Full Stack Analytics Course

The Full Stack Analytics course is a comprehensive program designed to equip students with the skills and knowledge required to excel in the dynamic fields of data and business analytics. This course covers the entire data analytics lifecycle, including data collection, cleaning, processing, and visualization, as well as advanced statistical analysis, machine learning, and predictive modeling. Students will learn to use popular tools and technologies such as Python, R, SQL, Tableau, and Power BI to analyze complex datasets and derive actionable business insights.

|| Full Stack Analytics Certification Course

The goal of the Full Stack Analytics Training course is to give students a thorough understanding of the entire analytics process, from advanced analytics techniques and visualization to data collection and preparation. Students who complete this course will have the theoretical knowledge and practical experience necessary to derive insights from data and make well-informed business decisions. Full Stack Analytics course covers Data Analytics and Business Analytics concepts.


The act of obtaining, analyzing, and arranging data in order to draw conclusions, generate forecasts, and improve decision-making is known as data analytics. The goal of the full-stack data analytics course is to help you develop into a competent data analyst. Discover how to work with SQL databases, create data visualizations, and use statistics and predictive analytics in a business setting by employing the top analytics tools and techniques.


The Full Stack Analytics course is a comprehensive program designed to equip students with a holistic set of skills necessary for the entire data analytics pipeline. The course covers a wide array of topics, starting with foundational knowledge in Relational Databases (SQL), mathematics, and both basic and advanced statistics. Students can specialize in either Python or R programming, with tailored modules such as Python for Analytics and R for Analytics, ensuring they can effectively apply these languages to data analysis tasks. Business intelligence tools are extensively covered, including Microsoft Power BI (MS Power BI Desktop and Server) and Tableau (Tableau Desktop and Server), alongside advanced tools like Snowflake, Qlik View, and Alteryx for data warehousing and visualization.



The curriculum also includes cloud computing, offering hands-on experience with leading platforms such as AWS and Azure, and emphasizes modern development practices with GitHub Action for version control and deployment. Agile Scrum methodologies are integrated to enhance project management and teamwork skills. By mastering these diverse tools and techniques, students are prepared to tackle real-world data challenges across various industries.



The benefits of this Full Stack Analytics course are substantial. Graduates gain a versatile skill set that makes them highly valuable in the job market, capable of handling everything from data collection and processing to advanced analysis, visualization, and deployment. This comprehensive training ensures they can deliver actionable insights and drive data-driven decision-making within organizations. The practical, hands-on approach of the course also ensures that students are job-ready, equipped with the latest industry tools and methodologies, and capable of adapting to the rapidly evolving field of data analytics. After completion of full stack Analytics Course you will become Data Analyst, Business Analyst.

 

Please contact the nearest BIT training institute or send an email to inquiry@bitbaroda.com with any additional questions you may have regarding our Full Stack Analytics training course. We offer a free demo by calling us at +91-9328994901. We offer top-notch Full Stack Analytics classes in Vadodara-Sayajigunj, Vadodara - Waghodia Road, Vadodara - Manjalpur, Ahmedabad, Anand, and Nadiad.

|| Choose Full Stack Analytics Course From BIT

full stack analytic course features.pngfull stack analytics course features in bit.png

full stack analytics course features in india.pngfull stack analytics course features in vadoadara.png

|| 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.

|| 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.

|| 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 encompasses a diverse range of topics to equip students with essential skills for the analytics domain. The curriculum starts with Relational Databases (SQL), providing foundational knowledge in querying and managing structured data. Mathematics and Basic + Advanced Statistics form the backbone of data analysis, ensuring students can understand and apply statistical methods effectively. Students can choose between Python and R programming, with dedicated modules for each—Python Programming and R Programming, along with specialized courses like Python for Analytics and R for Analytics to tailor these languages for data-centric tasks.


    The course dives into business intelligence tools, offering in-depth training on Microsoft Power BI, including MS Power BI Desktop and MS Power BI Server. Similarly, Tableau is covered comprehensively through Tableau Desktop and Tableau Server. Snowflake and Qlik View are introduced for advanced data warehousing and visualization capabilities, respectively, while Alteryx is included for its robust data blending and advanced analytics features.


    Cloud computing is another crucial area, where students choose between Cloud - AWS and Cloud - Azure, gaining hands-on experience with these leading cloud platforms. The curriculum also incorporates version control and automation using GitHub Action, ensuring students can manage and deploy code efficiently. Agile Scrum methodologies are taught to facilitate effective project management and collaborative work environments.


    Overall, this Full Stack Analytics course provides a well-rounded education, integrating technical skills with practical tools and methodologies to prepare students for a dynamic career in data analytics.

     


    Full Stack Analytics course Leaning pathway.png

    Full Stack Analytics Learning pathway in vadodara.png

    Full Stack Analytics course learning pathway in vadodara.png

    full stack Analytics course road map.png

    • 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

     

    • 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.

    • 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 and R are both powerful programming languages widely used in data analytics, each offering unique strengths that cater to different aspects of the data analysis process.


    Python is renowned for its versatility and ease of use, making it a popular choice among data analysts and scientists. It boasts a rich ecosystem of libraries and frameworks, such as NumPy, Pandas, and Scikit-learn, which are robust for data manipulation, statistical analysis, and machine learning. Python's syntax is straightforward and readable, making it accessible for those new to programming or transitioning from other languages. Its flexibility extends beyond data analysis to web development, automation, and scripting, making it a versatile tool in various industries.


    On the other hand, R is specifically designed for statistical computing and data analysis. It excels in handling and manipulating data frames, making it particularly strong for statistical modeling, data visualization (with packages like ggplot2), and advanced analytics. R's extensive collection of statistical packages and libraries, coupled with its strong community support in academia and research, make it a preferred choice for statisticians and analysts who require sophisticated data analysis capabilities.


    Choosing between Python and R often depends on specific project requirements and personal preferences. Python is favored for its general-purpose nature, broader application across different domains, and seamless integration with other technologies. Meanwhile, R remains the go-to language for statistical analysis and research-driven projects where data visualization and advanced statistical modeling are paramount.


    Ultimately, both Python and R are valuable tools in the data analyst's toolkit. Many professionals choose to learn both languages to leverage their respective strengths depending on the task at hand, ensuring they have the flexibility to tackle a wide range of data analytics challenges effectively.

    • Python Basic Building
    • Python Keywords and identifiers
    • Comments, indentation, statements
    • Variables and data types in Python
    • Standard Input and Output
    • Operators
    • Control flow: if else elif
    • Control flow: while loop
    • Control flow: for loop
    • Control flow: break & continue


    • Python Data Structures
    • Strings
    • Lists, Lists comprehension
    • Tuples
    • Sets
    • Dictionary, Dictionary Comprehension


    • Python Functions
    • Python Builtin Functions.
    • Python Userdefined Functions.
    • Python Recursion Functions.
    • Python Lambda Functions.
    • Python Exception Handling, 
    • Logging And Debugging


    • Exception Handling 
    • Custom Exception Handling
    • Logging With Python
    • Debugging With Python


    • Python OOPS
    • Python Objects And Classes
    • Python Constructors
    • Python Inheritance
    • Abstraction In Python
    • Polymorphism in Python
    • Encapsulation in Python


    • File Handling
    • Create 
    • Read
    • Write
    • Append

    • Introduction to NumPy
    • NumPy Array
    • Creating NumPy Array
    • Array Attributes, 
    • Array Methods
    • Array Indexing, 
    • Slicing Arrays
    • Array Operation
    • Iteration through Arrays


    • Introduction to Pandas
    • Pandas Series
    • Creating Pandas Series
    • Accessing Series Elements
    • Filtering a Series
    • Arithmetic Operations
    • Series Ranking and Sorting
    • Checking Null Values
    • Concatenate a Series


    • Data Frame Manipulation
    • Pandas Dataframe 
    • Introduction Dataframe Creation
    • Reading Data from Various Files
    • Understanding Data
    • Accessing Data Frame Elements using Indexing
    • Dataframe Sorting
    • Ranking in Dataframe
    • Dataframe Concatenation
    • Dataframe Joins, 
    • Dataframe Merge
    • Reshaping Dataframe
    • Pivot Tables, 
    • Cross Tables
    • Dataframe Operations


    • Checking Duplicates
    • Dropping Rows and Columns
    • Replacing Values
    • Grouping Dataframe
    • Missing Value Analysis & Treatment
    • Visualization using Matplotlib
    • Plot Styles & Settings
    • Line Plot, 
    • Multiline Plot
    • Matplotlib Subplots
    • Histogram, Boxplot
    • Pie Chart ,Scatter Plot
    • Visualization using Seaborn
    • Strip Plot ,Distribution Plot
    • Joint Plot, 
    • Violin Plot, 
    • Swarm Plot
    • Pair Plot,
    • Count Plot
    • Heatmap
    • Visualization using Plotly
    • Boxplot
    • Bubble Chart
    • Violin Plot
    • 3D Visualization


    • EDA and Feature Engineering
    • Introduction of EDA
    • Dataframe Analysis using Groupby
    • Advanced Data Explorations

    • Introduction to R
    • What is R?
    • Installing R
    • R environment
    • Understanding R data structure 
    • Variables , Scalars
    • Vectors, Matrices, List
    • Data frames, functions, Factors
    • Importing data
    • Reading Tabular Data files
    • Loading and storing data with a clipboard
    • Accessing database, Writing data to file
    • Writing text & output from analyses to file
    • Manipulating Data
    • Selecting rows/observations
    • Rounding Number
    • Merging data
    • Relabeling the column names
    • Data sorting
    • Data aggregation
    • Using functions in R
    • Commonly used Mathematical Functions
    • Commonly used Summary Functions
    • Commonly used String Functions
    • User-defined functions
    • local and global variable
    • Working with dates
    • Looping
    • While loop ,
    • If loop
    • Charts and Plots
    • Box plot, Histogram, 
    • Pie graph ,Line chart
    • Scatterplot, Developing graphs

    • Introduction to R Programming
    • Overview of R and RStudio IDE
    • Basic syntax, data types, and variables in R


    • Data Import and Manipulation
    • Importing data from various sources (e.g., CSV files, Excel spreadsheets, databases)
    • Cleaning and preprocessing data using dplyr and tidyr packages


    • Exploratory Data Analysis (EDA)
    • Summarizing and visualizing data distributions, correlations, and patterns
    • Identifying outliers, missing values, and data inconsistencies


    • Data Visualization with ggplot2
    • Creating static and interactive plots: scatter plots, histograms, bar charts, etc.
    • Customizing plot aesthetics and themes for effective storytelling


    • Statistical Analysis with R
    • Descriptive statistics: mean, median, standard deviation, etc.
    • Inferential statistics: hypothesis testing, confidence intervals, p-values


    • Advanced Data Analytics Techniques
    • Predictive modeling: linear regression, logistic regression, decision trees
    • Cluster analysis: k-means clustering, hierarchical clustering
    • Time series analysis: forecasting, seasonality, trend detection


    • Reporting and Deployment
    • Generating dynamic reports and presentations using RMarkdown
    • Building interactive web applications with Shiny for data visualization and analysis

    Power BI and Tableau are both leading business intelligence (BI) tools used extensively in data analytics, each offering distinct features and capabilities tailored to different user needs.


    Power BI, developed by Microsoft, is known for its integration with the Microsoft ecosystem, particularly Excel and Azure services. It excels in data connectivity and integration, allowing users to easily connect to various data sources, clean and transform data using Power Query, and create interactive visualizations and reports. Power BI's strength lies in its user-friendly interface and seamless integration with other Microsoft products, making it a preferred choice for organizations already invested in Microsoft technologies.


    Tableau, on the other hand, is celebrated for its powerful data visualization capabilities and ease of use. Tableau enables users to create visually appealing and interactive dashboards with simple drag-and-drop functionality. It supports a wide range of data sources and provides robust analytics features, including advanced statistical analysis, predictive modeling, and geographic mapping. Tableau's intuitive interface and strong emphasis on visual storytelling make it popular among analysts and data professionals who prioritize data visualization and storytelling.

    • Introduction to Power BI Desktop
    • Overview of Power BI
    • Key Features and Benefits
    • Comparison with other BI tools


    • Getting Started with Power BI Desktop
    • Installation and Setup
    • Tour of the Interface
    • Navigating Power BI Ribbon and Panes


    • Connecting to Data Sources
    • Importing Data from Excel
    • Connecting to Databases (SQL Server, MySQL, etc.)
    • Using Web and Text Data Sources


    • Transforming and Cleaning Data
    • Understanding Power Query Editor
    • Data Cleaning and Shaping
    • Merging and Appending Queries


    • Data Modeling in Power BI
    • Introduction to Data Modeling
    • Creating Relationships between Tables
    • Defining Calculated Columns and Measures


    • Creating Visualizations
    • Types of Visualizations (Bar charts, Line charts, Pie charts, etc.)
    • Formatting and Customizing Visuals
    • Using Interactive Filters and Slicers


    • Advanced Visualizations and Techniques
    • Hierarchies and Drill-downs
    • Using Custom Visuals
    • Applying Themes and Templates


    • Working with Maps and Geographic Data
    • Mapping Data Points
    • Using Shapefiles and Custom Maps
    • Geocoding and Location Analytics


    • Creating Dashboards
    • Designing Effective Dashboards
    • Using Tiles and Q&A Features
    • Sharing Dashboards


    • Data Analysis Expressions (DAX)
    • Introduction to DAX
    • Writing DAX Formulas
    • Calculating Totals, Ratios, and Percentages


    • Advanced Data Modeling with DAX
    • Understanding CALCULATE and FILTER Functions
    • Time Intelligence Functions (DATESYTD, SAMEPERIODLASTYEAR, etc.)
    • Implementing Row-level Security


    • Power BI Service Integration
    • Publishing Reports to Power BI Service
    • Setting up Scheduled Data Refresh
    • Sharing and Collaborating on Reports


    • Data Insights and AI Features
    • Introduction to AI Insights in Power BI
    • Using Quick Insights and AI Visuals
    • Integrating Azure AI Services


    • MS Power BI Desktop Exercises
    • Importing and Transforming Data
    • Task: Import sales data from Excel, clean and transform data using Power Query.
    • Outcome: Create a clean dataset ready for analysis.


    • Creating Basic Visualizations
    • Task: Build a bar chart and a line chart to visualize sales trends.
    • Outcome: Understand basic visualization types and formatting options.


    • Creating Advanced Visualizations
    • Task: Create a slicer-based dashboard page with interactive visuals.
    • Outcome: Learn how to use slicers, filters, and drill-down capabilities.


    • Implementing DAX Calculations
    • Task: Write DAX formulas to calculate year-to-date sales and growth percentages.
    • Outcome: Gain proficiency in using DAX for calculations and analysis.


    • Publishing and Sharing Reports
    • Task: Publish a completed sales dashboard to Power BI Service, set up scheduled refresh.
    • Outcome: Understand the workflow of publishing and sharing reports.

    • Introduction to Power BI Server
    • Overview of Power BI Ecosystem
    • Key Features and Capabilities
    • Understanding Power BI Server vs. Power BI Online


    • Installation and Configuration
    • System Requirements and Installation Steps
    • Configuring Power BI Server
    • Integration with Active Directory


    • Power BI Server Architecture
    • Components Overview (Gateway, Data Sources, Reports)
    • Understanding Data Gateways
    • Security and Permissions


    • Data Sources and Connectivity
    • Connecting to Various Data Sources
    • Live vs. DirectQuery vs. Import
    • Refreshing Data


    • Creating Reports and Dashboards
    • Using Power BI Desktop for Report Authoring
    • Building Interactive Visualizations
    • Designing Effective Dashboards


    • Publishing and Managing Reports
    • Publishing Reports from Power BI Desktop to Power BI Server
    • Organizing Content in Workspaces
    • Version Control and Sharing Reports


    • Data Security and Governance
    • Implementing Row-level Security
    • Applying Security Policies
    • Data Encryption and Compliance


    • Advanced Analytics and AI Integration
    • Introduction to AI Features in Power BI
    • Using Custom Visuals and R/Python Scripts
    • Integrating Azure AI Services


    • Performance Optimization
    • Optimizing Query Performance
    • Improving Report Rendering Speed
    • Monitoring and Troubleshooting


    • Customizing and Extending Power BI
    • Creating and Using Custom Themes
    • Developing Custom Visuals
    • Using Power BI APIs for Automation


    • Practical Exercises
    • Exercise 1: Setting up Power BI Server
    • Exercise 2: Creating and Publishing Reports
    • Exercise 3: Implementing Security Measures
    • Exercise 4: Performance Optimization Tasks
    • Exercise 5: Customizing Reports and Dashboards


    • Case Studies and Real-world Applications
    • Industry-specific Use Cases
    • Success Stories and Best Practices


    • MS Power BI Server Exercise
    • Setting up Power BI Server
    • Install Power BI Server on a local machine or VM.
    • Configure basic settings and connect to a sample database.


    • Creating and Publishing Reports
    • Design a sales dashboard using Power BI Desktop.
    • Publish the dashboard to Power BI Server and configure data refresh.


    • Implementing Security Measures
    • Set up row-level security based on user roles.
    • Configure encryption settings and access policies.


    • Performance Optimization Tasks
    • Identify slow-performing reports and optimize queries.
    • Monitor resource usage and apply performance tuning techniques.


    • Customizing Reports and Dashboards
    • Customize the appearance of reports using custom themes.
    • Create a custom visual using Power BI SDK and integrate it into a dashboard.

    • Introduction to Tableau Desktop:
    • Overview of Tableau Desktop and its features.
    • Understanding the Tableau interface and terminology.


    • Connecting to Data:
    • Importing data into Tableau from various sources (Excel, CSV, databases, etc.).
    • Understanding data source connection options and considerations.


    • Basic Visualization:
    • Creating basic visualizations such as bar charts, line charts, scatter plots, and maps.
    • Applying formatting and customization to visualizations.


    • Working with Data:
    • Data organization and structuring.
    • Filtering and sorting data.
    • Grouping and aggregating data.


    • Advanced Visualization Techniques:
    • Creating more complex visualizations such as dual-axis charts, treemaps, and heatmaps.
    • Implementing reference lines, bands, and distributions.


    • Calculations and Expressions:
    • Introduction to Tableau Calculated Fields.
    • Writing basic calculations (e.g., arithmetic calculations, string calculations, date calculations).


    • Dashboard Creation:
    • Building dashboards to combine multiple visualizations into a single view.
    • Implementing interactivity with dashboard actions and filters.


    • Data Blending and Joins:
    • Working with multiple data sources and blending data.
    • Understanding different types of joins and their implications.


    • Advanced Data Analysis:
    • Implementing advanced calculations using Tableau Calculated Fields and Parameters.
    • Utilizing Level of Detail (LOD) expressions for complex analysis.


    • Geospatial Analysis:
    • Mapping geographic data in Tableau.
    • Creating custom geocoding and using spatial files for analysis.


    • Performance Optimization:
    • Optimizing workbook performance for large datasets.
    • Understanding Tableau data extracts and incremental refreshes.


    • Advanced Dashboard Techniques:
    • Designing interactive and responsive dashboards.
    • Incorporating storytelling and guided analytics into dashboards.


    • Tableau Desktop Exercises
    • Data Connection and Basic Visualizations:
    • Import a dataset (e.g., CSV, Excel) into Tableau Desktop.
    • Create a bar chart to visualize sales by product category.
    • Create a line chart to show trends in monthly sales.
    • Add filters to interactively explore the data.


    • Geographic Visualization:
    • Use a geographic dataset (e.g., countries, states) to create a map visualization.
    • Color code the map based on a measure such as sales or population.
    • Drill down from country-level to state-level data using hierarchical filters.


    • Advanced Visualizations:
    • Create a dual-axis chart to compare two measures on the same axis.
    • Build a treemap to visualize hierarchical data such as sales by product category and subcategory.
    • Design a dashboard to display multiple visualizations together.


    • Calculations and Expressions:
    • Create a calculated field to calculate profit margin (profit divided by sales).
    • Use a LOD (Level of Detail) expression to calculate the total sales regardless of filters applied.
    • Implement a parameter to dynamically change the view (e.g., switch between different metrics).


    • Advanced Analytics:
    • Implement forecasting to predict future sales trends.
    • Use clustering algorithms to segment customers based on their purchasing behavior.
    • Apply trend lines and statistical models to analyze data patterns.


    • Dashboard Design and Interactivity:
    • Design a dynamic dashboard with interactivity (e.g., use of parameters, dashboard actions).
    • Incorporate user input controls like dropdowns and sliders to filter data dynamically.
    • Implement URL actions to link Tableau visualizations to external web pages or documents.


    • Sales Performance Analysis:
    • Analyze sales performance by region, product, and time period.
    • Identify top-performing products and regions.
    • Visualize sales trends and seasonality.


    • Customer Segmentation:
    • Segment customers based on demographics, purchasing behavior, or lifetime value.
    • Identify key characteristics of each segment and tailor marketing strategies accordingly.


    • Profitability Analysis:
    • Analyze profitability by product line, customer segment, or sales channel.
    • Identify low-margin products or unprofitable customer segments and recommend actions to improve profitability.

    • Introduction to Tableau Server:
    • Overview of Tableau Server
    • Introduction to Tableau Server architecture and components.
    • Understanding the role of Tableau Server in the Tableau ecosystem.


    • Installation and Configuration:
    • Installation prerequisites and best practices.
    • Step-by-step installation and configuration of Tableau Server.


    • User Management:
    • User authentication options (local authentication, Active Directory, SAML).
    • Managing users, groups, and permissions.


    • Content Management:
    • Publishing workbooks and data sources to Tableau Server.
    • Managing projects and content permissions.
    • Versioning and revision history.


    • Tableau Server Administration:
    • Server Administration Tasks:
    • Monitoring server status and performance.
    • Configuring server settings and resource management.
    • Backup and restore procedures.


    • Data Source Management:
    • Connecting to data sources and configuring data connections.
    • Managing data source permissions and connections.


    • Security and Governance:
    • Implementing security best practices.
    • Enforcing data governance policies.
    • Auditing and logging user activities.


    • High Availability and Scalability:
    • Configuring high availability and load balancing.
    • Scaling Tableau Server for increased capacity.


    • Advanced Topics:
    • Customization and Integration:
    • Customizing Tableau Server interface and branding.
    • Integrating Tableau Server with other applications and services.


    • Automation and Scripting:
    • Automating server tasks using Tableau Server REST API.
    • Scripting common administrative tasks for efficiency.


    • Disaster Recovery and Failover:
    • Planning and implementing disaster recovery strategies.
    • Configuring failover and redundancy options.


    • Tableau Server Exercises
    • Setting Up Tableau Server:
    • Installation and Configuration:
    • Install Tableau Server on a virtual machine or server environment.
    • Configure server settings, including authentication method (local, Active Directory, SAML).


    • Adding Users and Groups:
    • Add users to Tableau Server and assign them to appropriate groups.
    • Configure permissions to control access to projects, workbooks, and data sources.
    • Publishing Content to Tableau Server


    • Publishing Workbooks:
    • Publish a workbook from Tableau Desktop to Tableau Server.
    • Set permissions for the published workbook to control who can view and interact with it.


    • Publishing Data Sources:
    • Publish a data source to Tableau Server.
    • Configure data source permissions and refresh schedules.
    • Managing Content on Tableau Server:


    • Managing Projects:
    • Create new projects on Tableau Server to organize content.
    • Move workbooks and data sources between projects.


    • Content Permissions:
    • Modify permissions for existing content on Tableau Server.
    • Assign permissions to specific users or groups for projects, workbooks, and data sources.


    • Collaboration and Interactivity:
    • Creating and Managing Comments:
    • Add comments to workbooks and views on Tableau Server.
    • Reply to comments and manage comment threads.


    • Subscriptions and Alerts:
    • Set up email subscriptions to receive scheduled updates of workbook views.
    • Configure alerts to be notified when certain data thresholds are met.


    • Monitoring and Administration:
    • Server Status and Performance Monitoring:
    • Monitor server status, including CPU usage, memory usage, and disk space.
    • Identify performance bottlenecks and optimize server resources.


    • Backup and Restore:
    • Perform a backup of Tableau Server data and configuration.
    • Practice restoring Tableau Server from a backup in a test environment.


    • Security and Governance:
    • Security Best Practices:
    • Review and implement security best practices for Tableau Server.
    • Ensure compliance with data governance policies and regulations.


    • Auditing and Logging:
    • Review audit logs to track user activity on Tableau Server.
    • Analyze logs to identify security incidents or compliance issues.


    • Scaling and High Availability:
    • Scaling Tableau Server:
    • Add additional nodes to scale Tableau Server for increased capacity.
    • Configure load balancing to distribute traffic across multiple nodes.


    • High Availability Configuration:
    • Configure Tableau Server for high availability to ensure uptime and reliability.
    • Test failover and disaster recovery procedures to ensure continuity of service.

    • 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


    • Set Analysis in Qlik View
    • Date functions
    • What If analysis
    • Calculated Dimensions
    • Conditional Objects
    • Securing your document and document tuning
    • Cross tables
    • Bookmarks
    • Chart-level and script-level functions
    • Security measures and access points in QlikView
    • Integrating visualizations with dashboards

    • 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

    AWS (Amazon Web Services) and Azure (Microsoft Azure) are two of the leading cloud computing platforms offering robust data analytics services, each with its own strengths and capabilities tailored to diverse business needs.


    AWS provides a comprehensive suite of data analytics services under its Amazon Web Services umbrella. Key services include Amazon Redshift for data warehousing, Amazon EMR (Elastic MapReduce) for big data processing using Apache Hadoop and Spark, and Amazon Athena for querying data stored in Amazon S3 using standard SQL. AWS also offers analytics services like Amazon QuickSight for business intelligence and visualization, AWS Glue for ETL (Extract, Transform, Load) tasks, and AWS Data Pipeline for orchestrating data workflows. AWS's ecosystem is extensive, with a broad range of integrations and support for various programming languages and frameworks, making it a preferred choice for organizations seeking flexibility and scalability in their data analytics solutions.


    Azure, Microsoft's cloud platform, provides a robust set of data analytics services integrated with its suite of tools and services. Azure Synapse Analytics (formerly SQL Data Warehouse) offers enterprise-level data warehousing capabilities, supporting both relational and big data analytics. Azure HDInsight provides managed Apache Hadoop, Spark, HBase, and Storm clusters for big data processing. Azure Data Lake Store and Azure Databricks further enhance data storage and analytics capabilities, while services like Azure Machine Learning enable advanced predictive analytics and machine learning model development. Azure also includes Power BI for business intelligence and visualization, tightly integrating with other Microsoft products like Excel and SharePoint. Azure's strength lies in its seamless integration with Microsoft's enterprise ecosystem, making it an attractive option for organizations already using Microsoft technologies.

    • S3 Basics
    • Storage Classes 
    • Data Management
    • security & Access Control 
    • Cost Optimization
    • Monitoring & Logging 
    • Use Cases 
    • Data Replications and Disaster recovery
    • Course Overview 
    • Introducing our Hands-On Case Study
    • Collection Section 
    • Introduction Kinesis Data Streams Overview 
    • Hot shard 
    • Kinesis Producers
    • Kinesis Consumers 
    • Kinesis Enhanced Fan Out 
    • Kinesis Scaling
    • Kinesis - Handling Duplicate Records part 1 
    • Kinesis - Handling Duplicate Records part 2 
    • Kinesis Security 
    • Kinesis Data Firehose
    • CloudWatch Subscription Filters with Kinesis 
    • Kinesis Data Streams vs SQS 
    • IoT Overview 
    • IoT Components Deep Dive
    • Database Migration Service (DMS)
    • Direct Connect 
    • S3 Overview 
    • S3 Hands On 
    • S3 Security Bucket Policy
    • S3 Security Bucket Policy Hands On 
    • S3 Website Overview 
    • S3 Website Hands On
    • S3 Overview 
    • S3 Versioning Hands On 
    • S3 Server Access Logging
    • S3 Server Access Logging Hands On 
    • S3 Replication Overview
    • S3 Replication Hands On
    • S3 Storage Classes Overview 
    • S3 Storage Classes Hands On 
    • S3 Glacier Vault Lock & S3 Object Lock 
    • S3 Encryption
    • Shared Responsibility Model for S3 


    • DynamoDB Overview 
    • DynamoDB RCU & WCU
    • DynamoDB Partitions 
    • dynamodb api 
    • DynamoDB Indexes LSI & GSI
    • DynamoDB DAX 
    • DynamoDB Streams 
    • DynamoDB TTL 
    • DynamoDB Security
    • DynamoDB Storing Large Objects 
    • Lambda Overview 
    • Lambda Hands On
    • Why Cloud & Big Data on Cloud 
    • What is Virtual Machine 
    • On-Premise vs Cloud Setup
    • Major Vendors of Hadoop Distribution 
    • Hdfs vs S3 
    • Important Instances in AWS
    • Spark Basics 
    • Why spark is difficult 
    • Overview of EMR part 1 
    • Overview of EMR part 2 
    • What is EMR
    • Tez vs mapreduce 
    • Launching an emr cluster 
    • connecting to your cluster
    • Create a tunnel for web ui 
    • Use Hue to interact with EMR
    • Part 1 analyze movie ratings with hive on emr 
    • Part 2 analyze movie ratings with hive on emr
    • Transient vs Long Running Cluster Running 
    • Copy File From S3 to Local Zeppelin Notebook
    • How to Create a 
    • VM S3 & EBS 
    • Public ip Vs Private Ip
    • Aws Command Line Interface 
    • AWS Glue
    • Introduction to Amazon Redshift 
    • Redshift Master Slave Architecture 
    • Redshift demo
    • redshift specturm 
    • Redshift Distribution Styles
    • Redshift Fault Tolerance 
    • Redshift Sort Keys

    • Getting started with Azure
    • Creating Microsoft Azure account 
    • Understanding regions and availability zones in Azure
    • Getting started with Azure virtual machines 
    • Creating your first virtual machine in azure
    • Connecting to the Azure virtual machine and running commands 
    • Understanding Azure VM-key concepts
    • Simplifying installing software on the Azure virtual machine 
    • Increasing availability for azure VM
    • Virtual machine scale sets 
    • Exploring scaling and load balancing 
    • Static IP, monitoring and reducing costs
    • Designing a good solution with Azure VM 
    • Exploring Azure virtual machine scenarios
    • Azure Web Service Plan 
    • Azure Storage 
    • What is Data Factory
    • data factory in azure ecosystem 
    • Provision Azure data factory instance
    • data factory components 
    • data factory pipeline and activities
    • data factory linked service and datasets 
    • data factory integration runtime 
    • data factory triggers
    • data factory copy data activity demo 
    • copy data activity using author demo
    • secure input and output property 


    • user properties 
    • Data factory parameters
    • data flow concept 
    • mapping data flow
    • Wrangling data flow 
    • Monitoring
    • metrics and diagnostic settings 
    • why warehouse in cloud?
    • Traditional vs modern warehouse architecture 
    • what is synapse analytics service
    • demo create dedicated sql pool 
    • demo connect sql pool with ssms
    • demo create azure synapse analytics workspace 
    • Demo explore synapse studio v2
    • demo create dedicated sql pool and spark pool from inside synapse studio
    • demo analyse data using dedicated sql pool
    • analyse data using apache spark notebook
    • demo analyse data using serverless sql
    • demo data factory copy tool from synapse integrate tab
    • demo monitor synapse analytics studio
    • azure synapse a game-changer
    • azure synapse benefits

    • 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.

Get in touch

|| Tools to Matser

full stack analytics course-tools.png

Certificate

|| Skills to Master

Full Stack Data Analytics course.png

Certificate

|| Scope and Demand for Full Stack Analytics in India

The scope and demand for analytics in India are significant and growing rapidly, driven by the country's digital transformation, technological advancements, and the increasing importance of data-driven decision-making across industries. Here are key points detailing the scope and demand for analytics in India:

  • High Demand for Analytics Professionals:
  • Increasing Data Generation: The exponential growth in data from digital activities, IoT devices, social media, and other sources has heightened the need for skilled analytics professionals.
  • Wide Adoption Across Industries: Sectors such as IT, finance, healthcare, e-commerce, telecommunications, manufacturing, and retail are heavily investing in analytics to gain competitive advantages.


  • Key Roles in Analytics:
  • Data Analyst: Focuses on interpreting data and generating actionable insights.
  • Data Scientist: Develops models and algorithms to solve complex problems.
  • Business Intelligence Analyst: Utilizes data visualization tools to present data in an understandable manner.
  • Data Engineer: Designs and manages data infrastructure.
  • Machine Learning Engineer: Implements machine learning models into production.
  • Big Data Specialist: Works with large data sets and tools like Hadoop and Spark.


  • Educational and Training Opportunities:
  • Academic Institutions: Leading institutions like IITs, IIMs, and other universities offer specialized programs in data science and analytics.
  • Online Courses: Platforms like Coursera, edX, Udacity, and UpGrad provide flexible learning options in analytics.
  • Certification Programs: Professional certifications from organizations like SAS, Microsoft, and IBM enhance job prospects.


  • Technological Advancements:
  • Big Data Technologies: Tools like Hadoop, Spark, and cloud-based solutions are driving the need for big data analytics.
  • Machine Learning and AI: Advanced analytics applications in AI and machine learning are creating new job roles and opportunities.
  • Data Visualization Tools: Software like Tableau, Power BI, and Qlik are in high demand for business intelligence and reporting.


  • Industry-Specific Applications:
  • Finance: Risk management, fraud detection, customer segmentation, and algorithmic trading.
  • Healthcare: Predictive analytics for patient care, personalized medicine, and healthcare management.
  • Retail and E-commerce: Customer behavior analysis, inventory management, and personalized marketing.
  • Manufacturing: Predictive maintenance, quality control, and supply chain optimization.
  • Telecommunications: Network optimization, customer churn prediction, and service personalization.


  • Startups and Innovation:
  • Analytics Startups: A vibrant startup ecosystem focused on analytics and AI, contributing to job creation and innovation.
  • Entrepreneurship: Growing opportunities for entrepreneurs to develop analytics-based solutions and services.


  • Government and Policy Support:
  • Digital India Initiative: Promoting digital infrastructure and data analytics across sectors.
  • Skill Development Programs: Government-sponsored training programs aimed at building a skilled workforce in emerging technologies.


  • Future Trends:
  • Integration with Emerging Technologies: Analytics will increasingly integrate with IoT, blockchain, and edge computing.
  • Increased Use of AI and ML: Broader adoption of AI and machine learning across various applications.
  • Focus on Data Privacy and Ethics: Growing importance of data governance, privacy, and ethical considerations in analytics.


The scope and demand for analytics in India are robust, fueled by the need for data-driven decision-making and technological advancements. With continuous investments in education, training, and innovation, India is well-positioned to be a leader in the global analytics landscape. The field offers diverse career opportunities and is critical to the strategic growth of businesses across multiple sectors.

placement report placement report

|| Full Stack Analytics holds Prominent Position in Indian Job Market 

The Full Stack Analytics course offers excellent placement opportunities in India, reflecting the growing demand for data analytics professionals across various sectors. Graduates of this course are highly sought after by leading IT firms, financial institutions, healthcare organizations, e-commerce giants, and manufacturing companies. With comprehensive training in data management, statistical analysis, machine learning, business intelligence tools like Microsoft Power BI and Tableau, and cloud computing platforms such as AWS and Azure, students are well-prepared for roles like Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, and Machine Learning Engineer.


Top multinational companies and innovative startups alike are continuously seeking skilled analytics professionals to help them leverage data for strategic decision-making and operational efficiency. Additionally, the integration of modern development practices and project management methodologies in the course ensures that graduates are not only technically proficient but also adept at managing projects and collaborating effectively within teams. With the rapid digitization of the Indian economy and the increasing importance of data-driven insights, the Full Stack Analytics course significantly enhances the employability and career prospects of its graduates, making them valuable assets to any organization.

 

|| Empowering Your Career Transition From Learning To Leading

User Image
Rajvi Suthar

Rajvi Suthar, excelling as a Data Analyst at Tata Consultancy Services (TCS), leverages unique tools such as Python for scripting, R for statistical analysis, and Alteryx for data blending. Her adept use of these cutting-edge tools contributes to efficient and advanced data analysis solutions.

Company logo
User Image
Sarthak Gupta

Sarthak Gupta, demonstrating mastery as a Business Data Analyst at Accenture, leverages unique tools such as Power BI for visual analytics, Python for data scripting, and Alteryx for data blending. His adept use of these cutting-edge tools contributes to efficient and advanced business data analysis solutions.

Company logo
User Image
Megha Bhatt

Megha Bhatt, demonstrating prowess as a ML Engineer at Cognizant, leverages unique tools such as Alteryx for advanced data blending, Google BigQuery for large-scale data analytics. Her adept use of these cutting-edge tools contributes to innovative and efficient data analysis.

Company logo
User Image
Rishabhjit Saini

Rishabhjit Saini, demonstrating mastery as a Senior Data Processing professional at NielsenIQ, leverages unique tools such as Talend for data integration, Apache Spark for big data processing, and Trifacta for advanced data wrangling. His adept use of these cutting-edge tools contributes to efficient and innovative data handling.

Company logo
User Image
Darshna Dave

Darshna Dave, excelling as a Data Engineer at Deepak Foundation post-IT institute, showcases expertise in unique tools such as KNIME for data analytics workflows, Apache Superset for interactive data visualization, and RapidMiner for advanced predictive analytics.

Company logo
User Image
Shubham Ambike

Shubham Ambike, excelling as a Digital MIS Executive at Alois post-IT institute, showcases expertise in tools like Microsoft Excel, Power BI, and Google Analytics. His adept use of these tools contributes to efficient data management and analysis. Congratulations on his placement.

Company logo
User Image
Mehul Sirohi

Mehul Sirohi, excelling as a Data Associate at Numerator post-IT institute, skillfully employs unique tools such as Alteryx for data blending, Jupyter Notebooks for interactive data analysis, and Looker for intuitive data visualization. His mastery of these advanced tools contributes to Numerator's data processing success.

Company logo

|| Full Stack Analytics Course Career Option & Job Opportunities in India

Data Analytics Career Options:

  • Data Analyst:: Data collection, cleaning, and interpretation. Creating reports and visualizations to communicate insights.
  • Job Opportunities: Common in IT services, finance, healthcare, e-commerce, and telecom sectors.
  • Data Scientist: Advanced statistical analysis, machine learning model development, and data-driven problem solving.
  • Job Opportunities: High demand in technology companies, startups, financial institutions, and research organizations.
  • Data Engineer: Building and maintaining data pipelines, ensuring data quality and integrity.
  • Job Opportunities: Essential in organizations with large-scale data processing needs, such as tech firms, online retailers, and banking institutions.
  • Business Intelligence Analyst: Designing and developing BI solutions, analyzing complex datasets to support business decisions.
  • Job Opportunities: Predominantly in large enterprises across industries such as retail, healthcare, and manufacturing.
  • Machine Learning Engineer:  Implementing machine learning algorithms and models, optimizing performance for business applications.
  • Job Opportunities: High in tech firms, AI startups, financial services, and any sector leveraging AI for innovation.


Business Analytics Career Options:

  • Business Analyst: Identifying business needs, analyzing processes, recommending data-driven solutions.
  • Job Opportunities: Widely available across industries including IT, finance, consulting, and manufacturing.
  • Financial Analyst: Analyzing financial data, forecasting financial performance, investment decision support.
  • Job Opportunities: Common in banking, investment firms, corporate finance departments, and insurance companies.
  • Marketing Analyst: Analyzing market trends, customer data, and campaign performance to inform marketing strategies.
  • Job Opportunities: Found in marketing departments of companies across sectors, digital marketing agencies, and market research firms.
  • Operations Analyst: Improving business processes, optimizing supply chains, and enhancing operational efficiency.
  • Job Opportunities: Present in manufacturing, logistics, retail, and service industries.
  • Product Analyst:
  • Responsibilities: Analyzing product performance data, customer feedback, and market conditions to guide product development.
  • Job Opportunities: Common in tech companies, consumer goods firms, and any product-centric organizations.

|| Job Roles And Salary

Full stack Analytics job roles.png

Certificate

|| Average Salary for Full Stack Analytics in India

The average salary for full stack developers in India varies based on experience levels, company size, and location. Here is a level-wise breakdown of salaries for full stack developers in India for 2024:

  • Entry-Level (0-2 years of experience): The average annual salary is approximately ₹3-5 lakhs. Freshers often start at the lower end, with companies like Infosys and Wipro offering salaries around ₹3-4 lakhs per annum.
  • Mid-Level (2-5 years of experience):Developers with a few years of experience can expect to earn between ₹6-12 lakhs per annum. Companies like IBM and Accenture offer salaries in this range, with IBM offering higher salaries averaging around ₹6.5 lakhs per annum.
  • Senior-Level (5+ years of experience):Experienced full stack developers can earn anywhere from ₹12-20 lakhs per annum. Higher salaries are often found in tech hubs like Bangalore and Hyderabad. Companies like Oracle and high-growth startups may offer even higher packages, ranging from ₹13-17 lakhs per annum.


Additionally, top tech companies and MNCs in India such as Nuclei and Oracle provide some of the highest salaries for full stack developers, recognizing their crucial role in handling both front-end and back-end development tasks.


These salary figures are influenced by several factors, including educational background, technical skillset, company size, and location. Developers in tech hubs like Bangalore and Hyderabad generally command higher salaries due to the higher demand for tech talent and the higher cost of living.


|| Top Hiring Companies

Top Hiring Companies -2.png

Certificate

|| Some Prominent Companies in India that use Full Stack Analytics Course

IT and Technology:

  • Tata Consultancy Services (TCS): Uses data analytics for improving operational efficiencies and offering analytics services to clients.
  • Infosys: Employs data analytics for internal optimization and provides analytics solutions to global clients.
  • Wipro: Uses advanced analytics to drive digital transformation and provides data-driven insights to clients.
  • HCL Technologies: Implements data analytics for service improvement and client projects.

E-commerce:

  • Flipkart: Utilizes data analytics for personalized recommendations, inventory management, and customer experience enhancement.
  • Amazon India: Leverages data analytics for supply chain optimization, personalized marketing, and customer insights.
  • Myntra: Uses analytics to understand customer preferences and optimize product offerings and marketing strategies.

Banking and Financial Services:

  • HDFC Bank: Employs data analytics for fraud detection, customer segmentation, and personalized banking services.
  • ICICI Bank: Uses analytics to improve customer service, risk management, and targeted marketing.
  • Axis Bank: Leverages data analytics for credit scoring, fraud prevention, and enhancing customer engagement.
  • SBI: Utilizes analytics for operational efficiency, customer insights, and risk management.

Telecommunications:

  • Bharti Airtel: Uses data analytics for network optimization, customer experience improvement, and targeted marketing campaigns.
  • Reliance Jio: Leverages data analytics for user behavior analysis, network performance, and personalized services.
  • Vodafone Idea: Implements analytics for customer retention, network performance monitoring, and marketing strategies.

Healthcare and Pharmaceuticals:

  • Apollo Hospitals: Utilizes data analytics for patient care optimization, hospital operations, and predictive analytics for healthcare outcomes.
  • Dr. Reddy’s Laboratories: Uses analytics for drug development, market analysis, and supply chain optimization.
  • Cipla: Leverages data analytics for research and development, market insights, and operational efficiency.

Retail:

  • Reliance Retail: Uses data analytics for inventory management, customer behavior analysis, and personalized marketing.
  • Big Bazaar (Future Group): Employs analytics for demand forecasting, supply chain management, and customer insights.
  • D-Mart: Leverages data analytics for optimizing store operations, inventory management, and customer preferences.

Manufacturing:

  • Tata Steel: Uses data analytics for process optimization, predictive maintenance, and supply chain efficiency.
  • Mahindra & Mahindra: Implements analytics for improving manufacturing processes, supply chain management, and customer insights.
  • Larsen & Toubro: Utilizes data analytics for project management, operational efficiency, and predictive maintenance.

Consulting and Professional Services:

  • Deloitte: Provides business analytics services to clients for strategy, operations, and customer insights.
  • PwC India: Uses business analytics to help clients with decision-making, risk management, and performance improvement.
  • EY (Ernst & Young): Employs business analytics for financial advisory, risk management, and strategic consulting.
  • KPMG India: Provides business analytics solutions for improving client business processes and decision-making.

FMCG (Fast-Moving Consumer Goods):

  • Hindustan Unilever: Uses business analytics for market analysis, consumer insights, and optimizing supply chain operations.
  • Nestlé India: Leverages analytics for demand forecasting, marketing strategies, and improving operational efficiencies.
  • ITC: Employs business analytics for market research, consumer behavior analysis, and supply chain optimization.

Automobile:

  • Tata Motors: Uses business analytics for market analysis, product development, and optimizing manufacturing processes.
  • Maruti Suzuki: Leverages analytics for consumer insights, sales forecasting, and improving operational efficiency.
  • Hero MotoCorp: Employs business analytics for market research, customer insights, and optimizing production processes.

Real Estate:

  • Godrej Properties: Uses business analytics for market analysis, sales strategies, and project management.
  • DLF: Leverages analytics for customer insights, market trends, and operational efficiency.
  • Oberoi Realty: Employs business analytics for demand forecasting, marketing strategies, and optimizing project timelines.

Travel and Hospitality:

  • Make My Trip: Uses business analytics for customer behavior analysis, personalized recommendations, and improving user experience.
  • OYO Rooms: Leverages analytics for market analysis, pricing strategies, and optimizing room occupancy.
  • Taj Hotels: Employs business analytics for enhancing customer experience, market research, and operational efficiency.

||  Get Full Stack Analytics Certification

Three easy steps will unlock your Full Stack Analytics Certification:


  • Finish the online / offline course of Full Stack Analytics Course and the Assignment.
  • Take on and successfully complete a number of industry-based Projects
  • Pass the Full Stack Analyst certification exam


The certificate for this Full Stack Analytics course will be sent to you through our learning management system, where you can also download it. Add  a link to your certificate to your CV or LinkedIn profile

Certificate

|| Frequently asked question

This course is ideal for anyone interested in pursuing a career in analytics, data science, or related fields. It caters to beginners with little to no experience in analytics as well as professionals looking to enhance their skills or transition into analytics roles.

Most reputable Full Stack Analytics Courses offer a certificate of completion that can be shared on your resume or LinkedIn profile. However, it's essential to check the accreditation and recognition of the issuing institution before enrolling.

Completing a Full Stack Analytics Course can open doors to various career opportunities, including roles such as data analyst, business analyst, data scientist, analytics consultant, and more. Industries such as finance, healthcare, e-commerce, marketing, and technology actively seek professionals with analytics skills.

BIT offers a wide range of programs catering to various interests and career paths. These may include academic courses, vocational training, professional development, and more. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs.

For any questions or assistance regarding the enrolment process, admissions requirements, or program details, please don't hesitate to reach out to our friendly admissions team. Please visit our website – www.bitbaroda.com or contact our admissions office at M.9328994901 for a complete list of programs or Visit Our Centers – Sayajigunj, Waghodia Road, Manjalpur in Vadodara, Anand, Nadiad, Ahmedabad

BIT prides itself on providing high-quality education, personalized attention, and hands-on learning experiences. Our dedicated faculty, state-of-the-art facilities, industry partnerships, and commitment to student success make us a preferred choice for students seeking a rewarding educational journey.

BIT committed to supporting students throughout their academic journey. We offer a range of support services, including academic advising, tutoring, career counselling, and wellness resources. Our goal is to ensure that every student has the tools and support they need to succeed.

Data Analyst Business Intelligence Analyst Data Scientist Machine Learning Engineer Data Engineer Analytics Consultant

Related courses

Data Analytics using SAS Course
Read more
MS Power BI Course
Read more
Tableau Course
Read more
Big Data Analytics Course
Read more
Data Analytics Course
Read more
Business Analytics Course
Read more
HR Data Analytics Course
Read more
Finance Data Analytics Course
Read more
Banking Data Analytics Course
Read more
Health Care Analytics Course
Read more
Data analytics using R Course
Read more
Data Analytics using Excel Course
Read more

Related courses

Data Analytics using SAS Course
MS Power BI Course
Tableau Course
Big Data Analytics Course
Data Analytics Course
Business Analytics Course
HR Data Analytics Course
Finance Data Analytics Course
Banking Data Analytics Course
Health Care Analytics Course
Data analytics using R Course
Data Analytics using Excel Course

Visit our latest webinars

Qwerty unleashed : Exploring Next -Gen Hacker Techniques for Passwords

Jeetu Singh

Qwerty unleashed : Exploring Next -Gen Hacker Techniques for Passwords
Dominate the Digital Space: A Deep Dive into Google Ads Mastery.

Gaurang Parekh

Dominate the Digital Space: A Deep Dive into Google Ads Mastery.
Design the Future: React JS for Next-Gen UIs

Nikhil Bhowate

Design the Future: React JS for Next-Gen UIs
Immersing Tomorrow: Exploring the Future of AR & VR Today

Ankit Patel

Immersing Tomorrow: Exploring the Future of AR & VR Today
Workshop on Artificial Intelligence Learn Sentiment Analysis

Jalpa Desai

Workshop on Artificial Intelligence Learn Sentiment Analysis
-->

Useful links

Categories

IT Infrastructure

Development

Data Science

Work with us

Company

Copyright Baroda Institute of Technology© 2024 | Design and Developed By Bit infotech