Learn how to extract data for analysis. Join now and learn under an expert data analyst.
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Corporate Served- Data Analytics refers to the science of analyzing raw data to make conclusions about the information. Moreover, data analytics procedures get automated into the mechanical process. It helps out in the consumption of mechanical processes through the help of data. Moreover, data analytics compasses a diverse set of data analyses. As a result of these factors demand Data Analytics Training Institute in Delhi increasing.
- However, data analytics can reduce the bottlenecks in the production units of the organization. Gaming companies use data analytics set out the reward schedule for the players who are competing in the market. Content companies also use the tantrums of data analytics for keeping, checking as well as re-organizing. Resultantly, the demand for Data Analytics Training in Delhi getting out the acceleration in demand.
Data Analytics Training in Delhi
- The course offers an overview of Big Data and helps out the candidates in assisting with different complicated procedures. Moreover, emphasizes a variety of Analytics things needed to make out organization efficient. Additionally, Data Analyst Training in Delhi increasing its demand;
Gathering out the user-relevant data for handling processes.
Conducting the data analytics using scientific methods.
Learning the techniques of quantitative analysis.
Adapting out to the real-life problems in Data Analytics.
Handling out the large amount of data related to organizational success.
- After completing the Data Analyst Online course the salary gets out in the range of Rs 1.8 lakh to Rs 11.9 lakh. Moreover, as per the reports of the ambition box, the average salary gets struck out at the rate of 15 lakh per annum.
- Getting out of a job in Data Analytics is the first step in growing out of the career in the domain of Data Analytics. In case you have no previous experience in this domain then you can go through the below-mentioned course prospects after Data Analytics Online Training;
The financial analyst is the first position that encompasses all the core components of data analytics. Moreover, this type of role can also include business analysts, investment managers, etc.
According to the latest research by the Bureau of Labor Classification, the salary expectations of a data analyst are so high in the market. As of 2020, the job category shows an average salary wage of $ 31.64.
With the evolving business world, the demand for data technology, machine learning as well as machine learning is seeking momentum. Moreover, these types of tech get integrated with the data analysis.
Overall the data analysts have multiple sets of skills as they are good at working with the numbers & reports. Moreover, they use strong presentation skills which can easily appease the customers.
Technology companies change out rapidly with the organizational procedures taking place. Departments are constantly adapting to the new changes which are taking place inside the industrial verticals.
- Undoubtedly, everyone heard about Data Analytics in modern businesses because they are responsible for handling organizational functions. Simply, data analytics is critical to handling organizational projects. As the Data Analyst training in Delhi gets completed you have to overcome the reasons behind its popularity:
The first step is the business understanding whenever any type of data comes out in front of data analysts they analyze the business situation & act according to the need of the hour.
Data analytics helps out in the exploration of data from different perspectives. Moreover, the data which gets collected from various sources need to be accessed accurately for future analysis.
Evaluating the analytic process for the further development as well as deployment functions in the organization. Moreover, we get to produce the final report & review it accordingly.
Big Data is the hottest topic going across the industrial scenario; the main reason behind it is that a huge amount of data is generated regularly. With increasing the competition in the market more & more companies are going through data analytics.
Almost every organization can adopt the new technologies which are trending in the market. Organizations have seen amazing growth due to the use of data analytics technologies.
- Data analysts are the professionals who get responsible for the senior leadership in the organization who make out strategic decisions. After completing the course from the Data Analytics training institute in Delhi you have to follow the below-mentioned primary duties:
Evaluation of business proceedings, anticipating out requirements as well as others.
Leading out the ongoing business operations for improving automation techniques.
Prioritization of new business requirements.
Effectively communicating the new insights of business to consumers & stakeholders.
Providing business solutions to the new enterprises.
- After completing the Data Analyst training in Delhi multiple organizations will get to hire out from the pool of candidates. If we talk regarding the organizations then it is Infogain Solutions Pvt Ltd, PubMatic, Serving Skill, Reliance Retail, Huquo Consulting Pvt Ltd, Wipro, CBRE, etc.
- While completing the Data Analytics Course in Delhi you get out personalized training with specialized faculty members. Moreover, you also get out 100% globally recognized training certificate which will boost the chances of getting a job in a competitive market.
- Related Courses to Data Analytics Training in Delhi
Why should you learn Data Analytics?
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Plenary for Data Analytics Certification Training
| Track | Week Days | Weekends | Fast Track |
|---|---|---|---|
| Course Duration | 40-45 Days | 7 Weekends | 8Days |
| Hours | 1 Hrs. Per Day | 2Hrs. Per Day | 6+ Hrs. Per Day |
| Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
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Data Analytics Certification Training
- Data Analyst 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. It's a 9 months Master’s Program in Data Analyst with Power BI, Tableau & R (including Data Visualization & Cloud Implementation) which includes a 6 months online project internship.
- Analytics Microsoft Power BI
- Analytics with Tableau
- Analytics with R Proramming
- Cloud: AWS(Amazon Web Services)
- Cloud: Microsoft Azure Fundamentals
- Data Analyst - Live Projects
- Python for Data Analyst
- Data Analysis and Visualization
- Databases – MS SQL and SQL Queries
- Statistics for Data Analyst
- Analytics with Excel
Things you will learn:
- 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.
- 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
- Installing SMTP Python Module
- Sending Email
- Reading from le and sending emails to all users
- 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
- 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
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Raising Exception
- Try and Finally
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specic Exception in Python
- Remove elements from a Set
- PythonSet Operations
- What is Set
- Set Creation
- Add element to a Set
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Looping Dictionaries
- Ordered Dictionaries
- Default Dictionaries
- Dict Comprehension
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Values
- Dict Keys (Updating, Removing and Iterating)
- Dict Creation
- Changing a Tuple
- Tuple Deletion
- Tuple Count
- Tuple Index
- Tuple Membership
- TupleBuilt in Function (Length, Sort)
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- String Split to create a List
- List having Multiple Reference
- List Sorting
- List Revers
- List related Keyword in Python
- List Delete
- What is List.
- List Append & Extend using “+” and Keyword
- List Remove
- List Insert
- List Append
- List Creation
- List Length
- Locale’s appropriate date and time
- Format Code list of Data, Time and Cal
- 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
- More example of Python Function
- Map, Filter and Reduce function with Lambda Function
- Programming using functions, modules & external packages
- Random functions in python
- Understanding Packages
- Importing own module as well as external modules
- Organizing python projects into modules
- Organizing python codes using functions
- Recursive Function and Its Advantage and Disadvantage
- Nested Loop in Python Function
- Scope and Life Time of Variable in Python Function
- Python Syntax
- Function Call
- Return Statement
- Arguments in a function – Required, Default, Positional, Variable-length
- Help function in Python
- *args and **kwargs
- Lamda/ Anonymous Function
- Write an Empty Function in Python –pass statement.
- How to use IN or NOT IN keyword in Python Loop.
- Examples of Looping with Break and Continue Statement
- Use If and Else in for and While Loop
- How to use nested Loop in Python
- Generator in Python
- Elegant way of Python Iteration
- Use of Switch Function in Loop
- How to use if and else with Loop
- Programs for printing Patterns in Python
- Statements – if, else, elif
- How to use nested IF and Else in Python
- Loops
- Loops and Control Statements.
- Jumping Statements – Break, Continue, pass
- How to use Range function in Loop
- Looping techniques in Python
- Sets
- Dictionary
- Tuples
- List
- Strings
- Understanding the concept of Casting and Boolean.
- Declaring and using Numeric data types
- Using string data type and string operations
- Understanding Non-numeric data types
- Variable Scope
- Byte objects vs. string in Python
- Type Casting in Python
- Packing and Unpacking Arguments
- Global and Local Variables in Python
- Variables, expression condition and function
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- Python Indentation
- Python Comments, Multiline Comments.
- Understanding the Python blocks.
- Python basic Operators
- Understanding Python variables
- Installation and Working with Python
Complete Understanding of OS Module of Python
Contacting user Through Emails Using Python
Reading an excel
Python Database Interaction
Python File Handling
Python Exception Handling
Strings
Sets
Dictionary
Tuple
List
Python Date Time and Calendar
Python Function, Modules and Packages
Control Structure & Flow
Python Data Type
Introduction To Variables
Python Keyword and Identiers
Introduction To Python
- 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.
- Customizing Seaborn Plots
- Well done! What’s next
- Bar plot with subgroups and subplots
- Box plot with subgroups
- Putting it all together
- Rotating x-tics labels
- Adding title and labels Part 2
- Adding a title and axis labels
- Adding a title to a face Grid object
- Face Grids vs. Axes Subplots
- Adding titles and labels Part 1
- Using a custom palette
- Changing the scale
- Changing style and palette
- Changing plot style and colour
- Visualizing a Categorical and a Quantitative Variable
- Point plot with subgroups
- Customizing points plots
- Point plots
- Adjusting the whisk
- Omitting outliers
- Box plots
- Create and interpret a box plot
- Customizing bar plots
- Bar plot with percentages
- Count plots
- Current plots and bar plots
- Visualizing Two Quantitative Variables
- Plotting subgroups in line plots
- Visualizing standard deviation with line plots
- Interpreting line plots
- Introduction to line plots
- Changing the style of scatter plot points
- Changing the size of scatter plot points
- Customizing scatters plots
- Creating subplots with col and row
- Introduction to relational plots and subplots
- Introduction to Seaborn
- Hue and count plots
- Hue and scattera plots
- Adding a third variable with hue
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Using Pandas with seaborn
- Making a count plot with a list
- Making a scatter plot with lists
- Introduction to Seaborn
- MatPlotLib
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Create Charts as Image
- Legend Alignment of Chart using MatPlotLib
- Understanding plt. subplots () notation
- Export the Chart as Image
- Play with Charts Properties Using 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
- NumPy
- NumPy’s Sort Function and More
- NumPy’s Mode, Median and Sum Function
- NumPy’s Mean and Axis
- Indexing NumPy elements using conditionals
- Logical Operation with Arrays
- Selecting Elements from 2-D Array
- Selecting Elements from 1-D Array
- Two-Dimensional Array
- Operations with NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from a CSV
- NumPy Arrays
- Importing NumPy and Its Properties
- Introduction to NumPy Numerical Python
- Pandas
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Python | Pandas Data Frame Inner Join
- Exporting the results to Excel Joins
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
- Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Exploring the Data Plotting, Correlations, and Histograms
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
- How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
- Statistics
- Box plot
- Standard Deviation
- Variance
- Correlation
- Interquartile range
- Range
- Outliers
- Mode
- Median
- Numerical Data
- Mean
- Categorical Data
Introduction to Data Visualization with Seaborn
Data Analysis and Visualization using NumPy and MatPlotLib
Data Analysis and Visualization using Pandas.
- 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.
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- SAVEPOINT and Query Blocking
- AUTOCOMMIT Transaction and usage
- IMPLICIT Transactions and options
- EXPLICIT Transaction types
- ACID Properties and Scope
- Bulk Operations with Triggers
- Database Triggers and Server Triggers
- Data Audit operations & Sampling
- INSERTED and DELETED memory tables
- DML Triggers and Performance impact
- Why to use Triggers
- GROUPING Functions
- ROW_COUNT
- String and Operational Functions
- Time Functions
- Date Functions
- System Functions and usage
- SCHEMABINDING and ENCRYPTION
- Types of Table Valued Functions
- Scalar Valued Functions
- Dynamic SQL and parameterization
- System level Stored Procedures
- INPUT and OUTPUT parameters
- SCHEMABINDING and ENCRYPTION
- Use of Variables and parameters
- Types of Stored Procedures
- Why to use Stored Procedures
- Primary Keys & Non-Clustered Indexes
- Indexes and Table Constraints
- Composite Indexed Columns & Keys
- Materializing Views (storage level)
- INCLUDED Indexes & Usage
- Index SCAN and SEEK
- Indexing Table & View Columns
- Need for Indexes & Usage
- Working with JOINS inside views
- Common Dynamic Management views
- Common System Views and Metadata
- Issues with Views and ALTER TABLE
- SCHEMA BINDING and ENCRYPTION
- Views on Tables and Views
- Benets of Views in SQL Database
- Disabling Constraints & Other Options
- Naming Composite Primary Keys
- CHECK and DEFAULT Constraints
- PRIMARY KEY Constraint & Usage
- UNIQUE KEY Constraint and NOT NULL
- NULL and IDENTITY properties
- Table creation using Constraints
- ROWCOUNT and CUBE Functions
- HAVING
- GROUPING
- ORDER BY
- Variants of SELECT statement
- Use of WHERE, IN and BETWEEN
- SELECT queries and Schemas
- DELETE
- UPDATE
- Basic INSERT
- Table creation using Schemas
- Table Aliases
- Column Aliases & Usage
- Single Row and Multi-Row Inserts
- Table creation using T-SQL Scripts
- Naming Conventions for Columns
- SQL Server Database Tables
- Database Structure modications
- File locations and Size parameters
- DB Design using Files and File Groups
- Database Creation using T-SQL scripts
- Database Creation using GUI
- SQL Database Architecture
- Conventions & Collation
- Conguration Tools & SQLCMD
- Using Management Studio (SSMS)
- SQL Server Features & Purpose
- Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
- SQL Server 2019 Installation
Cursors and Memory Limitations
Transactions Management
Triggers, cursors, memory limitations
System functions and Usage
Stored Procedures and Benets
Indexes and Query tuning
Views and Row Data Security
Data Validation and Constraints
SQL Tables in MS SQL Server
SQL Server 2019 Database Design
SQL Server Fundamentals
- 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.
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
- Dimensionality Reduction
- Anomaly Detection
- Datasets
- Feature Scaling
- Feature Engineering
- Data Preparation
- Imbalanced Data Techniques
- Identify outliers’ data
- Identify missing data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Need for structured exploratory data
- 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
- Correlation and Co-variance
- Spread and Dispersion
- Gaussian distribution
- Z-score
- Normal distribution
- Expected value
- Random variables
- Probability distribution functions
- Sample vs Population Statistics
- Descriptive Statistics
- 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.)
- Label encoding/one hot encoding
- Feature Engineering
- Feature Selection
- Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
- Normalizing data
- Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
- Filling missing values using lambda function and concept of Skewness.
- Cleansing Data with Python
- Important python modules Pandas
- Exporting Data to various formats
- Viewing Data objects - sub setting, methods
- Database Input (Connecting to database)
- Importing Data from various sources (Csv, txt, excel, access etc)
- Why Python for data science
- Build Resource plan for Data Science project
- Project plan for Data Science project & key milestones based on effort estimates
- Identify the most appropriate solution design for the given problem statement
- List of steps in Data Science projects
- Data Science Methodology & problem-solving framework.
- Overview of Data Science tools & their popularity.
- Critical success drivers.
- How leading companies are harnessing the power of analytics
- Types of problems and business objectives in various industries
- Relevance in industry and need of the hour
- What is data
- Classication of data
- Common Terms in Data Science
- What is Analytics & Data Science
Data Pre-Processing & Data Mining
EDA (Exploratory Data Analysis)
Introduction to Predictive Modelling
Introduction to Statistics
Data Analysis Visualization Using Python
Feature Engineering in Data Science
Data Manipulation Cleansing - Munging Using Python Modules
Accessing/Importing and Exporting Data
Introduction to Data Analytics
- 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.
- Invoice Creation
- Loan Assumption Sheet
- Salary Slip
- Payroll Report
- Inventory Statement
- Stock Controller MIS Reporting
- Expenses Report
- Calling Reports
- Prospect Analysis Report
- Collections Report
- Channel Stock Report
- Customer-Wise Sales Report
- Member Performance Report
- Product Performance Report
- Data Analysis
- MIS Reporting For Store And Billing
- MIS Report For Manufacturing Company
- Costing Budgeting Mis Reporting
- MIS Report Preparation Supplier, Exporter
- HR Mis Reports
- Audit Report
- Accounting MIS Reports
- Type of Reporting In India
- Reporting Analyst
- Indian Print Media Reporting
- Interactive Dashboards
- Dashboard Elements
- Dashboard Background
- Functions for Calculation Depreciation
- Financial Functions
- Statistical Functions
- Logical Functions & Date and Time Functions
- Database Functions
- Lookup & Reference Function
- Text Functions
- Generating MIS Report In Excel
- Math & Trig Functions
- Advance Functions of Excel
- Pivot Table
- Slicer with Pivot Table & Chart
- Pivot Chat
- Data Table
- Advance use of Data Tables in Excel
- Reporting and Information Representation
- Specified Range Protection in Worksheet
- Excel Data Analysis
- Scenario Manager
- Goal Seek
- Printing of Raw & Column Heading on Each Page
- Workbook Protection and Worksheet Protection
- Filter
- Advance Filter
- Auto Filter
- Consolidation With Several Worksheets
- Grouping Features
- Row Wise
- Column Wise
- Subtotal, Multi-Level Subtotal
- Sort by colours
- Lookup Functions
- VLookup
- HLookup
- Lookup
- Sort by icons
- Restoring data to original order after performing sorting
- Advance Data Sorting
- Multi-level sorting
- Real Life Assignment work
- Solver, Freeze Panes
- Various Simple Functions in Excel(Sum, Average, Max, Min)
- What if Analysis (Data Table, Goal Seek, Scenario)
- Auditing, (Trace Precedents, Trace Dependents)Print Area
- Data Validations, Consolidate, Subtotal
- Different type of Chart Creations
- Sort, Filter, Advance Filter
- Range Name, Format Painter
- Conditional Formatting, Wrap Text, Merge & Centre
- Creation of Excel Sheet Data
MIS Reporting & Dash Board
Ms Excel Advance
Understanding Concepts of Excel
- The Power BI course assists the user to understand the way to install Power BI desktop also by understanding and developing the workshop and insights using the data. It offers tools and techniques that are used to visualize and analyze data. The course will help you to learn and grab insights on everything an organization need; to manage the data with Power BI.
- Replacing a Dataset and Troubleshooting Refreshing
- Understanding Data Refresh
- Personal Gateway (Power BI Pro and 64-bit Windows)
- Export to PowerPoint and Sharing Options Summary
- Export Data from a Visualization
- Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
- Print or Save as PDF and Row Level Security (Power BI Pro)
- Publish from Power BI Desktop and Publish to Web
- Share Dashboard with Power BI Service
- Introduction and Sharing Options Overview
- Content packs
- Update content packs
- Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
- Connecting directly to SQL Server
- Connectivity with CSV & Text Files
- Custom Data Gateways
- Exploring live connections to data with Power BI
- Quick Insights in Power BI
- Why Dashboard and Dashboard vs Reports
- Creating Dashboards
- Conguring a Dashboard Dashboard Tiles, Pinning Tiles
- Power BI Q&A
- Drill through and Custom Report Themes
- Managing and Arranging
- Custom Visuals
- Map Visualizations
- KPI's, Cards & Gauges
- Tables, Matrices & Conditional Formatting
- Hierarchies and Reference/Constant Lines
- Drill Down/Up
- Visual, Page and Report Level Filters
- Grouping and Binning and Selection Pane, Bookmarks & Buttons
- Data Binding and Power BI Report Server
- Cross Filtering and Highlighting
- Tooltips and Slicers, Timeline Slicers & Sync Slicers
- Setting Sort Order
- Scatter & Bubble Charts & Play Axis
- Creating Visualisations and Colour Formatting
- What Are Custom Visuals
- How to use Visual in Power BI
- In-Memory Processing DAX Performance
- Quick Measures in DAX - Auto validations
- Operators in DAX - Real-time Usage
- ROW Context and Filter Context in DAX
- Measures in DAX
- Measures and Calculated Columns
- DAX Functions
- Text and Aggregate
- Mathematical
- Statistical
- Information
- Logical
- Date and Time
- Time Intelligence
- Data Types in DAX
- Calculation Types
- Syntax, Functions, Context Options
- What is DAX
- Creating Calculated Columns
- Creating Measures & Quick Measures
- Default Summarization & Sort by
- Optimize Data Models
- Cardinality and Cross Filtering
- Manage Data Relationship
- Introduction to Modelling
- Modelling Data
- Improving Performance and Loading Data into the Data Model
- Creating the FACT Table
- Performing Basic Mathematical Operations
- Creating Conditional Columns
- Creating an Index Column
- Duplicating Columns and Extracting Information
- Introducing the another DimensionTable
- Merging Queries
- Finishing the Dimension Table
- Entering Data Manually
- Duplicating and Referencing Queries
- Creating the Dimension Tables
- Introducing the Star Schema
- Creating a New Group for our Queries
- Formatting Data
- Pivoting and Unpivoting Columns
- Splitting Columns
- Replacing Values
- Editing Columns
- Understanding Append Queries
- Editing Rows
- Connecting Power BI Desktop to our Data Sources
- Query Editor
- Data Transformation
- Workspaces in Power BI
- Extracting data from various sources
- Power BI Desktop
- Sharing Dashboards and Reports
- Interacting with your Dashboards
- Dashboard in Minutes
- Introduction to Tools and Terminology
- Get Power BI Tools
- Getting started with Power BI Desktop
- Building Blocks of Power BI
- Why Power BI
- What is Power BI
- SSBI Tools
- Introduction to SSBI
- Why we need BI
- Overview of BI concepts
Refreshing Datasets
Publishing and Sharing
Direct Connectivity
Introduction to Power BI Dashboard and Data Insights
Power BI Desktop Visualisations
Data Analysis Expressions (DAX)
Modelling with Power BI
Power BI Data Transformation
Power BI Desktop
Introduction to Power BI
- Tableau is an end-to-end data analytics platform that allows you to prep, analyze, collaborate, and share your data insights. Tableau excels in self-service visual analysis, allowing people to ask new questions of governed big data and easily share those insights across the organization.
- Data Security with Filters in Tableau Online
- Managing Permissions on Tableau Online
- Understand Scheduling
- AI-Powered features in Tableau Online (Ask Data and Explain Data)
- Data Management through Tableau Catalog
- Interacting with Content on Tableau Online
- Publishing Workbooks to Tableau Online
- Data Visualization best practices
- Format Style
- Choosing the right type of Chart
- Tableau Tips and Tricks
- Story Points
- Designing Dashboards for devices
- Dashboard Layouts and Formatting
- Interactive Dashboards with actions
- Building a Dashboard
- Introduction to Dashboards
- The Dashboard Interface
- Dashboard Objects
- Donut Chart
- Bump Chart
- Step and Jump Lines
- Word Cloud
- Control Chart
- Funnel Chart
- Waterfall Chart
- Pareto Chart
- Bar in Bar Chart
- Gantt Chart
- Bullet Chart
- Box and Whisker’s Plot
- Web Map Services
- Background Images
- Polygon Maps
- Spatial Files
- Custom Geocoding
- Types of Maps
- Manually assigning Geographical Locations
- Introduction to Geographic Visualizations
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Forecasting
- Clustering
- Trend lines
- Reference lines
- Tool tips
- Parameters
- Using R within Tableau for Calculations
- Level of Detail (LOD) Calculations
- Operators and Syntax Conventions
- Table Calculations
- Types of Calculations
- Built-in Functions (Number, String, Date, Logical and Aggregate)
- Sets
- Grouping
- Highlighting
- Sorting
- Filtering
- Data Granularity
- Hierarchies
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Visual Analytics
- Tableau Desktop User Interface
- Data Blending
- Joins and Unions
- Types of Connections
- Connect to data from File and Database
- Features of Tableau Desktop
- Data Preparation techniques using Tableau Prep Builder tool
- VizQL Fundamentals
- Introduction to Tableau Prep
- Tableau Prep Builder User Interface
- Tableau Architecture
- Tableau Server Architecture
- Business Intelligence tools
- Introduction to Tableau
- Data Visualization
Exploring Tableau Online
Get Industry Ready
Dashboards and Stories
Advanced charts in Tableau
Geographic Visualizations in Tableau
Level of Detail (LOD) Expressions in Tableau
Advanced Visual Analytics
Calculations in Tableau
Basic Visual Analytics
Data Connection with Tableau Desktop
Introduction to Data Preparation using Tableau Prep
- Many data scientists use R while analyzing data because it has static graphics that produce good-quality data visualizations. Moreover, the programming language has a comprehensive library that provides interactive graphics and makes data visualization and representation easy to analyze.
- Useful plots from regression models
- Evaluating residuals
- Scoring new data from models (prediction)
- Confounding / interaction in regression
- Regression plots
- Understanding formulas
- Linear and logistic regression models
- T-test and non-parametric equivalents
- Chi-squared test
- Bivariate correlation
- Exporting graphics
- Controlling legends and axes
- Understanding geoms (geometries)
- Linking chart elements to variable values
- Building graphics by pieces (ggplot function)
- Quick plots (qplot function)
- Understanding the grammar of graphics
- Exporting graphics to different formats
- Labels, legends, titles, axes
- Scatterplots, histograms, bar charts, box and whiskers, dot plots
- Base graphics system in R
- Programming with map and purr
- Sapply, lapply, apply
- Exception handling
- Applying functions across dimensions
- Return values
- Variable scope
- Looping
- Functions
- Parameters
- Truth testing
- Branching
- Categorical data wrangling with forcats
- Introduction to regular expressions in R
- Stringer package for text manipulation
- Finding and matching patterns in text
- Reshaping and pivoting data in R (long to wide with aggregation)
- Melt and cast
- Split-apply-combine
- Bar plots
- Group by calculations
- Tables
- Categorical data
- Bi-modal distributions
- Histograms, box-plots
- Quantiles, mean
- Distributions
- Continuous data
- Formatting dates for modeling
- Date and date-time classes in R
- Merging datasets together
- Stacking datasets together (concatenation)
- Handling missing data
- Transforming variables
- Combining categorical values
- Binning data (continuous to categorical)
- Adding new columns
- Renaming columns
- Introduction to tables, enhanced data frames
- Reading data using ODBC
- Reading data from structured text files
- Built-in data
- Naming conventions
- Objects
- Viewing data and summaries
- Indexing, sub-setting
- Assigning new values
- Data types
- Numeric, character, Boolean, and factors
- Data structures
- Vectors, matrices, arrays, data frames, lists
- Variables and assignment
- History of R
- Advantages and disadvantages
- Downloading and installing
- How to find documentation
- Saving your work
- Installing and loading packages
- Working directory
- Introduction to data frames
- Object oriented programming
- Introduction to vectorised calculations
- Using the R console and R Studio
- Getting help
- Learning about the environment
- Writing and executing scripts
General Linear Regression Models in R :
Inferential Statistics :
Advanced R graphics :
Graphics in R Overview :
Control flow & functions :
Working with text data :
Exploratory Data Analysis (Descriptive Statistics) :
Handling dates in R :
Data frame manipulation :
Getting data into the R environment :
Variable types and data structures in base R :
Overview :
R Programming Basics :
- AWS allows you to easily move data between the data lake and purpose-built data services. For example, AWS Glue is a serverless data integration service that makes it easy to prepare data for analytics, machine learning, and application development.
- In this module, you will get an overview of multiple AWS services. We will talk about how do you manage life cycle of AWS resources and follow the DevOps model in AWS. We will also talk about notification and email service of AWS along with Content Distribution Service in this module.
- Cloud Trail,
- In this module, you will learn how to manage relational database service of AWS called RDS.
- RDS Failover
- RDS Subnet
- RDS Migration
- Dynamo DB (No SQL DB)
- Redshift Cluster
- SQL workbench
- JDBC / ODBC
- Amazon RDS
- Type of RDS
- Policy
- Role
- Group
- User
- Amazon IAM
- manage passwords,
- log in with IAM-created users.
- add users to groups,
- In this module, you will learn how to achieve distribution of access control with AWS using IAM.
- In this module, you will learn introduction to Amazon Virtual Private Cloud. We will cover how you can make public and private subnet with AWS VPC. A demo on creating VPC. We will also cover overview of AWS Route 53.
- Cross region Peering
- Gateways
- Route Tables
- Subnet
- Amazon VPC with subnets
- In this module, you will learn about 'Scaling' and 'Load distribution techniques' in AWS. This module also includes a demo of Load distribution & Scaling your resources horizontally based on time or activity.
- Auto scaling policy with real scenario based
- Type of Load Balancer
- Hands on with scenario based
- Amazon Auto Scaling
- In this module, you will learn how to monitoring AWS resources and setting up alerts and notifications for AWS resources and AWS usage billing with AWS CloudWatch and SNS.
- Cloud Watch with Agent
- Amazon Cloud Watch
- SNS - Simple Notification Services
- In this module, you will learn how AWS provides various kinds of scalable storage services. In this module, we will cover different storage services like S3, Glacier, Versioning, and learn how to host a static website on AWS.
- Hands-on all above
- AWS CloudFront
- Real scenario Practical
- Classes of Storage
- AWS-CLI
- Life cycle
- Cross region Replication
- Static website
- Policy
- Permission
- Versioning
- In this module, you will learn about the introduction to compute offering from AWS called EC2. We will cover different instance types and Amazon AMIs. A demo on launching an AWS EC2 instance, connect with an instance and host ing a website on AWS EC2 instance. We will also cover EBS storage Architecture (AWS persistent storage) and the concepts of AMI and snapshots.
- Hands on both Linux and Windows
- Exercise
- Demo of AMI Creation
- EC2 Type
- Installation of Web server and manage like (Apache/ Nginx)
- Amazon EC2
- EC2 Pricing
- In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.
- A Short history
- Client Server Computing Concepts
- Challenges with Distributed Computing
- Introduction to Cloud Computing
- Why Cloud Computing
- Benefits of Cloud Computing
- In this module, you will cover various architecture and design aspects of AWS. We will also cover the cost planning and optimization techniques along with AWS security best practices, High Availability (HA) and Disaster Recovery (DR) in AWS.
- AWS High Availability Design
- AWS Best Practices (Cost +Security)
- AWS Calculator & Consolidated Billing
- Public DNS
- Private DNS
- Routing policy
- Records
- Register DNS
- Work with third party DNS as well
- Stack
- Templet
- Json / Ymal
- Installation of Linux
- Configuration
- Manage
- Installation of app on Linux (apache / Nginx etc)
- AWS cli configuration on Linux
- Complete hands-on on Linux.
- Scenario based lab and practical
- Each topic and services will be cover with lab and theory.
Multiple AWS Services and Managing the Resources' Lifecycle
Amazon Relational Database Service (RDS)
Identity and Access Management Techniques (IAM)
AWS VPC
Scaling and Load Distribution in AWS
Cloud Watch & SNS
Amazon Storage Services S3 (Simple Storage Services)
Amazon EC2 and Amazon EBS
Introduction to Cloud Computing
AWS Architecture and Design
Migrating to Cloud & AWS
Router S3 DNS
Cloud Formation
Elastic Beanstalk
EFS / NFS (hands-on practice)
Hands-on practice on various Topics
Linux
- Data Scientists know how to train Predictive Models. So, by enabling them to work together, Microsoft Azure Data Science ensures high-quality models at scale in production. With MLOps incorporated as a part of the Microsoft Azure Data Science platform, Data Scientists can create a discrete pipeline for each model
- Identify the benefits and considerations of using cloud services Cloud Computing Basics.
- Identify the benefits of cloud computing, such as High Availability,Scalability, Elasticity,
- Agility, and Disaster Recovery
- Identify the differences between Capital Expenditure (Cap Ex) and Operational.
- Expenditure (Op Ex)
- Describe the consumption-based model
- Describe the differences between categories of cloud services
- Describe the shared responsibility model
- Describe Infrastructure-as-a-Service (IaaS),
- Describe Platform-as-a-Service (PaaS)
- Describe server less computing
- Describe Software-as-a-Service (SaaS)
- Identify a service type based on a use case
- Describe the differences between types of cloud computing
- Define cloud computing
- Describe Public cloud
- Describe Private cloud
- Describe Hybrid cloud
- Compare and contrast the three types of cloud computing Describe Core Azure Services
- Manage Azure AD objects
- create users and groups
- manage user and group properties
- manage device settings
- perform bulk user updates
- manage guest accounts
- configure Azure AD Join
- configure self-service password reset
- NOTE Azure AD Connect; PIM
- Manage role-based access control (RBAC)
- create a custom role
- provide access to Azure resources by assigning roles
- subscriptions
- resource groups
- resources (VM, disk, etc.)
- interpret access assignments
- manage multiple directories
- Manage subscriptions and governance
- configure Azure policies
- configure resource locks
- apply tags
- create and manage resource groups
- move resources
- remove RGs
- manage subscriptions
- configure Cost Management
- configure management groups
- Manage storage accounts
- configure network access to storage accounts
- create and configure storage accounts
- generate shared access signature
- manage access keys
- implement Azure storage replication
- configure Azure AD Authentication for a storage account
- Manage data in Azure Storage
- export from Azure job
- import into Azure job
- install and use Azure Storage Explorer
- copy data by using AZ Copy
- Configure Azure files and Azure blob storage
- create an Azure file share
- create and configure Azure File Sync service
- configure Azure blob storage
- configure storage tiers for Azure blobs
- Configure VMs for high availability and scalability
- configure high availability
- deploy and configure scale sets
- Create and configure VMs
- configure Azure Disk Encryption
- move VMs from one resource group to another
- manage VM sizes
- add data discs
- configure networking
- redeploy VMs
- Create and configure Web Apps
- create and configure App Service
- create and configure App Service Plans
- Implement and manage virtual networking
- create and configure VNET peering
- configure private and public IP addresses, network routes, network interface,
- subnets, and virtual network
- Configure name resolution
- configure Azure DNS
- configure custom DNS settings
- configure a private or public DNS zone
- Secure access to virtual networks
- create security rules
- associate an NSG to a subnet or network interface
- evaluate effective security rules
- deploy and configure Azure Firewall
- deploy and configure Azure Bastion Service
- NOT Implement Application Security Groups; DDoS
- Configure load balancing
- configure Application Gateway
- configure an internal load balancer
- configure load balancing rules
- configure a public load balancer
- troubleshoot load balancing
- NOT Traffic Manager and Front Door and Private Link
- Implement backup and recovery
- configure and review backup reports
- perform backup and restore operations by using Azure Backup Service
- create a Recovery Services Vault
- use soft deletes to recover Azure VMs
Describe Cloud Concepts
Manage Azure identities and governance (15-20%)
Implement and Manage Storage (10-15%)
Deploy and Manage Azure Compute Resources (25-30%)
Configure and Manage Virtual Networking (30-35%)
Monitor and Back up Azure Resources (10-15%)
- The training offers complete career transitioning projects based on the current needs of the organization. These projects are guided by experts and help you to add more value to your profile. You will learn to initiate data analytics projects based on a high-level perspective helping you to understand and articulate the innovative solutions.
- Managing credit card Risks
- Bank Loan default classification
- YouTube Viewers prediction
- Super store Analytics (E-commerce)
- Buying and selling cars prediction (like OLX process)
- Advanced House price prediction
- Analytics on HR decisions
- Survival of the fittest
- Twitter Analysis
- Flight price prediction
Here is the project list you will going to work on
+ More Lessons
Mock Interviews
Projects
Phone (For Voice Call):
+91-971 152 6942WhatsApp (For Call & Chat):
+918287060032self assessment
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FAQ's
Croma campus is one of the best institutes for the training of IT professional jobs. It is one of the most prestigious and certified organizations that has been associated with the top most MNCs. Croma Campus is really famous for its innovative and technical teaching methods. So, if you want to get linked with Data Analytics then do a Collab with Croma Campus.
Data Analytics is a process of examining datasets to know about the information they contain. There are a lot of works and jobs under Data Analytics. The first thing you need to do is to always keep your profile updated on LinkedIn because they are directly associated with Data Analytics. So, if you get a Data Analytics certificate you can work as Data Analyst.
Data Analytics is nowadays becoming a very important certification that can lead you to get a good job. To get any Data Analyst job you need to get a certification in Data Analytics. There is a list of things after which you can get a certificate that contains in-depth training, many simultaneous exams, live demos, and other industrial projects that can make you a perfect Data Analyst. After all this training, you can get a Data Analytics certification.
Croma Campus India program sizes a powerful training tool that can be applied in classrooms as well as in manufacturing. We offer a wide range of agendas for Live Project Data Analytics Training in Delhi under the leadership of the best industrial experts. We are always awarded for the past 10 years as the Best Data Analytics Training Institute in Delhi.
The ways to connect Croma Campus
- Phone Number: - +91-120-4155255, +91-9711526942
- Email: - [email protected]
- Address: - G-21, Sector-03, Noida (201301)
- - Build an Impressive Resume
- - Get Tips from Trainer to Clear Interviews
- - Attend Mock-Up Interviews with Experts
- - Get Interviews & Get Hired
If yes, Register today and get impeccable Learning Solutions!
Training Features
Instructor-led Sessions
The most traditional way to learn with increased visibility,monitoring and control over learners with ease to learn at any time from internet-connected devices.
Real-life Case Studies
Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Assignment
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
Lifetime Access
Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.
24 x 7 Expert Support
With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.
Certification
Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.
Training Certification
Your certificate and skills are vital to the extent of jump-starting your career and giving you a chance to compete in a global space.
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Video Reviews
Corporate Training at Croma Campus helped our employees in getting all the required skills through an organized method of instructions. It was truly amazing getting associated with Croma campus with the amazing content delivery and constant support.
Manager at Genpact
Batch-2020
Through corporate training at Croma campus, our employees got all capabilities and knowledge that is required to achieve company goals and success. Furthermore, the training also helped in improving teamwork, employee’s satisfaction, retention, and more.
Manager at Tech Mahindra
Batch - 2021
Adding skills and certifications to employers' resume was a wise decision that helped us to increase our revenue by 200 percent and also improved our reputation in terms of quality and on-time delivery.
Manager at hCentive
Batch-2021
Croma Campus is the best place to learn with amazing support and customer services. The team is always ready to help and assist. They helped me to understand all salesforce concept and today I am working as a Salesforce Professional in a leading industry.
Training in Salesforce
Batch-2022
I did complete two courses from Croma Campus. One was web designing and the other was PHP. The mentors are so skillful and knowledgeable. They helped me to learn all concepts with ease without any hassle and I am highly satisfied with my choice.
Training in PHP
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Earlier, I was not sure how online training can help in building a career but the career assistance and mentorship at Croma was so good that I would like to recommend everyone to join the Croma Learning Campus and boost your confidence level too..
Training in Software Testing
Batch-2021
I am a Data Science professional and working with a leading IT industry for more than 15 years. I do have practical exposure to different Data Science services and technologies. I love to teach people having aspiring minds.
Specialization in Data Science
I am a Data Analytics professional with over 10 years of experience. I am associated with Croma campus from last two years. I have trained hundreds of students and guided them towards their career goals and enhancements.
Specialization in Data Analytics
I am a Cloud Computing expert with more than 12 years of experience in an MNC. Till date, I have trained and mentored hundreds of students and professionals globally. Working at Croma is an enriching experience and a wonderful journey too.
Specialization in Cloud Computing
Testimonials & Reviews
I was enrolled for the R programming under the guidance of Mr. Anantha Rao sir. He has a very unique way of teaching and daily presentations by the students were the main highlight of the class. Whatever he taught is actually really he
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Tinish Sharma
TCS R programming
Best training institute for learning Tableau. Anurag Mishra sir is the best trainer for learning Tableau.
Read More...Ishani
Data Analytics
Hi. I have done this course Croma campus. The trainer give the sessions very nicely nd in-depth knowledge. My experience is good throughout the learning program. Management is good. The course helped me in increasing my knowledge and c
Read More...Aadesh
Data Analytics
If you are looking to get a reputed job after SAS certification then you should go for the top which is Croma Campus. I have recently completed my SAS certification and got a placement in a reputed MNC. Moreover, the training offered o
Read More...Aakash
Data Analytics
Hi, my name is Anup Singh and I have recently completed my SAS certification from Croma Campus. I am fully satisfied after getting a world-class experience at affordable costs. Besides offering out the training they also guided me rega
Read More...Anup Singh
Data Analytics
Hi, my name is Manoj Diwakar I have recently completed our Microsoft Excel VBA Certification from Croma Campus Pvt Ltd. It helps out me in clearing the doubts related to training and prepares me for further job interviews.
Read More...Manoj Diwakar
Data Analytics
Croma Campus is a very nice training center. I have taken SPSS training in Croma Campus. They are very experienced and the way of teaching is very good. I got the job based on training; it was my great experience at Croma Campus. All t
Read More...Aanav
Data Analytics
Hi, my name is Neelam. I currently work as a Microsoft Excel at HCL. My interview in this company was scheduled by Croma Campus as I have done 2 months of Microsoft Advance Excel Certification Training course from it. I managed to crac
Read More...Neelam
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