Data Analytics Course in Gurgaon, Delhi - Job Oriented

Machine Learning introduction will provide an overview of machine learning and its applications. We will discuss the difference between supervised and unsupervised learning and common machine learning algorithms such as regression, classification, clustering, bootstrapping, K-fold validation, etc.

• Statistical learning vs. Machine learning
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)

• Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
• Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
• Iteration and Model Evaluation

It provides a deep understanding of Python's data analysis libraries, such as Pandas, Numpy, and Matplotlib. Using these libraries, we will learn how to perform exploratory data analysis (EDA) techniques on data sets, including data cleaning, transformation, and visualization.

• Python Numpy (Data Manipulation)
• Python Pandas(Data Extraction & Cleansing)
• Python Matplot (Data Visualization)
• Python Scikit-Learn (Data Modelling)
• EDA – Quantitative Technique

Data preprocessing in machine learning alludes to the procedure of planning (cleaning and coordinating) the crude information to make it reasonable for structuring and preparing machine learning models. We will learn the information mining strategy that changes crude information into a reasonable and coherent organization, such as Data Exploration Techniques, Correlation Analysis, Data Wrangling, and many more. 

• Data Exploration Techniques
• Sea-born | Matplotlib
• Correlation Analysis
• Data Wrangling
• Outliers Values in a DataSet
• Data Manipulation
• Missing & Categorical Data
• Splitting the Data into Training Set & Test Set
• Feature Scaling
• Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
• Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)

Information can be in different structures, for example, mathematical, straight-out, or time-series information, and can emerge from different sources like data sets, calculation sheets, or APIs.

• Basic Data Structure & Data Types in Python language.
• Working with data frames and Data handling packages.
• Importing Data from various file sources like csv, txt, Excel, HDFS and other files types.
• Reading and Analysis of data and various operations for data analysis.
• Exporting files into different formats.
• Data Visualization and concept of tidy data.
• Handling Missing Information.

In this capstone, students will apply their profound learning information and mastery to a certifiable test. They will utilize a library of their decision to create and test a profound learning model. Finally, they will stack and pre-process information for a genuine issue and fabricate and approve the model.

• Calls Data Capstone Project
• Finance Project : Perform EDA of stock prices. We will focus on BANK Stocks(JPMorgan, Bank Of America, Goldman Sachs, Morgan Stanley, Wells Fargo) & see how they progressed throughout the financial crisis all the way to early 2016.

Machine learning is a significant part of the developing field of information science. Using measurable techniques, calculations are prepared to make characterizations or forecasts, uncovering key knowledge inside information mining projects.

• Fundamental of descriptive Statistics and Hypothesis testing (t-test, z-test).
• Probability Distribution and analysis of Variance.
• Correlation and Regression.
• Linear Modeling.
• Advance Analytics.
• Poisson and logistic Regression

Include Decrease or Dimensionality Decrease is a procedure that lessens the time it takes to handle complex calculations. A low number of elements likewise implies that you have more extra room. These advantages are equipment related. In any case, Principal Component Analysis(PCA), the most prominent advantage is its capacity to tackle the multicollinearity issue.

• Feature Selection
• Principal Component Analysis(PCA)
• Linear Discriminant Analysis (LDA)
• Kernel PCA
• Feature Reduction

Relapse is one more regulated learning strategy that utilizes a calculation to determine the connection between reliant and free factors. Relapse models are useful for foreseeing mathematical qualities in light of various interest data, like deals income projections for a given business.

• Simple Linear Regression
• Multiple Linear Regression
• Perceptron Algorithm
• Regularization
• Recursive Partitioning (Decision Trees)
• Ensemble Models (Random Forest , Bagging & Boosting (ADA, GBM)
• Ensemble Learning Methods
• Working of Ada Boost
• AdaBoost Algorithm & Flowchart
• Gradient Boosting
• XGBoost
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Evaluating Regression Models Performance

Characterization is a directed machine learning technique where the model attempts to foresee the right mark of a given information. In characterization, the model is completely prepared to utilize the preparation information. Afterward, Logistic Regression is assessed on test information before being utilized to forecast new concealed information. 

• Logistic Regression
• K-Nearest Neighbours(K-NN)
• Support Vector Machine(SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification
• Evaluating Classification Models Performance

In machine learning, we also frequently bunch models as an initial step to figuring out a subject (informational collection) in a machine learning framework. Gathering unlabeled models is called grouping.

• K-Means Clustering
• Challenges of Unsupervised Learning and beyond K-Means
• Hierarchical Clustering

Machine learning calculations that assist clients with finding new items and administrations are known as recommender frameworks. 

• Purpose of Recommender Systems
• Collaborative Filtering

Constructed a powerful suggestions motor that coordinates work searchers with possible managers. Input information for the suggestion motor is acquired by grouping information from transferred text archives, for example, Market Basket Analysis. 

• Market Basket Analysis
• Collaborative Filtering
• Content-Based Recommendation Engine
• Popularity Based Recommendation Engine
• Anomaly Detection and Time Series Analysis

Support Learning is a sort of machine learning strategy that empowers a specialist to learn in an intelligent climate by experimentation utilizing criticism from its own decisions and encounters.

• Upper Confidence Bound (UCB)
• Thompson Sampling

Machine learning for NLP and message investigation includes measurable strategies for distinguishing grammatical features, elements, feeling, and different message parts. The methods can be communicated as a model that is then applied to other text, otherwise called managed AI.

• Spacy Basics
• Tokenization
• Stemming
• Lemmatization
• Stop-Words
• Vocabulary-and-Matching
• NLP-Basics Assessment
• TF-IDF

The Word2Vec model is utilized for Word portrayals in Vector Space, established by Tomas Mikolov and a gathering of exploration groups from Google in 2013. It is a brain network model that endeavors to make sense of the word embeddings given a text corpus. These models work utilizing settings.

• Understanding Word Vectors
• Training the Word2Vec Model
• Exploring Pre-trained Models

In AI, Grammatical form Labeling or POS Labeling is an idea of regular language handling where we relegate a tag to each word in a text in light of the setting of the text. It helps understand the linguistic parts of a text to perform different errands of regular language handling.

• POS-Basics
• Visualizing POS
• NER-Named-Entity-Recognition
• Visualizing NER
• Sentence Segmentation

Microsoft's Power BI is an interactive data visualization software program with a main emphasis on business intelligence. The Microsoft Power Platform includes it. Power BI is a group of software services, applications, and connections that combine to transform disparate data sources into coherent, engaging visuals and interactive insights.

• Power BI Installation
• Power BI Desktop
• Data Imports into Power BI
• Views in Power BI
• Building a Dashboard
• Publishing Reports
• Creating Dashboard in Power BI

Power BI's data sources are merely the places from which data is gathered, either by importation or by setting up a live service to obtain the data. Because Power BI has limited storage capacity, the data received in both cases is only in compressed form.

• Power Query
• Power Pivot
• Power View
• Power Map
• Power BI Service
• Power BI & QA
• Data Management Gateway
• Data Catalog

An Excel workbook or CSV file can have rows of data manually entered into it, or you can connect to an external data source to query and load data into your file. Once you have a file with some data, you can import it as a dataset into Power BI. For Power BI to import data from Excel workbooks, the data must be in a table or data model.

• Connect to DataSources
• Clean and Transform using Query Editor
• Advanced Datasources and transformations
• Cleaning irregularly formatted data.

We cannot use the data exactly as it is while working with a vast quantity of data. Therefore, some adjustments must be made to examine the data we are interested in. This procedure, known as Data Transformation in Power BI, can be carried out in various ways.

• Bins
• Change Datatype of column
• Combine Multiple tables
• Clusters
• Format dates
• Groups
• Hierarchies
• Joins
• Pivot table
• Query Groups
• Split Columns
• Unpivot table

Data models are made with Power BI utilizing this tool. It gives data administrators and analysts the resources to create fluid table associations. As a result, you may use enhanced data to create interactive, intricate, and perceptive graphics and reports.

• How Data Model Looks Like
• Database Normalization
• Data Tables vs Lookup Tables
• Primary Key vs Foreign Key
• Relationships vs Merged Table
• Creating Table Relationships
• Creating Snowflake Schemas
• Managing & Editing Relationships
• Active vs Inactive Relationships
• Relationship Cardinality
  • Many to Many
  • One to One
• Connecting Multiple Data Tables
• Filter Flow
• Two Way Filter
• Two Way Filters Conditions
• Hiding Fields from Report View

You may view a specific data collection from several angles with the help of Power BI Charts. These graphs show various conclusions and learnings from certain data collection. You can get reports from charts containing only one visualization or pages and pages of visualizations.

• Area Chart
• Bar chart
• Card
• Clustered Bar chart
• Clustered Column chart
• Donut chart
• Funnel chart
• Heat Map
• Line Chart
• Clustered Column and Line chart
• Line and Stacked Column chart
• Matrix
• Multi-Row Card
• Pie chart
• Ribbon chart
• Stacked Area char
• Scatter Chart
• Stacked Bar chart
• Waterfall chart
• Map
• Filled Map

Power BI filters are tools that let you quickly isolate and analyze a specific subset of your entire data collection. Filters separate the information that is now relevant from unnecessary or irrelevant facts that won't aid in your decision-making. 

• Slicer Basic
• Filters
• Advanced Filters
• Top N Filters
• Filters on Measures
• Page-Level Filters
• Report Level Filters
• Drill through Filters

Visualizations, or visuals for short, present data insights that have been found. A Power BI report may have several or just one page of visuals. Visuals from reports can be pinned to dashboards in the Power BI service.

• Visuals in PowerBI
• Create and Customize simple visualizations
• Combination charts
• Slicers
• Map visualizations
• Matrixes and tables
• Scatter charts
• Waterfall and funnel charts
• Gauges and single-number cards
• Modify colors in charts and visuals
• Z-Order
• Duplicate a report page

Through interactive, visual features, data exploration tools make data analysis simpler to show and comprehend, making it simpler to share and communicate crucial insights. Software for data visualization and business intelligence platforms like Microsoft Power BI, Qlik, and Tableau are examples of tools for data exploration.

• PowerBI Service
• QuickSights in PowerBI
• Create and configure a dashboard
• Share dashboards with organizations
• Install and configure the personal gateway

Microsoft's Power BI is a tool for business analytics that enables the creation of several dashboards and reports and has a quick processing speed for data sets with millions of rows. On the other hand, Excel is a Microsoft product with various tools and features that we may use for forecasting, graphing, and charting, as well as mathematical computations.

• Using Excel Data in PowerBI
• Upload Excel Data to PowerBI
• Import Power View and Power Pivot to PowerBI
• Connect OneDrive for Business to PowerBI

You may produce official packaged content with Power BI and then make it available as an app to a large audience. For example, you build apps in workspaces where you and your coworkers can work together on Power BI content. The resulting app can then be distributed to sizable employee groups inside your company.

• Introduction to content packs, security, and groups
• Publish PowerBI Desktop reports
• Print and export dashboards and reports
• Manually republish and refresh your data
• Introduction PowerBI Mobile
• Creating groups in PowerBI
• Build content packs
• Use content packs
• Update content packs
• Integrate OneDrive for Business with PowerBI
• Publish to web

The formula expression language, Data Analysis Expressions (DAX), is used in Excel's Power Pivot, Analysis Services, and Power BI. To execute complex computations and queries on data in connected tables and columns in tabular data models, DAX formulas comprise functions, operators, and values.

• Introduction to DAX
• DAX calculation types
• DAX functions:

                       a) Aggregate Functions

                       b) Date Functions

                       c) Logical Functions

                       d) String Functions

                       e) Trigonometric Functions

• Using variables in DAX expressions
• Table relationships and DAX
• DAX tables and filtering

You may create a data model for business analysis and connect to several data sources simultaneously with Power BI. The Power Query editor that comes with Power BI can be used to add new sources. Before the data files are imported into Power BI, it is used to alter or edit them.

• PowerBI SQL Server Integration
• PowerBI Mysql Integration
• PowerBI Excel Integration
• R Integration with PowerBI Desktop

A Power BI report is a group of data and visualizations arranged in a certain way to offer insights into your data. Reports, which frequently contain in-depth analysis, can be lengthy and include numerous pages. On the other hand, a Power BI dashboard is a group of customizable graphics on one page that gives you a rapid overview of your data.

• Objects & Charts
• Formatting Charts
• Report Interactions
• Bookmarks
• Managing Roles
• Custom Visuals
• Desktop vs Phone Layout

Artificial Intelligence Visuals refer to the use of graphical representations to illustrate and communicate AI concepts and insights. These visuals may include diagrams, flowcharts, interactive dashboards, and other graphical tools to help people better understand and use AI technologies.

Apache Hadoop is an open-source software framework for processing and storing large datasets. It can be deployed on the Amazon Web Services (AWS) cloud platform to create scalable, cost-effective big data solutions. In addition, AWS provides several managed Hadoop services, such as Amazon EMR and Redshift, to simplify Hadoop deployment on the cloud.

This module will help you understand how to configure Hadoop Cluster on AWS Cloud:

1. Introduction to Amazon Elastic MapReduce
2. AWS EMR Cluster
3. AWS EC2 Instance: Multi-Node Cluster Configuration
4. AWS EMR Architecture
5. Web Interfaces on Amazon EMR
6. Amazon S3
7. Executing MapReduce Job on EC2 & EMR
8. Apache Spark on AWS, EC2 & EMR
9. Submitting Spark Job on AWS
10. Hive on EMR
11. Available Storage types: S3, RDS & DynamoDB
12. Apache Pig on AWS EMR
13. Processing NY Taxi Data using SPARK on Amazon EMR

Learning Big Data and Hadoop involves acquiring skills in handling massive datasets using distributed computing frameworks. Hadoop is an open-source software framework that supports the processing and storage of big data. Learning Hadoop involves understanding its architecture, components, and tools, such as HDFS, MapReduce, Hive, Sqoop, Pig, and Oozie.

This module will help you understand Big Data:

1. Common Hadoop ecosystem components
2. Hadoop Architecture
3. HDFS Architecture
4. Anatomy of File Write and Read
5. How MapReduce Framework works
6. Hadoop high-level Architecture
7. MR2 Architecture
8. Hadoop YARN
9. Hadoop 2.x core components
10. Hadoop Distributions
11. Hadoop Cluster Formation

Hadoop Architecture is a distributed computing model that allows for storing and processing large data sets across clusters of computers. The Hadoop Distributed File System (HDFS) is the primary storage component of Hadoop Architecture. It is designed to store and manage large data sets across a cluster of machines.

This module will help you to understand Hadoop & HDFS Cluster Architecture:

1. Configuration files in Hadoop Cluster (FSimage & edit log file)
2. Setting up of Single & Multi-node Hadoop Cluster
3. HDFS File permissions
4. HDFS Installation & Shell Commands
5. Daemons of HDFS
   1. Node Manager
   2. Resource Manager
   3. NameNode
   4. DataNode
   5. Secondary NameNode
   6.  YARN Daemons
   7. HDFS Read & Write Commands
   8. NameNode & DataNode Architecture
   9. HDFS Operations
   10. Hadoop MapReduce Job
   11. Executing MapReduce Job

Hadoop MapReduce is a programming model and software framework for processing large data sets in parallel. It is a core component of Hadoop and allows for distributed processing of large data sets across clusters of computers. The framework consists of two main functions: Map and Reduce.

This module will help you to understand Hadoop MapReduce framework:

1. How MapReduce works on HDFS data sets
2. MapReduce Algorithm
3. MapReduce Hadoop Implementation
4. Hadoop 2.x MapReduce Architecture
5. MapReduce Components
6. YARN Workflow
7. MapReduce Combiners
8. MapReduce Partitioners
9. MapReduce Hadoop Administration
10. MapReduce APIs
11. Input Split & String Tokenizer in MapReduce
12. MapReduce Use Cases on Data sets

Apache Hive is a data warehousing and SQL-like query language for Hadoop. It allows users to access and manage structured and semi-structured data stored in Hadoop Distributed File System (HDFS) using SQL-like queries. In addition, hive translates queries into MapReduce jobs for execution on Hadoop.

1. Hive Installation
2. Hive Data types
3. Hive Architecture & Components
4. Hive Meta Store
5. Hive Tables(Managed Tables and External Tables)
6. Hive Partitioning & Bucketing
7. Hive Joins & Sub Query
8. Running Hive Scripts
9. Hive Indexing & View
10. Hive Queries (HQL); Order By, Group By, Distribute By, Cluster By, Examples
11. Hive Functions: Built-in & UDF (User Defined Functions)
12. Hive ETL: Loading JSON, XML, Text Data Examples
13. Hive Querying Data
14. Hive Tables (Managed & External Tables)
15. Hive Used Cases
16. Hive Optimization Techniques
    1. Partioning(Static & Dynamic Partition) & Bucketing
    2. Hive Joins > Map + BucketMap + SMB (SortedBucketMap) + Skew
    3. Hive FileFormats ( ORC+SEQUENCE+TEXT+AVRO+PARQUET)
    4. CBO
    5. Vectorization
    6. Indexing (Compact + BitMap)
    7. Integration with TEZ & Spark
17. Hive SerDer ( Custom + InBuilt)
18. Hive integration NoSQL (HBase + MongoDB + Cassandra)
19. Thrift API (Thrift Server)
20. Hive LATERAL VIEW
21. Incremental Updates & Import in Hive  Hive Functions: 
    1.  LATERAL VIEW EXPLODE   
    2. 2) LATERAL VIEW JSON_TUPLE ...........others...
22. Hive SCD Strategies :1) Type - 1      2) Type – 2         3) TYpe - 3
23. UDF, UDTF & UDAF
24. Hive Multiple Delimiters
25. XML & JSON Data Loading HIVE.
26. Aggregation & Windowing Functions in Hive
27. Hive integration NoSQL(HBase + MongoDB + Cassandra)
28. Hive Connect with Tableau

Apache Sqoop is a tool designed to transfer data between Hadoop and relational databases. It provides a command-line interface for importing and exporting data to and from Hadoop Distributed File System (HDFS) and other relational databases.

1. Sqoop Installation
2. Loading Data form RDBMS using Sqoop
3. Fundamentals & Architecture of Apache Sqoop
4. Sqoop Tools
   1. Sqoop Import & Import-All-Table
   2. Sqoop Job
   3. Sqoop Codegen
   4. Sqoop Incremental Import & Incremental Export
   5. Sqoop  Merge
   6. Sqoop : Hive Import
   7. Sqoop Metastore
   8. Sqoop Export
5. Import Data from MySQL to Hive using Sqoop
6. Sqoop: Hive Import
7. Sqoop Metastore
8. Sqoop Use Cases
9. Sqoop- HCatalog Integration
10. Sqoop Script
11. Sqoop Connectors
12. Batch Processing in Sqoop
13. SQOOP Incremental Import
14. Boundary Queries in Sqoop
15. Controlling Parallelism in Sqoop
16. Import Join Tables from SQL databases to Warehouse using Sqoop
17. Sqoop Hive/HBase/HDFS integration

Apache Pig is a high-level platform for creating MapReduce programs used with Hadoop. It provides a scripting language called Pig Latin, which simplifies the development of complex MapReduce programs. In addition, pig Latin is translated into MapReduce jobs for execution on Hadoop.

1. Pig Architecture
2. Pig Installation
3. Pig Grunt shell
4. Pig Running Modes
5. Pig Latin Basics
6. Pig LOAD & STORE Operators
7. Diagnostic Operators
   1. DESCRIBE Operator
   2. EXPLAIN Operator
   3. ILLUSTRATE Operator
   4. DUMP Operator
8. Grouping & Joining
   1. GROUP Operator
   2. COGROUP Operator
   3. JOIN Operator
   4. CROSS Operator
9. Combining & Splitting
   1. UNION Operator
   2. SPLIT Operator
10. Filtering
    1. FILTER Operator
    2. DISTINCT Operator
    3. FOREACH Operator
11. Sorting
    1. ORDERBYFIRST
    2. LIMIT Operator
12. Built in Fuctions
    1. EVAL Functions
    2. LOAD & STORE Functions
    3. Bag & Tuple Functions
    4. String Functions
    5. Date-Time Functions
    6. MATH Functions
13. Pig UDFs (User Defined Functions)
14. Pig Scripts in Local Mode
15. Pig Scripts in MapReduce Mode
16. Analysing XML Data using Pig
17. Pig Use Cases (Data Analysis on Social Media sites, Banking, Stock Market & Others)
18. Analysing JSON data using Pig
19. Testing Pig Sctipts

Confluent Kafka is a distributed streaming platform for building real-time data pipelines and streaming applications. It provides various APIs and services such as Kafka Connect, Kafka Streams, and Schema Registry to simplify the development of real-time data processing applications.

1. KAFKA Confluent HUB
2. KAFKA Confluent Cloud
3. KStream APIs
4. Difference between Apache KAFKA / Confluence KAFKA
5. KSQL (SQL Engine for Kafka)
6. Developing Real-time application using KStream APIs
7. KSQL (SQL Engine for Kafka)
8. Kafka Connectors
9. Kafka REST Proxy
10. Kafka Offsets

An Orchestration Engine is a tool that automates and manages data processing workflows in big data applications. It provides a graphical user interface for designing and scheduling ETL (Extract, Transform, Load) jobs. In addition, it can handle batch and real-time data processing jobs across multiple clusters of machines.

Apache Oozie is a workflow scheduler system that manages and schedules Hadoop jobs. It is a web-based application with a graphical user interface for designing and scheduling workflows for Hadoop jobs. Oozie supports various Hadoop components such as HDFS, MapReduce, Hive, Pig, and Sqoop.

1. Oozie Introduction
2. Oozie Workflow Specification
3. Oozie Coordinator Functional Specification
4. Oozie H-catalog Integration
5. Oozie Bundle Jobs
6. Oozie CLI Extensions
7. Automate MapReduce, Pig, Hive, Sqoop Jobs using Oozie
8. Packaging & Deploying an Oozie Workflow Application

Apache Airflow is an open-source platform to create, schedule, and monitor workflows. It provides a flexible architecture that allows developers to create custom workflows and integrate them with third-party services. In addition, airflow supports various data processing frameworks like Hadoop, Spark, and Hive.

1. Apache Airflow Installation
2. Work Flow Design using Airflow
3. Airflow DAG
4. Module Import in Airflow
5. Airflow Applications
6. Docker Airflow
7. Airflow Pipelines
8. Airflow KUBERNETES Integration
9. Automating Batch & Real Time Jobs using Airflow
10. Data Profiling using Airflow
11. Airflow Integration:
    1. AWS EMR
    2. AWS S3
    3. AWS Redshift
    4. AWS DynamoDB
    5. AWS Lambda
    6. AWS Kines
12. Scheduling of PySpark Jobs using Airflow
13. Airflow Orchestration
14. Airflow Schedulers & Triggers
15. Gantt Chart in Apache Airflow
16. Executors in Apache Airflow
17. Airflow Metrices

Apache Spark is a distributed computing framework used for large-scale data processing. It provides an in-memory processing engine that allows faster data processing than traditional big data processing tools. In addition, spark supports various data processing tasks such as batch processing, real-time streaming, and machine learning.

1. Spark RDDs Actions & Transformations.
2. Spark SQL : Connectivity with various Relational sources & its convert it into Data Frame using Spark SQL.
3. Spark Streaming
4. Understanding role of RDD
5. Spark Core concepts : Creating of RDDs: Parrallel RDDs, MappedRDD, HadoopRDD, JdbcRDD.
6. Spark Architecture & Components.

???????AWS provides a range of big data-related services on its cloud platform. These include data storage and processing services such as Amazon S3, Amazon EMR, Amazon Redshift, and Amazon Athena, as well as real-time data processing services such as Amazon Kinesis and AWS Lambda.???????

• AWS Lambda:
  • AWS Lambda Introduction
  • Creating Data Pipelines using AWS Lambda & Kinesis
  • AWS Lambda Functions
  • AWS Lambda Deployment

• • AWS GLUE :
    • GLUE Context
    • AWS Data Catalog
    • AWS Athena
    • AWS Quiksight

• • AWS Kinesis
  • AWS S3
  • AWS Redshift
  • AWS EMR & EC2
  • AWS ECR & AWS Kubernetes

Docker and Kubernetes are containerization technologies that deploy and manage applications in production environments. By containerizing big data applications, developers can create scalable, portable, and consistent deployment environments that can be easily managed with Kubernetes orchestration.

1. How to manage & Monitor Apache Spark on Kubernetes
2. Spark Submit Vs Kubernetes Operator
3. How Spark Submit works with Kubernetes
4. How Kubernetes Operator for Spark Works.
5. Setting up of Hadoop Cluster on Docker
6. Deploying IMR , Sqoop & Hive Jobs inside Hadoop Dockerized environment.

Docker is an open-source containerization technology for packaging applications and their dependencies into lightweight, portable containers. It simplifies the deployment process of big data applications by providing an isolated environment for running applications and ensuring consistency across multiple deployment environments.

1) Docker Installation

2) Docker Hub

3) Docker Images

4) Docker Containers & Shells

5) Working with Docker Containers

6) Docker Architecture

7) Docker Push & Pull containers

8) Docker Container & Hosts

9) Docker Configuration

10) Docker Files (DockerFile)

11) Docker Building Files

12) Docker Public Repositories

13) Docker Private Registeries

14) Building WebServer using DockerFile

15) Docker Commands

16) Docker Container Linking?

17) Docker Storage

18) Docker Networking

19) Docker Cloud

20) Docker Logging

21) Docker Compose

22) Docker Continuous Integration

23) Docker Kubernetes Integration

24) Docker Working of Kubernetes

25) Docker on AWS

Kubernetes is an open-source container orchestration platform for automating containerized applications' deployment, scaling, and management. It provides a scalable and highly available infrastructure for big data applications, making managing and monitoring them in production environments easier.

1) Overview

2) Learn Kubernetes Basics

3) Kubernetes Installation

4) Kubernetes Architecture

5) Kubernetes Master Server Components

a) etcd

b) kube-apiserver

c) kube-controller-manager

d) kube-scheduler

e) cloud-controller-manager

6) Kubernetes Node Server Components

a) A container runtime

b) kubelet

c) kube-proxy

d) kube-scheduler

e) cloud-controller-manager

7) Kubernetes Objects & Workloads

a) Kubernetes Pods

b) Kubernetes Replication Controller & Replica Sets

8) Kubernetes Images

9) Kubernetes Labels & Selectors

10) Kubernetes Namespace

11) Kubernetes Service

12) Kubernetes Deployments

a) Stateful Sets

b) Daemon Sets

c) Jobs & Cron Jobs

13) Other Kubernetes Components:

a) Services

b) Volume & Persistent Volumes

c) Lables, Selectors & Annotations

d) Kubernetes Secrets

e) Kubernetes Network Policy

14) Kubernetes API

15) Kubernetes Kubectl

16) Kubernetes Kubectl Commands

17) Kubernetes Creating an App

18) Kubernetes App Deployment

19) Kubernetes Autoscaling

20) Kubernetes Dashboard Setup

21) Kubernetes Monitoring

22) Federation using kubefed

The last phase of the training will cover PySpark, a Python-based API for Apache Spark, and its various components. In addition, it will cover PySpark Core, RDD, DataFrames, DataSets, Streaming, GraphX, and best practices for deploying big data applications using Docker and Kubernetes.

• CREATING DATA PIPELINES USING CLOUD & ON-PREMISE INFRASTRUCTURES
• HYBRID INFRASTRUCTURES
• Automation Test Cases(TDD & BDD Test Cases for Spark Applications)
• Data Security
• Data Governance
• Deployment Automation using CI/CD Pipelines with Docker & KUBERNETES.
• DESIGNING BATCH & REAL-TIME APPLICATION
• LATENCY & OPTIMIZATION in REAL-TIME APPLICATIONS
• Resolving Latency & Optimization issues using other Streaming Platforms like :

The introduction will provide an overview of Hadoop & Apache PySpark's role in big data processing. It will also cover both advantages over other big data processing frameworks.

Overview of Hadoop & Apache PySpark 

This will cover the introduction to the Hadoop ecosystem with a little insight into Apache Hive.

Introduction to Big Data and Data Science–

• Learn about big data and see examples of how data science can leverage big data
• Performing Data Science and Preparing Data – explore data science definitions and topics, and the process of preparing data

PySpark Core is the fundamental API of PySpark, which provides various functionalities for distributed data processing. It includes data structures such as RDD and DataFrame, and transformation and action operations for manipulating data.

1) Introduction

2) PySpark Installation

3) PySpark & its Features

    > Speed

    > Reusability

    > Advance Analytics

    > In Memory Computation

    > RealTime Stream processing

    > Lazy Evaluation

    > Dynamic in Nature

    > Immutability

    > Partitioning

4) PySpark with Hadoop

5) PySpark Components

6) PySpark Architecture

PySpark RDD is a fundamental data structure representing a distributed collection of objects across the cluster. It allows for parallel processing of data and provides fault tolerance.

1) RDD Overview

2) RDD Types (Ways to Create RDD):

• Parallelized RDDs(PairRDDs) > RDD from Collection Objects : Example: map() & flatMap() function so that we able to perform groupByKey() & others....
• RDD from External Datasets (CSV,JSON,XML.....)
  • rowRDD
  • schemaRDD (Adding schema in RDD particularly Dataframes)

3) RDD Operations:

• Basic Operations like map() & flatMap() to convert into pairRDD.
• Actions
• Transformations : 

PySpark provides various transformation and action operations for manipulating data. Transformations create a new RDD from an existing one, whereas actions return a value or write data to external storage.

Passing Functions to PySpark

Working with Key-Value Pairs (pairRDDs)

SuffleRDD : ShuffleOperations Background & Performance Impact

RDD Persistence & Unpersist

PySpark Deployment :

    1) Local

    2) Cluster Modes:

        a) Client mode

        b) Cluster mode

Shared Variables:

    1) Broadcast Variables

    2) Accumulators

Launching PySpark Jobs from Java/Scala

Unit Test Cases

PySpark API :

    1) PySpark Core Module

    2) PySpark SQL Module

    3) PySpark Streaming Module

    4) PySpark ML Module

Integrating with various Datasources (SQL+ NoSQL + Cloud)

PySpark DataFrames are similar to tables in a relational database and provide a structured data view. PySpark SQL is a module in PySpark that allows for querying structured data using SQL.

Dataframes: 

1) Overview

• An introduction to the PySpark framework in cluster computing space.
• Reading data from different PySpark sources.
• PySpark Dataframes Actions & Transformations.
• PySpark SQL & Dataframes
• Basic Hadoop functionality
• Windowing functions in Hive
• PySpark Architecture and Components
• PySpark SQL and Dataframes
• PySpark Data frames
• PySpark SQL
• Running PySpark on cluster
• PySpark Performance Tips

2) Creating Dataframes using PySparkSession

3) Untyped Transformations

4) Running SQL Queries Programmatically

5) Global Temporary View

6) Interoperating with RDDs:

1. Inferring Schema using Reflection
2. Programatically specifying the Schema

7) Aggregations:

1. Untyped User-Defined Aggregate Functions
2. Type-Safe User-Defined Aggregare Functions

PySpark allows for processing data from various data sources such as CSV, JSON, and Parquet into a DataFrame. This module will cover loading data from different sources into a data frame.

1) Generic LOAD/Save Functions

• Manually Specifying Options
• Run SQL on files directly (avro/parquet files)
• Saving to Persistent Tables ( persistent tables into HiveMetastore)
• Bucketing, Sorting & Partitioning (  repartition() &  coalesc() )

 2) Parquet Files

1.  Loading Data Programatically
2.  Partition Discovery
3.  Schema Merging
4.  Hive Metastore Parquet Table Conversion 

• Hive Parquet/Schema Reconcialation
• Metadata Refreshing

5. Configuration

3) ORC Files

4) JSON Datasets

5) Hive Tables:

• Specifying storage format for Hive Tables
• Interacting with various versions of Hive Metastore.

a) JDBC to other databases (SQL+ NoSQL databases)

b) Troubleshooting

c) PySpark SQL Optimizer

d) Transforming Complex Datatypes

e) Handling BadRecords & Files 

f) Task Preemptionh Concurrency  for Hig

Optional>>Only for DataBricks Cloud

g) Handling Large Queries in Interactive Flows

• Only for DataBricks Cloud (Using WatchDog)

h) Skew Join Optimization

i) PySpark UDF Scala 

j) PySpark UDF Python

k) PySpark UDAF Scala

l) Peformance Tuning/Optimization PySpark Jobs:

• Caching Data in Memory
• Other Configurations like:

                      >maxPartitionBytes

                      >openCostInBytes

                     >broadcastTimeout

                     >autoBroadcastJoinThreshold

                     >partitions

• BHJ (BroadcastHashJoin)
• Serializers

m) Distributed SQL Engine: (Accessing PySpark SQL using JDBC/ODBC using Thrift API)

1.  Running the Thrift JDBC/ODBC server
2.  Running the PySpark SQL CLI (Accessing PySpark SQL using SQL shell)

n) PySpark for Pandas using Apache Arrow:

1. Apache Arrow in PySpark (To convert PySpark data frames into Python data frames)
2. Conversion to/from Pandas
3. Pandas UDFs

o) PySpark SQL compatibility with Apache Hive

p) Working with DataFrames Python & Scala

q) Connectivity with various DataSources

PySpark DataSets are an extension of DataFrames and provide type safety and optimization opportunities. This module will cover the differences between DataFrames and DataSets and when to use them.

a) Introduction to DataSets

b) DataSet API - DataSets Operators

c) DataSet API - Typed Tranformations

d) Datasets API - Basic Actions

e) DataSet API - Structured Query with Encoder

f) Windowing & Aggregation DataSet API

g) DataSet Checking & Persistence

h) DataSet Checkpointing

i) Datsets Vs Dataframes Vs RDDs

PySpark Streaming allows for processing real-time streaming data using RDDs. In addition, it provides a scalable and fault-tolerant system for processing streaming data.
 

PySpark Streaming also supports DStreams based on RDD APIs, allowing for parallel data processing across a cluster.

a) Overview

b) Example to demonstrate DStreams

c) PySpark Streaming Basic Concepts:

1. Linking
2. Initialize Streaming Context
3. Discretized Streams (DStreams)
4. Input DStreams & Receivers
5. Transformations on DStreams
6. Output Operations on DStreams : print(), saveAsTextFile(), saveAsObjectFiles(), saveAsHadoopFiles(), foreachRDD()
7. Dataframe & SQL Operations on DStreams (Converting DStreams into Dataframe)
8. Streaming Machine Learning on streaming data
9. Caching/Persistence
10. Checkpointing
11. Accumulators, Broadcast Variables, and Checkpoints
12. Deploying PySpark Streaming Applications.
13. Monitoring PySpark Streaming Applications

d) Performance Tuning PySpark Streaming Applications:

1. Reducing the Batch Processing Times
2. Setting the Right Batch Interval
3. Memory Tuning

e) Fault Tolerance Semantics

f) Integration:

1. Kafka Integration
2. Kinesis Integration
3. Flume Integration

g) Custom Receivers : creating client/server application PySpark Streaming.

PySpark GraphX is a distributed graph processing framework that allows for graph computation on a large scale. This module will cover the various operations and transformations available in GraphX.

a) Overview

b) Example to demonstrate GraphX

c) PropertyGraph: Example Property Graph

d) Graph Operators:

1. Summary List of Operators
2. Property Operators
3. Structural Operators
4. Join Operators
5. Neighborhood Aggregation:

             >Aggregation Messages (aggregate messages)

            >Map Reduce Triplets Transition Guide (Legacy)

            >Computing Degree Information

             >Collecting Neighbours

6. Caching & Uncaching

e) Pregel API

f) Graph Builders

g) Vertex & Edge RDDs:

1. VertexRDDs
2. EdgeRDDs

h) Optimisation Representation

i) Graph Algorithms

1. PageRank
2. Connected Components
3. Triangle Counting

j) Examples

k) GraphFrames & GraphX

This module will cover best practices for deploying big data applications using Docker and Kubernetes. In addition, it will provide an overview of containerization and container orchestration technologies.

• How to manage & Monitor Apache Spark on Kubernetes
• Spark Submit Vs Kubernetes Operator
• How Spark Submit works with Kubernetes
• How Kubernetes Operator for Spark Works.
• Setting up of Hadoop Cluster on Docker
• Deploying MR, Sqoop & Hive Jobs inside Hadoop Dockerized environment.

This module will cover best practices for PySpark development and deployment. In addition, it will provide tips and tricks for optimizing PySpark code and performance.

• Building & running applications using PySpark API.
• PySpark SQL with mySQL (JDBC) source :
• Now that we have PySpark SQL experience with CSV and JSON, connecting and using a MySQL database will be easy. So, let’s cover how to use PySpark SQL with Python and a MySQL database input data source.
• Overview
• We’re going to load some NYC Uber data into a database. Then, we’re going to fire up PySpark with a command-line argument to specify the JDBC driver needed to connect to the JDBC data source. We’ll make sure we can authenticate and then start running some queries.

** will also be going through some of the concepts like caching and UDFs.

The last training phase will conclude with a case study and Q&A session. Participants will be able to apply their knowledge of PySpark and ask any questions.

A real-world business case built on top of PySpark API. 

1) Web Server Log Analysis with Apache PySpark – use PySpark to explore a NASA Apache web server log.

2) Introduction to Machine Learning with Apache PySpark – use PySpark’s MLB Machine Learning library to perform collaborative filtering on a movie dataset

3) PySpark SQL with New York City Uber Trips CSV Source: PySpark SQL uses a type of Resilient Distributed Dataset called DataFrames which are composed of Row objects accompanied by a schema. The schema describes the data types of each column. A DataFrame may be considered similar to a table in a traditional relational database.

Methodology

We’re going to use the Uber dataset and the PySpark-CSV package available from PySpark Packages to make our lives easier. The PySpark-CSV package is described as a “library for parsing and querying CSV data with Apache PySpark, for PySpark SQL and DataFrames” This library is compatible with PySpark 1.3 and above.

 PySpark SQL with New York City Uber Trips CSV Source

Software Required :

·         VMWare Workstation

·         Ubuntu ISO Image setup on Virtual Environment(VMWare)

·         Cloudera Quickstart VM (version: 5.3.0)

·         Putty Client

·         WinSCP

·         Hadoop software version 2.6.6

·         PySpark 2.x

Data Analytics Certification is designed to build your expertise in Data Science concepts like Machine Learning, Python, Big Data Analytics using Spark, Reporting tools like Power BI, Tableau. The course is designed for professionals looking for a 360 degree change in Data Engineering, Data Analytics and Data Visualization.

After the completion of Data Analytics Course, you will be able to:

• Understand Scala & Apache Spark implementation
• Spark operations on Spark Shell
• Spark Driver & its related Worker Nodes
• Spark + Flume Integration
• Setting up Data Pipeline using Apache Flume, Apache Kafka & Spark Streaming
• Spark RDDs and Spark Streaming
• Spark MLib : Creating Classifiers & Recommendations systems using MLib 
• Spark Core concepts : Creating of RDDs: Parrallel RDDs, MappedRDD, HadoopRDD, JdbcRDD.
• Spark Architecture & Components
• Spark SQL experience with CSV , XML & JSON
• Reading data from different Spark sources
• Spark SQL & Dataframes
• Develop and Implement various Machine Learning Algorithms in daily practices & Live Environment
• Building Recommendation systems and Classifiers
• Perform various type of Analysis (Prediction & Regression)
• Implement plotting & graphs using various Machine Learning Libraries
• Import data from HDFS & Implement various Machine Learning Models
• Building different Neural networks using NumPy and TensorFlow
• Power BI Visualization
• Power BI Components
• Power BI Transformations
• Dax functions
• Data Exploration and Mapping
• Designing Dashboards
• Time Series, Aggregation & Filters

We at Gyansetu understand that teaching any course is not difficult but to make someone job-ready is the essential task. That's why we have prepared capstone projects which will drive your learning through real-time industry scenarios and help you clearing interviews.

All the advanced level topics will be covered at Gyansetu in a classroom/online Instructor-led mode with recordings.

No prerequisites. This course is for beginners.

Gyansetu is providing complimentary placement service to all students. Gyansetu Placement Team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training.

• Our placement team will add Data Analytics skills & projects to your CV and update your profile on Job search engines like Naukri, Indeed, Monster, etc. This will increase your profile visibility in top recruiter search and ultimately increase interview calls by 5x.
• Our faculty offers extended support to students by clearing doubts faced during the interview and preparing them for the upcoming interviews.
• Gyansetu’s Students are currently working in Companies like Sapient, Capgemini, TCS, Sopra, HCL, Birlasoft, Wipro, Accenture, Zomato, Ola Cabs, Oyo Rooms, etc.

• Gyansetu trainers are well known in Industry; who are highly qualified and currently working in top MNCs.
• We provide interaction with faculty before the course starts.
• Our experts help students in learning Technology from basics, even if you are not good at basic programming skills, don’t worry! We will help you.
• Faculties will help you in preparing project reports & presentations.
• Students will be provided Mentoring sessions by Experts.