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Corporate Served- Embark on an exciting journey in the data analytics industry with our comprehensive Data Analytics Course in Bangalore at Croma Campus. Unlock a world of opportunities in a dynamic and data-driven field. With a high demand for data analytics specialists worldwide, it's a promising career for dedicated learners.
- Whether you choose self-study or a paid program, consider the Data Analytics Certification Course in Bangalore. As the leading Data Analytics training institute in Bangalore, we work hard to help you become a successful Data Analyst, with the potential for substantial earnings in top industries.
Data Analytics Course in Bangalore
- Our Data Analytics courses at Croma Campus are designed to provide a comprehensive education, encompassing fundamental concepts and advanced data analytics. The primary objectives of our Data Analytics Course in Bangalore include:
Data Proficiency: Equip students to work proficiently with both structured and unstructured data across diverse business settings.
Informed Decision-Making: Empower students to utilize data effectively for making informed, strategic decisions that impact organizational success.
Complete Skill Set: Provide students with a complete package of data analytics skills, including mastery of tools, algorithms, complex data modeling, identifying business needs, and conducting in-depth data analysis.
Diverse Learning: Cover a wide range of topics, such as statistics, computer science, data analytics, data visualization, and programming languages like R and Python.
Advanced Topics: Introduce advanced concepts like big data technologies, machine learning, artificial intelligence, and deep learning.
- Let's delve into the salary expectations for fresh graduates pursuing a data analytics career:
High Demand: The data analytics field is witnessing increasing demand for skilled professionals.
Salary Projections: Salary expectations vary, with opportunities for competitive compensation.
- Course Data analyst in Bangalore at Croma Campus caters to a diverse audience:
Aspiring Data Analysts: Enthusiastic learners looking to kickstart a rewarding career in the field of data analytics.
IT Professionals: Experienced IT experts looking to upgrade their skills and seize data analytics opportunities.
Business Professionals: Executives and decision-makers eager to enhance their data analysis capabilities.
Academics and Researchers: Scholars and researchers aiming to explore data analytics as an academic or research specialization.
Professionals Seeking a Career Change: Individuals from various backgrounds contemplating a transition into data analytics for a fresh career trajectory.
- With intensive training that covers a broad spectrum of data analytics topics, individuals can anticipate remarkable career growth upon completing our data analyst classes in Bangalore. Students are empowered to:
1. Apply Practical Knowledge: Implement acquired concepts in real-world scenarios, effectively addressing complex challenges.
2. Elevate Earning Potential: Attain certifications that enhance employability and earning potential.
3. Unlock Job Opportunities: Access job openings across diverse industries and organizations.
4. Drive Industry Transformation: Play a pivotal role in the transformation of various sectors through data-driven insights and decision-making.
5. Pursue Leadership Positions: Develop the competence to advance into leadership roles, such as Data Analytics Manager, or other technical positions like Machine Learning Engineer or Data Analyst.
- The future of data analytics is exceedingly promising, with data professionals set to play a pivotal role in harnessing the power of data across various industries. Key trends shaping the future scope of data analytics include:
Growing Demand: The increasing reliance on data in organizations will continue to drive demand for data professionals.
Emerging Technologies: The convergence of data analytics with emerging technologies promises diverse job roles and opportunities.
Varied Job Roles: Data analytics professionals will have the option to explore diverse job profiles and industries.
Evolving Data Landscape: As organizations increasingly rely on data, the field of data analytics will adapt to changing data landscapes.
- Embarking on a data analytics career opens doors to a plethora of exciting job profiles in the data-driven industry, each offering unique challenges and opportunities. Let's explore some of the prominent job roles and their corresponding salary expectations in the world of data analytics:
Data Analyst: Data analysts work with datasets to uncover valuable insights. Their average salary varies, depending on factors such as experience and location.
Business Intelligence Analyst: Business intelligence analysts focus on data interpretation to support decision-making. Their salary is competitive, and they play a crucial role in business success.
Data Scientist: Data scientists specialize in complex data analysis, deriving actionable insights from data. Their salary varies based on experience and expertise.
Data Engineer: Data engineers design and maintain data infrastructure. Their salary is competitive, and their role is pivotal in ensuring data availability.
Statistician: Statisticians analyze data to generate valuable statistics. Their skills are in high demand, with competitive salaries.
Machine Learning Engineer: Machine learning engineers develop AI models and algorithms. Their role is critical in AI development and commands a competitive salary.
- Leading industries in data analytics include prominent companies in Bangalore and beyond. Opportunities abound in:
IT Sector: Leading tech companies seek skilled data analytics professionals.
Healthcare and Medical Sector: The healthcare industry relies on data for decision-making, creating a high demand for data professionals.
Banking & Finance: Financial institutions leverage data analytics for risk assessment, fraud detection, and customer insights.
Transportation: Data analytics is essential for optimizing routes, managing logistics, and improving transportation services.
Travel Industry: The travel sector employs data analytics for recommendations, personalization, and enhancing the customer experience.
eCommerce: Companies employ data analysts to enhance user experiences and boost sales.
Media & Entertainment: Data analysis plays a pivotal role in content recommendation, user engagement, and personalization.
Non-Profit Industries: Non-profit organizations utilize data analytics to drive social impact and optimize operations.
Insurance Sector: Data analytics helps insurers analyze risks, set premiums, and predict claims effectively.
- Upon course completion at the Data Analytics Training Institute in Bangalore, you'll receive a training certificate. This certificate is earned by completing projects and skill assessments at various points during the course.
- Having a well-thought-out career plan is always a smart choice, no matter where you are in your IT career. So, don't hesitate to explore our comprehensive Data Analytics training in Bangalore.
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CURRICULUM & PROJECTS
Data Analytics Training Program
- Introduction
- What is Data Analytic
- Common Terms in Data Analytics
- 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 Analytics tools & their popularity.
- Data Analytics Methodology & problem-solving framework.
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- 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
- Subtotal, Multi-Level Subtotal
- Grouping Features
- Consolidation With Several Worksheets
- Filter
- Printing of Raw & Column Heading on Each Page
- Workbook Protection and Worksheet Protection
- Specified Range Protection in Worksheet
- Excel Data Analysis
- Data Table
- Pivot Table
- Generating MIS Report In Excel
- Text Functions
- Lookup & Reference Function
- Logical Functions & Date and Time Functions
- Database Functions
- Statistical Functions
- Financial Functions
- Functions for Calculation Depreciation
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- 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
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- 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
- 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
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- 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
- 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
- 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
- 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 folders details using OS
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- Data Analysis and Visualization using Pandas.
- Statistics
- Pandas
- Data Analysis and Visualization using NumPy and MatPlotLib
- NumPy
- MatPlotLib
- Introduction to Data Visualization with Seaborn
- Introduction to Seaborn
- Visualizing Two Quantitative Variables
- Visualizing a Categorical and a Quantitative Variable
- Customizing Seaborn Plots
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- Capstone Project
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Course Duration
80 Hrs.
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