Original Price | ₹13,500.00 |
Total | ₹13,500.00 |
🚨 Full Stack Data Powerhouse -
✅ Complete Data Engineering With AWS - Basic To Advance
✅ Data Analyst Course With Azure Data Analytics
✅ Machine Learning - Basic To Advance
✅ ETL Pipelines With Informatica
✅ Complete Python With Project
✅ Tableau Complete Course
✅ Data Science Projects With Azure
What is there for you in this course 👇
🔹Recorded Video Content
🔹Each topic covered from Basic to Advance
🔹Quality teaching from top mentor working in Fortune 100 Companies
🔹Covered most demanding skills and tools from companies
🔹Multiple Industry level real world projects covered from scratch
🔹Hands on exercises, quizzes & assignments with solutions in each module
🔹Proper interview guidance & curated interview questions
🔹Resume making skills
🔹Complete guidance for LinkedIn, Naukri & Other Job Hunting Platforms
🔹Access to GrowDataSkills Discord Community where you can network with thousands of Like Minded Data Professionals
🔹Certificate of course completion
==============================================================
👉 Detailed Curriculum: Data Engineering Course (Self Paced)
Curriculum Details - https://growdataskills.com/course-data-engineering
✅ Module 1 - SQL
🔹MySQL Installation guide Connection & set up
🔹DDL, DML, DCL in SQL
🔹Data type of SQL
🔹Create, Insert, Update, Alter, Delete, Drop, Truncate Operations
🔹Views in SQL
🔹Operators in SQL
🔹WHERE clause, ORDER BY clause, GROUP BY & Having Clause
🔹Aggregations Group Concat & Roll UP
🔹CASE-WHEN Statement
🔹Joins in SQL - Inner, Left, Right, Full, Self
🔹Correlated subqueries IN, NOT IN, ANY, ALL, EXISTS, NOT EXISTS
🔹Window Functions
🔹Frame Clause in Window Functions
🔹Common Table Expressions - Recursive, Iterative
✅ Module 2 - BigData Fundamentals & Hadoop
🔹5 V’s of BigData
🔹Distributed Computation
🔹Distributed Storage
🔹Cluster, Commodity Hardware
🔹File Formats
🔹Types of Data
🔹Hadoop Complete Architecture - HDFS, Map Reduce, YARN
✅ Module 3 - Apache Hive & Hadoop Cluster On GCP
🔹Hive Architecture
🔹Hadoop & Hive Setup on GCP Dataproc Cluster
🔹HQL (Hive Query Language)
🔹Working with different file formats in Hive
🔹Partitioning and Bucketing in Hive
🔹Optimized Joins in Hive
🔹Optimizing Hive Queries
✅ Module 4 - Confluent Kafka & GCP Pub-Sub
🔹Kafka Cluster
🔹Brokers
🔹Topics
🔹Partitions
🔹Producer-Consumer
🔹Offset Management
🔹Replicas
🔹Commits
🔹Sync & Async Commits
🔹Confluent Kafka Setup
🔹Topic Creation
🔹Schema Registry
🔹Key, Value Message
🔹Message in Kafka Topics based on Random and Constant Keys
🔹Kafka Producer Code with Serialisation
🔹Kafka Consumer Code with De-Serialization
🔹Consumer Groups
🔹Working with JSON, CSV Data
✅ Module 5 - MongoDB (NoSQL DataBase)
🔹CAP Theorem
🔹What is MongoDB and MongoDB Atlas?
🔹MongoDB vs Relational Database
🔹MongoDB features
🔹MongoDB use cases and applications
🔹MongoDB architecture
🔹Node
🔹Data Centre
🔹Cluster
🔹Data replication
🔹Write operation
🔹Read operation
🔹Indexing
🔹MongoDB Atlas Setup
🔹Understanding different ways to communicate with MongoDB
🔹Query data from MongoDB tables
🔹Create APIs for MongoDB table data
🔹Queries on MongoDB using Python
🔹Bonus - Apache Cassandra Complete (Recorded Session)
✅ Module 6 - Apache Spark (PySpark) On Databricks & GCP Cloud
🔹Spark Complete Architecture
🔹Memory Management in Spark
🔹Spark Core, Dataframes & Functions in PySpark
🔹Use SQL in PySpark
🔹PySpark Structured Streaming With Kafka
🔹Common Failures in Spark and their solutions
🔹Optimization Techniques in Spark
🔹Implement PySpark Application on GCP Dataproc Cluster
🔹What is Databricks?
🔹Databricks Setup
🔹Create Cluster on Databricks
🔹Data Import & Export in Databricks
🔹PySpark Notebook on Databricks
🔹Excute PySpark Code on Databricks Cluster
✅ Module 7 - Apache Airflow With GCP Cloud Composer
🔹Introduction of Airflow
🔹Different Components of Airflow
🔹Installing Airflow
🔹Setup Airflow Using GCP Cloud Composer
🔹Understanding Airflow Web UI
🔹DAG Operators & Tasks in Airflow Job
🔹Create & Schedule Airflow Jobs For Data Processing
✅ Module 8 - Data Warehousing With SnowFlake & GCP BigQuery
🔹OLAP vs OLTP
🔹What is a Data Warehouse?
🔹Difference between Data Warehouse, Data Lake and Data Mart
🔹Fact Tables
🔹Dimension Tables
🔹Slowly changing Dimensions
🔹Types of SCDs
🔹Star Schema Design
🔹Snowflake Schema Design
🔹Data Warehousing Case Studies
🔹Inroduction of SnowFlake Data Warehousing Service
🔹SnowFlake Architecture
🔹Complete Setup of SnowFlake
🔹Create Data Warehouse on SnowFlake
🔹Analytical Queries on SnowFlake Data Warehouse
🔹Undestanding SnowPark (Execute PySpark Application on SnowFlake)
🔹What is GCP BigQuery?
🔹GCP BigQuery Architecture
🔹Complete Setup of GCP BigQuery
🔹BigQuery Tables & Data Load
🔹Create Data Warehouse on GCP BigQuery
🔹Analytical Queries on GCP BigQuery Tables
🔹Analytical Queries on GCP BigQuery Tables
🔹Data Read & Write From BigQuery Tables in PySpark
✅ Module 9 - AWS
🔹S3
🔹Lambda
🔹IAM
🔹CLOUDWATCH
🔹EC2
🔹SNS
🔹SQS
🔹Event Bridge Scheduler
🔹Event Bridge Pipe
🔹Kinesis
🔹Kinesis Firehose
🔹DynamoDB
🔹Step Function
🔹EMR
🔹GLUE
🔹RDS
🔹ATHENA
🔹REDSHIFT
✅ Module 10 - Industrial Projects
🔹Batch Data Pipeline Project - 1
(GCP/AWS, Hive, PySpark, Airflow)
🔹Batch Data Pipeline Project - 2
(GCP/AWS, Hive, PySpark, MongoDB/Cassandra, Airflow)
🔹Real Time Data Pipeline Project - 1
(GCP/AWS, Kafka, PySpark Structure Streaming)
🔹Real Time Data Pipeline Project - 2
(GCP/AWS, Kafka, PySpark Structure Streaming)
🔹Batch & Real Time Data Pipeline Project on AWS
(S3, Kinesis, DynamoDB, EventBridge Pipe, Lambda, Redshift, Athena, Cloud Composer, BigQuery, PySpark)
=================================================================
👉 Detailed Curriculum: Data Analyst Course With Azure Data Analytics (Self Paced)
Curriculum Details - https://growdataskills.com/course-data-analyst
✅ Module 1 - Python Programming
🔹Python Introduction
🔹Intro to Jupyter Notebook and installation
🔹Variables,Data Types and Operators in Python
🔹Data Structures in Python: Tuple, List, Dictionary & Set
🔹Python Object-Oriented Programming: Class & Object
🔹Searching and Sorting
🔹Data Analysis Libraries in Python - NumPy,Pandas
🔹Data Visualization with Python: Matplotlib,SeaBorn
🔹Data Cleaning and Exploratory Data Analysis(EDA)
✅ Module 2 - SQL
🔹MySQL Installation guide Connection & set up
🔹DDL, DML, DCL in SQL
🔹Data type of SQL
🔹Create, Insert, Update, Alter, Delete, Drop, Truncate Operations
🔹Views in SQL
🔹Operators in SQL
🔹WHERE clause, ORDER BY clause, GROUP BY & Having Clause
🔹Aggregations Group Concat & Roll UP
🔹CASE-WHEN Statement
🔹Joins in SQL - Inner, Left, Right, Full, Self
🔹Correlated subqueries IN, NOT IN, ANY, ALL, EXISTS, NOT EXISTS
🔹Window Functions
🔹Frame Clause in Window Functions
🔹Common Table Expressions - Recursive, Iterative
✅ Module 3 - MS-Excel
🔹Introduction to excel
🔹Phases of Data Analytics Project
🔹Data Cleaning
🔹Functions
🔹Lookup Functions
🔹Conditional Formatting
🔹Data Validation
🔹Pivot Tables
🔹Data Visualisation using Excel
✅ Module 4 - Statistics
🔹Introduction to Statistics
🔹Standard Deviation
🔹Correlation and Covariation
🔹Introduction to Probability
🔹Hypothesis Testing
🔹Marginal and Conditional Probability
🔹Normal Distribution
✅ Module 5 - Power BI (Dashboarding)
🔹Power BI: Introduction and Setup
🔹Load and clean-up data with Power BI
🔹Build a data model in Power BI
🔹Use DAX & Power Pivot to build measures and calculations
🔹Creating visuals-Answering business questions with Power BI
🔹Customizing visuals & interaction effects
🔹Creating a comprehensive sales performance report
🔹Updating and data refresh process
🔹Advance Data Analysis and Dashboarding on Power BI
✅ Module 6 - Python in Excel
🔹Introduction to Python in Excel
🔹Troubleshoot Python in Excel Errors
🔹Data Security
🔹Python in Excel DataFrames
🔹Create Python in Excel Plots & Charts
🔹Use Power Query data with Python in Excel
✅ Module 7 - Data Analytics on Azure Cloud
🔹Introduction to Microsoft Azure Cloud
🔹Data Ingestion
🔹Data Storage
🔹Data Processing and Transformations
🔹Data Analysis
🔹Monitoring and Optimizations
✅ Module 8 - Industrial Projects
🔹Supply Chain Data Analytics Projects
🔹IPL (Indian Premier League) Data Analytics Projects
==============================================================
👉 Detailed Curriculum: Machine Learning Course (Self Paced)
Curriculum Details - https://growdataskills.com/course-machine-learning
✅ Module 1 - Basic Understanding of ML
🔹Introduction to the Course
🔹Python For ML
🔹Data Collection + EDA
🔹Mathematics for ML
🔹Statistics for ML
✅ Module 2 - Deep Dive to ML Algorithms
🔹Introductions to the ML Algorithms
🔹Regression
🔹Classification
🔹Clustering
🔹Dimensionality Reduction
🔹Capstone Project & Deployment
✅ Module 3 - Advance ML
🔹Introductions to the Advance ML Algorithms
🔹Advance ML Algorithms use-cases
🔹Probabilistic Programming
🔹Bayesian Algorithm
🔹Frequentist vs Bayesian
✅ Module 4 - Introduction Of End-to-End ML Project Life Cycle
🔹Introduction to ML Deployments
🔹Understanding of ML Tools
🔹AWS Basics
🔹Project Deployments Architecture
🔹Final Task : Competition
✅ Module 5 - Industrial Projects
🔹Build Industry Ready AI Solution
a). Problem Statement
b). Data Acquisition
c). Build data pipelines
d). Build a Machine Learning model
e). Tune the model based on performance metrics
f). Deploy the model for real time scoring
g). Refresh the model in real time
=========================================================================
👉 Detailed Curriculum: ETL Pipelines With Informatica (Self Paced)
Curriculum Details - https://growdataskills.com/course-etl
✅ Module 1 - ETL Basics and Introduction
🔹 Data Architectures Overview : Databases, Data Warehouses, DataMarts, OLAP, OLTP, and Data Marts
🔹 ETL Overview : Need of ETL, Tools available in ETL, and ETL developer’s Roles & Responsibilities
🔹 Informatica Overview : Introduction, Market Share, Architecture Overview, Download and Installation
✅ Module 2 - Getting Started with Informatica
🔹 Informatica Setup
🔹 Informatica UI Walkthrough
🔹 Data Transformations : Active Transformations, Passive transformations, and Data Flows
🔹 All the Transformation functions: Routing, Grouping, Filtering, Sorting, aggregates, Joins, Ranking, and lookups
✅ Module 3 - Advanced Concepts
🔹 Dimensional Modeling, Slowly Changing Dimensions
🔹 Reusable Transformations
🔹 Workflow Monitoring
🔹 Integration with Other tools
✅ Module 4 - Versioning, Documentation, and Deployment
🔹 Version Control
🔹 Deployment : Static and Dynamic Deployment
🔹 Documentation of Pipelines and Data Models
✅ Module 5 - Portfolio Building | Industrial Projects
🔹 ETL Project 1: Building an end-to-end ETL pipeline to extract, load, and transform sales data from an e-commerce platform from multiple sources into a centralised repository.
🔹 ETL Project 2: Building an end-to-end ETL pipeline with governance and quality management, Will include Pipeline development, Error Handling, Performance optimization, Access Controls, Scheduling, Documentation and Reporting
✅ Module 6 - Resume, LinkedIn & Interview Strategies
🔹 Attention Seeking Resume Preparation and Interview Strategies
🔹 Strategies To Crack Tech Interviews
🔹 Linkedin Profile Making
🔹 How To Expand Your Professional Network On Linkedin
🔹 How To Use Various Job Portals
🔹 How To Approach For Referrals
====================================================================================
👉 Detailed Curriculum: Complete Python With Project (Self Paced)
Curriculum Details - https://growdataskills.com/course-complete-python
✅ Module 1 - Introduction to Programming
🔹 What is Programming?
🔹 What is Automation?
🔹 What is Python?
🔹 Why we choose Python?
🔹 Installing and Setup
🔹 Working with IDE's
🔹 Quiz
🔹 Assignment
✅ Module 2 - Variables and Data Types
🔹 What are Variables?
🔹 Numerical Variable
🔹 Working with Integers
🔹 Working with Floats
🔹 Booleans
🔹 Working with booleans
🔹 Introduction to string
🔹 String Slicing
🔹 Working with String
🔹 Data Type Conversion
🔹 Quiz
🔹 Assignment
✅ Module 3 - Structure of Code
🔹 Python Indentation
🔹 Statements
🔹 Expressions
🔹 Quiz
✅ Module 4 - Conditionals
🔹 Understanding operators
🔹 IF Condition
🔹 ELSE Condition
🔹 Introducing ELIF
🔹 Truth Falsy Values
🔹 Quiz
✅ Module 5 - Loops and Iterations
🔹 Understading Iterations
🔹 Why iterate?
🔹 For Loop
🔹 Working with for loops
🔹 Nested Loops
🔹 Quiz
🔹 While Loop
🔹 Loop Controls
🔹 Quiz
🔹 Assignment
✅ Module 6 - Functions
🔹 Introduction Functions
🔹 Structure of a Python Function
🔹 Scope of a Function
🔹 Kwargs and args
🔹 Working on Function
🔹 Building a Basic Python Backend Project
🔹 Quiz
🔹 Assignment
✅ Module 7 - Data Structures
🔹 Intro to Data Structures
🔹 Understading List
🔹 Working with List
🔹 List Questions
🔹 Quiz
🔹 Understading Tuples
🔹 Working with Tuples
🔹 Quiz
🔹 Understading set theory
🔹 Working with set
🔹 Quiz
🔹 Assignment
🔹 What is a Dictionary
🔹 Ordered dictionaries
🔹 Working with Dictionary
🔹 Quiz
🔹 Assignment
✅ Module 8 - Advance Functions with Data Structures
🔹 Lambda Function
🔹 Map Function
🔹 Zip Function
🔹 Regular Expression
🔹 Reduce
🔹 Filter
🔹 Evaluate
🔹 Quiz
🔹 Assignment
✅ Module 9 - Objected Oriented Programming
🔹 What is a Object
🔹 What are classes
🔹 Attributes
🔹 Structure of classes
🔹 What is Self?
🔹 What is init?
🔹 Module vs Function - Revisit
🔹 Inheritance
🔹 Polymorphism
🔹 Quiz
🔹 Assignment
✅ Module 10 - Production Ready Code
🔹 Modular programming
🔹 Python packaging
🔹 What is Production grade?
🔹 Integrating a whole backend code
✅ Module 11 - Errors and Exceptions
🔹 Types of Error
🔹 Understading Errors
🔹 The Idea of Debugging
🔹 Exception Handling
🔹 Try - Except -Finally
🔹 How to raise a exception
🔹 Capturing exceptions
🔹 Quiz
🔹 Assignment
✅ Module 12 - Code Optimization and BigO
🔹 Optimization of Code
🔹 BigO Notation
🔹 Quiz
🔹 Giving BigO Notation
🔹 Complexity Rules
🔹 Space Complexity
🔹 Quiz
🔹 Assignment
✅ End-To-End Project
🔹 Complete Finance Tracker Backend Application
=========================================================================
👉 Detailed Curriculum: Tableau Complete Course (Self Paced)
Curriculum Details - https://growdataskills.com/course-tableau
✅ Module 1 - Introduction to Tableau
🔹Tableau Introduction - Tableau products, their website & its versions
🔹Tableau Use cases
🔹Tableau Market Share - Demand in analytics market, Companies using Tableau , Tableau Competitors
🔹Tableau Starter - Installation Guide, Interface walkthrough
🔹Tableau Help - Community & Resources
✅ Module 2 - Exploring & Connecting Datasources
🔹Data Connection Types
🔹Data Source Interface
🔹Transforming Data
🔹Joining Data - Union, Join, Blending
🔹Live vs Extract Connections
🔹Tableau Shortcuts
✅ Module 3 - Creating Charts & playing with Data in Tableau
🔹Creating Basic Charts - Tables, Heat maps, Highlight Tables, Pie charts, Bar charts, Tree maps, Circle chart, Line Chart, Area Chart, Marks Card, Formatting of Charts
🔹Creating Advanced Charts - Dual Combination charts, Scatter Plot, Histogram chart, Box and whisker Plot, Packed bubbles charts, Gantt chart
🔹Playing with Data - Sorting Data, Grouping Data, Creating Hierarchies
🔹Filtering the Data - Filter Shelf, Dimension Filters, Filter Customisations, Context Filters, Measure Filters, Date Filters
✅ Module 4 - Creating Calculations in Tableau
🔹Calculated Fields - Intro, Syntax, Types
🔹Aggregations - Types, Common aggregation functions
🔹Basic Functions - String functions, Logical functions, Date functions, Type conversion functions
🔹Advanced Functions - LOD functions, Table calculations, Parameter calculations
🔹Calculation Best Practices
✅ Module 5 - Portfolio Building | Industrial Projects
🔹Tableau Dashboards & Stories Introduction
🔹Dashboard Best Practices- Layouts, Sizings, Types, Objects, Formatting
🔹Project 1 - E-commerce Dashboard
🔹Project 2 - Hotels Business Analysis Dashboard
🔹Project 3 - Health Care Analysis Dashboard
==========================================================================
👉 Detailed Curriculum: Data Science Projects With Azure (Self Paced)
Curriculum Details - https://growdataskills.com/project-data-science
✅ Project 1 - Renewable Energy Forecasting (Solar And Wind) For Power Generation
🔹Problem Overview
🔹Data Understanding & Cleaning
🔹Exploratory Data Analysis On Jupyter Notebook Using Pandas, Numpy, Matplotlib
🔹Machine Learning Model Training Using Linear Regression, Decision Tree Regressor, Random Forest Regressor, Support Vector Regressor (SVR)
🔹Model Testing & Evaluation
🔹Result & Model Accuracy Analysis
✅ Project 2 - Face Detection and Recognition
🔹Problem Overview
🔹Image Data Collection For Training
🔹Data Preprocessing/Cleaning
🔹Model Training Using OpenCV & k-nearest neighbors (KNN)
🔹Face Detection & Recognition
🔹Model Testing & Evaluation
✅ Project 3 - Patient Cost prediction based on medical History Data
🔹Problem Overview
🔹Data Understanding & Cleaning
🔹Exploratory Data Analysis On Jupyter Notebook Using Pandas, Numpy, Matplotlib, Seaborn
🔹Model Training Using Random Forest Regressor
🔹Django Local Setup
🔹Model Deployment In Local Using Django Web App
🔹Model Deployment With CICD On Azure Web App
🔹Test & Result Showcase
✅ Project 4 - Customer Purchase Prediction for Retail Stores
🔹Problem Overview
🔹Data Understanding & Cleaning
🔹Exploratory Data Analysis On Google Colab Using Pandas, Numpy, Matplotlib, Seaborn
🔹Model Training Using Keras Framework & Sequential Deep Learning Algorithm
🔹Model Testing & Validation
🔹Django Local Setup
🔹Model Deployment In Local Using Django Web App
🔹Model Deployment With CICD On Azure Web App
✅ Project 5 - Resume Analyzer using Generative AI - LLM
🔹Problem Overview
🔹Resume Data Extraction On Jupyter Notebook Using PDF Reader
🔹Analyzing Resume Using OpenAI/LLM
🔹Strength & Weakness Finding In Resume Using OpenAI
🔹Building LinkedIn Web Scrapper Using Selenium
🔹Stream Lit Local Setup
🔹Model Deployment In Local Using Stream Lit
🔹Model Deployment With CICD On Stream Lit Cloud