Learn how to extract data for analysis. Join now and learn under an expert data analyst.
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32 Hrs.Live Project
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Speciality
200+
Professionals Trained
3+
Batches every month
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Countries & Counting
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Corporate Served- Looking for the best provider of Data Analytics Courses that can impart at the right price If so, your search ends here! Croma Campus offers the best Data Analytics Training that is industriously designed by our team of experts to help you improve your skills and make you proficient so that you can deploy them in your workplace, confidently.
- No matter if you are from an IT background or not, our Data Analytics Course offers a holistic approach to provide its training participants. So, what are you waiting for Simply enrol for the best Data Analytics Certification and become a certified professional like never before.
Enterprises are using data in their business processes to make the right decisions.
Avail of suitable learning materials and knowledge that will enhance the necessary skills.
Our Data Analytics Course will help you to kick-start an incredible career journey.
Our Data Analytics Training will help you understand real-life case studies and hands-on practice to let you gain a proper understanding of the concepts.
Get an immersive understanding of the relative domain.
Data Analytics Certification Training
- Our Data Analytics Training may help you with a robust foundation of data analysis concepts. Here are a few of the major objectives of the Data Analytics Course:
- Once you have mastered the fundamentals of the Data Analytics Course, you’ll learn more advanced concepts of R studio, Tableau, Spotfire, statistics, etc.
We offer an optimal distribution of theoretical and practical knowledge.
Sharpen your data analysis skills and various data analytics terminologies.
Familiarize yourself with open-source data analytics tools i.e., R Studio or Spotfire, etc.
Our Data Analytics Course makes you an expert in Data Analytic concepts and, principles.
Know how to extract meaningful insights from the preferred data such as hidden patterns, market trends unknown correlations, and customer preferences.
- Data Analytics are one of the most in-demand and highly-paid professionals across the world. However, the salary of a Data Analytics varies depending on various factors such as work experience, location, size of the organization, job role, etc.
- Scroll down a bit to know the salary structures offered post completing the Data Analytics Training.
Even a professional at the beginner level may earn $40,585 to $78,832.
An experienced data Analytics can earn up to $45,280 to $99,542.
It is one of the most in-demand and high-paid professionals across the globe.
The average salary of a Data Analytics is more than $ 65,000 per year in the USA.
The salary will grow as you gain experience, knowledge, and a job role.
- Data Analysis has become a part of every organization. However, the huge demand and therefore the salary of Data Analytics is significantly higher. Some of the job titles after successfully completing the Data Analytics Certification are data engineer, data scientist, data architect, database administrator, data Analytics, etc.
- If you want to avail the benefits of a lucrative career, you need to enrol in one of the leading Data Analytics Training.
Our Live session is empowered with delivering the recordings of the live class, study material, projects, etc.
MNCs and corporate giants are hiring data Analytics professionals across the globe.
Popular job titles include data engineer, data architect, data scientist, database administrator, etc.
You will work with leading IT giants in the marketplace and at a higher salary package.
- It wouldn’t be wrong to say that data is increasingly popular, and so is the demand for a data Analytics professional. Well, Data Analytics Certification plays a crucial role in helping business operations by making effective business decisions.
There’s a shortage of skilled experts across the globe.
Professionals are likely to bag huge salary packages and even work abroad.
Most of the top MNCs are getting into data analytics.
The demand for skilled professionals is likely to rise in the future.
Students will learn how to analyse and manage the data that could benefit the organization.
- After the completion of your Data Analytics Training, here are some of the major job roles and responsibilities that you must need to perform at work.
- Our Data Analytics Course such as the projects, webinars, & assignments will make you industry-ready, allowing you to grab a high-paying job at a top-notch company.
A pro should know all Data Analytics concepts and related skills.
Apply for advanced-level training such as advanced analytics, R studio, Tableau, Spotfire, statistics, and much more.
Implement basic R programming, and Tableau programming and familiarize yourself with open-source data analytics tools.
You must know about various data analytics terminologies like acquisition, manipulation, evaluation, experimentation, etc.
- Upon Data Analytics Training completion, you will be bestowed with a training certificate that will assure your potential employers trust your skillset and knowledge. So, it will be a wise decision on your part to enrol in our Data Analytics Course and successfully complete the training.
Get yourself ready to face any challenges that come your way.
Our Data Analytics Course provide an interview training session regarding job interviews
Major companies include Google, Intuit, Cisco Systems, Facebook, Apple, Capital, Maruti, etc.
Establish yourself as a certified data Analytics expert right away.
- Our Data Analytics Certification includes comprehensive knowledge of all the related Data Analytics tools and techniques to cater to the specific needs of the learners.
Get sufficient knowledge to clear any certification exam successfully.
As it is accepted worldwide, you can work abroad.
Increase your worth in today’s competitive landscape.
Our experts also support you by providing interview tips.
Get noticed by potential recruiters, and get hired right away.
Why Should You Learn Data Analyst Training?
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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 |
Course Price at :
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Program Core Credentials
Trainer Profiles
Industry Experts
Trained Students
10000+
Success Ratio
100%
Corporate Training
For India & Abroad
Job Assistance
100%
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CURRICULUM & PROJECTS
Data Analytics Certification Training
- 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.
- 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
- How to get user prole detail
- How to split path and extension
- Check Dirs. (exist or not)
- More example of Python Function
- Creating and Removing Sheets
- Formatting the Excel File Data
- Play with Workbook, Sheets and Cells in Excel using Python
- ArithmeticOperation in Excel le.
- Adjusting Rows and Column using Python
- Python | Writing an excel le
- Writing to an excel sheet using Python
- Python| Reading an excel le
- Working With Excel
- Reading an excel le using Python
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Installing SMTP Python Module
- Sending Email
- Reading from le and sending emails to all users
- Changing Directory
- Making New Directory
- List Directories and Files
- Python Directory and File Management
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Opening a File
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Try and Finally
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specic Exception in Python
- Raising Exception
- 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 Remove
- List Append & Extend using “+” and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- List Append
- List Insert
- List Length
- List Creation
- What is List.
- Locale’s appropriate date and time
- Format Code list of Data, Time and Cal
- Strftime
- Strptime
- Create 12-month Calendar
- Lots of Example on Python Calendar
- Itermonthdates
- Time stamp and Date Format
- Month Calendar
- Day, Month, Year, Today, Weekday
- StrfTime, Now
- Time Delta and UTC
- Time, Hour, Minute, Sec, Microsec
- Date Time
- IsoWeek day
- 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
- Arguments in a function – Required, Default, Positional, Variable-length
- Scope and Life Time of Variable in Python Function
- Help function in Python
- *args and **kwargs
- Lamda/ Anonymous Function
- Write an Empty Function in Python –pass statement.
- Return Statement
- Function Call
- Python Syntax
- 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
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- Programs for printing Patterns in Python
- How to use Range function in Loop
- Looping techniques in Python
- Jumping Statements – Break, Continue, pass
- Loops and Control Statements.
- Loops
- Statements – if, else, elif
- How to use nested IF and Else in Python
- Tuples
- Dictionary
- Sets
- List
- Strings
- Understanding the concept of Casting and Boolean.
- Understanding Non-numeric data types
- Using string data type and string operations
- Declaring and using 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
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
Complete Understanding of OS Module of Python
Reading an excel
Python Database Interaction
Contacting user Through Emails Using Python
Python File Handling
Dictionary
Sets
Strings
Python Exception Handling
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
- Adding titles and labels Part 1
- Face Grids vs. Axes Subplots
- Adding a title to a face Grid object
- Adding title and labels Part 2
- Adding a title and axis labels
- Rotating x-tics labels
- Changing style and palette
- Changing plot style and colour
- Using a custom palette
- Changing the scale
- Visualizing a Categorical and a Quantitative Variable
- Point plot with subgroups
- Bar plot with percentages
- Customizing bar plots
- Box plots
- Create and interpret a box plot
- Omitting outliers
- Adjusting the whisk
- Point plots
- Customizing points plots
- Count plots
- Current plots and bar plots
- Visualizing Two Quantitative Variables
- Interpreting line plots
- Visualizing standard deviation with line plots
- Plotting subgroups in line plots
- Changing the size of scatter plot points
- Changing the style of scatter plot points
- Introduction to line plots
- 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
- Making a count plot with a Dataframe
- Tidy vs Untidy data
- Using Pandas with seaborn
- Making a scatter plot with lists
- Introduction to Seaborn
- Making a count plot with a list
- MatPlotLib
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Play with Charts Properties Using MatPlotLib
- Other Useful Properties of Charts.
- Create Charts as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Complete Understanding of Histograms
- Export the Chart as Image
- Scatter Plot Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Bar Chart using Python MatPlotLib
- NumPy
- NumPy’s Mean and Axis
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPy’s Mode, Median and Sum Function
- NumPy’s Sort Function and More
- Two-Dimensional Array
- Operations with NumPy Arrays
- Operations an Array from a CSV
- Creating an Array from a CSV
- NumPy Arrays
- Importing NumPy and Its Properties
- Introduction to NumPy Numerical Python
- Pandas
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- 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
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- 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
- Variance
- Box plot
- Correlation
- Standard Deviation
- Range
- Interquartile range
- Outliers
- Mode
- Mean
- Median
- Numerical Data
- 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.
- SAVEPOINT and Query Blocking
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
- EXPLICIT Transaction types
- ACID Properties and Scope
- Cursor declaration and Life cycle
- KEYSET Cursors with Complex SPs
- FORWARD_ONLY and LOCAL Cursors
- SCROLL Cursors
- DYNAMIC
- STATIC
- Database Triggers and Server Triggers
- Bulk Operations with Triggers
- INSERTED and DELETED memory tables
- Data Audit operations & Sampling
- DML Triggers and Performance impact
- Why to use Triggers
- ROW_COUNT
- GROUPING Functions
- Time Functions
- String and Operational Functions
- SCHEMABINDING and ENCRYPTION
- System Functions and usage
- Date Functions
- Scalar Valued Functions
- Types of Table Valued Functions
- Dynamic SQL and parameterization
- INPUT and OUTPUT parameters
- System level Stored Procedures
- SCHEMABINDING and ENCRYPTION
- Types of Stored Procedures
- Use of Variables and parameters
- Why to use Stored Procedures
- Indexes and Table Constraints
- Primary Keys & Non-Clustered Indexes
- Materializing Views (storage level)
- Composite Indexed Columns & Keys
- Indexing Table & View Columns
- Index SCAN and SEEK
- INCLUDED Indexes & Usage
- Need for Indexes & Usage
- Common Dynamic Management views
- Working with JOINS inside views
- Issues with Views and ALTER TABLE
- Common System Views and Metadata
- Views on Tables and Views
- SCHEMA BINDING and ENCRYPTION
- Benets of Views in SQL Database
- Naming Composite Primary Keys
- Disabling Constraints & Other Options
- PRIMARY KEY Constraint & Usage
- CHECK and DEFAULT Constraints
- Table creation using Constraints
- NULL and IDENTITY properties
- UNIQUE KEY Constraint and NOT NULL
- File locations and Size parameters
- Database Structure modications
- Database Creation using T-SQL scripts
- DB Design using Files and File Groups
- Database Creation using GUI
- SQL Database Architecture
- Conguration Tools & SQLCMD
- Conventions & Collation
- Using Management Studio (SSMS)
- SQL Server Features & Purpose
- SQL Server 2019 Installation
- Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
- ROWCOUNT and CUBE Functions
- HAVING
- GROUPING
- ORDER BY
- Variants of SELECT statement
- Use of WHERE, IN and BETWEEN
- SELECT queries and Schemas
- DELETE
- SQL Server Database Tables
- Table creation using T-SQL Scripts
- Naming Conventions for Columns
- Single Row and Multi-Row Inserts
- Table Aliases
- Column Aliases & Usage
- Table creation using Schemas
- Basic INSERT
- UPDATE
Transactions Management
Cursors and Memory Limitations
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 Server 2019 Database Design
SQL Server Fundamentals
SQL Tables in MS SQL Server
- 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.
- Data and Knowledge
- Selected Applications in Data Mining
- Parameter Estimation
- Anomaly Detection
- Dimensionality Reduction
- Datasets
- Data Preparation
- Feature Engineering
- Feature Scaling
- Imbalanced Data Techniques
- Identify missing data
- Identify outliers’ data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Need for structured exploratory data
- Types of Business problems - Mapping of Techniques
- Different Phases of Predictive Modelling
- Popular Modelling algorithms
- Common terminology used in Analytics & Modelling process
- Concept of model in analytics and how it is used
- Correlation and Co-variance
- Z-score
- Spread and Dispersion
- Descriptive Statistics
- Sample vs Population Statistics
- Random variables
- Probability distribution functions
- Expected value
- Normal distribution
- Gaussian distribution
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ densi ty etc.)
- Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Descriptive statistics, Frequency Tables and summarization
- Introduction exploratory data analysis
- Label encoding/one hot encoding
- Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
- Feature Engineering
- Feature Selection
- 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
- Exporting Data to various formats
- Important python modules Pandas
- 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.
- How leading companies are harnessing the power of analytics
- Critical success drivers.
- Overview of Data Science tools & their popularity.
- 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
- Salary Slip
- Loan Assumption Sheet
- Payroll Report
- Inventory Statement
- Stock Controller MIS Reporting
- Data Analysis
- Costing Budgeting Mis Reporting
- MIS Report For Manufacturing Company
- MIS Reporting For Store And Billing
- Product Performance Report
- Member Performance Report
- Customer-Wise Sales Report
- Collections Report
- Channel Stock Report
- Prospect Analysis Report
- Calling Reports
- Expenses Report
- MIS Report Preparation Supplier, Exporter
- HR Mis Reports
- Audit Report
- Accounting MIS Reports
- Dashboard Background
- Dashboard Elements
- Interactive Dashboards
- Type of Reporting In India
- Indian Print Media Reporting
- Reporting Analyst
- Database Functions
- Statistical Functions
- Financial Functions
- Functions for Calculation Depreciation
- Logical Functions & Date and Time Functions
- Text Functions
- Lookup & Reference Function
- Generating MIS Report In Excel
- Math & Trig Functions
- Advance Functions of Excel
- Pivot Table
- Slicer with Pivot Table & Chart
- Pivot Chat
- Data Table
- Reporting and Information Representation
- Advance use of Data Tables in Excel
- Excel Data Analysis
- Goal Seek
- Scenario Manager
- Specified Range Protection in Worksheet
- Workbook Protection and Worksheet Protection
- Printing of Raw & Column Heading on Each Page
- Consolidation With Several Worksheets
- Filter
- Advance Filter
- Auto Filter
- Grouping Features
- Column Wise
- Row Wise
- Subtotal, Multi-Level Subtotal
- Sort by icons
- Sort by colours
- Lookup Functions
- HLookup
- VLookup
- Lookup
- Multi-level sorting
- Restoring data to original order after performing sorting
- Advance Data Sorting
- Real Life Assignment work
- Various Simple Functions in Excel(Sum, Average, Max, Min)
- Solver, Freeze Panes
- 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
- Conditional Formatting, Wrap Text, Merge & Centre
- Range Name, Format Painter
- 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
- Print or Save as PDF and Row Level Security (Power BI Pro)
- Share Dashboard with Power BI Service
- Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
- Publish from Power BI Desktop and Publish to Web
- 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
- Exploring live connections to data with Power BI
- Custom Data Gateways
- Quick Insights in Power BI
- Power BI Q&A
- Why Dashboard and Dashboard vs Reports
- Conguring a Dashboard Dashboard Tiles, Pinning Tiles
- Creating Dashboards
- Grouping and Binning and Selection Pane, Bookmarks & Buttons
- Data Binding and Power BI Report Server
- Hierarchies and Reference/Constant Lines
- Tables, Matrices & Conditional Formatting
- KPI's, Cards & Gauges
- Map Visualizations
- Custom Visuals
- Managing and Arranging
- Drill through and Custom Report Themes
- Cross Filtering and Highlighting
- Visual, Page and Report Level Filters
- Drill Down/Up
- Tooltips and Slicers, Timeline Slicers & Sync Slicers
- Scatter & Bubble Charts & Play Axis
- Setting Sort Order
- Creating Visualisations and Colour Formatting
- What Are Custom Visuals
- How to use Visual in Power BI
- Quick Measures in DAX - Auto validations
- In-Memory Processing DAX Performance
- Measures and Calculated Columns
- ROW Context and Filter Context in DAX
- Operators in DAX - Real-time Usage
- Measures in DAX
- What is DAX
- Data Types in DAX
- Calculation Types
- Syntax, Functions, Context Options
- DAX Functions
- Statistical
- Text and Aggregate
- Date and Time
- Time Intelligence
- Information
- Logical
- Mathematical
- Creating Measures & Quick Measures
- Creating Calculated Columns
- 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 Dimension Tables
- Entering Data Manually
- Merging Queries
- Finishing the Dimension Table
- Introducing the another DimensionTable
- Creating an Index Column
- Duplicating Columns and Extracting Information
- Creating Conditional Columns
- Creating the FACT Table
- Performing Basic Mathematical Operations
- Pivoting and Unpivoting Columns
- Splitting Columns
- Creating a New Group for our Queries
- Introducing the Star Schema
- Duplicating and Referencing Queries
- Understanding Append Queries
- Editing Columns
- Replacing Values
- Formatting Data
- Connecting Power BI Desktop to our Data Sources
- Editing Rows
- Data Transformation
- Query Editor
- Extracting data from various sources
- Workspaces in Power BI
- Power BI Desktop
- Sharing Dashboards and Reports
- Get Power BI Tools
- Introduction to Tools and Terminology
- Dashboard in Minutes
- Interacting with your Dashboards
- Getting started with Power BI Desktop
- Building Blocks of Power BI
- SSBI Tools
- Why Power BI
- What is Power BI
- Why we need BI
- Introduction to SSBI
- 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
- Understand Scheduling
- Managing Permissions on Tableau Online
- AI-Powered features in Tableau Online (Ask Data and Explain Data)
- Data Management through Tableau Catalog
- Publishing Workbooks to Tableau Online
- Interacting with Content on Tableau Online
- Data Visualization best practices
- Format Style
- Tableau Tips and Tricks
- Choosing the right type of Chart
- Story Points
- Designing Dashboards for devices
- Interactive Dashboards with actions
- Dashboard Layouts and Formatting
- Building a Dashboard
- Dashboard Objects
- The Dashboard Interface
- Introduction to Dashboards
- Step and Jump Lines
- Word Cloud
- Donut Chart
- Bump Chart
- Funnel Chart
- Control Chart
- Waterfall Chart
- Pareto Chart
- Bar in Bar Chart
- Gantt Chart
- Box and Whisker’s Plot
- Bullet Chart
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Polygon Maps
- Web Map Services
- Background Images
- Spatial Files
- Custom Geocoding
- Types of Maps
- Manually assigning Geographical Locations
- Introduction to Geographic Visualizations
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Parameters
- Tool tips
- Using R within Tableau for Calculations
- Level of Detail (LOD) Calculations
- Table Calculations
- Operators and Syntax Conventions
- Built-in Functions (Number, String, Date, Logical and Aggregate)
- Types of Calculations
- Sets
- Filtering
- Grouping
- Highlighting
- Sorting
- Data Granularity
- Hierarchies
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Visual Analytics
- Tableau Desktop User Interface
- Joins and Unions
- Data Blending
- Features of Tableau Desktop
- Connect to data from File and Database
- Types of Connections
- Data Preparation techniques using Tableau Prep Builder tool
- VizQL Fundamentals
- Introduction to Tableau Prep
- Tableau Prep Builder User Interface
- Introduction to Tableau
- Tableau Server Architecture
- Tableau Architecture
- Business Intelligence tools
- Data Visualization
Exploring Tableau Online
Get Industry Ready
Dashboards and Stories
Advanced charts in Tableau
Level of Detail (LOD) Expressions in Tableau
Geographic Visualizations in Tableau
Advanced Visual Analytics
Calculations in Tableau
Basic Visual Analytics
Data Connection with Tableau Desktop
Introduction to Data Preparation using Tableau Prep
- 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.
- Templet
- Json / Ymal
- Complete hands-on on Linux.
- Scenario based lab and practical
- Each topic and services will be cover with lab and theory.
- AWS cli configuration on Linux
- Configuration
- Manage
- Installation of app on Linux (apache / Nginx etc)
- Installation of Linux
- Stack
- Register DNS
- Work with third party DNS as well
- Records
- Routing policy
- Private DNS
- Public DNS
- 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 Calculator & Consolidated Billing
- AWS Best Practices (Cost +Security)
- AWS High Availability Design
- 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.
- SQL workbench
- JDBC / ODBC
- Redshift Cluster
- Dynamo DB (No SQL DB)
- RDS Subnet
- RDS Migration
- Type of RDS
- RDS Failover
- Amazon RDS
- Policy
- User
- Group
- Role
- Amazon IAM
- log in with IAM-created users.
- add users to groups,
- manage passwords,
- 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
- Route Tables
- Subnet
- Gateways
- 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.
- Hands on with scenario based
- Type of Load Balancer
- Amazon Auto Scaling
- Auto scaling policy with real scenario based
- 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.
- SNS - Simple Notification Services
- Cloud Watch with Agent
- Amazon Cloud Watch
- 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
- Real scenario Practical
- AWS CloudFront
- Classes of Storage
- AWS-CLI
- Life cycle
- Cross region Replication
- Policy
- Permission
- Versioning
- Static website
- 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
- Installation of Web server and manage like (Apache/ Nginx)
- Amazon EC2
- EC2 Pricing
- EC2 Type
- 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.
- Benefits of Cloud Computing
- Why Cloud Computing
- Introduction to Cloud Computing
- Challenges with Distributed Computing
- A Short history
- Client Server Computing Concepts
Linux
Hands-on practice on various Topics
EFS / NFS (hands-on practice)
Elastic Beanstalk
Cloud Formation
Migrating to Cloud & AWS
Router S3 DNS
AWS Architecture and Design
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
- 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
- Implement backup and recovery
- use soft deletes to recover Azure VMs
- create a Recovery Services Vault
- perform backup and restore operations by using Azure Backup Service
- configure and review backup reports
- Configure load balancing
- NOT Traffic Manager and Front Door and Private Link
- troubleshoot load balancing
- configure load balancing rules
- configure a public load balancer
- configure an internal load balancer
- configure Application Gateway
- Secure access to virtual networks
- NOT Implement Application Security Groups; DDoS
- deploy and configure Azure Firewall
- deploy and configure Azure Bastion Service
- evaluate effective security rules
- create security rules
- associate an NSG to a subnet or network interface
- Configure name resolution
- configure Azure DNS
- configure custom DNS settings
- configure a private or public DNS zone
- Implement and manage virtual networking
- subnets, and virtual network
- create and configure VNET peering
- configure private and public IP addresses, network routes, network interface,
- Create and configure Web Apps
- create and configure App Service Plans
- create and configure App Service
- Create and configure VMs
- redeploy VMs
- configure networking
- add data discs
- manage VM sizes
- move VMs from one resource group to another
- configure Azure Disk Encryption
- Configure VMs for high availability and scalability
- deploy and configure scale sets
- configure high availability
- Configure Azure files and Azure blob storage
- configure Azure blob storage
- configure storage tiers for Azure blobs
- create an Azure file share
- create and configure Azure File Sync service
- Manage data in Azure Storage
- copy data by using AZ Copy
- export from Azure job
- import into Azure job
- install and use Azure Storage Explorer
- Manage storage accounts
- implement Azure storage replication
- configure Azure AD Authentication for a storage account
- manage access keys
- generate shared access signature
- configure network access to storage accounts
- create and configure storage accounts
- 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
- manage subscriptions
- configure Cost Management
- configure management groups
- create and manage resource groups
- remove RGs
- move resources
- configure Azure policies
- configure resource locks
- apply tags
- Manage Azure AD objects
- NOTE Azure AD Connect; PIM
- configure self-service password reset
- configure Azure AD Join
- manage device settings
- perform bulk user updates
- manage guest accounts
- create users and groups
- manage user and group properties
- Describe the differences between types of cloud computing
- Compare and contrast the three types of cloud computing Describe Core Azure Services
- Describe Hybrid cloud
- Describe Public cloud
- Describe Private cloud
- Define cloud computing
- Describe the differences between categories of cloud services
- Identify a service type based on a use case
- Describe Software-as-a-Service (SaaS)
- Describe server less computing
- Describe Platform-as-a-Service (PaaS)
- Describe Infrastructure-as-a-Service (IaaS),
- Describe the shared responsibility model
- Identify the benefits and considerations of using cloud services Cloud Computing Basics.
- Describe the consumption-based model
- Expenditure (Op Ex)
- Identify the differences between Capital Expenditure (Cap Ex) and Operational.
- Agility, and Disaster Recovery
- Identify the benefits of cloud computing, such as High Availability,Scalability, Elasticity,
Monitor and Back up Azure Resources (10-15%)
Configure and Manage Virtual Networking (30-35%)
Deploy and Manage Azure Compute Resources (25-30%)
Implement and Manage Storage (10-15%)
Manage Azure identities and governance (15-20%)
Describe Cloud Concepts
- 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.
- Survival of the fittest
- Twitter Analysis
- Flight price prediction
- Bank Loan default classification
- Managing credit card Risks
- YouTube Viewers prediction
- Super store Analytics (E-commerce)
- Buying and selling cars prediction (like OLX process)
- Advanced House price prediction
- Analytics on HR decisions
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
Learn, Grow & Test your skill with Online Assessment Exam to achieve your Certification Goals
FAQ's
Professionals or fresher’s who are willing to make a career in Data Analytics can do this course.
Well, with our training, participants can enhance their careers as Data analysts.
We, at Croma Campus, have a dedicated team of instructors with nearly a decade of relevant teaching experience.
We will train you accordingly, allowing you to get attractive job offers at the time of an interview.
We, at Croma Campus, don't ask for any requisites.
- - 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.
Talk about it on Linkedin, Twitter, Facebook, boost your resume or frame it- tell your friend and colleagues about it.
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
Batch-2021
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
Currently working at RBL .The knowledge and the environment which I got in the croma campus was very helpful to achieve the success.
Read More...Upasana Singh
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Master in Cloud Computing Training
Master in Cloud Computing Training