Master Data Analytics Course
Master Data Analytics certification course
Looking to master the entire data analytics process from end to end? Our Full Stack Analytics certification course in Vadodara is designed to equip you with a complete skill set to thrive in today’s data-driven world. This hands-on program takes you through every stage of analytics—from data extraction and transformation to advanced data visualization and predictive modeling.
Perfect for aspiring data analysts and business intelligence professionals, this comprehensive course combines both Data Analytics and Business Analytics concepts. You’ll work on real-world datasets, learn to apply statistical techniques, and gain the confidence to make impactful, data-driven decisions.
Key highlights of our program include:
- Proficiency in SQL for database querying and management.
- Specialization in Python or R, with dedicated modules such as Python for Analytics or R for Analytics.
- In-depth training in BI tools like Power BI, Tableau, Alteryx, Snowflake, and QlikView.
- Cloud computing fundamentals using AWS and Azure for real-world deployment scenarios.
- Project collaboration and version control with GitHub Actions, integrated with Agile/Scrum methodologies.
As one of the best Full Stack Analytics training in Vadodara, this course emphasizes practical, job-ready skills. Every student gets hands-on exposure through projects that mimic real-world industry problems—ensuring you’re well-prepared to work as a Data Analyst or Business Analyst upon graduation.
Whether you’re a fresher or an experienced professional looking to upgrade your skill set, our Full Stack Analytics coaching classes in Vadodara offer the perfect blend of theory and application. By the end of the course, you’ll have the confidence to handle full-scale analytics projects and contribute meaningfully to data-driven decision-making in any business environment.
Join the best Full Stack Analytics certification course in Vadodara and take your analytics career to the next level.
What will I learn?
- Students should be able to collect data from various sources, including databases, APIs, and web scraping, and clean it to prepare it for analysis.
- Students will learn how to apply statistical and machine learning techniques to analyze data and uncover patterns, trends, and relationships.
- Visualizing data effectively is key to communicating insights to stakeholders. Students will learn how to create meaningful visualizations using tools like Matplotlib, Seaborn, Tableau, or Power BI to effectively convey their findings.
- Students should be proficient in applying machine learning algorithms to build predictive models and make data-driven decisions.
- Gain proficiency in Python for data manipulation and analysis.
- Develop skills in R for statistical analysis and data visualization.
- Learn essential mathematical foundations for data analysis.
- Study descriptive and inferential statistics and regression analysis.
- Create interactive dashboards and reports with Microsoft Power BI.
- Get familiar with Snowflake, QlikView, and Alteryx for advanced data analytics.
- Master querying and managing structured data using SQL databases and understand handling unstructured data with NoSQL databases.
- Learn ETL processes and tools for integrating data from various sources.
- In a full stack analytics course, students may also learn how to deploy analytical models into production environments and integrate them with existing systems and applications.
- Learn to process large-scale data with Apache Spark.
Requirements
- While there are no strict prerequisites, participants are expected to have a basic understanding of programming concepts and some familiarity with data analysis tools and techniques.
- Knowledge of Python or R programming languages is beneficial but not required.
Full Stack Analytics Course Content
- SQL Fundamentals
- Various types of databases
- Introduction to Structured Query Language
- Distinction between client server and file server databases
- Understanding SQL Server Management Studio
- SQL Table basics
- Data types and functions
- Transaction-SQL
- Authentication for Windows
- Data control language
- The identification of the keywords in T-SQL, such as Drop Table
- Database Normalization
- Entity Relationship Model
- SQL Operators
- Working with SQL
- Join
- Tables
- Variables
- Advanced concepts of SQL tables
- SQL functions
- Operators & queries
- Table creation
- Data retrieval from tables
- Combining rows from tables using inner, outer, cross, and self joins
- Deploying operators such as ‘intersect,’ ‘except,’ ‘union,’
- Temporary table creation
- Set operator rules
- Table variables•
- Deep Dive into SQL Functions
- Working with Subqueries
- SQL Views, Functions, and Stored Procedures
- Deep Dive into User-defined Functions
- SQL Optimization and Performance
- SQL Server Management Studio
- Using pivot in MS Excel and MS SQL Server
- Differentiating between Char, Varchar, and NVarchar
- XL path, indexes and their creation
- Records grouping, advantages, searching, sorting, modifying data
- Clustered indexes creation
- Use of indexes to cover queries
- Common table expressions
- Index guidelines
- Managing Data with Transact-SQL
- Querying Data with Advanced Transact-SQL Components
- Programming Databases Using Transact-SQL
- Creating database programmability objects by using T-SQL
- Implementing error handling and transactions
- Implementing transaction control in conjunction with error handling in stored procedures
- Implementing data types and NULL
- Designing and Implementing Database Objects
- Implementing Programmability Objects
- Managing Database Concurrency
- Optimizing Database Objects
- Advanced SQL
- Correlated Subquery, Grouping Sets, Rollup, Cube
- Implementing Correlated Subqueries
- Using EXISTS with a Correlated subquery
- Using Union Query
- Using Grouping Set Query
- Using Rollup
- Using CUBE to generate four grouping sets
- Perform a partial CUBE
Basic Math
- Linear Algebra
- Probability
- Calculus
- Develop a comprehensive understanding of coordinate geometry and linear algebra.
- Build a strong foundation in calculus, including limits, derivatives, and integrals.
- What is Keras?
- How to Install Keras?
- Why to Use Keras?
- Different Models of Keras
- Preprocessing Methods
- What are the Layers in Keras?
- Descriptive Statistics
- Sampling Techniques
- Measure of Central Tendency
- Measure of Dispersion
- Skewness and Kurtosis
- Random Variables
- Bassells Correction Method
- Percentiles and Quartiles
- Five Number Summary
- Gaussian Distribution
- Lognormal Distribution
- Binomial Distribution
- Bernoulli Distribution
- Inferential Statistics
- Standard Normal Distribution
- ZTest
- TTest
- ChiSquare Test
- ANOVA / FTest
- Introduction to Hypothesis Testing
- Null Hypothesis
- Alternet Hypothesis
- Probability Theory
- What is Probability?
- Events and Types of Events
- Sets in Probability
- Probability Basics using Python
- Conditional Probability
- Expectation and Variance
- Python Programming
- Python for Analytics
- R Programming
- R for Anlalytics
- Introduction
- Roles
- Snowflake Pricing
- Resource Monitor – Track Compute Consumption
- Micro-Partitioning in Snowflake
- Clustering in Snowflake
- Query History & Caching
- Load Data from AWS – CSV / JASON / PARQUET & Stages
- Snow pipe – Continuous Data Ingestion Service
- Different Type of Tables
- Time Travel – Work with History of Objects & Fail Safe
- Task in Snowflake – Scheduling Service
- Snowflake Stream – Change Data Capture (CDC)
- Zero-Copy Cloning
- Snowflake SQL – DDL
- Snowflake SQL – DML & DQL
- Snowflake SQL – Sub Queries & Case Statement
- Snowflake SQL – SET Operators
- Snowflake SQL – Working with ROW NUMBER
- Snowflake SQL – Functions & Transactions
- Procedures
- User defined function
- Types of Views
- Intro to Qlik View
- Installation of Qlik view
- Data Modelling in Qlik View
- Circular reference
- Link Tables to your model
- Joins in Qlik view
- ETL in Qlik View
- Handling Null Values
- Visualizations in Qlik View
- Pivot Table in Qlik View
- KPI Development in Qlik View
- Introduction to Alteryx
- Download and Install Alteryx
- User Interface of Alteryx
- Get Data from Excel
- Get Data from CSV
- Append All CSV files
- Browse Tool
- Output Tool – Update Existing Data
- Directory Tool
- Directory Tool – Specific Files
- Text Input Tool
- Date and Time Tool
- Auto Field Tool
- Data Cleansing Tool
- Filter Tool (Text Example)
- Filter Tool (Number Example)
- Filter Tool ( Date Example)
- Formula Tool ( Basic Example )
- Formula Tool – (Multiple Examples)
- Generate Rows Tool
- Imputation Tool
- Multi-Field Binning Tool
- Multi-Field Formula
- Multi Row Formula
- Random % Sample Tool
- Sample Tool
- Record Id Tool
- Select Tool
- Sort
- Create Sample Tool
- Tile Tool
- Unique Tool
- Append Fields Tool
- Find And Replace Tool
- Fuzzy Match Tool
- Join Tool
- Join Multiple Tool
- Union Tool
- Regex Tool
- Text To Columns
- Cross Tab Tool
- Transpose Tool
- Running Total Tool
- Summarize Tool
- Table Tool
- Interactive Chart Tool
- Join Table And Chart
- Add Annotation
- Report Text Tool
- Report Header Tool
- Report Footer Tool
- Report Layout Tool
- Comment Tool
- Explorer Tool
- Container Tool
- Cloud AWS
- Cloud Azure
- Introduction to GIT
- Version Control System
- Introduction and Installation of Git
- History of Git
- Git Features
- Introduction to GitHub
- Git Repository
- Git Features
- Bare Repositories in Git
- Git Ignore
- Readme.md File
- GitHub Readme File
- GitHub Labels
- Difference between CVS and GitHub
- Git – SubGit
- Git Environment Setup
- Using Git on CLI
- How to Setup a Repository
- Working with Git Repositories
- Using GitHub with SSH
- Working on Git with GUI
- Difference Between Git and GitHub
- Working on Git Bash
- States of a File in Git Working Directory
- Use of Submodules in GitHub
- How to Write Good Commit Messages on GitHub?
- Deleting a Local GitHub Repository
- Git Workflow Etiquettes
- Git Packfiles
- Git Garbage Collection
- Git Flow vs GitHub Flow
- Git – Difference Between HEAD, Working Tree and Index
- Git Ignore
- Introduction of Scum and Agile
- How to differentiate between Waterfall and Agile
- Agile Framework
- Agile Manifesto
- Agile Principles
- Top Agile Methodologies
- Scrum terminology and roles
- Managing tasks and events within a Sprint
- Scrum Framework
- Introduction to Scrum Framework
- Three pillars of Scrum Framework
- Values of Scrum
- When to use Scrum
- Cross-Functional, Self-Organizing Teams
- Scrum Team philosophy
- Developers
- Product Owner
- Scrum Master
- Scrum Events and Planning
- Scrum Events
- Understanding Sprint
- Sprint Planning
- Daily Scrum Meeting
- Sprint Review Meeting
- Sprint Retrospective
- Scrum Planning with backlog
- Product Backlog
- Refining Backlog
- Backlog items Estimation
- Planning Poker
- T-Shirt Sizing
- Defining Product Goals
- User Stories and INVEST
- Sprint Backlog
- Definition of Done
- Product Increment
- Definition of Done
- Objective and Scope
- Objective: To improve the effectiveness of marketing campaigns by analyzing customer behavior data.
- Scope: Focus on a retail company’s email marketing campaigns over the past year.
- Background Information
- Business Context: A retail company wants to increase sales through targeted email marketing campaigns.
- Data Sources: Customer transaction data, email campaign engagement metrics (open rates, click-through rates), demographic information.
- Data Collection and Preparation
- Data Collection: Gather transactional data from the company’s CRM system and email campaign data from marketing tools.
- Data Cleaning: Handle missing values, remove duplicates, and ensure data consistency.
- Data Transformation: Merge datasets, perform segmentation based on customer demographics and purchase history.
- Data Analysis and Exploration
- Descriptive Analytics: Analyze email open rates, click-through rates, and conversion rates over time.
- Exploratory Data Analysis (EDA): Identify trends in customer behavior and preferences.
- Customer Segmentation: Use clustering algorithms to group customers based on their purchasing patterns and demographics.
- Modeling and Analysis
- Predictive Analytics: Build a predictive model to forecast customer response to different types of email campaigns.
- Campaign Optimization: Use A/B testing to compare the effectiveness of different campaign strategies.
- ROI Calculation: Estimate the return on investment (ROI) for each campaign based on sales uplift.
- Interpretation of Results
- Business Insights: Discover that customers in certain demographics respond better to personalized product recommendations.
- Impact Analysis: Show that targeted campaigns led to a 15% increase in sales compared to generic promotions.
- Case Study Structure
- Introduction: Overview of the retail company’s marketing challenges and objectives.
- Methodology: Detailed explanation of data collection, analysis techniques, and modeling approach.
- Results: Presentation of findings including visualizations (charts, graphs) and statistical analysis.
- Discussion: Interpretation of results, implications for marketing strategy, and actionable recommendations.
- Conclusion: Summary of key findings and the impact of data-driven insights on business outcomes.
- Review and Validation
- Peer Review: Obtain feedback from marketing experts and data analysts within the company.
- Validation: Validate findings by comparing with historical performance and conducting sensitivity analyses.
- Presentation and Documentation
- Presentation: Prepare a compelling slide deck with key findings, visualizations, and actionable insights.
- Documentation: Document methodologies, assumptions, and data sources for transparency and reproducibility.
- Dissemination
- Publishing: Share the case study internally with stakeholders and externally through industry conferences or publications.
- Feedback: Gather feedback from stakeholders to refine strategies and improve future campaigns.
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Why Choose Full Stack Analytics Certification Course from Bright Computer Education?
Full Stack Analytics courses are structured to offer a complete, hands-on, and industry-relevant learning experience for those looking to dive deep into data-driven decision-making. Whether you’re aiming to Learn Full Stack Analytics in Vadodara, starting fresh with Full Stack Analytics for beginners in Vadodara, or advancing your expertise through Advanced Full Stack Analytics training in Vadodara, these programs are built to support learners at all levels. The curriculum combines data collection, processing, analysis, visualization, and deployment using tools like Python, SQL, Power BI, and machine learning models. With real-world case studies, project-based learning, and expert guidance, students gain the technical proficiency and confidence required to turn complex data into actionable business insights.
Designed Curriculum
Our curriculum covers everything from basic to advanced topics. Topics include variables, data types, control structures, functions, OOP, STL, and more.
Hands-on Learning
Dive into practical exercises and coding projects that reinforce learning and help you build real-world applications.
Experienced Instructors
Learn from industry experts with years of experience in C programming and software development.
Flexible Learning
Choose from flexible scheduling options, including self-paced learning or live virtual classes to fit your busy lifestyle.
Career Development
Gain valuable skills sought after by employers in various industries, from software development to embedded systems and beyond.
Interactive Learning
Engage with fellow learners and instructors through live Q&A sessions, discussion forums, and collaborative coding exercises.
Diverse Career Opportunities in Full Stack Analytics: Exploring Paths in India's Technology Sector
Full Stack Analytics combines data extraction, processing, analysis, and visualization with the ability to build end-to-end analytics solutions. This course prepares learners to handle the entire data lifecycle—making them valuable assets in data-driven industries such as finance, healthcare, e-commerce, and tech.
In India, professionals completing a Full Stack Analytics course can expect starting salaries between ₹6–12 lakhs per annum. With hands-on experience in tools like Python, SQL, Power BI, Tableau, and cloud platforms, candidates are highly sought after. Internationally, especially in the U.S., UK, Germany, and Australia, full stack analytics professionals earn between $90,000 to $130,000 annually.
After 2–4 years of experience, learners can grow into roles like Data Analyst, Business Intelligence Analyst, Analytics Engineer, or Data Consultant. The combination of technical and analytical skills makes them versatile in a competitive job market.
In summary, a Full Stack Analytics course opens up diverse and high-growth career opportunities in both Indian and global markets—ideal for those looking to blend data science with practical business insights.
Frequently Asked Questions
Typically, learners complete the Full Stack Analytics course in about 4 to 6 months. The actual duration can vary based on how much time you dedicate each week, especially since the course covers a wide range of tools and techniques from data collection to visualization.
It helps if you have a basic understanding of mathematics, statistics, and some familiarity with programming concepts. However, many Full Stack Analytics programs are structured to guide beginners, starting from the fundamentals and moving into more advanced topics as you progress.
You’ll gain expertise in data extraction, data cleaning, analysis, and visualization. The course typically covers tools like SQL, Python, Power BI, and Tableau, along with machine learning basics, cloud services, and dashboard development, enabling you to manage end-to-end analytics projects.
Yes. Upon finishing the course and any final project or exam, you will be awarded a Full Stack Analytics certification. This certificate validates your skills across data handling, analysis, and reporting, making you a strong candidate for data-related roles in various industries.
Learners usually get access to recorded sessions, real-world projects, assignments, and quizzes. Many programs also offer mentorship opportunities, one-on-one guidance, doubt-clearing sessions, and peer discussion groups to ensure you stay motivated and on track throughout your learning journey.
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