Career Transition
Career Gap
Placement Activities
Placement Drives
Latest Hiring
Whats App Reviews
Course Design By
Nasscom & Wipro
Learn data analysis, Excel, SQL, Python, and visualization tools with hands-on training in Chandigarh.
4.2/5
4.8/5
4.6/5
4.3/5
4.5/5Course Duration
32 Hrs.Live Project
2 ProjectCertification Pass
GuaranteedTraining Format
Live Online /Self-Paced/ClassroomWatch Live Classes
Speciality
300+
Professionals Trained
4+
Batches every month
20+
Countries & Counting
100+
Corporate Served- Data Analytics Course in Chandigarh in Chandigarh is an exhaustive course of study that trains people with the necessary skills and knowledge to make a mark in the data analytics profession. Chandigarh, a city synonymous with education and technology, presents a snapshot of perfect weather for the blooming of a data analytics career. The rising demand for data-driven insights has made Data Analyst Training in Chandigarh the key to a hot seat in the employment market. In this training course, you get the hands-on experience and additionally improve your understanding of the data analytics field.
- Things You Will Learn in Data Analyst Training:
- Data analyst training aims to equip people with data analytics skills employing different methods, including data visualization, statistical analysis, and machine learning. Data Analytics Certification gives you hands-on experience with the software and technologies in data analytics, for example, Excel, SQL, and Tableau. With these competencies, you will unlock the door to a rewarding career in data analytics and be the one who drives organizations in data-based decision-making. Data Analyst Training can be your place to acquire knowledge in the data analytics field and improve your job prospects.
- Prerequisites:
- To take part in Data Analyst Institute in Chandigarh, first of all, you must have a good understanding of statistics and mathematics at a basic level. If you have also good skills in programming languages such as Python or R, it will facilitate the process of your work here. Besides this, it is very important that you have a lot of analytical skills and are able to think critically. In case you meet such conditions, there is no doubt that you have the maximum benefit of your Data Analyst Training.
Comprehensive curriculum: The Data Analyst Course in Chandigarh covers intense learning in multiple areas such as data visualization and machine learning.
Experienced trainers: The trainers are experts in the industry who have good experience in the field of data analytics.
Hands-on training: Trainees implement data analytics tools and technologies practically.
Placement assistance: Numerous training centers offer placement support to students to enable them to find jobs without difficulty.
Data visualisation: Leveraging interactive and dynamic visualisation functionalities of Tableau and Power BI for a better understanding of data.
Statistical analysis: Implementing statistical methods, including regression and hypothesis testing, to comprehend data.
Machine learning: Employing machine learning principles to develop forecasting models.
Data mining: Applying data mining methods to research the large data pools for useful information.
Basic statistics: Understanding of statistical concepts, such as mean, median, and standard deviation.
Mathematical skills: Familiarity with mathematical concepts, such as algebra and calculus.
Programming skills: Knowledge of programming languages, such as Python or R.
Analytical mindset: Ability to think critically and analyze complex data.
Data Analyst Course in Chandigarh
- Data Analyst Training in Chandigarh is a perfect fit for individuals who want to start a data analytics career. A training program like this is perfect for students, working professionals, and freshers who want to improve their data analytics skills. You will gain practical experience and learn data analytics in detail by signing up for this training program. Data Analyst Training not only equips you to have a prosperous career in data analytics but also enables you to assist organizations in making data-driven decisions.
Students: Those who are in the process of obtaining a degree in mathematics, statistics, or computer science.
Working professionals: Those who want to improve their skills or change their job to data analytics.
Freshers: Those who want to start a career in data analytics.
Data enthusiasts: People who love data and are passionately working with it.
- Data Analyst Coaching in Chandigarh has a primary objective of empowering people with the essential skills and knowledge of the data analytics field. The course is designed to give you the best possible knowledge of data analytics and enhance your career prospects. Data Analyst Training allows you to establish a successful career in the data analytics industry and also provides support to business enterprises in implementing data-driven decisions.
Develop analytical skills: Capacity to understand complicated information and find out the gist.
Obtain practical experience: Through the use of data analytics tools and technologies.
Get ready for industry-standard certifications: Examples of such are Certified Data Analyst (CDA).
Improve career opportunities: By building a solid base in data analytics.
- Salary expectations for fresher in data analytics differ depending on several factors such as location, industry, and company size. Nevertheless, you can boost your earning capacity and broaden your career prospects with the help of Data Analyst Training. Establishing a solid base in data analytics will definitely help you to create a prosperous career and get a competitive salary.
Average salary: 3-5 lakhs per annum for freshers in data analytics.
Salary growth: A significant increase in your salary is expected if you gain more experience.
Industry demand: The need for data analysts is very high in the market.
Career prospects: By getting trained as a Data Analyst, you can boost your career options and make more money.
- Data Analyst Training in Chandigarh offers a great opportunity to improve your career prospects and also to increase your salary. The Data Analyst Training is a perfect way to learn core concepts of data analytics and create a solid base for a career in data analytics. You can keep on growing and succeeding in your career by being aware of trends and technological developments in the industry.
- Chandigarh, the place where the IT industry thrives and where there is a constant need for data-driven insights, is a place where data analyst training is sought after. Chandigarh is a city where the academic and industrial sectors are not only blended but also an excellent place for people who are looking to invest in learning data analytics. The city of Chandigarh, which is full of different training institutes and vibrant industry, provides many opportunities for individuals who want to make a career in data analytics. Moreover, the city is located nearby major IT hubs, which is like a bonus for those data analytics professionals who want to move to another place.
Increasing need: Data analysts are required by various industries.
Presence of educational institutions: Many institutions conduct extensive courses in data analytics.
Thriving IT sector: The IT sector in Chandigarh is buzzing with lots of jobs for data analytics specialists.
Job openings: By undertaking a data analyst training course, you can upgrade your career options and potentially get a raise.
- Data analysts are those individuals that deal with the collection, analysis, and interpretation of complicated data in order to help organisations to be able to make the correct decisions. They partner with various stakeholders to understand their data needs and then find the most appropriate solutions. They are the ones who also have to make reports and visualize the data in order to be able to communicate the insights to the stakeholders. Choosing a career as a data analyst will not only benefit you in terms of personal development, but it will also make you the one that any organization can depend on for leading their business expansion.
Data analysis: Gathering, evaluating, and understanding complicated data.
Data visualization: Generating documents and graphical representations for the explanation of the data.
Stakeholder communication: Collaborating with the stakeholders so that their data requirements become clear.
Solution development: Designing answers that fit the requirements of the stakeholders.
- People who analyze data are greatly needed in several sectors, such as IT, finance, and healthcare. Data Analytics Classes in Chandigarh have the potential to change one's professional path in any of these domains. Upgrading your data analytics knowledge will not only open up more career paths for you but also raise your earning capability. You can pursue higher positions or become an expert in certain fields with experience.
IT and software development: Firms whose core competency is in the provision of data analytics solutions.
Finance and banking: Organizations looking for data-driven insights to generate business decisions.
Healthcare: Entities employing data analytics for the betterment of patient outcomes.
E-commerce: Businesses leveraging data analytics to fuel business expansion.
- Upon successful completion of the Data Analyst Training in Chandigarh, you will get a certificate that proves you have skills in data analytics. This certificate can be an effective resume booster and can make you stand out in the job market. A Data Analyst Training certificate can be the key to unlocking new career paths and to a higher salary.
Industry recognition: The certificate is accepted by the industry.
Career benefits: The certificate can improve your career options and raise your salary.
Proof of expertise: The certificate confirms your skills in data analytics.
Competitive advantage: The certificate may provide you with a competitive edge when applying for a job.
Why You Should Get Started with The Data Analyst?
Not just learning –
we train you to get hired.
By registering here, I agree to Croma Campus Terms & Conditions and Privacy Policy
CURRICULUM & PROJECTS
Data Analyst Certification Training
- Things you will learn:
- Python for Data Analyst
- Data Analysis and Visualization
- Databases – MS SQL and SQL Queries
- Statistics for Data Analyst
- Analytics with Excel
- Analytics Microsoft Power BI
- Analytics with Tableau
- Analytics with R Proramming
- Cloud: AWS(Amazon Web Services)
- Cloud: Microsoft Azure Fundamentals
- Data Analyst - Live Projects
Get full course syllabus in your inbox
- Introduction To Python
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Keyword and Identiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- Introduction To Variables
- Variables, expression condition and function
- Global and Local Variables in Python
- Packing and Unpacking Arguments
- Type Casting in Python
- Byte objects vs. string in Python
- Variable Scope
- Python Data Type
- Declaring and using Numeric data types
- Using string data type and string operations
- Understanding Non-numeric data types
- Understanding the concept of Casting and Boolean.
- Strings
- List
- Tuples
- Dictionary
- Sets
- Control Structure & Flow
- Statements – if, else, elif
- How to use nested IF and Else in Python
- Loops
- Loops and Control Statements.
- Jumping Statements – Break, Continue, pass
- Looping techniques in Python
- How to use Range function in Loop
- Programs for printing Patterns in Python
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- How to use nested Loop in Python
- Use If and Else in for and While Loop
- Examples of Looping with Break and Continue Statement
- How to use IN or NOT IN keyword in Python Loop.
- Python Function, Modules and Packages
- Python Syntax
- Function Call
- Return Statement
- Arguments in a function – Required, Default, Positional, Variable-length
- Write an Empty Function in Python –pass statement.
- Lamda/ Anonymous Function
- *args and **kwargs
- Help function in Python
- Scope and Life Time of Variable in Python Function
- Nested Loop in Python Function
- Recursive Function and Its Advantage and Disadvantage
- Organizing python codes using functions
- Organizing python projects into modules
- Importing own module as well as external modules
- Understanding Packages
- Random functions in python
- Programming using functions, modules & external packages
- Map, Filter and Reduce function with Lambda Function
- More example of Python Function
- Python Date Time and Calendar
- Day, Month, Year, Today, Weekday
- IsoWeek day
- Date Time
- Time, Hour, Minute, Sec, Microsec
- Time Delta and UTC
- StrfTime, Now
- Time stamp and Date Format
- Month Calendar
- Itermonthdates
- Lots of Example on Python Calendar
- Create 12-month Calendar
- Strftime
- Strptime
- Format Code list of Data, Time and Cal
- Locale’s appropriate date and time
- List
- What is List.
- List Creation
- List Length
- List Append
- List Insert
- List Remove
- List Append & Extend using “+” and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- Tuple
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- Changing a Tuple
- Tuple Deletion
- Tuple Count
- Tuple Index
- Tuple Membership
- TupleBuilt in Function (Length, Sort)
- Dictionary
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- Strings
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Python Exception Handling
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specic Exception in Python
- Raising Exception
- Try and Finally
- Python File Handling
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- Python Database Interaction
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Contacting user Through Emails Using Python
- Installing SMTP Python Module
- Sending Email
- Reading from le and sending emails to all users
- Reading an excel
- Working With Excel
- Reading an excel le using Python
- Writing to an excel sheet using Python
- Python| Reading an excel le
- Python | Writing an excel le
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel le.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
- Complete Understanding of OS Module of Python
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user prole detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any les and folder’s details using OS
Get full course syllabus in your inbox
- Data Analysis and Visualization using Pandas.
- Statistics
- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- Pandas
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
- How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
- Exploring the Data Plotting, Correlations, and Histograms
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
- Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Exporting the results to Excel Joins
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- Data Analysis and Visualization using NumPy and MatPlotLib
- NumPy
- Introduction to NumPy Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from a CSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPy’s Mean and Axis
- NumPy’s Mode, Median and Sum Function
- NumPy’s Sort Function and More
- MatPlotLib
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Data Visualization with Seaborn
- Introduction to Seaborn
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Adding a third variable with hue
- Hue and scattera plots
- Hue and count plots
- Visualizing Two Quantitative Variables
- Introduction to relational plots and subplots
- Creating subplots with col and row
- Customizing scatters plots
- Changing the size of scatter plot points
- Changing the style of scatter plot points
- Introduction to line plots
- Interpreting line plots
- Visualizing standard deviation with line plots
- Plotting subgroups in line plots
- Visualizing a Categorical and a Quantitative Variable
- Current plots and bar plots
- Count plots
- Bar plot with percentages
- Customizing bar plots
- Box plots
- Create and interpret a box plot
- Omitting outliers
- Adjusting the whisk
- Point plots
- Customizing points plots
- Point plot with subgroups
- Customizing Seaborn Plots
- Changing plot style and colour
- Changing style and palette
- Changing the scale
- Using a custom palette
- Adding titles and labels Part 1
- Face Grids vs. Axes Subplots
- Adding a title to a face Grid object
- Adding title and labels Part 2
- Adding a title and axis labels
- Rotating x-tics labels
- Putting it all together
- Box plot with subgroups
- Bar plot with subgroups and subplots
- Well done! What’s next
Get full course syllabus in your inbox
- SQL Server Fundamentals
- SQL Server 2019 Installation
- Service Accounts & Use, Authentication Modes & Usage, Instance Congurations
- SQL Server Features & Purpose
- Using Management Studio (SSMS)
- Conguration Tools & SQLCMD
- Conventions & Collation
- SQL Server 2019 Database Design
- SQL Database Architecture
- Database Creation using GUI
- Database Creation using T-SQL scripts
- DB Design using Files and File Groups
- File locations and Size parameters
- Database Structure modications
- SQL Tables in MS SQL Server
- SQL Server Database Tables
- Table creation using T-SQL Scripts
- Naming Conventions for Columns
- Single Row and Multi-Row Inserts
- Table Aliases
- Column Aliases & Usage
- Table creation using Schemas
- Basic INSERT
- UPDATE
- DELETE
- SELECT queries and Schemas
- Use of WHERE, IN and BETWEEN
- Variants of SELECT statement
- ORDER BY
- GROUPING
- HAVING
- ROWCOUNT and CUBE Functions
- Data Validation and Constraints
- Table creation using Constraints
- NULL and IDENTITY properties
- UNIQUE KEY Constraint and NOT NULL
- PRIMARY KEY Constraint & Usage
- CHECK and DEFAULT Constraints
- Naming Composite Primary Keys
- Disabling Constraints & Other Options
- Views and Row Data Security
- Benets of Views in SQL Database
- Views on Tables and Views
- SCHEMA BINDING and ENCRYPTION
- Issues with Views and ALTER TABLE
- Common System Views and Metadata
- Common Dynamic Management views
- Working with JOINS inside views
- Indexes and Query tuning
- Need for Indexes & Usage
- Indexing Table & View Columns
- Index SCAN and SEEK
- INCLUDED Indexes & Usage
- Materializing Views (storage level)
- Composite Indexed Columns & Keys
- Indexes and Table Constraints
- Primary Keys & Non-Clustered Indexes
- Stored Procedures and Benets
- Why to use Stored Procedures
- Types of Stored Procedures
- Use of Variables and parameters
- SCHEMABINDING and ENCRYPTION
- INPUT and OUTPUT parameters
- System level Stored Procedures
- Dynamic SQL and parameterization
- System functions and Usage
- Scalar Valued Functions
- Types of Table Valued Functions
- SCHEMABINDING and ENCRYPTION
- System Functions and usage
- Date Functions
- Time Functions
- String and Operational Functions
- ROW_COUNT
- GROUPING Functions
- Triggers, cursors, memory limitations
- Why to use Triggers
- DML Triggers and Performance impact
- INSERTED and DELETED memory tables
- Data Audit operations & Sampling
- Database Triggers and Server Triggers
- Bulk Operations with Triggers
- Cursors and Memory Limitations
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- Transactions Management
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
- SAVEPOINT and Query Blocking
Get full course syllabus in your inbox
- Introduction to Data Analytics
- What is Analytics & Data Science
- Common Terms in Data Science
- What is data
- Classication of data
- Relevance in industry and need of the hour
- Types of problems and business objectives in various industries
- How leading companies are harnessing the power of analytics
- Critical success drivers.
- Overview of Data Science tools & their popularity.
- Data Science Methodology & problem-solving framework.
- List of steps in Data Science projects
- Identify the most appropriate solution design for the given problem statement
- Project plan for Data Science project & key milestones based on effort estimates
- Build Resource plan for Data Science project
- Why Python for data science
- Accessing/Importing and Exporting Data
- Importing Data from various sources (Csv, txt, excel, access etc)
- Database Input (Connecting to database)
- Viewing Data objects - sub setting, methods
- Exporting Data to various formats
- Important python modules Pandas
- Data Manipulation Cleansing - Munging Using Python Modules
- Cleansing Data with Python
- Filling missing values using lambda function and concept of Skewness.
- Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.
- Normalizing data
- Feature Engineering in Data Science
- Feature Engineering
- Feature Selection
- Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.
- Label encoding/one hot encoding
- Data Analysis Visualization Using Python
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ densi ty etc.)
- Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)
- Introduction to Statistics
- Descriptive Statistics
- Sample vs Population Statistics
- Random variables
- Probability distribution functions
- Expected value
- Normal distribution
- Gaussian distribution
- Z-score
- Spread and Dispersion
- Correlation and Co-variance
- Introduction to Predictive Modelling
- Concept of model in analytics and how it is used
- Common terminology used in Analytics & Modelling process
- Popular Modelling algorithms
- Types of Business problems - Mapping of Techniques
- Different Phases of Predictive Modelling
- EDA (Exploratory Data Analysis)
- Need for structured exploratory data
- EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
- Identify missing data
- Identify outliers’ data
- Imbalanced Data Techniques
- Data Pre-Processing & Data Mining
- Data Preparation
- Feature Engineering
- Feature Scaling
- Datasets
- Dimensionality Reduction
- Anomaly Detection
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
Get full course syllabus in your inbox
- Understanding Concepts of Excel
- Creation of Excel Sheet Data
- Range Name, Format Painter
- Conditional Formatting, Wrap Text, Merge & Centre
- Sort, Filter, Advance Filter
- Different type of Chart Creations
- Auditing, (Trace Precedents, Trace Dependents)Print Area
- Data Validations, Consolidate, Subtotal
- What if Analysis (Data Table, Goal Seek, Scenario)
- Solver, Freeze Panes
- Various Simple Functions in Excel(Sum, Average, Max, Min)
- Real Life Assignment work
- Ms Excel Advance
- Advance Data Sorting
- Multi-level sorting
- Restoring data to original order after performing sorting
- Sort by icons
- Sort by colours
- Lookup Functions
- Lookup
- VLookup
- HLookup
- Subtotal, Multi-Level Subtotal
- Grouping Features
- Column Wise
- Row Wise
- Consolidation With Several Worksheets
- Filter
- Auto Filter
- Advance Filter
- Printing of Raw & Column Heading on Each Page
- Workbook Protection and Worksheet Protection
- Specified Range Protection in Worksheet
- Excel Data Analysis
- Goal Seek
- Scenario Manager
- Data Table
- Advance use of Data Tables in Excel
- Reporting and Information Representation
- Pivot Table
- Pivot Chat
- Slicer with Pivot Table & Chart
- Generating MIS Report In Excel
- Advance Functions of Excel
- Math & Trig Functions
- Text Functions
- Lookup & Reference Function
- Logical Functions & Date and Time Functions
- Database Functions
- Statistical Functions
- Financial Functions
- Functions for Calculation Depreciation
- MIS Reporting & Dash Board
- Dashboard Background
- Dashboard Elements
- Interactive Dashboards
- Type of Reporting In India
- Reporting Analyst
- Indian Print Media Reporting
- Audit Report
- Accounting MIS Reports
- HR Mis Reports
- MIS Report Preparation Supplier, Exporter
- Data Analysis
- Costing Budgeting Mis Reporting
- MIS Report For Manufacturing Company
- MIS Reporting For Store And Billing
- Product Performance Report
- Member Performance Report
- Customer-Wise Sales Report
- Collections Report
- Channel Stock Report
- Prospect Analysis Report
- Calling Reports
- Expenses Report
- Stock Controller MIS Reporting
- Inventory Statement
- Payroll Report
- Salary Slip
- Loan Assumption Sheet
- Invoice Creation
Get full course syllabus in your inbox
- Introduction to Power BI
- Overview of BI concepts
- Why we need BI
- Introduction to SSBI
- SSBI Tools
- Why Power BI
- What is Power BI
- Building Blocks of Power BI
- Getting started with Power BI Desktop
- Get Power BI Tools
- Introduction to Tools and Terminology
- Dashboard in Minutes
- Interacting with your Dashboards
- Sharing Dashboards and Reports
- Power BI Desktop
- Power BI Desktop
- Extracting data from various sources
- Workspaces in Power BI
- Power BI Data Transformation
- Data Transformation
- Query Editor
- Connecting Power BI Desktop to our Data Sources
- Editing Rows
- Understanding Append Queries
- Editing Columns
- Replacing Values
- Formatting Data
- Pivoting and Unpivoting Columns
- Splitting Columns
- Creating a New Group for our Queries
- Introducing the Star Schema
- Duplicating and Referencing Queries
- 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
- Improving Performance and Loading Data into the Data Model
- Modelling with Power BI
- Introduction to Modelling
- Modelling Data
- Manage Data Relationship
- Optimize Data Models
- Cardinality and Cross Filtering
- Default Summarization & Sort by
- Creating Calculated Columns
- Creating Measures & Quick Measures
- Data Analysis Expressions (DAX)
- What is DAX
- Data Types in DAX
- Calculation Types
- Syntax, Functions, Context Options
- DAX Functions
- Date and Time
- Time Intelligence
- Information
- Logical
- Mathematical
- Statistical
- Text and Aggregate
- Measures in DAX
- Measures and Calculated Columns
- ROW Context and Filter Context in DAX
- Operators in DAX - Real-time Usage
- Quick Measures in DAX - Auto validations
- In-Memory Processing DAX Performance
- Power BI Desktop Visualisations
- How to use Visual in Power BI
- What Are Custom Visuals
- Creating Visualisations and Colour Formatting
- Setting Sort Order
- Scatter & Bubble Charts & Play Axis
- Tooltips and Slicers, Timeline Slicers & Sync Slicers
- Cross Filtering and Highlighting
- Visual, Page and Report Level Filters
- Drill Down/Up
- 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
- Grouping and Binning and Selection Pane, Bookmarks & Buttons
- Data Binding and Power BI Report Server
- Introduction to Power BI Dashboard and Data Insights
- Why Dashboard and Dashboard vs Reports
- Creating Dashboards
- Conguring a Dashboard Dashboard Tiles, Pinning Tiles
- Power BI Q&A
- Quick Insights in Power BI
- Direct Connectivity
- Custom Data Gateways
- Exploring live connections to data with Power BI
- Connecting directly to SQL Server
- Connectivity with CSV & Text Files
- Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel
- Content packs
- Update content packs
- Publishing and Sharing
- Introduction and Sharing Options Overview
- Publish from Power BI Desktop and Publish to Web
- Share Dashboard with Power BI Service
- Workspaces (Power BI Pro) and Content Packs (Power BI Pro)
- Print or Save as PDF and Row Level Security (Power BI Pro)
- Export Data from a Visualization
- Export to PowerPoint and Sharing Options Summary
- Refreshing Datasets
- Understanding Data Refresh
- Personal Gateway (Power BI Pro and 64-bit Windows)
- Replacing a Dataset and Troubleshooting Refreshing
Get full course syllabus in your inbox
- Introduction to Data Preparation using Tableau Prep
- Data Visualization
- Business Intelligence tools
- Introduction to Tableau
- Tableau Architecture
- Tableau Server Architecture
- VizQL Fundamentals
- Introduction to Tableau Prep
- Tableau Prep Builder User Interface
- Data Preparation techniques using Tableau Prep Builder tool
- Data Connection with Tableau Desktop
- Features of Tableau Desktop
- Connect to data from File and Database
- Types of Connections
- Joins and Unions
- Data Blending
- Tableau Desktop User Interface
- Basic Visual Analytics
- Visual Analytics
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Hierarchies
- Data Granularity
- Highlighting
- Sorting
- Filtering
- Grouping
- Sets
- Calculations in Tableau
- Types of Calculations
- Built-in Functions (Number, String, Date, Logical and Aggregate)
- Operators and Syntax Conventions
- Table Calculations
- Level of Detail (LOD) Calculations
- Using R within Tableau for Calculations
- Advanced Visual Analytics
- Parameters
- Tool tips
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Level of Detail (LOD) Expressions in Tableau
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Geographic Visualizations in Tableau
- Introduction to Geographic Visualizations
- Manually assigning Geographical Locations
- Types of Maps
- Spatial Files
- Custom Geocoding
- Polygon Maps
- Web Map Services
- Background Images
- Advanced charts in Tableau
- Box and Whisker’s Plot
- Bullet Chart
- Bar in Bar Chart
- Gantt Chart
- Waterfall Chart
- Pareto Chart
- Control Chart
- Funnel Chart
- Bump Chart
- Step and Jump Lines
- Word Cloud
- Donut Chart
- Dashboards and Stories
- Introduction to Dashboards
- The Dashboard Interface
- Dashboard Objects
- Building a Dashboard
- Dashboard Layouts and Formatting
- Interactive Dashboards with actions
- Designing Dashboards for devices
- Story Points
- Get Industry Ready
- Tableau Tips and Tricks
- Choosing the right type of Chart
- Format Style
- Data Visualization best practices
- Exploring Tableau Online
- Publishing Workbooks to Tableau Online
- Interacting with Content on Tableau Online
- Data Management through Tableau Catalog
- AI-Powered features in Tableau Online (Ask Data and Explain Data)
- Understand Scheduling
- Managing Permissions on Tableau Online
- Data Security with Filters in Tableau Online
Get full course syllabus in your inbox
- Overview :
- History of R
- Advantages and disadvantages
- Downloading and installing
- How to find documentation
- R Programming Basics :
- Using the R console and R Studio
- Getting help
- Learning about the environment
- Writing and executing scripts
- Object oriented programming
- Introduction to vectorised calculations
- Introduction to data frames
- Installing and loading packages
- Working directory
- Saving your work
- Variable types and data structures in base R :
- Variables and assignment
- Data types
- Numeric, character, Boolean, and factors
- Data structures
- Vectors, matrices, arrays, data frames, lists
- Indexing, sub-setting
- Assigning new values
- Viewing data and summaries
- Naming conventions
- Objects
- Getting data into the R environment :
- Built-in data
- Reading data from structured text files
- Reading data using ODBC
- Data frame manipulation :
- Introduction to tables, enhanced data frames
- Renaming columns
- Adding new columns
- Binning data (continuous to categorical)
- Combining categorical values
- Transforming variables
- Handling missing data
- Merging datasets together
- Stacking datasets together (concatenation)
- Handling dates in R :
- Date and date-time classes in R
- Formatting dates for modeling
- Exploratory Data Analysis (Descriptive Statistics) :
- Continuous data
- Distributions
- Quantiles, mean
- Bi-modal distributions
- Histograms, box-plots
- Categorical data
- Tables
- Bar plots
- Group by calculations
- Split-apply-combine
- Reshaping and pivoting data in R (long to wide with aggregation)
- Melt and cast
- Working with text data :
- Finding and matching patterns in text
- Stringer package for text manipulation
- Introduction to regular expressions in R
- Categorical data wrangling with forcats
- Control flow & functions :
- Truth testing
- Branching
- Looping
- Functions
- Parameters
- Return values
- Variable scope
- Exception handling
- Applying functions across dimensions
- Sapply, lapply, apply
- Programming with map and purr
- Graphics in R Overview :
- Base graphics system in R
- Scatterplots, histograms, bar charts, box and whiskers, dot plots
- Labels, legends, titles, axes
- Exporting graphics to different formats
- Advanced R graphics :
- Understanding the grammar of graphics
- Quick plots (qplot function)
- Building graphics by pieces (ggplot function)
- Understanding geoms (geometries)
- Linking chart elements to variable values
- Controlling legends and axes
- Exporting graphics
- Inferential Statistics :
- Bivariate correlation
- T-test and non-parametric equivalents
- Chi-squared test
- General Linear Regression Models in R :
- Understanding formulas
- Linear and logistic regression models
- Regression plots
- Confounding / interaction in regression
- Evaluating residuals
- Scoring new data from models (prediction)
- Useful plots from regression models
Get full course syllabus in your inbox
- Introduction to Cloud Computing
- 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
- Amazon EC2 and Amazon EBS
- 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.
- Amazon EC2
- EC2 Pricing
- EC2 Type
- Installation of Web server and manage like (Apache/ Nginx)
- Demo of AMI Creation
- Exercise
- Hands on both Linux and Windows
- Amazon Storage Services S3 (Simple Storage 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.
- Versioning
- Static website
- Policy
- Permission
- Cross region Replication
- AWS-CLI
- Life cycle
- Classes of Storage
- AWS CloudFront
- Real scenario Practical
- Hands-on all above
- Cloud Watch & SNS
- 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.
- Amazon Cloud Watch
- SNS - Simple Notification Services
- Cloud Watch with Agent
- Scaling and Load Distribution in AWS
- 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.
- Amazon Auto Scaling
- Auto scaling policy with real scenario based
- Type of Load Balancer
- Hands on with scenario based
- AWS VPC
- 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.
- Amazon VPC with subnets
- Gateways
- Route Tables
- Subnet
- Cross region Peering
- Identity and Access Management Techniques (IAM)
- In this module, you will learn how to achieve distribution of access control with AWS using IAM.
- Amazon IAM
- add users to groups,
- manage passwords,
- log in with IAM-created users.
- User
- Group
- Role
- Policy
- Amazon Relational Database Service (RDS)
- In this module, you will learn how to manage relational database service of AWS called RDS.
- Amazon RDS
- Type of RDS
- RDS Failover
- RDS Subnet
- RDS Migration
- Dynamo DB (No SQL DB)
- Redshift Cluster
- SQL workbench
- JDBC / ODBC
- Multiple AWS Services and Managing the Resources' Lifecycle
- 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,
- AWS Architecture and Design
- 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
- Migrating to Cloud & AWS
- Router S3 DNS
- Public DNS
- Private DNS
- Routing policy
- Records
- Register DNS
- Work with third party DNS as well
- Cloud Formation
- 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.
- Elastic Beanstalk
- EFS / NFS (hands-on practice)
- Hands-on practice on various Topics
- Linux
Get full course syllabus in your inbox
- Describe Cloud Concepts
- 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 identities and governance (15-20%)
- 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
- Implement and Manage Storage (10-15%)
- 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
- Deploy and Manage Azure Compute Resources (25-30%)
- 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
- Configure and Manage Virtual Networking (30-35%)
- 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
- Monitor and Back up Azure Resources (10-15%)
- 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
Get full course syllabus in your inbox
- Here is the project list you will going to work on
- 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
Get full course syllabus in your inbox
+ More Lessons
Course Design By
Course Offered By
Croma Campus
Real
Stories
success
inspiration
Abhishek
career upgrad
Upasana Singh
career upgrad
Shashank
career upgrad
Abhishek Rawat
career upgrad
Course Duration
32 Hrs.
Weekday1 Hr/Day
Weekend2 Hr/Day
Training ModeClassroom/Online
Flexible Batches For You
12-Jul-2025*
- Weekend
- SAT - SUN
- Mor | Aft | Eve - Slot
14-Jul-2025*
- Weekday
- MON - FRI
- Mor | Aft | Eve - Slot
-
16-Jul-2025*
- Weekday
- MON - FRI
- Mor | Aft | Eve - Slot
-
12-Jul-2025*
- Weekend
- SAT - SUN
- Mor | Aft | Eve - Slot
-
14-Jul-2025*
- Weekday
- MON - FRI
- Mor | Aft | Eve - Slot
-
16-Jul-2025*
- Weekday
- MON - FRI
- Mor | Aft | Eve - Slot
Course Price :
For Indian
Want To Know More About
This Course
Program fees are indicative only* Know moreGet exclusive
access to career resources
upon completion
Mock Session
You will get certificate after
completion of program
LMS Learning
You will get certificate after
completion of program
Career Support
You will get certificate after
completion of program
Showcase your Course Completion Certificate to Recruiters
-
Training Certificate is Govern By 12 Global Associations.
-
Training Certificate is Powered by “Wipro DICE ID”
-
Training Certificate is Powered by "Verifiable Skill Credentials"
in Collaboration with
Not Just Studying
We’re Doing Much More!
Empowering Learning Through Real Experiences and Innovation
Mock Interviews
Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
How Croma Campus Mock Interview Works?
Not just learning –
we train you to get hired.
Phone (For Voice Call):
+91-971 152 6942WhatsApp (For Call & Chat):
+91-971 152 6942Download Curriculum
Get a peek through the entire curriculum designed that ensures Placement Guidance
Course Offered By
Request Your Batch Now
Ready to streamline Your Process? Submit Your batch request today!
.webp)
+91-971 152 6942
+91-971 152 6942 
+16473706470
+91-971 152 6942
Interested in Course?
Master in Cloud Computing Training