Data Analytics Certification Course in Gurgaon/Gurugram|Best Data Analyst Training Institute

Data Analytics Training Course

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Accounting & Taxation Certification

Furnish the skills of fresher/Experienced at corporate by delivering industry admirable certification for Data Analytics training.

Experienced Faculty

Complete practical training by expert Sr. Lead Analyst/Technical Analyst With having 10+ Years of experience.

Placement Assistance

Placement assistance would be 100% after completion of 70% of Data Analytics training our dedicated placement team arrange interview till placement.

LAB Facility

Unfold your Advanced Excel skills with corporate level with technical test series with Practical Training to furnish aptitude

Accounting & Taxation Workshop Sessions

Experience unique and lively – Real time projects learning & case study for learners at SLA’s workshop.

Admin Support

Complete assistance for fresh batch schedule/re-scheduling classes/arrange doubt classes to the Learners.

Data Analytics Program details

Data Analytics Practical with Interview Guarantee

Our Trainer:- 10+ Years of industry experience as a Data Analyst in MNC, Education:- B.Tech

Course Duration: 150 (Hours) with Highly Skilled Corporate Trainers

(Data Analytics Training) for below Modules – Weekdays/Weekend

SLA Consultants Gurgaon – Data Analytics Training Delivery Method:

Web / Desktop Base.
Instructor-Led Classroom Sessions.
Presentations/Demonstration/Practicals of concepts.
Handouts/Assignments/Real-time Exposure to work on Clients.

Data Analytics Programme Modules

Data Analytics Training Course

Module 1 - Excel & VBA/Macros Analytics

Section A – Basic & Advanced Excel

Introduction to Excel, Basic Understanding Menu and Toolbar, Introduction to different category of functions like Basics, Mathematical and Statistical, Date and Time, Logical, Lookup and References, Text and Information.

Mathematical Functions:- Sum, Sumif, Sumifs, Count, Counta, Countblank, Countif, Countifs, Average, Averagea, Averageif, Averageifs, Subtotal, Aggregate, Rand, Randbetween, Roundup, Rounddown, Round, Sumproduct

Date & Time Function:- Date, Day, Month, Year, Edate, Eomonth, Networkdays, Workday, Weeknum, Weekday, Hour, Minute, Second, Now, Today, Time

Text Functions & Data Validation:- Char, Clean, Code, Concatenate, Find, Search, Substitute, Replace, Len, Right, Left, Mid, Lower, Upper, Proper, Text, Trim, Value, Large, Small Filters (Basic, Advanced, Conditional), Sort (Ascending, Descending, Cell/ Font Color), Conditional Formatting, Data Validation, Group & Ungroup, Data split.

Statistical Function & Other Functions:- Isna, Isblank, Iserr, Iseven, Isodd, Islogical, Isytext, Max, Min, Len, Right, Left, Mid, ,Maxa, Maxifs, Median, Minifs, Mina, Vara, Correl, Geomen

Logical Functions:- And, Or, If, Iferror, Not, Nested If

Lookup & Reference Functions:- VLookup, HLookup, Index, Match, Offset, Indirect, Address, Column, Columns, Row, Rows, Choose, Arrays Concept In Lookup Formula’s, Past Special, Past link

Section B – Analysis Using Microsoft Advanced Excel

Lesson 1: Introduction to Business Analytics

What is Analytics?
Overview of different Analytics with Segment
Customer Analytics
Understand Business Analysis Key Concepts
Performance Analytics
Risk Analytics

Lesson 2:Formatting, Auto Formatting & Data Manipulation

Conditional Formatting
Freezing Concept
Data Validations
Logical Functions
And, If, Nested IF, Not, OR, True, False
SUMPRODUCT, IFERROR, CONCATENATE, MOD, LEN, RIGHT, LEFT
Vlookup/Hlookup, Index, Match, Offset
Statistical and Mathematical Functions
Rank, Quartile, Percentile, Correl, Geomen
Trace Precedents and Trace Dependents
Round, Roundup, Round down, Max, Min, Large, Small
Standard Deviation, Correlation, Mean, Median, Sum, Count, , Countifs , Sumif, Sumifs

Lesson 3: Data Analysis & Visualization – Power Pivot

Pivot Table
Power Pivot
What-if Analysis
Data Table –One Variable and Two Variables
Data Analysis Using Statistics
Descriptive Statistics
ANOVA
Moving Average
Testing Hypothesis
Measuring Covariance and Correlation
Distribution
Regression
Graphs & Charts
Analysis Tool Pack
Solver
Histogram
Pareto
Water Fall

Lesson 4: What is Dashboards and Excel Dashboard

Overview of Chart types, Chart Formatting
Active X Form Controls
Principle of great dashboard design
Selecting Correct Chart to display data
Interactive Charts with Form Controls
Combo box, Check Box, Scroll Bar and Radio Button
Interactive Dashboard with Form Controls
Form Controls for reports automation
Data Models using Power Pivot

Module 1.1 – VBA/Macros

Macros I

Introduction to Macros & Advanced Application Building in Excel
Key Concepts in Macros
Macros as recorded Robots!
Form Controls and Active X controls
Implementing user forms in Excel Macros
Linking forms to excel cells
Building flexible Charts in Excel

Macros II

Understanding VBA – Sub & Functions
Understanding the Relevant Language Constructs
IDC Implementation using Macros
Breaking Circular Loops using Macro
Monte Carlo Simulation
Basics of Debugging VBA Code

Module 2 - SQL & Access

Access is a relational database management system (RDBMS) from Microsoft that combines the relational Microsoft Jet Database Engine with a graphical user interface and software-development tools. … It can also import or link directly to data stored in other applications and databases. Introduction to SQL

SQL Course overview
Installing the test environment
What is SQL?
Editors and Platforms to learn SQL

Complete SQL in a Class

Quick-start introduction
Using the basic SELECT statement
Selecting Rows
Selecting columns
Counting rows
Inserting data
Updating data
Deleting data
Import and Export data

Fundamentals of SQL

Databases and tables
SQL syntax overview
Data Definition, Data Manipulation, Data Control, Transactional Control statements
Creating tables
Deleting a table
Inserting rows into a table
Deleting rows from a table
What is NULL?
Controlling column behaviours with constraints
Changing a schema with ALTER
Filtering data with WHERE, LIKE, and IN
Removing duplicates with SELECT DISTINCT
Sorting with ORDER BY

How Relationships Work in SQL

Understanding Joins
Accessing related tables with JOIN
Multiple related tables

Explaining SQL Strings

About SQL strings
Finding the length of a string
Selecting part of a string
Removing spaces with TRIM
Making strings uppercase and lowercase

Numbers and SQL

About numeric types
Finding the type of a value
Integer division and remainders
Rounding numbers

SQL Functions and Clause

The Aggregate functions MIN, MAX, AVG, SUM and COUNT, UPPER, LENGTH, LOWER
The GROUP BY and HAVING clauses Grouping in a combination with joining
Triggers in SQL

Concept of Trigger
Create Trigger for (Insert, Update, Delete)
Alter Trigger

What are Subselects and Views in SQL

Creating a simple subselect
Searching within a result set
Creating a view
Creating a joined view

Maintaining SQL Server Database

Backup Database
Restore Database

SQL Server Job Creation

How to create job in SQL Server Agent
How to schedule job

Topics Covered as follows:

Access environment and tools
Database terminology and concept
Designing database in Access
Understanding RDBMS
Working with the Design side of Tables
- Create Query
- Join Tables That Have No Common Fields
- Work with Subdatasheets
- Create Sub queries
Working with the runtime of Tables
Data Migration and Importing
Working with the Design side of Queries
Working with the runtime of Queries
Working with the Design side of Forms
- Adding Controls to Forms
- Creating Sub forms
- Organizing Information with Tab Pages
- Displaying a Summary of Data in a Form
- Applying Conditional Formatting
Working with the runtime of Forms, Managing Switchboard
Working with the Design side of Reports
- Organize Report Information
- Format Reports Include Charts in a Report
- Add a Calculated Field to a Report
- Add a Sub report to an Existing Report
Working with the runtime of Reports
Working with the Design side of Macros
- Creating a Macro Restricting Records Using a Condition
- Automating Data Entry Using a Macro
Working with the runtime of Macros
How to create a functional specification
Build a real-world business application
Putting altogether and deployment

Module 3 - SAS Base & Advanced

Module 3.1 – SAS Base

INTRODUCTION TO SAS PROGRAMMING

Working in the SAS Environment
Working with the Windows
Enhance Editor
Result Window
Log Window
Explorer Window
Output Window
Program Editor Window

BASICS OF SAS LANGUAGE

SAS Libraries
Data Sets
Cube
Data View
Referencing Files in Catalog
Creating a SAS Program
Components of SAS Program
Characteristics of SAS Program
Layout of SAS Program

ACCESSING DATA

Use FORMATTED, LIST and COLUMN input to read raw data files
Use INFILE statement options to control processing when reading raw data files
Use various components of an INPUT statement to process raw data files including column and line pointer controls, and trailing @ controls
Combine SAS data sets using the DATA step

CREATING SAS DATA SETS

Create temporary and permanent SAS data sets
Create and manipulate SAS date values
Control which observations and variables in a SAS data set are processed and output

DATA STEP PROCESSING

Program Data Vector (PDV)
Execution Phase and Compilation Phase
Get Started with Output Delivery System (ODS)

STATEMENT OPTIONS

Global Statements
Local Statements
List Input
Modified Input
Column Input
Named Input

MANAGING DATA

Exploring SAS data libraries using base SAS utility procedures
Sorting observations in a SAS data set
Conditionally execute SAS statements
Use assignment statements in the DATA step
Modify variable attributes using options and statements in the DATA step
Use SAS functions to manipulate character data, numeric data, and SAS date values
Use SAS functions to convert character data to numeric and vice versa

COMBINING SAS DATA SETS

Concatenation
One-One Merging
Match Merging
One-One Reading
Updating or Setting

CONDITIONAL STATEMENTS

IF & IF THEN statement
IF ELSE statement
Where condition
Like , Into Statements
Loops ( Do, While )

WRITING DATA TO EXTERNAL FILES

File Statement
Put Statement

GENERATING REPORTS

Generate list reports using the PRINT and REPORT procedures
Enhance reports through the use of labels, SAS formats, user-defined formats
Generate summary reports and frequency tables using base SAS procedures

DEBUGGING AND HANDLING ERRORS

Identify and resolve programming logic errors
Checking for Errors, Warnings, Un-Initialization , missing values
Recognize and correct syntax errors
Examine and resolve data errors

PROCEDURES

Proc Sort
Proc Transpose
Proc Print
Proc Contents
Proc Formats, Proc Compare
Proc Append, Proc Tabulate
Proc Report
Proc Import, Proc Export
Proc Freq
Proc Means

Module 3.2 – SAS Base

ADVANCE SAS TRAINING SYLLABUS

Introduction to SAS Macro Language Elements
Introduction to Macro Variables
Automatic Macro Variables
User define Macro Variables
How Macro processing will undergo inside the SAS system
Macro Statements
Macro Functions
Autocall Macros

SAS MACROS

ACCESSING DATA USING SQL

Introduction to Proc SQL
Generate detail reports by working with a single table or joining tables using PROC SQL
Generate summary reports by working with a single table or joining tables using PROC SQL
Inserting data into Tables
Alter & update the tables
Construct sub queries within a PROC SQL step
SQL pass through Facility
Connecting to DBMS
Natural Joins
Inner Joins
Outer Joins
Access Dictionary Tables using the SQL procedure
Work with indexes using the macro interface/creating macro variables with SQL, defining integrity constraints

MACRO PROCESSING

Creating and using user-defined and automatic macro variables within the SAS Macro Language
Automating programs by defining and calling macros using the SAS Macro Language
Various system options available for macro debugging
Displaying values of user-defined and automatic macro variables in SAS log
Understand the use and implantation of macro functions

Module 4 –Tableau OR MS Power BI (Any one)

Module 4 a) Tableau

Module 1 – Getting Started with Tableau

Overview Of Tableau
Tableau Architecture
Installation And Configuration Of Tableau 10

Module 2 – Connecting to The Data

Managing Metadata
Managing Extracts
Data Sources
Cross-Database Joins
Data Aggregation And Data Ports
Tableau Charts
Bar Charts and Stacked Bars Data Blending
Tree Maps and Scatter Plots
Individual Axes, Blended Axes, Dual Axes and Combinational Chart

Module 3 – Visual Analytics

Drill Down and Hierarchies
Sorting, Filtering and Grouping
Parameters and Formatting
Trend and Reference Lines
Forecasting and Clustering
Analysis with Cubes and MDX

Module 4 – Developing First Bar Chart

Connecting Tableau to Data File
Navigating Tableau
Calculated Fields
Adding Colors, Labels and Formatting

Module 5 – Time Series, Maps and Aggregation

Data Extracts and Time Series
Understanding Granularity, Aggregation and Level Of Details
Default Location in Maps
Custom Geo Coding
Symbol Map and Filled Map

Module 6 – First Dashboard

Into Section
Joining Data In Tableau
Working With Maps and Hierarchies
Scatter Plot and Applying Filters in Different Sheets
Creating First Dashboard

Module 7 – Blending Data and Dual Axis Charts

Duplicate Values
Multiple Fields
Data Blending
Dual Axis Chart
Building Calculated Fields

Module 8 – Table Calculation and Storytelling

Downloading Data set and Connection
Mapping
Building Table Calculation for Gender
Bins and Distributions for Age
Tree Map Chart
Advanced Dashboard
Storyline and Storytelling

Module 9 – Data Preparation

Data Format
Data Interpreter
Multiple Columns And Pivot
Metadata Grid
Advanced Data Preparation

Module 4 b) MS Power BI

A Quick Introduction

What is MSPBI and its scope
Learn the common work flow in MSPBI
Building blocks of MSPBI and its relations
Quick demo how to create a business dashboard in MSPBI
MSPBI components
Old vs. new technologies
Power BI Desktop/Service/Mobile

Getting Business Data

Get data in shape for use with MSPBI
Combining two or more data sets (source data) for reporting
Tackling messy data in MSPBI
Clean and transform data

Modelling in Power BI

How to connect many different data sources
Manage data source (database) relationships
Unique keys
Calculated columns and more
Custom calculations to evaluate time-based functions
Build calculated tables based on DAX formulas and expressions
Creating and viewing visuals easier with optimized models
Discover hierarchal drill-down tools for date fields

Data Visualization

Create and customize visualization in MSPBI and its power
Use combination charts
Create and format slicers with it
Map visualizations and its utilization
Use tables and matrixes
Long live bubbles
scatter charts in action
Advanced funnel and waterfall charts
Drive fast dashboard insights with gauges and numbers
Color your visualization world with colors
shapes and scales
Adding personal touch
logo etc. to reports and dashboards
Display and present your dashboard in a way you want with summarize data
Control how your report elements overlap with each other
Learn to drill into hierarchies
Manage how levels are shared (Z-order in reports)
How to use R visuals in MSPBI

Data Exploring & Sharing

Quick insights in Power BI Service
Create and configure a dashboard
Share dashboard with your organization
Display and edit visuals- tiles
full screen
Get more space on your dashboard
Install and configure a personal gateway
Excel and MSPBI
Import and excel table into Power BI
Import excel files with data models and power view sheets
Connect One Drive for business to MSPBI
Excel data in Power BI summary

DAX (Data Analysis Expression) Application

DAX and its basic building blocks
Create calculated columns (fields) and measures in MSPBI using DAX formulas
Breadth of functions available in DAX
User variables in DAX
Create expressions across multiple tables with relational functions;
Filter and evaluate tables using advanced table functions

Module 5 - R-Programming OR Python (Any One)

Module 5 a) R-Programming

Module 1- Introduction to Business Analytics

Introduction to Business Analytics
Types of Analytics
Case study on Walmart, Signet Bank
Data Science and its importance

Module 2- Introduction to R

Introduction to R
Installing R
Installing R Studio
Workspace Setup
R Packages

Module 3- R Programming

R Programming
if statements
for statements
while statements
repeat statements
break and next statements
switch statement
scan statement
Executing the commands in a File

Module 4 – R Data Structure

Data structures
Vector
Matrix
Array
Data frame
List
Factors

Module 5 – Apply Functions

DPLYR & apply Function
Import Data File
DPLYP – Selection
DPLYP – Filter
DPLYP – Arrange
DPLYP – Mutate
DPLYP – Summarize

Module 6 – Apply Functions

Data visualization in R
Bar chart, Dot plot
Scatter plot, Pie chart
Histogram and Box plot
Heat Maps
World Cloud

Module 7 – Apply Functions

Introduction to statistics
Type of Data
Distance Measures (Similarity, dissimilarity, correlation)
Euclidean space.
Manhattan
Minkowski
Cosine similarity
Mahalanobis distance
Pearson’s correlation coefficient
Probability Distributions

Module 8 – Hypothesis Testing I

Hypothesis Testing
Introduction
Hypothesis Testing – T Test, Anova

Module 9 – Hypothesis Testing II

Hypothesis Testing about population
Chi Square Test
F distribution and F ratio

Module 10 – Regression Analysis

Regression
Linear Regression Models
Non Linear Regression Models

Module 11 – Classification

Classification
Decision Tree
Logistic Regression
Bayesian
Support Vector Machines

Module 12 – Clustering

Clustering
K-means Clustering and Case Study
DBSCAN Clustering and Case study
Hierarchical Clustering

Module 13 – Association

Association
Apriori Algorithm
Candidate Generation
Visualization on Associated Rules
Summary
Conclusion

Module 5 b) Python

Module 1 – Introduction to Python

Python overview
Advantages and disadvantages
Installation and configuration
Interpreted languages

Module 2 – Programming with Python

Python script
Standalone scripts under Unix and Windows
Using variables and operators
Command line parameters
Understanding expressions

Module 3 – Flow Control

IF and Else If Statement
The While and Loop statement
Continue statement
Break statement
Range () function
Using lists

Module 4 – Sequence Data

List operations and methods
Sets
Dictionaries
Tuples
Strings

Module 5 – Functions

Defining functions
Parameters and variables
Using global statement
Keyword arguments
Keyword only parameters
The return statement
VarArgs parameters
DocStrings

Module 6 – Errors and Exception Handling

Dealing syntax errors
Exception handling
Cleaning up

Module 7 – Modules

Creating modules
The from and import statement
Package
Dir function
Module name

Module 8 – Importing and Exporting Data

Importing data from different sources
Connecting to databse
Viewing data objects and sets
Exporting data to other formats

Module 9 – Data Manipulation and Data Analysis

Cleansing Data With Python
Data Manipulation
Python Tools And In-Built Functions
Formatting And Normalizing Data
User Defined Functions
Data Analysis Using Statistics And Graphical Representation

Module 10 – Data Structures and Regular Expressions

List, Tuples, Dictionaries And Set
Re Objects
Pattern Matching
Sub Expressions
Parsing Data
Complex Substitutions

Module 11 – Live Practice Sessions

Projects and Assignments
Live Training

Find the Data Analytics course details below.

Duration of Module

Data Analytics Course Module	Data Analytics Course Duration
Module 1-5 – Data Analytics	150 (Hours)

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