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- 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
- 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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- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
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- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- 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
- Data Preparation
- Feature Engineering
- Feature Scaling
- Datasets
- Dimensionality Reduction
- Anomaly Detection
- Parameter Estimation
- Data and Knowledge
- Selected Applications in Data Mining
- 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
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- Statistics
- Pandas
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- 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 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 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
- 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
- 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
- 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
- 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
- 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
- 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
- Cursor declaration and Life cycle
- STATIC
- DYNAMIC
- SCROLL Cursors
- FORWARD_ONLY and LOCAL Cursors
- KEYSET Cursors with Complex SPs
- ACID Properties and Scope
- EXPLICIT Transaction types
- IMPLICIT Transactions and options
- AUTOCOMMIT Transaction and usage
- AI Tools:
- Copilot in Azure Data Studio or GitHub for SQL suggestions
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- 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
- 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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- 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
- Extracting data from various sources
- Workspaces in Power BI
- 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
- 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
- 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
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- 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
- Features of Tableau Desktop
- Connect to data from File and Database
- Types of Connections
- Joins and Unions
- Data Blending
- Tableau Desktop User Interface
- Visual Analytics
- Basic Charts Bar Chart, Line Chart, and Pie Chart
- Hierarchies
- Data Granularity
- Highlighting
- Sorting
- Filtering
- Grouping
- Sets
- 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
- Parameters
- Tool tips
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Count Customer by Order
- Profit per Business Day
- Comparative Sales
- Profit Vs Target
- Finding the second order date
- Cohort Analysis
- Introduction to Geographic Visualizations
- Manually assigning Geographical Locations
- Types of Maps
- Spatial Files
- Custom Geocoding
- Polygon Maps
- Web Map Services
- Background Images
- Box and Whiskers 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
- 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
- Tableau Tips and Tricks
- Choosing the right type of Chart
- Format Style
- Data Visualization best practices
- 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
- Ask Data: Natural Language Data Exploration
- Explain Data: Automatic statistical insights
- Tableau GPT (Einstein Copilot - Salesforce)
- AI forecasting in visualizations
- Integration with Python (TabPy) and R
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