable Scope

Python Data Type

Declaring and using Numeric data types

Using string data type and string operations

Understanding Non-numeric data types

Understanding the concept of Casting and Boolean.

Strings

List

Tuples

Dictionary

Sets

Control Structure & Flow

Statements – if, else, elif

How to use nested IF and Else in Python

Loops

Loops and Control Statements.

Jumping Statements – Break, Continue, pass

Looping techniques in Python

How to use Range function in Loop

Programs for printing Patterns in Python

How to use if and else with Loop

Use of Switch Function in Loop

Elegant way of Python Iteration

Generator in Python

How to use nested Loop in Python

Use If and Else in for and While Loop

Examples of Looping with Break and Continue Statement

How to use IN or NOT IN keyword in Python Loop.

Python Function, Modules and Packages

Python Syntax

Function Call

Return Statement

Arguments in a function – Required, Default, Positional, Variable-length

Write an Empty Function in Python –pass statement.

Lamda/ Anonymous Function

*args and **kwargs

Help function in Python

Scope and Life Time of Variable in Python Function

Nested Loop in Python Function

Recursive Function and Its Advantage and Disadvantage

Organizing python codes using functions

Organizing python projects into modules

Importing own module as well as external modules

Understanding Packages

Random functions in python

Programming using functions, modules & external packages

Map, Filter and Reduce function with Lambda Function

More example of Python Function

Python Date Time and Calendar

Day, Month, Year, Today, Weekday

IsoWeek day

Date Time

Time, Hour, Minute, Sec, Microsec

Time Delta and UTC

StrfTime, Now

Time stamp and Date Format

Month Calendar

Itermonthdates

Lots of Example on Python Calendar

Create 12-month Calendar

Strftime

Strptime

Format Code list of Data, Time and Cal

Locale’s appropriate date and time

List

What is List.

List Creation

List Length

List Append

List Insert

List Remove

List Append & Extend using “+” and Keyword

List Delete

List related Keyword in Python

List Revers

List Sorting

List having Multiple Reference

String Split to create a List

List Indexing

List Slicing

List count and Looping

List Comprehension and Nested Comprehension

Tuple

What is Tuple

Tuple Creation

Accessing Elements in Tuple

Changing a Tuple

Tuple Deletion

Tuple Count

Tuple Index

Tuple Membership

TupleBuilt in Function (Length, Sort)

Dictionary

Dict Creation

Dict Access (Accessing Dict Values)

Dict Get Method

Dict Add or Modify Elements

Dict Copy

Dict From Keys.

Dict Items

Dict Keys (Updating, Removing and Iterating)

Dict Values

Dict Comprehension

Default Dictionaries

Ordered Dictionaries

Looping Dictionaries

Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets

What is Set

Set Creation

Add element to a Set

Remove elements from a Set

PythonSet Operations

Frozen Sets

Strings

What is Set

Set Creation

Add element to a Set

Remove elements from a Set

PythonSet Operations

Python Exception Handling

Python Errors and Built-in-Exceptions

Exception handing Try, Except and Finally

Catching Exceptions in Python

Catching Specic Exception in Python

Raising Exception

Try and Finally

Python File Handling

Opening a File

Python File Modes

Closing File

Writing to a File

Reading from a File

Renaming and Deleting Files in Python

Python Directory and File Management

List Directories and Files

Making New Directory

Changing Directory

Python Database Interaction

SQL Database connection using

Creating and searching tables

Reading and Storing cong information on database

Programming using database connections

Contacting user Through Emails Using Python

Installing SMTP Python Module

Sending Email

Reading from le and sending emails to all users

Reading an excel

Working With Excel

Reading an excel le using Python

Writing to an excel sheet using Python

Python| Reading an excel le

Python | Writing an excel le

Adjusting Rows and Column using Python

ArithmeticOperation in Excel le.

Play with Workbook, Sheets and Cells in Excel using Python

Creating and Removing Sheets

Formatting the Excel File Data

More example of Python Function

Complete Understanding of OS Module of Python

Check Dirs. (exist or not)

How to split path and extension

How to get user prole detail

Get the path of Desktop, Documents, Downloads etc.

Handle the File System Organization using OS

How to get any les and folder’s details using OS

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Data visualization is the graphical way to representation of information and data. By using visual elements like graphs, maps and charts. Data visualization tools provide an accessible easy way to see and understand the data.

Data Analysis and Visualization using Pandas.

Statistics

Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot

Pandas

Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
Exploring the Data Plotting, Correlations, and Histograms
Getting statistical information about the data Analysis Concepts, Handle the None Values
Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
Exporting the results to Excel Joins
Python | Pandas Data Frame Inner Join
Under sting the properties of Data Frame Left Join (Left Outer Join)
Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
Pandas | How to Group Data How to apply Lambda / Function on Data Frame
Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

Data Analysis and Visualization using NumPy and MatPlotLib

NumPy

Introduction to NumPy Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from a CSV
Operations with NumPy Arrays
Two-Dimensional Array
Selecting Elements from 1-D Array
Selecting Elements from 2-D Array
Logical Operation with Arrays
Indexing NumPy elements using conditionals
NumPy’s Mean and Axis
NumPy’s Mode, Median and Sum Function
NumPy’s Sort Function and More

MatPlotLib

Bar Chart using Python MatPlotLib
Column Chart using Python MatPlotLib
Pie Chart using Python MatPlotLib
Area Chart using Python MatPlotLib
Scatter Plot Chart using Python MatPlotLib
Play with Charts Properties Using MatPlotLib
Export the Chart as Image
Understanding plt. subplots () notation
Legend Alignment of Chart using MatPlotLib
Create Charts as Image
Other Useful Properties of Charts.
Complete Understanding of Histograms
Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Data Visualization with Seaborn

Introduction to Seaborn

Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using Pandas with seaborn
Tidy vs Untidy data
Making a count plot with a Dataframe
Adding a third variable with hue
Hue and scattera plots
Hue and count plots

Visualizing Two Quantitative Variables

Introduction to relational plots and subplots
Creating subplots with col and row
Customizing scatters plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots

Visualizing a Categorical and a Quantitative Variable

Current plots and bar plots
Count plots
Bar plot with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whisk
Point plots
Customizing points plots
Point plot with subgroups

Customizing Seaborn Plots

Changing plot style and colour
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels Part 1
Face Grids vs. Axes Subplots
Adding a title to a face Grid object
Adding title and labels Part 2
Adding a title and axis labels
Rotating x-tics labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots
Well done! What’s next

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Microsoft SQL Server is a relational database management system (RDBMS) that supports a wide variety of transaction processing, business intelligence and analytics applications in corporate IT environments. In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool, and to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL.

SQL Server Fundamentals

SQL Server 2019 Installation

Service Accounts & Use, Authentication Modes & Usage, Instance Congurations

SQL Server Features & Purpose

Using Management Studio (SSMS)

Conguration Tools & SQLCMD

Conventions & Collation

SQL Server 2019 Database Design

SQL Database Architecture

Database Creation using GUI

Database Creation using T-SQL scripts

DB Design using Files and File Groups

File locations and Size parameters

Database Structure modications

SQL Tables in MS SQL Server

SQL Server Database Tables

Table creation using T-SQL Scripts

Naming Conventions for Columns

Single Row and Multi-Row Inserts

Table Aliases

Column Aliases & Usage

Table creation using Schemas

Basic INSERT

UPDATE

DELETE

SELECT queries and Schemas

Use of WHERE, IN and BETWEEN

Variants of SELECT statement

ORDER BY

GROUPING

HAVING

ROWCOUNT and CUBE Functions

Data Validation and Constraints

Table creation using Constraints

NULL and IDENTITY properties

UNIQUE KEY Constraint and NOT NULL

PRIMARY KEY Constraint & Usage

CHECK and DEFAULT Constraints

Naming Composite Primary Keys

Disabling Constraints & Other Options

Views and Row Data Security

Benets of Views in SQL Database

Views on Tables and Views

SCHEMA BINDING and ENCRYPTION

Issues with Views and ALTER TABLE

Common System Views and Metadata

Common Dynamic Management views

Working with JOINS inside views

Indexes and Query tuning

Need for Indexes & Usage

Indexing Table & View Columns

Index SCAN and SEEK

INCLUDED Indexes & Usage

Materializing Views (storage level)

Composite Indexed Columns & Keys

Indexes and Table Constraints

Primary Keys & Non-Clustered Indexes

Stored Procedures and Benets

Why to use Stored Procedures

Types of Stored Procedures

Use of Variables and parameters

SCHEMABINDING and ENCRYPTION

INPUT and OUTPUT parameters

System level Stored Procedures

Dynamic SQL and parameterization

System functions and Usage

Scalar Valued Functions

Types of Table Valued Functions

SCHEMABINDING and ENCRYPTION

System Functions and usage

Date Functions

Time Functions

String and Operational Functions

ROW_COUNT

GROUPING Functions

Triggers, cursors, memory limitations

Why to use Triggers

DML Triggers and Performance impact

INSERTED and DELETED memory tables

Data Audit operations & Sampling

Database Triggers and Server Triggers

Bulk Operations with Triggers

Cursors and Memory Limitations

Cursor declaration and Life cycle

STATIC

DYNAMIC

SCROLL Cursors

FORWARD_ONLY and LOCAL Cursors

KEYSET Cursors with Complex SPs

Transactions Management

ACID Properties and Scope

EXPLICIT Transaction types

IMPLICIT Transactions and options

AUTOCOMMIT Transaction and usage

SAVEPOINT and Query Blocking

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This module offers knowledge to introduce you to the basic principles based on statistical methods and procedures followed in data analysis. This course will help you to understand the work process involved with summarizing the data, data storage, visualizing the data results, and a hands-on approach with statistical analysis with python.

Introduction to Data Analytics

What is Analytics & Data Science

Common Terms in Data Science

What is data

Classication of data

Relevance in industry and need of the hour

Types of problems and business objectives in various industries

How leading companies are harnessing the power of analytics

Critical success drivers.

Overview of Data Science tools & their popularity.

Data Science Methodology & problem-solving framework.

List of steps in Data Science projects

Identify the most appropriate solution design for the given problem statement

Project plan for Data Science project & key milestones based on effort estimates

Build Resource plan for Data Science project

Why Python for data science

Accessing/Importing and Exporting Data

Importing Data from various sources (Csv, txt, excel, access etc)

Database Input (Connecting to database)

Viewing Data objects - sub setting, methods

Exporting Data to various formats

Important python modules Pandas

Data Manipulation Cleansing - Munging Using Python Modules

Cleansing Data with Python

Filling missing values using lambda function and concept of Skewness.

Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

Normalizing data

Feature Engineering in Data Science

Feature Engineering

Feature Selection

Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

Label encoding/one hot encoding

Data Analysis Visualization Using Python

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/ densi ty etc.)

Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Introduction to Statistics

Descriptive Statistics

Sample vs Population Statistics

Random variables

Probability distribution functions

Expected value

Normal distribution

Gaussian distribution

Z-score

Spread and Dispersion

Correlation and Co-variance

Introduction to Predictive Modelling

Concept of model in analytics and how it is used

Common terminology used in Analytics & Modelling process

Popular Modelling algorithms

Types of Business problems - Mapping of Techniques

Different Phases of Predictive Modelling

EDA (Exploratory Data Analysis)

Need for structured exploratory data

EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

Identify missing data

Identify outliers’ data

Imbalanced Data Techniques

Data Pre-Processing & Data Mining

Data Preparation

Feature Engineering

Feature Scaling

Datasets

Dimensionality Reduction

Anomaly Detection

Parameter Estimation

Data and Knowledge

Selected Applications in Data Mining

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Excel is one of the most popular data analysis tool, to help visualize and gain insights from your data. Analytics with Excel helps you to boost your Microsoft Excel skills.

Understanding Concepts of Excel

Creation of Excel Sheet Data

Range Name, Format Painter

Conditional Formatting, Wrap Text, Merge & Centre

Sort, Filter, Advance Filter

Different type of Chart Creations

Auditing, (Trace Precedents, Trace Dependents)Print Area

Data Validations, Consolidate, Subtotal

What if Analysis (Data Table, Goal Seek, Scenario)

Solver, Freeze Panes

Various Simple Functions in Excel(Sum, Average, Max, Min)

Real Life Assignment work

Ms Excel Advance

Advance Data Sorting

Multi-level sorting

Restoring data to original order after performing sorting

Sort by icons

Sort by colours

Lookup Functions

Lookup
VLookup
HLookup

Subtotal, Multi-Level Subtotal

Grouping Features

Column Wise
Row Wise

Consolidation With Several Worksheets

Filter

Auto Filter
Advance Filter

Printing of Raw & Column Heading on Each Page

Workbook Protection and Worksheet Protection

Specified Range Protection in Worksheet

Excel Data Analysis

Goal Seek
Scenario Manager

Data Table

Advance use of Data Tables in Excel
Reporting and Information Representation

Pivot Table

Pivot Chat
Slicer with Pivot Table & Chart

Generating MIS Report In Excel

Advance Functions of Excel
Math & Trig Functions

Text Functions

Lookup & Reference Function

Logical Functions & Date and Time Functions

Database Functions

Statistical Functions

Financial Functions

Functions for Calculation Depreciation

MIS Reporting & Dash Board

Dashboard Background

Dashboard Elements

Interactive Dashboards

Type of Reporting In India

Reporting Analyst
Indian Print Media Reporting

Audit Report

Accounting MIS Reports

HR Mis Reports

MIS Report Preparation Supplier, Exporter

Data Analysis

Costing Budgeting Mis Reporting
MIS Report For Manufacturing Company
MIS Reporting For Store And Billing

Product Performance Report

Member Performance Report

Customer-Wise Sales Report

Collections Report

Channel Stock Report

Prospect Analysis Report

Calling Reports

Expenses Report

Stock Controller MIS Reporting

Inventory Statement

Payroll Report

Salary Slip

Loan Assumption Sheet

Invoice Creation

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SPSS Certification Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.8 (64790)

View Details Course Curriculum

Our Social Science Statistics Certificate provides the skills necessary to gather, analyze, and assess data for activities like making policy decisions, answering cultural questions, studying behavioral changes, and informing business decisions. This ten course program – including six required courses and four electives – cover the principal statistical concepts used to design, sample, collect, interpret, and present data as it applies to behaviors of groups of people in their environment and special situations.

In this program you will learn:

Introduction to SPSS

Input and Data Cleaning

Data Import [Excel File]

Data Import [Text Data / CSV Data]

Scales

Data Manipulation

Visualisation in SPSS

Descriptive analysis of data

What is Significance

Statistical Tests

Correlation

Regression

Sampling

Tables

Multivariate Analysis [Factor Analysis]

Multivariate Analysis [Cluster Analysis]

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Introduction to SPSS:

Data Analysis with SPSS: General Aspects, Workflow, Critical Issues

SPSS: General Description, Functions, Menus, Commands

SPSS File Management

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Input and Data Cleaning:

Defining Variables

Manual Input of Data

Automated Input of Data and File Import

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Data Import [Excel File]:

Excel Data : Initial Preparation

Check Cell Range of Data

Give Variable Names

Store basic Checks Max Min Mean

Import into SPSS

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Data Import [Text Data / CSV Data]:

In Notepad or Text Pad

Check Total No of Rows

How many Rows Per Case

How Many Variables

Import Text File

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SAS Base Certification Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.9 (65879)

View Details Course Curriculum

This course is for users who want to learn how to write SAS programs to access, explore, prepare, and analyze data. It is the entry point to learning SAS programming for data science, machine learning, and artificial intelligence. It is a prerequisite to many other SAS courses You will also learn data manipulation techniques using the SAS DATA step and procedures to access, transform, and summarize data.

SAS Certification Training is intended to make you an expert in SAS programming and Analytics. You will be able to analyse and write SAS code for real problems, learn to use SAS to work with datasets, perform advanced statistical techniques to obtain optimized results with BASE SAS programming.

In this program you will learn :

SAS Programming Essentials

SAS Programming - Data Manipulation Techniques

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Introduction

The SAS programming process.

Submitting a SAS program

Using SAS programming tools.

Understanding SAS syntax.

Diagnosing and correcting syntax errors

Mastering fundamental concepts

Accessing Data

Understanding SAS data.

Accessing data through libraries.

Importing data into SAS.

Exploring and Validating Data

Exploring data.

Filtering rows.

Formatting columns.

Sorting data and removing duplicates.

Preparing Data

Reading and filtering data.

Computing new columns.

Conditional processing.

Analyzing and Reporting on Data

Enhancing reports with titles, footnotes, and labels.

Creating frequency reports.

Creating summary statistics reports.

Exporting Results

Exporting data.

Exporting reports.

Using SQL in SAS

Using Structured Query Language in SAS.

Joining tables using SQL in SAS.

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Controlling DATA Step Processing

setting up for this course

understanding DATA step processing

directing DATA step output

Summarizing Data

creating an accumulating column

processing data in groups

Manipulating Data with Functions

understanding SAS functions and CALL routines

using numeric and date functions

using character functions

using special functions to convert column type

Creating Custom Formats

creating and using custom formats

creating custom formats from tables

Combining Tables

concatenating tables

merging tables

identifying matching and nonmatching rows

Processing Repetitive Code

using iterative DO loops

using conditional DO loops

Restructuring Tables

restructuring data with the DATA step

restructuring data with the TRANSPOSE procedure

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Placement Guide

What is an Interview

Tips to clear an Interview

Common Interview questions and answers

SAS Interview Questions and Answers

Resume Building Guide

Career roadmap and certifications

Attempt for related Global Certification Exam

Start applying for Jobs

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SAS Adv Certification Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.9 (26599)

View Details Course Curriculum

This course is for SAS programmers who want to learn advanced techniques within the DATA step and procedures. Further, the course focuses on using the SAS macro facility to design, write, and debug macro programs, with an emphasis on understanding how programs that contain macro code are processed.

You will Perform text substitution in SAS code, learn to use macro variables and macro functions, automate and customize the production of SAS code, conditionally or iteratively construct SAS code, write self-modifying, data-driven programs. Moving ahead, you will also learn how to process SAS data using Structured Query Language (SQL).

In this program you will learn:

SAS Programming Advanced Techniques

SAS Macro Language Essentials

SAS SQL Essentials

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Getting Started

Setting up for this course.

DATA step review.

Using Advanced Functions

Using a variety of advanced functions.

Performing pattern matching with Perl regular expressions.

Defining and Processing Arrays

Defining and referencing one-dimensional arrays.

Doing more with one-dimensional arrays.

Defining and referencing two-dimensional arrays.

Defining and Processing Hash Objects

Declaring hash objects.

Defining hash objects.

Finding key values in a hash object.

Writing a hash object to a table.

Using hash iterator objects.

Using Utility Procedures

Creating picture formats with the FORMAT procedure.

Creating functions with the FCMP procedure.

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Introduction

Why SAS macro

Setting up for this course.

SAS Macro Facility

Program flow.

Creating and using macro variables.

Storing and Processing Text

Macro functions.

Using SQL to create macro variables.

Using the DATA step to create macro variables.

Indirect references to macro variables.

Working with Macro Programs

Defining and calling a macro.

Macro variable scope.

Conditional processing.

Iterative processing.

Developing Macro Applications

Storing macros.

Generating data-dependent code.

Validating parameters and documenting macros.

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Essentials

Setting up for this course.

Overview of SAS Foundation.

Course logistics.

Course data files.

Introducing the Structured Query Language.

Overview of the SQL procedure.

Exploring tables.

Specifying columns.

PROC SQL Fundamentals

Sub-setting data.

Presenting data.

Summarizing data.

Creating and managing tables.

Using DICTIONARY tables.

SQL Joins

Introduction to SQL joins.

Inner joins.

Outer joins.

Complex SQL joins.

Subqueries

Noncorrelated subqueries.

Correlated subqueries.

In-line views.

Creating views with the SQL procedure.

Subqueries in the SELECT clause.

Remerging summary statistics.

Set Operators

Introduction to set operators.

The INTERSECT operator.

The EXCEPT operator.

The UNION operator.

The OUTER UNION operator.

Using and Creating Macro Variables in SQL

Interfacing PROC SQL with the macro language.

Creating data-driven macro variables with a query.

Using macro variables in SQL.

Accessing DBMS Data with SAS/ACCESS

Overview of SAS/ACCESS technology.

SQL pass-through facility.

SAS/ACCESS LIBNAME statement.

PROC Fed SQL.

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Placement Guide

What is an Interview

Tips to clear an Interview

Common Interview questions and answers

SAS Advanced Interview Questions and Answers

Resume Building Guide

Career roadmap and certifications

Attempt for related Global Certification Exam

Start applying for Jobs

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Microsoft Power BI Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.8 (145790)

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Power BI stands tall for users with its significant features like minimal license cost, easy to use operations and high-end product performance. Industry insights specify significant growth in Power BIusers and thus provides a greater number of jobs to the next generations. Microsoft being one among top software providers, with a vision to make this power BI to lead the business intelligence sector started updating with new features to attract more clients in the market.

In this program you will learn :

Introduction to Power BI

Power BI Desktop

Modelling with Power BI

Data Analysis Expressions (DAX)

Modelling with Power BI

Power BI Desktop Visualisations

Introduction to Power BI Q&A and Data Insights

Direct Connectivity

BI and Azure ML Integrating Power

Publishing and Sharing

Refreshing Datasets

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Introduction to Power BI:

Overview of BI concepts

Why we need BI

Introduction to SSBI

SSBI Tools

Why Power BI

What is Power BI

Building Blocks of Power BI

Getting started with Power BI Desktop

Get Power BI Tools

Introduction to Tools and Terminology

Dashboard in Minutes

Refreshing Power BI Service Data

Interacting with your Dashboards

Sharing Dashboards and Reports

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Power BI Desktop:

Power BI Desktop

Extracting data from various sources

Workspaces in Power BI

Data Transformation

Measures and Calculated Columns

Query Editor

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Modelling with Power BI:

Introduction to Modelling

Modelling Data

Manage Data Relationship

Optimize Data Models

Cardinality and Cross Filtering

Default Summarization & Sort by

Creating Calculated Columns

Creating Measures & Quick Measures

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Data Analysis Expressions (DAX):

What is DAX

Data Types in DAX

Calculation Types

Syntax, Functions, Context Options

DAX Functions

Date and Time

Time Intelligence

Information

Logical

Mathematical

Statistical

Text and Aggregate

Measures in DAX

ROW Context and Filter Context in DAX

Operators in DAX -Real-time Usage

Quick Measures in DAX -Auto validations

PowerPivot xVelocity & Vertipaq Store

In-Memory Processing: DAX Performance

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Tableau Certification Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.9 (66427)

View Details Course Curriculum

This Tableau course is for anyone who works with data regardless of technical or analytical background. Users can create and distribute interactive and shareable dashboards, that depict large volumes of data into easily readable graphs and charts.

Our Tableau Training is designed to help you prepare for the Tableau Desktop Certified Associate Certification examination by providing you rich hands-on training on various Tableau tools.

In this program you will learn :

Data Preparation using Tableau Prep.

Data Connection with Tableau Desktop.

Basic Visual Analytics.

Calculations in Tableau.

Advanced Visual Analytics.

Level of Detail (LOD) Expressions in Tableau.

Geographic Visualizations in Tableau.

Advanced charts in Tableau.

Dashboards and Stories.

Get Industry Ready.

Exploring Tableau Online.

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Data Preparation using Tableau Prep:

Data Visualization

Business Intelligence tools

Introduction to Tableau

Tableau Architecture

Tableau Server Architecture

VizQL Fundamentals

Introduction to Tableau Prep

Tableau Prep Builder User Interface

Data Preparation techniques using Tableau Prep Builder tool

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Data Connection with Tableau Desktop:

Features of Tableau Desktop

Connect to data from File and Database

Types of Connections

Joins and Unions

Data Blending

Tableau Desktop User Interface

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Basic Visual Analytics:

Visual Analytics

Basic Charts Bar Chart, Line Chart, and Pie Chart

Hierarchies

Data Granularity

Highlighting

Sorting

Filtering

Grouping

Sets

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Calculations in Tableau:

Types of Calculations

Built-in Functions (Number, String, Date, Logical and Aggregate)

Operators and Syntax Conventions

Table Calculations

Level of Detail (LOD) Calculations

Using R within Tableau for Calculations

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R Programming Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.9 (98569)

View Details Course Curriculum

This course is suggested for all those novices and specialists who are keen on working in the analytics industry. R is suitable for all IT professionals like Big data analytics, Business analytics, scientific research, statistical reporting, Econometrics, social science, business intelligence, and business development. Researchers who perform data analysis with higher dimensional graphs. Students who need R for their courses. Prerequisite It is not necessary to have any prior knowledge of R. R is now well thought out to be not just the most popular open-source analytic tool but the most popular analytics tool in the world

In this program you will learn:

Overview :

R Programming Basics.

Variable types and data structures in base R.

Getting data into the R environment.

Data frame manipulation.

Handling dates in R.

Exploratory Data Analysis (Descriptive Statistics).

Working with text data.

Control flow & functions.

Graphics in R Overview.

Advanced R graphics.

Inferential Statistics.

General Linear Regression Models in R.

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Overview:

History of R

Advantages and disadvantages

Downloading and installing

How to find documentation

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R Programming Basics:

Using the R console and R Studio

Getting help

Learning about the environment

Writing and executing scripts

Object oriented programming

Introduction to vectorised calculations

Introduction to data frames

Installing and loading packages

Working directory

Saving your work

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Variable types and data structures in base R:

Variables and assignment

Data types

Numeric, character, Boolean, and factors

Data structures

Vectors, matrices, arrays, data frames, lists

Indexing, sub-setting

Assigning new values

Viewing data and summaries

Naming conventions

Objects

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Getting data into the R environment:

Built-in data

Reading data from structured text files

Reading data using ODBC

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Microsoft Advance Excel Certification Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.8 (36216)

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In this course, you will gain advanced understanding of the Excel environment and have the ability to guide others to the proper use of the program’s features. You will learn to create, manage, and distribute professional spreadsheets for a variety of specialized purposes and situations. You will learn to customize Excel environments to meet project needs and to enhance the productivity. Become an expert in managing custom business templates, multiple-axis financial charts, amortization tables, and inventory schedules.

In this program you will learn:

Presenting and Managing Data in Excel

Manage Workbook Options and Settings

Apply custom data formats and layouts

Create Advanced Formulas

Create advanced charts and tables

MIS Reporting and Dashboards

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Presenting and Managing Data in Excel:

Basic Understanding Menu and Toolbar

Introduction to different category of functions

Creation of Excel Sheet Data

Range Name, Format Painter

Conditional Formatting, Wrap Text, Merge & Centre

Sort, Filter, Advance Filter

Different type of Chart Creations

Auditing, (Trace Precedents, Trace Dependents) Print Area

Data Validations, Consolidate, Subtotal

What if Analysis (Data Table, Goal Seek, Scenario)

Solver, Freeze Panes

Various Simple Functions in Excel (Sum, Average, Max, Min)

Real Life Assignment work

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Manage workbook:

Save a workbook as a template,

copy macros between workbooks,

reference data in another workbook,

reference data by using structured references,

enable macros in a workbook,

display hidden ribbon tabs

Manage workbook review:

Restrict editing,

protect a worksheet

configure formula calculation options

protect workbook structure

manage workbook versions

encrypt a workbook with a password

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Apply custom data formats and validation:

Create custom number formats

populate cells by using advanced Fill Series options

configure data validation

Apply advanced conditional formatting and filtering:

Create custom conditional formatting rules

create conditional formatting rules that use formulas

manage conditional formatting rules

Create and modify custom workbook elements:

Create custom color formats,

create and modify cell styles,

create and modify custom themes,

create and modify simple macros

insert and configure form controls

Prepare a workbook for internationalization:

Display data in multiple international formats

apply international currency formats,

manage multiple options for Body and Heading fonts

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Apply functions in formulas:

Perform logical operations by using AND, OR, and NOT functions;

perform logical operations by using nested functions

perform statistical operations by using SUMIFS, AVERAGEIFS, COUNTIFS functions

Look up data by using functions:

Look up data by using the VLOOKUP function,

look up data by using the HLOOKUP function,

look up data by using the MATCH function,

look up data by using the INDEX function

Apply advanced date and time functions:

Reference the date and time by using the NOW and TODAY functions,

serialize numbers by using date and time functions

Perform data analysis and business intelligence:

Reference the date and time by using the NOW and TODAY functions

import, transform, combine, display, and connect to data

consolidate data

perform what-if analysis by using Goal Seek and Scenario Manager

use cube functions to get data out of the Excel data model

calculate data by using financial functions

Troubleshoot formulas:

Trace precedence and dependence

monitor cells and formulas by using the Watch Window

validate formulas by using error checking rules,

Evaluate formulas

Define named ranges and objects:

Name cells,

name data ranges,

name tables,

manage named ranges and objects

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Microsoft Excel VBA Certification Training

30k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.8 (24900)

View Details Course Curriculum

It is estimated that the job opportunities for VBA programmers are set to grow exponentially given the current demand and estimated growth in the number of companies seeking out such individuals. With the VBA (Visual Basic for Applications) training by Croma Campus, learn how to create macros to automate functions and generate graphs and reports.

In this program you will learn:

VBA Basics.

Control Structures: Logical Expressions.

Control Structures: Loops.

Sub-Procedure and Functions.

Debugging.

Handling Text files, Pictures and charts.

VBA forms.

Database Connectivity:

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What is VBA:

Overview

The History of VBA

Why VBA

VBA Summary

Available Data Types and Usage

Micro Structure & Basic Macro

Active sheet, Active cell, offset, sheet add, rename, etc.

Options Base and Explicit

Arrays

Static & Dynamic

Array functions

Macro with Excel

Basic Macro with daily use

Filter, cut, copy, paste, remove duplication, etc.

Macro with File

File system basics

Understanding file permissions

Setting file permissions

Accessing files

Writing to files

Deleting files

Reading files

Examining file details

Working with directories

Viewing directory content

Form and Activity X Control

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Control Structures: Logical Expressions:

If statements

Else and elseif statements

Logical operators

And. OR, Not. Go to Statements

Switch statements

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Control Structures: Loops:

While loops

For loops

For each loops

do while

do until

Continue

Break

Understanding array pointers

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Sub-Procedure and Functions:

Subroutine

Passing value to sub- procedure

Message box

VBA Inbuilt functions

Passing values in functions

Workbook and worksheet functions

Keywords

Difference between sub-routines and functions

Creating own functions in VBA for Excel

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Business Analyst Certification Training

40k LearnersWeekend/WeekdayLive Class

2 Live Project
Self-Paced/ Classroom
Certification Pass Guaranteed

4.9 (21223)

View Details Course Curriculum

Software Development Life Cycle

Software Development Methodologies Water Fall, Iterative, Agile, etc.

Types of Software Projects Greenfield, Migration, Product Customization, Maintenance etc.

IT Implementation

IT Deployment Environments SAAS, On Premise, Client Server etc.

Software Licensing, Subscription and Sale Models

Project Development Vs Product Development

RUP, DevOps Methodologies

Cloud Service and Deployment Models - Public, Private, Hybrid, Community, IAAS, PAAS,

SAAS

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Activities of an IT Business Analyst

Analysis, Business Analysis, IT Business Analysis defined

Objectives of Business Analysis

Who is an IT Business Analyst

Business Analysis Levels or Views - Process, Project, Enterprise and Industry

Business Analysis Framework and Techniques

IT Business Analysis Software

IT Business Analyst Skills

IT Business Analyst Career Path and Salaries

Meet some real Business Analysts

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What is a Business Process

Critical Analysis of a Business Process

Business Process Analysis Sparx System Framework

Business Process Mapping & Flow charting

As Is To Be Analysis for a Business Process

Business Process Mapping and Modelling Frameworks

Business Process Mapping and Modelling Software

Business Process Re-Engineering & Re-Design

Business Process Automation, Disinter mediation and Re-intermediation

Business Process Simulation

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What is a Requirement

Attributes of Requirements

Importance of Requirements for IT Projects

Types of Requirements: Business, Enterprise, Solution, Stakeholder, Transition

Overview of Requirements Engineering and Management

Requirements Basics: Mapping, Modelling, Traceability Matrix, Tracking

Business Process Re-Engineering & Re-Design

Important Requirement Process Steps: Elicitation, Analysis, Documentation, Validation, Management

Requirement Software Companies - JIRA, Rational Requisite Pro

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Stakeholder Definition

Importance of Stakeholder

Stakeholder Identification Onion Diagram

Stakeholder Identification Wheel

Stakeholder Classification Power Influence Matrix

Stakeholder Engagement Techniques Power/Influence Vs Interest

Stakeholder RACI Chart, its preparation through Visio

Stake Holder Persona

Stakeholder Alignment and Sponsorship

Managing Internal stakeholders

Preparing Stakeholder map through Excel

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Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.How Croma Campus Placement Process Works?

Projects

Validate your skills and knowledge by working on industry-based projects that includes significant real-time use cases.Gain hands-on expertize in Top IT skills and become industry-ready after completing our project works and assessments.Our projects are perfectly aligned with the modules given in the curriculum and they are picked up based on latest industry standards.Add some meaningful project works in your resume, get noticed by top industries and start earning huge salary lumps right away.

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