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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

Pivot Table and Charts, Import and Export data, Protect/Unprotect sheets/workbooks. Worksheet formatting and Print Display

Data Collection Method With Data Quality, Collaboration & Security Like Share Your Workbook On Share Drive With Quality

Analysis Single/Multidimensional Analysis, Like Three Dimensional (3D) Tables, Sensitive Analysis Like Data Table, Manual What-If Analysis, Threshold Values, Goal Seek, One-Variable Data Table, Two-Variable Data Table

Advanced Dashboard.

Report Development – (Real World Data)

DASHBOARD & VBA / Macros Programming Objective

Dashboard Reports Allow User to Get High-Level Overview of the Business and Help Them Make Quick Decisions.

Dashboards are Often Called as Management Information System (MIS), Which Provides Information that Organizations Require to Manage Themselves Efficiently and Effectively.

  • To Define KPIs (Key performance Indicator), Customer Service Dashboards or Project Management Dashboard (Gantt Chart)
  • Dashboard Reports Based on Tables and Number or Charts/Graphs or Both.
Introduction to Programming Introduction to logical thinking flowcharts & algorithms
  • Define Objective, Start & End Points; Identifying Solution & Breaking it Into Sequential Steps Writing a
  • Step-by-Step Instructions, Flowcharts, Process Flow Diagrams. Excel Macros – an Introduction
Complete Review of the VBA Language (Subs, Functions, Variables, Arrays, Loops, Logic…etc.)
  • Excel Macro Language Review (VBA) Including Variables, Data Types, Constants, Arrays, Operators, Expressions, Loops, Logic Decisions And Calling
  • Overview Of Commanding Excel Using VBA Including A Discussion Of Objects, Properties And Methods
  • The Power of Macros – What, When, How to use Macros. Introduction to Object Oriented Programming
  • Objects, Its Functions, Methods and Properties Introduction to Events
  • Details of Events, How & When to use of Events, Preparing to ‘Macro’ Visual Basic Editor (VBE) – Developer Tab, Security
  • Introduction to the VBE, Properties window, Project Explorer, Password Protection of Code How to use the VBE – Features, Options, Intelligence Technology
  • Debugging Mode, Bookmarks, Breakpoints, Watch Window, Immediate Window and Locals Window Inbuilt VBE Help Feature – Tips and Tricks
  • Form Controls vs. ActiveX Controls Getting into the Code
  • Message Box and Input Box Working with Data in Excel through VBA
  • Data Types, Constants and Variables
  • Different type of data type; How and When to use Variables to Store Information.
  • For-Next, For-Each, Do-While, Do until, Do Loop Decision-Making and Code Branching
  • If-Then-Else, Select-Case, And/or Nested Conditions
  • What is user’s Defined Functions? How to create & use them.
  • Use of Arrays in VBA programming with one dimensional, two dimensional or multi-dimensional analysis
Excel VBA Power Programming For VBA Macros
  • Working with Dynamic Ranges. Protecting Worksheets, Cells and Ranges. Working with Multiple Files. Opening & Saving Files
  • How to Analyze Data On Multi Worksheets And Build Summary Sheets
  • How to Access The Windows File And Folder System To Open And Close Workbooks
  • How to Protect Your Code Against Error
  • How to Use Excel And VBA To Create Basic Dash Boards
  • How to Create Your Own Custom Business Worksheet Functions In VBA
  • How to Create Basic Report Generation Tools Using Excel VBA, Microsoft Word And PowerPoint
  • How to Use The Excel Visual Basic Macro Recorder To Record Excel Tasks In VBA And Then Interpret The Code
Overview of Using User forms To Create Business Wizards
  • Working with User Forms & User Forms Events like List box, Combo box, Option Buttons, Check box, Text box, Labels, Command button, Toggle button.
  • How to create dynamic dashboard on user form with different controls
  • How to link various user form with each other to create a complete interface between user and system
Connection between Excel VBA & other platforms
  • How to Establish Connection Between VBA and Internet Explorer to Open any Internet Website through VBA
  • How to Establish Connection Between Excel VBA and power presentation to create power point through VBA
  • How to Establish Connection Between Excel VBA and Access database to update the data in access through VBA
  • How to Establish Connection Between Excel VBA and outlooks through VBA
  • How to Establish Connection Between Excel VBA and MS Word through VBA

Testing and Debugging Your Code

Effective Error Handling

Automation Development Reports & Live Projects

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.

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 1 - Process Studio
  • 1.1 Running a Process
  • 1.2 Basic Skills
  • 1.3 Process Validation
  • 1.4 Decision Stage
  • 1.5 Calculation Stage
  • 1.6 Data Items
Module 2 - Process Flow
  • 2.1 Circular Paths
  • 2.2 Controlling Play
  • 2.3 Set Next Stage
  • 2.4 Breakpoints
  • 2.5 Collections and Loops
  • 2.6 Pages for Organization
Module 3 - Inputs and Outputs
  • 3.1 Input Parameters
  • 3.2 Stepping and Pages
  • 3.3 Data Item Visibility
  • 3.4 Data Types
  • 3.5 Output Parameters
  • 3.6 Start Up Parameters
  • 3.7 Control Room
  • 3.8 Process outputs
Module 4 – Object Studio
  • 4.1 Creating a Business Object
  • 4.2 Application Modeler
  • 4.3 Spying Elements
  • 4.4 Attributes
  • 4.5 Attribute Selection
  • 4.6 Launch
  • 4.7 Wait
  • 4.8 Timeouts
  • 4.9 Terminate
  • 4.10 Write
  • 4.11 Press
  • 4.12 Attach and Detach
  • 4.13 Read
  • 4.14 Actions
  • 4.15 Action Inputs and Outputs
  • 4.16 Data Items as inputs
Module 5 – Error Management
  • 5.1 Exception Handling
  • 5.2 Recover and Resume
  • 5.3 Throwing Exceptions
  • 5.4 Preserving the current exception
  • 5.5 Exception Bubbling
  • 5.6 Exception Blocks
Module 6 – Case Management
  • 6.1 Work Queues
  • 6.2 Queue Items
  • 6.3 Work Queue Configuration
Module 7 – Additional Features
  • 7.1 Collection Actions
  • 7.2 Choice Stage
  • 7.3 Logging
  • 7.4 Log Viewer
  • 7.5 System Manager
  • 7.6 Process Grouping
  • 7.7 Export and Import
  • 7.8 Release Manager – Packages and Releases
Module 8 - Advanced Features
  • 8.1 Data Item Initialization
  • 8.2 Data Item Exposure (Environment Variables)
  • 8.3 Casting
  • 8.4 Code Stage
  • 8.5 Initialize and Cleanup
  • 8.6 Attribute Match Types
  • 8.7 Dynamic Attributes
  • 8.8 Active Accessibility
  • 8.9 Global Clicks and Keys
  • 8.10 Credentials
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 1 - SPSS - Basic
Module 1.1 - Developing the familiarity with SPSS Processer
Module 1.2 - Working with descriptive statistics
Module 1.3 - Hypothesis Testing
Module 1.4 - Testing the differences between group means
Module 1.5 - Correlational Analysis
Module 1.6 - Regression
Module 1.7 - Non-parametric tests
Module 2 - SPSS - Advanced
Module 2.1 - General Linear Models (GLM 1 to 5)
Module 2.2 - Factor Analysis
Module 2.3 - Cluster Analysis
Module 2.4 - Profile Analysis
Module 2.5 - Discriminant Analysis
Module 2.6 - Survival Analysis
Module 2.7 - Neural network Analysis
Module 2.8 - Time Series Analysis
Module 1 - Introduction to SAS and Analytics
  • Overview Of SAS
  • Analytical World
  • Library Structure And Definition
Module 2 - Getting Started with SAS
  • Installation And Configuration
  • Sas Graphic User Interface
  • Sas Programming Windows
  • Sas Library
  • Variable Attributes
  • Importing And Exporting Data
  • Understating Datasets
Module 3 - Accessing Data
  • Understanding Data Step Processing
  • Compilation And Execution Phase
  • Buffer Pdv Concept
  • Importing Raw Data Files
  • Reading One To Many And Many To One Records
  • Import Wizard
  • Datalines And Cards
  • Mixed Record Types
  • Hierarchical Files
Module 4 - Data Management and Manipulation
  • Proc Content and Proc Print
  • Creating New Column in SAS
  • Conditional Logic
  • Formatting Values
  • Using Filter in SAS Table and Data Task
  • Merging SAS Tables in Data Step
  • Combining Data Sets
Module 5 - Sas Functions
  • Arithmetic Functions
  • Date and Time Functions
  • Nested Functions
  • Text Manipulation Function
Module 6 - Sas Data Analysis and Reporting
  • Proc Fre
  • Proc Format
  • Proc Summary
  • Proc Tabulate
  • Proc Report
  • Output Delivery System
  • Ods Statement
Module 7 - SAS Data Mining
  • Sql Language
  • Creating Tables and Inserting Values
  • Storing and Retrieving Data
  • Sorting, Filtering and Grouping
  • Reporting and Summary Analysis
  • Creating Indexes and Using Joins
Module 8 - Advanced SAS Features and Macros
  • Defining Macros
  • Macro Parameters and Variables
  • Macros Options
  • Using Call Symput and Symget
  • Debugging SAS
  • Effective SAS Programming
  • Memory Saving Tips
  • Disk Management and Saving I/O Processing Time
Module 1 - Overview
  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation
Module 2 - Introduction
  • Using the R console
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Object oriented programming
  • Introduction to vectorized calculations
  • Introduction to data frames
  • Installing packages
  • Working directory
  • Saving your work
Module 3 - Variable types and data structures
  • Variables and assignment
  • Data types
  • Numeric, character, boolean, and factors
  • Data structures
  • Vectors, matrices, arrays, dataframes, lists
  • Indexing, subsetting
  • Assigning new values
  • Viewing data and summaries
  • Naming conventions
  • Objects
Module 4 - Getting data into the R environment
  • Built-in data
  • Reading data from structured text files
  • Reading data using ODBC
Module 5 - Data frame manipulation with dplyr
  • Renaming columns
  • Adding new columns
  • Binning data (continuous to categorical)
  • Combining categorical values
  • Transforming variables
  • Handling missing data
  • Long to wide and back
  • Merging datasets together
  • Stacking datasets together (concatenation)
Module 6 - Handling dates in R
  • Date and date-time classes in R
  • Formatting dates for modeling
  • Control flow
  • Truth testing
  • Branching
  • Looping
Module 7 - Functions in depth
  • Parameters
  • Return values
  • Variable scope
  • Exception handling
Module 8 - Applying functions across dimensions
  • Sapply, lapply, apply
Module 9 - Exploratory data analysis (descriptive statistics)
  • Continuous data
  • Distributions
  • Quantiles, mean
  • Bi-modal distributions
  • Histograms, box-plots
  • Categorical data
  • Tables
  • Barplots
  • Group by calculations with dplyr
  • Split-apply-combine
  • Melting and casting data
Module 10 - Inferential statistics
  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test
Module 11 - Base graphics
  • Base graphics system in R
  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, titles, axes
  • Exporting graphics to different formats
Module 12 - Advanced R graphics: ggplot2
  • Understanding the grammar of graphics
  • Quick plots (qplot function)
  • Building graphics by pieces (ggplot function)
Module 13 - General linear regression
  • Linear and logistic models
  • Regression plots
  • Confounding / interaction in regression
  • Scoring new data from models (prediction)
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
Module 1 - Introduction to Hadoop and Big Data
  • Big Data Overview
  • How to Process Big Data?
  • What is Hadoop?
  • Features and Elements of Hadoop
  • Uses of Hadoop
  • Hadoop Ecosystem
  • Data Analytics Structure
  • Rdbms Vs Hadoop
  • Installation and Configuration
Module 2 - Hadoop Distribution File System
  • Importance Of Hdfs
  • Hdfs Features
  • Daemons Of Hadoop
    • Name Node
    • Data Node
    • Secondary Name Node
    • Job Tracker
    • Task Tracker
  • Hadoop 2x Configuration Files
  • Data Storage In Hdfs
  • Accessing Hdfs
  • Fault Tolerance
  • Hdfs Federation
Module 3 - Map Reduce and Its Features
  • Understand Hadoop Mapreduce Framework
  • Working With Mapreduce In Hdfs
  • Concepts Of Input Splits, Combiner And Partitioner And Demos In Mapreduce
  • Traditional Wav S Mapreduce Way
  • Why Mapreduce
  • Hadoop 2x Mapreduce Architecture And Components
  • Yarn Workflow
  • How Mapreduce Works
  • Mapreduce Algorithms
  • Writing Mapreduce Program
  • 3mapper And Reducer
  • Input And Output Format In Mapreduce
  • Data Localization
  • Hadoop I/O
Module 4 - Pig
  • Understanding Pig
  • Pig Use Cases
  • Pig Vs Mapreduce
  • Pig Scripting And Running Modes
  • Programming Structure In Pig
  • Data Types In Pig
  • Execution Modes In Pig
  • Loading Data And Exploring Pig
  • Latin Commands
Module 5 - Hive
  • Learn About Hive Concepts
  • Data Types In Hive
  • Loading And Querying Data In Hive
  • Hive Scripts And Udf
  • Hive Vs Pig
  • Hive Architecture And Components
  • Partitions And Buckets
  • Hive Vs Rdbms
Module 6 - Hbase
  • Introduction To Hbase
  • Advanced Hive Concepts
  • Hbase Architecture And Design
  • Hbase Vs Rdbms
  • Read And Write Pipeline
  • Hbase Commands
  • Hbase Shell
  • Client Api And Data Loading Techniques
  • Zoopkeeper Service
Module 7 - Data Integration with Sqoop, Talend and Flume
  • Hadoop Integration
  • Introduction to Talend
  • Loading Data From Rdbms Into Hdfs Using Sqoop and Talend
  • Managing Real Time Data Using Flume
  • Other Important Data Analysis Features with Hadoop Elements
Module 8 - Hadoop Project and Oozie
  • Solving Big Data Issues
  • Discussing Data Sets
  • Live Training Under Expert Supervision
  • Module Wise Assignments

Inquiry for Advanced Excel Reporting & Analysis Training

FAQ

IS MS Access SQL Course Helps In Data Management ?

MS Access and SQL server are Database Management System (DBMS). It handles data management tasks, the large volume of data or complex data structure in to organize way and it can be retrieve as per requirement.

Who Will Be Trainer For Advanced Excel Course ?

Advanced excel training is provided by industry expert who are working in companies like Evalueserve, Accenture,Mercer etc. SLA advanced excel course in 100% practical workshop and batch size is 8-10 students.

How much can I earn after acquiring and SAS certification?

There is an abundance of job opportunities for both Freshers and experience candidate in SAS. Almost every organization that deals with customers and employees use the SAS tool as it helps them to maintain their database and perform valuable tasks such as data analysis and data modeling. This is the main reason more and more students are now moving towards this exceptional tool and learning how to operate it.
After acquiring a certification in SAS, the candidate will easily be able to find a relevant job opportunity in any reputed ECommerce industry where he or she can earn between 15000 to 25000 INR at the start, which will increase as experience. However, it also depends on from which institute you have garnered the certification.

How to acquire a decent job in Hadoop as a fresher?

There is too many job opportunities are there for people who can perform Hadoop operations such as data transfer and data competition in a large amount regardless of their experience. Hadoop is a Highly popular and open source platform for managing huge data from one server to several. However, in order to get an appointment as a Hadoop fresher, you must first gain specific knowledge and skills required for the purpose of data management, which you will easily be able to acquire through our specialized Hadoop training course.

Is R Programming beneficial for Data Analytics?

R Programming is Widely used among statisticians and data miners for developing statistical software and data analysis

Can I Start My Career By Learning Advanced Excel Course ?

Yes, Our advanced excel course is designed as per companies job requirement. Advanced excel course includes topic like Lookup, Data Validation, Graph & Charts, Power Pivot Table & Dashboard. By learning advanced excel course you can make reports & dashboard presentation.

Can a fresher in SAS acquire a decent job opportunity?

There are tons of job opportunities for SAS candidates regardless of their skills, knowledge, and experience. The scope of SAS Freshers is also huge as several banks, insurance companies and financial services are constantly looking for SAS operators. The fact that SAS is very cost efficient, provides greater security and allows the company is easily and effectively maintain a large amount of data makes it one of the most popular and worthy data analysis and business intelligence tool. The software is very easy to learn and operate, thus business Enterprises don’t have any issue in appointing Freshers for the job profile.

Why should I join Hadoop training course?

If you Aspire to become a data analyst are willing to pursue a career in big data and data management then it is highly necessary for you to join Hadoop training course as Hadoop is considered as one of the most popular and Powerful big data analysis and computation tool available in the current market. Even if you are working as a Hadoop operator in any business organization, you can still benefit greatly from this course in order to enhance your expertise and knowledge to become a valuable asset for the company.

Can I learn Python All By Myself?

It is possible for you to learn python by yourself as there are several reliable online tutorials available which will help you enhance your skills and become a professional in the Python programming language. However, if you are looking to make a promising career in the field without having any programming knowledge whatsoever, then it is highly suggestible that you attend a reputed training institute for Python training course where you will be guided by highly trained and certified experts along with the needed course material and equipment. SLA Consultants India is a leading Python certification course provider in Delhi, Gurgaon, and Noida at a very affordable price.

Can I get a job as a data analyst in R?

R is the name of a popular Programming language that has become the tool of choice for data scientists, data analytics and statisticians around the globe. It helps to get the job as a data analyst.

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