Big Data, Data Science Training - Combo Course

Name: Big Data, Data Science Training - Combo Course
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Our Big Data, Data Science Master program lets you gain proficiency in Big Data & Data Science. You will work on real world projects in Hadoop development, administration, testing, analyst, HDFS, MapReduce, Hive, Pig, Spark, Storm, Scala, Data analytics, Splunk, NoSQL databases, and more.

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About Course

This is an all-inclusive Big Data and Data Science Course that includes in-depth study of Hadoop and its ecosystem, the various programming languages, NoSQL database training along with business intelligence, statistics and probability. Taking this all-in-one 16 courses will equip you with all the skills needed to be a Data Scientist.

List of Courses included:

Hadoop Developer Training
Hadoop Analyst Training
Hadoop Administration Training
Hadoop Testing Training
Apache Spark And Scala Training
Apache Storm Training
Apache Solr Training
R Programming Training
Mahout Training
Data Science Training
Splunk Training
Splunk Admin Training
HBase Training
Cassandra Training
CouchBase Training
MongoDB Training

What you will learn in this Training Course?

Learn Hadoop basics, HDFS and MapReduce framework
Write MapReduce programs and deploy on Hadoop clusters
Design and develop applications for Big Data using Hadoop framework
Work on data analytics using Hive, Pig and YARN
Learn about Apache Storm, its architecture and Installation
Storm topology, Logic Dynamics, and its components
Learn Apache Solr and its scalable fault-tolerant features
Apache Solr Cloud configuration for distributed indexing and search
Learn about the concepts of Tables and Data Analysis
Deploy data conversion, collection and interpretation
Learn Splunk Search, visualizations techniques
Create Reports and different Chart Types
Learn NoSQL, its scope and various concepts
Understand Cassandra, MongoDB and HBase

Who should take this Training Course?

Software Developers, Data Scientist, Statistical Analysts

Information Architects, Project Managers, BI professionals.

What are the prerequisites for taking this Training Course?

Anybody can take this Training Course regardless of their prior skills.

Why should you take this Big Data, Data Science Combo Training Course?

Big data professionals are in huge demand and due to this there are not enough qualified professionals to meet the industry requirement. Taking this all-in-one Combo Course can prepare you to meet the challenges of the industry in the Data Scientist role. This will help you grow your career and go for big salary hikes too.

How Big is Big Data – Find out and Get Ahead!

Big Data Hadoop Course Content

Introduction to Big Data & Hadoop and its Ecosystem, Map Reduce and HDFS

What is Big Data, Where does Hadoop fit in, Hadoop Distributed File System – Replications, Block Size, Secondary Namenode, High Availability, Understanding YARN – ResourceManager, NodeManager, Difference between 1.x and 2.x

Hadoop Installation & setup

Hadoop 2.x Cluster Architecture , Federation and High Availability, A Typical Production Cluster setup , Hadoop Cluster Modes, Common Hadoop Shell Commands, Hadoop 2.x Configuration Files, Cloudera Single node cluster

Deep Dive in Mapreduce

How Mapreduce Works, How Reducer works, How Driver works, Combiners, Partitioners, Input Formats, Output Formats, Shuffle and Sort, Mapside Joins, Reduce Side Joins, MRUnit, Distributed Cache

Lab exercises:

Working with HDFS, Writing WordCount Program, Writing custom partitioner, Mapreduce with Combiner , Map Side Join, Reduce Side Joins, Unit Testing Mapreduce, Running Mapreduce in LocalJobRunner Mode

Graph Problem Solving

What is Graph, Graph Representation, Breadth first Search Algorithm, Graph Representation of Map Reduce, How to do the Graph Algorithm, Example of Graph Map Reduce,

Exercise 1: Exercise 2:Exercise 3:

Detailed understanding of Pig

A. Introduction to Pig

Understanding Apache Pig, the features, various uses and learning to interact with Pig

B. Deploying Pig for data analysis

The syntax of Pig Latin, the various definitions, data sort and filter, data types, deploying Pig for ETL, data loading, schema viewing, field definitions, functions commonly used.

C. Pig for complex data processing

Various data types including nested and complex, processing data with Pig, grouped data iteration, practical exercise

D. Performing multi-dataset operations

Data set joining, data set splitting, various methods for data set combining, set operations, hands-on exercise

E. Extending Pig

Understanding user defined functions, performing data processing with other languages, imports and macros, using streaming and UDFs to extend Pig, practical exercises

F. Pig Jobs

Working with real data sets involving Walmart and Electronic Arts as case study

Detailed understanding of Hive

A. Hive Introduction

Understanding Hive, traditional database comparison with Hive, Pig and Hive comparison, storing data in Hive and Hive schema, Hive interaction and various use cases of Hive

B. Hive for relational data analysis

Understanding HiveQL, basic syntax, the various tables and databases, data types, data set joining, various built-in functions, deploying Hive queries on scripts, shell and Hue.

C. Data management with Hive

The various databases, creation of databases, data formats in Hive, data modeling, Hive-managed Tables, self-managed Tables, data loading, changing databases and Tables, query simplification with Views, result storing of queries, data access control, managing data with Hive, Hive Metastore and Thrift server.

D. Optimization of Hive

Learning performance of query, data indexing, partitioning and bucketing

E. Extending Hive

Deploying user defined functions for extending Hive

F. Hands on Exercises – working with large data sets and extensive querying

Deploying Hive for huge volumes of data sets and large amounts of querying

G. UDF, query optimization

Working extensively with User Defined Queries, learning how to optimize queries, various methods to do performance tuning.

Impala

A. Introduction to Impala

What is Impala?, How Impala Differs from Hive and Pig, How Impala Differs from Relational Databases, Limitations and Future Directions, Using the Impala Shell

B. Choosing the Best (Hive, Pig, Impala)

C. Modeling and Managing Data with Impala and Hive

Data Storage Overview, Creating Databases and Tables, Loading Data into Tables, HCatalog, Impala Metadata Caching

D. Data Partitioning

Partitioning Overview, Partitioning in Impala and Hive

(AVRO) Data Formats

Selecting a File Format, Tool Support for File Formats, Avro Schemas, Using Avro with Hive and Sqoop, Avro Schema Evolution, Compression

Introduction to Hbase architecture

What is Hbase, Where does it fits, What is NOSQL

Apache Spark

A. Why Spark? Working with Spark and Hadoop Distributed File System

What is Spark, Comparison between Spark and Hadoop, Components of Spark

B. Spark Components, Common Spark Algorithms-Iterative Algorithms, Graph Analysis, Machine Learning

Apache Spark- Introduction, Consistency, Availability, Partition, Unified Stack Spark, Spark Components, Scalding example, mahout, storm, graph

C. Running Spark on a Cluster, Writing Spark Applications using Python, Java, Scala

Explain python example, Show installing a spark, Explain driver program, Explaining spark context with example, Define weakly typed variable, Combine scala and java seamlessly, Explain concurrency and distribution., Explain what is trait, Explain higher order function with example, Define OFI scheduler, Advantages of Spark, Example of Lamda using spark, Explain Mapreduce with example

Hadoop Cluster Setup and Running Map Reduce Jobs

Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster

Major Project – Putting it all together and Connecting Dots

Putting it all together and Connecting Dots, Working with Large data sets, Steps involved in analyzing large data

ETL Connectivity with Hadoop Ecosystem

How ETL tools work in Big data Industry, Connecting to HDFS from ETL tool and moving data from Local system to HDFS, Moving Data from DBMS to HDFS, Working with Hive with ETL Tool, Creating Map Reduce job in ETL tool, End to End ETL PoC showing big data integration with ETL tool.

Cluster Configuration

Configuration overview and important configuration file, Configuration parameters and values, HDFS parameters MapReduce parameters, Hadoop environment setup, ‘Include’ and ‘Exclude’ configuration files, Lab: MapReduce Performance Tuning

Administration and Maintenance

Namenode/Datanode directory structures and files, File system image and Edit log, The Checkpoint Procedure, Namenode failure and recovery procedure, Safe Mode, Metadata and Data backup, Potential problems and solutions / what to look for, Adding and removing nodes, Lab: MapReduce File system Recovery

Monitoring and Troubleshooting

Best practices of monitoring a cluster, Using logs and stack traces for monitoring and troubleshooting, Using open-source tools to monitor the cluster

Job Scheduler: Map reduce job submission flow

How to schedule Jobs on the same cluster, FIFO Schedule, Fair Scheduler and its configuration

Multi Node Cluster Setup and Running Map Reduce Jobs on Amazon Ec2

Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup, Running Map Reduce Jobs on Cluster

ZOOKEEPER

ZOOKEEPER Introduction, ZOOKEEPER use cases, ZOOKEEPER Services, ZOOKEEPER data Model, Znodes and its types, Znodes operations, Znodes watches, Znodes reads and writes, Consistency Guarantees, Cluster management, Leader Election, Distributed Exclusive Lock, Important points

Advance Oozie

Why Oozie?, Installing Oozie, Running an example, Oozie- workflow engine, Example M/R action, Word count example, Workflow application, Workflow submission, Workflow state transitions, Oozie job processing, Oozie security, Why Oozie security?, Job submission, Multi tenancy and scalability, Time line of Oozie job, Coordinator, Bundle, Layers of abstraction, Architecture, Use Case 1: time triggers, Use Case 2: data and time triggers, Use Case 3: rolling window

Advance Flume

Overview of Apache Flume, Physically distributed Data sources, Changing structure of Data, Closer look, Anatomy of Flume, Core concepts, Event, Clients, Agents, Source, Channels, Sinks, Interceptors, Channel selector, Sink processor, Data ingest, Agent pipeline, Transactional data exchange, Routing and replicating, Why channels?, Use case- Log aggregation, Adding flume agent, Handling a server farm, Data volume per agent, Example describing a single node flume deployment

Advance HUE

HUE introduction, HUE ecosystem, What is HUE?, HUE real world view, Advantages of HUE, How to upload data in File Browser?, View the content, Integrating users, Integrating HDFS, Fundamentals of HUE FRONTEND

Advance Impala

IMPALA Overview: Goals, User view of Impala: Overview, User view of Impala: SQL, User view of Impala: Apache HBase, Impala architecture, Impala state store, Impala catalogue service, Query execution phases, Comparing Impala to Hive

Hadoop Application Testing

Why testing is important, Unit testing, Integration testing, Performance testing, Diagnostics, Nightly QA test, Benchmark and end to end tests, Functional testing, Release certification testing, Security testing, Scalability Testing, Commissioning and Decommissioning of Data Nodes Testing, Reliability testing, Release testing

Roles and Responsibilities of Hadoop Testing Professional

Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion, ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation, User Authorization and Authentication testing (Groups, Users, Privileges etc), Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Validating new feature and issues in Core Hadoop.

Framework called MR Unit for Testing of Map-Reduce Programs

Report defects to the development team or manager and driving them to closure, Consolidate all the defects and create defect reports, Responsible for creating a testing Framework called MR Unit for testing of Map-Reduce programs.

Unit Testing

Automation testing using the OOZIE, Data validation using the query surge tool.

Test Execution

Test plan for HDFS upgrade, Test automation and result

Test Plan Strategy and writing Test Cases for testing Hadoop Application

How to test install and configure

Job and Certification Support

Cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques

R Programming Course Content

Introduction to R

R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.

R-Packages

R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.

Sorting Dataframe

R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.

Matrices and Vectors

Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.

Reading data from external files

Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.

Generating plots

Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.

Analysis of Variance (ANOVA)

Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.

K-means Clustering

K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.

Association Rule Mining

Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.

Regression in R

Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.

Analyzing Relationship with Regression

Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit

Advance Regression

Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.

Logistic Regression

Logistic Regression Mean, Logistic Regression in R.

Advance Logistic Regression

Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

Receiver Operating Characteristic (ROC)

Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.

Kolmogorov Smirnov Chart

Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.

Database connectivity with R

Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.

Integrating R with Hadoop

Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.

R Case Studies

Logistic Regression Case Study

In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) case study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

Apache Solr Course Content

Fundamentals of Search Engine and Apache Lucene

Introduction to the search engine, the Apache Lucene, understanding the inverted index, documents and fields & documents.

Analyzers in Lucene

Introduction to the various query types available in Lucene and clear understanding of these.

Exploring Apache Lucene

Understanding the prerequisites for using Apache Lucene, learning about the querying process, analyzers, scoring boosting, faceting, grouping, highlighting, the various types of geographical and spatial searches, introduction to Apache Tika.

Apache Lucene Demonstration

Demonstration of the Apache Lucene workings.

Apache Lucene advanced

Understanding the Analyzer, Query Parser in Apache Lucene, Query Object, Stopword.

Advance topics of Apache Lucene (practical)

Understanding the various aspects of Apache Lucene like Scoring, Boosting, Highlighting, Faceting and Grouping

Apache Solr

Introduction to Apache Solr, the advantages of Apache Solr over Apache Lucene, the basic system requirements for using Apache Solr, introduction to Cores in Apache Solr.

Apache Solr Indexing

Introduction to the Apache Solr indexing, index using built-in data import handler and post tool, understanding the Solrj Client and configuration of Solrj Client.

Solr Indexing continued

Demonstrating the Book Store use cases with Solr Indexing with practical examples, learning to build Schema, the field, field types, CopyField and Dynamic Field, understanding how to add, explore, update, and delete using Solrj.

Apache Solr Searching

The various aspects of Apache Solr search like sorting, pagination, an overview of the request parameters, faceting and highlighting.

Deep dive into Apache Solr

Understanding the Request Handlers, defining and mapping to search components, highlighting and faceting, updating managed schemas, request parameters hardwiring, adding fields to default search, the various types of Analyzers, Parsers, Tokenizers.

Apache Solr continued

Grouping of results in Apache Solr, Parse queries functions, fuzzy query in Apache Solr.

Extended Features

The extended features in Apache Solr, learning about Pseudo-fields, Pseudo-Joins, Spell Check, suggestions, Geospatial Search, multi-language search, stop words and synonyms.

Multicore

Understanding the concept of Multicore in Solr, the creation of Multicore in Solr, the need of Multicore, Joining of data, Replication and Ping Handler.

Administration & SolrCloud

Understanding the SolrCloud, the concept of Sharding, indexing, and replication in Apache SolrCloud, the working of Apache SolrCloud, distributed requests, reading and writng slide fault tolerance, cluster coordination using Apache ZooKeeper.

Splunk Developer Course Content

Splunk Development concepts

Introduction to Splunk, Splunk developer roles and responsibilities

Saving and Scheduling Searches

Writing Splunk query for search, sharing, saving, scheduling and exporting search results

Creating Alerts

Creation of alert, explaining alerts and viewing fired alerts

Tags and Event Types

Introduction to Tags in Splunk, deploying Tags for Splunk search, understanding event types and utility, generating and implementing event types in Search

Splunk Search Commands

Search Command study, search practices in general, detailed understanding of search, search field performance with different commands like table,multikv, rename, rex & erex

Reporting Commands

Using following commands and their functions: addcoltotals, addtotals,top, rare,stats

Splunk Reports & visualizations

Explore the available visualizations, create charts and time charts, omit null values and format results

Analyzing, Calculating and Formatting Results

Calculating and analyzing results, value conversion, round and format values, using eval command, conditional statements, filtering calculated search results

Correlating Events

Understanding Search Transactions

Enriching Data with Lookups

Learn about data lookups, example, lookup table, defining and configuring automatic lookup, deploying lookup in reports and searches

Creating Reports and Dashboards

Creating search charts, reports and dashboards

Getting started with Parsing

Working with raw data for data extraction, transformation, parsing and preview

Splunk Administration Course Content

Overview of Splunk

Introduction to the Splunk 3 tier architecture, understanding the Server settings, control, preferences and licensing, the most important components of Splunk tool, the hardware requirements, conditions for installation of Splunk.

Splunk Installation

Understanding how to install and configure Splunk, index creation, input configuration in standalone server, the search preferences, installing Splunk in the Linux environment.

Splunk Installation in Linux

Installing Splunk in the Linux environment, the various prerequisites, configuration of Splunk in Linux.

Distributed Management Console

Introduction to the Splunk Distributed Management Console, index clustering, forwarder management and distributed search in Splunk environment, providing the right authentication to users, access control.

Introduction to Splunk App

Introducing the Splunk app, managing the Splunk app, the various add-ons in Splunk app, deleting and installing apps from SplunkBase, deploying the various app permissions, deploying the Splunk app, apps on forwarder.

Splunk indexes and users

Understanding the index time configuration file and search time configuration file.

Splunk configuration files

Learning about the index time and search time configuration files in Splunk, installing the forwarders, configuring the output and inputs.conf, managing the Universal Forwarders.

Splunk Deployment Management

Deploying the Splunk tool, the Splunk deployment Server, setting up the Splunk deployment environment, deploying the clients grouping in Splunk.

Splunk Indexes

Understanding the Splunk Indexes, the default Splunk Indexes, segregating the Splunk Indexes, learning about Splunk Buckets and Bucket Classification, estimating index storage, creating new index.

User roles and authentication

Understanding the concept of role inheritance, Splunk authentications, native authentications, LDAP authentications.

Splunk Administration Environment

Splunk installation, configuration, data inputs, app management, Splunk important concepts, parsing machine-generated data, search indexer and forwarder.

Basic Production Environment

Introduction to Splunk Configuration Files, Universal Forwarder, Forwarder Management, data management, troubleshooting and monitoring.

Splunk Search Engine

Converting machine-generated data into operational intelligence, setting up Dashboard, Reports and Charts, integrating Search Head Clustering & Indexer Clustering.

Various Splunk Input Methods

Understanding the input methods, deploying scripted, Windows, network and agentless input types, fine-tuning it all.

Splunk User & Index Management

Splunk User authentication and Job Role assignment, learning to manage, monitor and optimize Splunk Indexes.

Machine Data Parsing

Understanding parsing of machine-generated data, manipulation of raw data, previewing and parsing, data field extraction.

Search Scaling and Monitoring

Distributed search concepts, improving search performance, large scale deployment and overcoming execution hurdles, working with Splunk Distributed Management Console for monitoring the entire operation.

Scala Course Content

Introduction of Scala

Introducing Scala and deployment of Scala for Big Data applications and Apache Spark analytics.

Pattern Matching

The importance of Scala, the concept of REPL (Read Evaluate Print Loop), deep dive into Scala pattern matching, type interface, higher order function, currying, traits, application space and Scala for data analysis.

Executing the Scala code

Learning about the Scala Interpreter, static object timer in Scala, testing String equality in Scala, Implicit classes in Scala, the concept of currying in Scala, various classes in Scala.

Classes concept in Scala

Learning about the Classes concept, understanding the constructor overloading, the various abstract classes, the hierarchy types in Scala, the concept of object equality, the val and var methods in Scala.

Case classes and pattern matching

Understanding Sealed traits, wild, constructor, tuple, variable pattern, and constant pattern.

Concepts of traits with example

Understanding traits in Scala, the advantages of traits, linearization of traits, the Java equivalent and avoiding of boilerplate code.

Scala java Interoperability

Implementation of traits in Scala and Java, handling of multiple traits extending.

Scala collections

Introduction to Scala collections, classification of collections, the difference between Iterator, and Iterable in Scala, example of list sequence in Scala.

Mutable collections vs. Immutable collections

The two types of collections in Scala, Mutable and Immutable collections, understanding lists and arrays in Scala, the list buffer and array buffer, Queue in Scala, double-ended queue Deque, Stacks, Sets, Maps, Tuples in Scala.

Use Case bobsrockets package

Introduction to Scala packages and imports, the selective imports, the Scala test classes, introduction to JUnit test class, JUnit interface via JUnit 3 suite for Scala test, packaging of Scala applications in Directory Structure, example of Spark Split and Spark Scala.

Spark Course Content

Introduction to Spark

Introduction to Spark, how Spark overcomes the drawbacks of working MapReduce, understanding in-memory MapReduce, Spark Hadoop YARN, HDFS Revision, YARN Revision, the overview of Spark and how it is better Hadoop, deploying Spark without Hadoop.

Spark Basics

Spark installation guide, working with Spark Shell, the concept of Resilient Distributed Datasets (RDD), learning to do functional programming in Spark, the architecture of Spark.

Working with RDDs in Spark

Deep dive into Spark RDDs, the RDD general operations, a read-only partitioned collection of records, using the concept of RDD for faster and efficient data processing.

Aggregating Data with Pair RDDs

Understanding the concept of Key-Value pair in RDDs, learning how Spark makes MapReduce operations faster, various operations of RDD.

Writing and Deploying Spark Applications

Comparing the Spark applications with Spark Shell, creating a Spark application using Scala or Java, deploying a Spark application, the web user interface of Spark application, a real world example of Spark and configuring of Spark.

Parallel Processing

Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions, file-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations.

Spark RDD Persistence

Understanding the RDD persistence overview, distributed persistence, RDD lineage

Basic Spark Streaming

Understanding the Spark streaming, creating a Spark stream application, processing of Spark stream, streaming request count and DStreams.

Advanced Spark Streaming

Introduction to Spark multi-batch operations, state operations, sliding window operations and advanced data sources.

Common Patterns in Spark Data Processing

Learning about the Spark common use cases, the concept of iterative algorithm in Spark, analyzing with Spark graph processing, introduction to K-Means and machine learning.

Improving Spark Performance

Introduction to various variables in Spark like shared variables, broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems.

Spark SQL and Data Frames

Learning about Spark SQL, the context of SQL in Spark for providing structured data processing, understanding the Data Frames in Spark, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL, deploying Hive on Spark as the execution engine.

Scheduling/ Partitioning

Learning about the scheduling and partitioning in Spark, scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling, Spark master high availability, standby Masters with Zookeeper, Single Node Recovery With Local File System, High Order Functions.

Capacity planning in Spark

Understanding how to design capacity planning in Spark, creation of Maps, Transformations, the concept of concurrency in Java and Scala.

Log analysis

Understanding about log analysis with Spark, first log analyzers in Spark, working with various buffers like array, compact and protocol buffer.

Apache Storm Course Content

Understanding Architecture of Storm

Big Data characteristics, understanding Hadoop distributed computing, the Bayesian Law, deploying Storm for real time analytics, the Apache Storm features, comparing Storm with Hadoop, Storm execution, learning about Tuple, Spout, Bolt.

Installation of Apache storm

Installing the Apache Storm, various types of run modes of Storm.

Introduction to Apache Storm

Understanding Apache Storm and the data model.

Apache Kafka Installation

Installation of Apache Kakfa and its configuration.

Apache Storm Advanced

Understanding of advanced Storm topics like Spouts, Bolts, Stream Groupings, Topology and its Life cycle, learning about Guaranteed Message Processing.

Storm Topology

Various Grouping types in Storm, reliable and unreliable messages, Bolt structure and life cycle, understanding Trident topology for failure handling, process, Call Log Analysis Topology for analyzing call logs for calls made from one number to another.

Overview of Trident

Understanding of Trident Spouts and its different types, the various Trident Spout interface and components, familiarizing with Trident Filter, Aggregator and Functions, a practical and hands-on use case on solving call log problem using Storm Trident.

Storm Components & classes

Various components, classes and interfaces in storm like – Base Rich Bolt Class, i RichBolt Interface, i RichSpout Interface, Base Rich Spout class and the various methodology of working with them.

Cassandra Introduction

Understanding Cassandra, its core concepts, its strengths and deployment.

Boot Stripping

Twitter Boot Stripping, detailed understanding of Boot Stripping, concepts of Storm, Storm Development Environment.

Cassandra Course Content

Advantages and Usage of Cassandra

Introduction to Cassandra, its strengths and deployment areas

CAP Theorem and No SQL DataBase

Significance of NoSQL, RDBMS Replication, Key Challenges, types of NoSQL, benefits and drawbacks, salient features of NoSQL database. CAP Theorem, Consistency.

Cassandra fundamentals, Data model, Installation and setup

Installation, introduction to Cassandra, key concepts and deployment of non relational database, column-oriented database, Data Model – column, column family,

Cassandra Configuration

Token calculation, Configuration overview, Node tool, Validators, Comparators, Expiring column, QA

Summarization, node tool commands, cluster, Indexes, Cassandra & MapReduce, Installing Ops-center

How Cassandra modelling varies from Relational database modelling, Cassandra modelling steps, introduction to Time Series modelling, comparing Column family Vs. Super Column family, Counter column family, Partitioners, Partitioners strategies, Replication, Gossip protocols, Read operation, Consistency, Comparison

Multi Cluster setup

Creation of multi node cluster, node settings, Key and Row cache, System Key space, understanding of Read Operation, Cassandra Commands overview, VNodes, Column family

Thrift/Avro/Json/Hector Client

JSON, Hector client, AVRO, Thrift, JAVA code writing method, Hector tag

Datastax installation part,• Secondary index

Cassandra management, commands of node tool, MapReduce and Cassandra, Secondary index, Datastax Installation

Advance Modelling

Rules of Cassandra data modelling, increasing data writes, duplication, and reducing data reads, modelling data around queries, creating table for data queries

Deploying the IDE for Cassandra applications

Understanding the Java application creation methodology, learning key drivers, deploying the IDE for Cassandra applications,cluster connection and data query implementation

Cassandra Administration

Learning about Node Tool Utility, cluster management using Command Line Interface, Cassandra management and monitoring via DataStax Ops Center.

Cassandra API and Summarization and Thrift

Cassandra client connectivity, connection pool internals, API, important features and concepts of Hector client, Thrift, JAVA code, Summarization.

Couchbase Course Content

Introduction to Couchbase

The Architecture of Couchbase, understanding Couchbase distributed NoSQL database engine, vBuckets for information distribution on Couchbase cluster, user and system requirements, Couchbase downloading and installation.

Single Node Implementation

Couchbase single node deployment for development purpose

Couchbase Web Console

Managing the Couchbase environment with the Web Console tool, configuring the Couchbase server and management, working with Couchbase data buckets, default bucket sizing, and administration.

Couchbase Multi Node Cluster

Methods for deploying Couchbase in multi node cluster – all Couchbase Servers on one machine and second with each Couchbase Server on own machine.

Couchbase Command-line Interface

The Couchbase Command-line Interface tools for managing and monitoring single node and multi node clusters, Severs and vBuckets, developing Reports for log data collection.

MongoDB Course Content

Introduction to NoSQL and MongoDB

RDBMS, types of relational databases, challenges of RDBMS, NoSQL database, its significance, how NoSQL suits Big Data needs, Introduction to MongoDB and its advantages, MongoDB installation, JSON features, data types and examples.

MongoDB Installation

Installing MongoDB, basic MongoDB commands and operations, MongoChef (MongoGUI) Installation, MongoDB Data types.

Importance of NoSQL

The need for NoSQL, types of NoSQL databases, OLTP, OLAP, limitations of RDBMS, ACID properties, CAP Theorem, Base property, learning about JSON/BSON, database collection & document, MongoDB uses, MongoDB Write Concern – Acknowledged, Replica Acknowledged, Unacknowledged, Journaled, Fsync.

CRUD Operations

Understanding CRUD and its functionality, CRUD concepts, MongoDB Query & Syntax, read and write queries and query optimization.

Data Modeling & Schema Design

Concepts of data modeling, difference between MongoDB and RDBMS modeling, Model tree structure, operational strategies, monitoring and backup.

Data Management & Administration

In this module you will learn MongoDB® Administration activities such as Health Check, Backup, Recovery, database sharding and profiling, Data Import/Export, Performance tuning etc.

Data Indexing and Aggregation

Concepts of data aggregation and types, data indexing concepts, properties and variations.

MongoDB Security

Understanding database security risks, MongoDB security concept and security approach, MongoDB integration with Java and Robomongo.

Working with Unstructured Data

Implementing techniques to work with variety of unstructured data like images, videos, log data, and others, understanding GridFS MongoDB file system for storing data.

What Hadoop Projects You will be working on?

Project 1 – Working with MapReduce, Hive, Sqoop

This project is involved with working on the various Hadoop components like MapReduce, Apache Hive and Apache Sqoop. Work with Sqoop to import data from relational database management system like MySQL data into HDFS. Deploy Hive for summarizing data, querying and analysis. Convert SQL queries using HiveQL for deploying MapReduce on the transferred data. You will gain considerable proficiency in Hive, and Sqoop after completion of this project.

Project 2 – Work on MovieLens data for finding top records

Data – MovieLens dataset

In this project you will work exclusively on data collected through MovieLens available rating data sets. The project involves the following important components:

You will write a MapReduce program in order to find the top 10 movies by working in the data file
Learn to deploy Apache Pig create the top 10 movies list by loading the data
Work with Apache Hive and create the top 10 movies list by loading the

Project 3 – Hadoop YARN Project – End to End PoC

In this project you will work on a live Hadoop YARN project. YARN is part of the Hadoop 2.0 ecosystem that lets Hadoop to decouple from MapReduce and deploy more competitive processing and wider array of applications. You will work on the YARN central Resource Manager. The salient features of this project include:

Importing of Movie data
Appending the data
Using Sqoop commands to bring the data into HDFS
End to End flow of transaction data
Processing data using MapReduce program in terms of the movie data, etc.

Project 4 – Partitioning Tables in Hive

This project involves working with Hive table data partitioning. Ensuring the right partitioning helps to read the data, deploy it on the HDFS, and run the MapReduce jobs at a much faster rate. Hive lets you partition data in multiple ways like:

Manual Partitioning
Dynamic Partitioning
Bucketing

This will give you hands-on experience in partitioning of Hive tables manually, deploying single SQL execution in dynamic partitioning, bucketing of data so as to break it into manageable chunks.

Project 5 – Connecting Pentaho with Hadoop Ecosystem

This project lets you connect Pentaho with the Hadoop ecosystem. Pentaho works well with HDFS, HBase, Oozie and Zookeeper. You will connect the Hadoop cluster with Pentaho data integration, analytics, Pentaho server and report designer. Some of the components of this project include the following:

Clear hands-on working knowledge of ETL and Business Intelligence
Configuring Pentaho to work with Hadoop Distribution
Loading, Transforming and Extracting data into Hadoop cluster

Project 6 – Multi-node cluster setup

This is a project that gives you opportunity to work on real world Hadoop multi-node cluster setup in a distributed environment. The major components of this project involve:

Running a Hadoop multi-node using a 4 node cluster on Amazon EC2
Deploying of MapReduce job on the Hadoop cluster

You will get a complete demonstration of working with various Hadoop cluster master and slave nodes, installing Java as a prerequisite for running Hadoop, installation of Hadoop and mapping the nodes in the Hadoop cluster.

Hadoop Multi-Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
Running Map Reduce Jobs on Cluster

Project 7 – Hadoop Testing using MR

In this project you will gain proficiency in Hadoop MapReduce code testing using MRUnit. You will learn about real world scenarios of deploying MRUnit, Mockito, and PowerMock. Some of the important aspects of this project include:

Writing JUnit tests using MRUnit for MapReduce applications
Doing mock static methods using PowerMock&Mockito
MapReduceDriver for testing the map and reduce pair

After completion of this project you will be well-versed in test driven development and will be able to write light-weight test units that work specifically on the Hadoop architecture.

Project 8 – Hadoop Weblog Analytics

Data – Weblogs

This project is involved with making sense of all the web log data in order to derive valuable insights from it. You will work with loading the server data onto a Hadoop cluster using various techniques. The various modules of this project include:

Aggregation of log data
Processing of the data and generating analytics

The web log data can include various URLs visited, cookie data, user demographics, location, date and time of web service access, etc. In this project you will transport the data using Apache Flume or Kafka, workflow and data cleansing using MapReduce, Pig or Spark. The insight thus derived can be used for analyzing customer behavior and predict buying patterns.

Project 9 – Hadoop Maintenance

This project is involved with working on the Hadoop cluster for maintaining and managing it. You will work on a number of important tasks like:

Administration of distributed file system
Checking the file system
Working with name node directory structure
Audit logging, data node block scanner, balancer
Learning about the properties of safe mode
Entering and exiting safe mode
HDFS federation and high availability
Failover, fencing, DISTCP, Hadoop file format

R Programming Project

Projects

Project Title – Restaurant Revenue Prediction

Data set – Sales

Project Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.

Data Science Project

Project 1 – Understanding Cold Start Problem in Data Science

Topics: This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the following:

Algorithms for Recommender
Ways of Recommendation
Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
Complete mastery in working with the Cold Start Problem.

Project 2 – Recommendation for Movie, Summary

Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provider data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

Recommendation for movie
Two Types of Predictions – Rating Prediction, Item Prediction
Important Approaches: Memory Based and Model-Based
Knowing User Based Methods in K-Nearest Neighbor
Understanding Item Based Method
Matrix Factorization
Decomposition of Singular Value
Data Science Project discussion
Collaboration Filtering
Business Variables Overview

Case study

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

Couchbase Project

Topics : This project involves working with the Couchbase command-line interface tools that are used for managing of clusters in a multi-node or single node setup, working with vBuckets in Couchbase server, deploying Reports for log data collection. You will gain hands-on experience in deploying commands like start, stop and report status for log collection. It also includes working with Couchbase-cli, cbcollect_info tool and so on. Upon completion of the project you will be proficient in using Couchbase CLI for managing and monitoring clusters, data replication using XDCR.

Apache Solr Project

Project – Running Function Queries on Apache Solr

Topics : In this project you will learn about the Function Queries and deploy it on the search results got in Apache Solr. You will understand how exactly the Function Queries are used to modify the search results based on certain conditions. It involves working on the index store that has dimensions of a box with arbitrary names, sort all the boxes through search and then modify the search results using Function Queries based on new parameters. Some of the query parsers used are DisMax, Extended DisMax and standard.

Splunk Developer Project

Topics: This project gives you hands-on experience in working with the Splunk tool. You will have the data set of employee details in a text file based on which you will create a dashboard and report. Then you will deploy the various Splunk commands to perform row operations, extract certain data fields, edit the event, add tags, search with tag name for event and then save the tag search. Upon completion of this project you will learn to create a searchable repository using data that is captured, correlated and indexed in real time and ultimately visualize it using dashboard, report and alert.

Splunk Administration Project

Project

Type – Field Extraction

Topics : In this project you will learn to extract fields from events using the Splunk field extraction technique. You will gain knowledge in the basics of field extractions, understand the use of field extractor, the field extraction page in Splunk web and field extract configuration in files. Learn about the regular expression and delimiters method of field extraction. Upon completion of the project you will gain expertise in building Splunk dashboard and use the extracted fields data in it to create rich visualizations in an enterprise setup.

Apache Spark – Scala Project

Project 1: Movie Recommendation

Topics – This is a project wherein you will gain hands-on experience in deploying Apache Spark for movie recommendation. You will be introduced to the Spark Machine Learning Library, a guide to MLlib algorithms and coding which is a machine learning library. Understand how to deploy collaborative filtering, clustering, regression, and dimensionality reduction in MLlib. Upon completion of the project you will gain experience in working with streaming data, sampling, testing and statistics.

Project 2: Twitter API Integration for tweet Analysis

Topics – With this project you will learn to integrate Twitter API for analyzing tweets. You will write codes on the server side using any of the scripting languages like PHP, Ruby or Python, for requesting the Twitter API and get the results in JSON format. You will then read the results and perform various operations like aggregation, filtering and parsing as per the need to come up with tweet analysis.

Project 3: Data Exploration Using Spark SQL – Wikipedia data set

Topics – This project lets you work with Spark SQL. You will gain experience in working with Spark SQL for combining it with ETL applications, real time analysis of data, performing batch analysis, deploying machine learning, creating visualizations and processing of graphs.

Apache Storm Project

Project 1. Call Log Analysis using Trident

Topics : In this project you will be working on call logs to decipher the data and gather valuable insights using Apache Storm Trident. You will extensively work with data about calls made from one number to another. The aim of this project is to resolve the call log issues with Trident stream processing and low latency distributed querying. You will gain hands-on experience in working with Spouts and Bolts along with various Trident functions, filters, aggregation, joins and grouping.

Project 2. Twitter Data Analysis using Trident

Topics : This is a project that involves working with Twitter data and processing it to extract patterns out of it. The Apache Storm Trident is the perfect framework for real-time analysis of tweets. Working with Trident you will be able to simplify the task of live Twitter feed analysis. In this project you will gain real world experience of working with Spouts, Bolts, and Trident filters, joins, aggregation, functions and grouping.

Project 3. US Presidential Election Result analysis using Trident DRPC Query

Topics : This is a project that lets you work on the US presidential election results and predict who is leading and trailing on a real-time basis. For this you exclusively work with Trident distributed Remote Procedure Call server. After completion of the project you will learn how to access data residing in a remote computer or network and deploy it for real-time processing, analysis and prediction.

Cassandra Project

Type : Deploying the IDE for Cassandra applications

Topics : This project gives you a hands-on experience in installing and working with Apache Cassandra which is a high performance and extremely scalable database for distributed data with no single point of failure. You will deploy the Java Integrated Development Environment for running Cassandra, learn about the key drivers, work with Cassandra applications in a cluster setup and implement data querying techniques.

MongoDB Project

Java is one of the most popular programming languages for working with MongoDB. This project tells you how to work with the MongoDB Java Driver, and using MongoDB as a Java Developer. Become proficient in creating a table for inserting video using Java programming. Some of the tasks and steps involved are as below–

Installation of Java
Setting up MongoDB JDBC Driver
Connecting to the database
Understanding about collections and documents
Reading and writing basics from the database
Learning about the Java Virtual Machine libraries

Sample Big Data and Data Science Video Tutorials

Hadoop Tutorial

Data Science Tutorial

Solr Tutorial

Existing Learner Feedback

Why should I learn Big Data, Data Science from Intellipaat?

Intellipaat is the pioneer of Big Data, Data Science training. Data Scientist is one of the most sought-after professional roles in the corporate world today. This Intellipaat all-in-one Combo course exclusively trains you to become a top-notch Big Data, Data Science professional. You will gain hands-on experience in mastering Big Data, Data Science beginner to advanced concepts including comprehensive study of Big Data Hadoop ecosystem, programming languages, Apache Solr, NoSQL databases like MongoDB, Cassandra, HBase, machine learning tool Splunk, Apache Mahout, advanced statistics and probability.

The entire training course content is fully aligned towards clearing the following Big Data and Data Science certification exams: CCA Spark and Hadoop Developer (CCA175), Cloudera Certified Administrator for Apache Hadoop (CCAH), Cloudera Data Scientist certification (CCP:DS), Apache Hbase certification exam CCB-400, C100DEV: MongoDB Certified Developer Associate, Apache Cassandra DataStax, Splunk Certified Power User & Admin.

This is a completely career-oriented training and it is designed by industry experts. Your training program includes real time Big Data & Data Science projects, step-by-step assignments to evaluate your progress and specially designed quizzes for clearing the requisite certification exams.

Intellipaat also offers lifetime access to videos, course materials, 24/7 Support, and course material upgrades to latest version at no extra fees. For Hadoop and Spark training you get the Intellipaat Proprietary Virtual Machine for Lifetime and free cloud access for 6 months for performing training exercises. All-in-one it is a one-time investment to become a successful Data Scientist and grab the best jobs at the best salaries in top MNCs around the world.

What are the various modes of training that Intellipaat offers?

Intellipaat basically offers the self-paced training and online instructor-led training. Apart from that we also provide corporate training for enterprises. All our trainers come with over 12 years of industry experience in relevant technologies and also they are subject matter experts working as consultants. You can check about the quality of our trainers in the sample videos provided.

Can I request for a support session if I find difficulty in grasping topics?

If you have any queries you can contact our 24/7 dedicated support to raise a ticket. We provide you email support and solution to your queries. If the query is not resolved by email we can arrange for a one-on-one session with our trainers. The best part is that you can contact Intellipaat even after completion of training to get support and assistance. There is also no limit on the number of queries you can raise when it comes to doubt clearance and query resolution.

Can you explain to me about the Intellipaat self-paced training and its various benefits?

The Intellipaat self-paced training is for people who want to learn at their own leisurely pace. As part of this program we provide you with one-on-one sessions, doubt clearance over email, 24/7 Live Support, lifetime LMS and upgrade to the latest version at no extra cost. The prices of self-paced training can be 75% lesser than online training. While studying should you face any unexpected challenges then we shall arrange a Virtual LIVE session with the trainer.

What kind of projects will I be working on as part of the training?

We provide you with the opportunity to work on real world projects wherein you can apply your knowledge and skills that you acquired through our training. We have multiple projects that thoroughly test your skills and knowledge of various aspect and components making you perfectly industry-ready. These projects could be in exciting and challenging fields like banking, insurance, retail, social networking, ecommerce, marketing, sales, high technology and so on. The Intellipaat projects are equivalent to six months of relevant experience in the corporate world.

Do you provide placement assistance?

Yes, Intellipaat does provide you with placement assistance. We have tie-ups with 80+ organizations including Ericsson, Cisco, Cognizant, TCS, among others that are looking for skilled & quality professionals and we would be happy to assist you with the process of preparing yourself for the interview and the job.

Can I switch from self-paced training to online instructor-led training?

Yes, if you would want to upgrade from the self-paced training to instructor-led training then you can easily do so by paying the difference of the fees amount and joining the next batch of classes which shall be separately notified to you.

How are your verified certificates awarded?

Upon successful completion of training you have to take a set of quizzes, complete the projects and upon review and on scoring over 60% marks in the qualifying quiz the official Intellipaat verified certificate is awarded.The Intellipaat Certification is a seal of approval and is highly recognized in 80+ corporations around the world including many in the Fortune 500 list of companies.

This is a comprehensive course that is designed to clear multiple certifications viz.

CCA Spark and Hadoop Developer (CCA175)
Cloudera Certified Administrator for Apache Hadoop (CCAH)
Cloudera Data Scientist certification (CCP:DS)
Apache Hbase certification exam CCB-400

The entire training course content is in line with respective certification program and helps you clear the requisite certification exam with ease and get the best jobs in the top MNCs.

As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.

At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.

Intellipaat Course Completion certificate will be awarded on the completion of Project work (on expert review) and upon scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

Kavita Mehra

Perfect Training!

The classes were highly interactive and also practical oriented. The office staff was cordial and co-operative. Every teaching session was recorded each day and was put on-line by the institute which was really helpful. The trainer was very patient and able to solve or give some hints to solve all the questions posed to him.
Sameer Gupta

Marvellous Content

I enjoyed this course from the very first session.The content guides you from very basic approach of the fundamentals to the advanced level with practical knowledge in just few days of training.
Vikrant Singh

Good work

It was a wonderful experience and learning from Intellipaat trainers. The trainers were hands on and provided real time scenario’s. For me learning cutting edge and latest technologies intellipaat is the right place.
Abhimanyu Balgopal

Excellent Training Package!

As a Big Data Engineer, this combo course doubled my interest in various technologies specifically Hadoop, Spark, Storm Scala and others. Very beneficial for those who are passionate about Big Data and Data science and want to learn everything at one place. The courses in this combo are well constructed and each topic is well explained. Thanks Intellipaat!!!
Narendra Kumar

Wonderful Integrated learning!

Nothing better than a combo like this! Being a Data scientist, I could gain insights into various Big Data platforms like Hadoop, Spark and Scala, which has really enriched my skill set and gave me an edge amongst coworkers. The idea of learning most demanding Big data and Data science technologies through a single course is just wonderful. The trainers are doing a great job. I just love the Integration of various technologies together.

Self Paced

$2300 | 131100

Lifetime Access and 24/7 Support

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Key Features

Course Duration : 237 Hrs

High quality interactive e-learning sessions for Self paced course. For online instructor led training ,total course will be divided into sessions.

Hands on Exercise and Project Work: 404 Hrs

Each module will be followed by practical assignments and lab exercises. Towards the end of the course, you will be working on a project where would be expected to complete a project based on your learning. Our support team is available to help through email, phone or Live Support for any help required.

Access Duration: Lifetime

You will get Lifetime access to high quality interactive tutorials along with life time access to complete Course Material .There will be 24/7 access to video tutorials with email support. If you stuck in any unexpected problem we will provide online interactive sessions with trainer for issue resolving.

24 x 7 Support

We provide 24X7 support by email for issues or doubts clearance for Self-paced training.

In online Instructor led training, trainer will be available to help you out with your queries regarding the course. If required, the support team can also provide you live support by accessing your machine remotely. This ensures that all your doubts and problems faced during labs and project work are clarified round the clock.

Get Certified

This course is designed for clearing CCA Spark and Hadoop Developer (CCA175),Cloudera Certified Administrator for Apache Hadoop (CCAH),Cloudera Data Scientist certification (CCP:DS) and Apache Hbase certification exam CCB-400.

At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.

Job Assistance

Intellipaat enjoys strong relationship with multiple staffing companies in US, UK and have +60 clients across the globe. If you are looking out for exploring job opportunities, you can pass your resumes once you complete the course and we will help you with job assistance. We don’t charge any extra fees for passing the resume to our partners and clients

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Latest Trends: Machine learning to make the search engines smarter!2 - Mar

All these years we have witnessed autonomous cars having face/speech recognition systems transforming the technology landscape radically. While the algorithms have already become a baseline of any technology, Google has taken it a level further by introducing RankBrain algorithm which is an artificial intelligence and machine learning system to be used to rank the search results. Google has remained the top search engines because of its rich set of algorithms like PageRank and Signals. RankBrain is an addition to that broad array which will help Google provide the relevant results even if the exact word is not present in the web page. That means if you enter “shoe”, it is going to show you pages that are relevant to “running”. Thus we can say that these innovations have made machine learning and artificial intelligence a new normal.

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