Data Analytics Course in Gurgaon, Delhi - Job Oriented

• 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

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

• 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)

• 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.

• 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.

• 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

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

• 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

• 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

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

• Purpose of Recommender Systems
• Collaborative Filtering

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

• Upper Confidence Bound (UCB)
• Thompson Sampling

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

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

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

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

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

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

• 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

• 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

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

• 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

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

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

• 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

• 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

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

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

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

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

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

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

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

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

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

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

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

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

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 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

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.

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

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

• 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 :

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

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

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 : 

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)

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

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

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

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.

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

• 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.

• 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.

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 Big Data 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.