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Big Data Training

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Key Features

OSACAD is committed to bringing you the best learning experience with high-standard features including

Key Features
Real-time Practice Labs

Learning by doing is what we believe. State-of-the-art labs to facilitate competent training.

Key Features
Physical & Virtual Online Classrooms

Providing the flexibility to learn from our classrooms or anywhere you wish considering these turbulent times.

Key Features
24/7 Support On Slack

Technical or Technological, we give you assistance for every challenge you face round-the-clock.

Key Features
Job Interview & Assistance

Guiding in & out, until you get placed in your dream job.

Key Features
Live projects with our industry partners

An inside look & feel at industry environments by handling real-time projects.

Key Features
Internship after course

Opportunity to prove your talent as an intern at our partner firms and rope for permanent jobs.

Why Big Data Analytics ?

Why Data Science
Making Confident Decisions

Big data can help you to make those choices with confidence, based on an in-depth analysis of what you know about your marketplace, industry, and customers

Why Data Science
Optimising and Understanding Business Processes

Big data technologies like cloud computing and machine learning help you to stay ahead of the curve by identifying inefficiencies and opportunities in your company practices

Why Data Science
Empowering the Next Generation

Finally, as a new generation of technology leaders enter the marketplace, big data delivers the agility and innovation top-tier talent needs from their employer

Who is This program for

  • Data Architects,Data Scientists, Software Developers,Fresher Graduates
  • Data Analysts,BI Analysts,BI Developers,SAS Developers
  • Others who analyze Big Data in Hadoop environment
  • Others who analyze Big Data in Hadoop environment
Who is this program

Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

  • Tips for Using This Course
  • If you have trouble downloading Hortonworks Data Platform
  • Overview of the Hadoop Ecosystem
  • HDFS: What it is, and how it works
  • [Activity] Install the MovieLens dataset into HDFS using the command line
  • MapReduce: What it is, and how it works
  • How MapReduce distributes processing
  • MapReduce example: Break down movie ratings by rating score
  • Troubleshooting tips: installing pip and mrjob
  • Installing Python, MRJob, and nano
  • Rank movies by their popularity
  • Check your results against mine!
  • Introducing Ambari
  • Introducing Pig
  • Example: Find the oldest movie with a 5-star rating using Pig
  • More Pig Latin
  • Exercise] Find the most-rated one-star movie
  • Pig Challenge: Compare Your Results to Mine!
  • Why Spark?
  • The Resilient Distributed Dataset (RDD)
  • Find the movie with the lowest average rating - with RDD's
  • Find the movie with the lowest average rating - with DataFrames
  • Filter the lowest-rated movies by number of ratings
  • Check your results against mine!
  • Why NoSQL?
  • What is HBase
  • Import movie ratings into HBase
  • Use HBase with Pig to import data at scale.
  • Cassandra overview
  • If you have trouble installing Cassandra
  • Installing Cassandra
  • Write Spark output into Cassandra
  • MongoDB Overview
  • Install MongoDB, and integrate Spark with MongoDB
  • Using the MongoDB shell
  • Choose a database for a given problem
  • What is Hive?
  • Use Hive to find the most popular movie
  • Use Hive to find the movie with the highest average rating
  • Compare your solution to mine.
  • Integrating MySQL with Hadoop
  • Install MySQL and import our movie data
  • Use Sqoop to import data from MySQL to HFDS/Hive
  • Use Sqoop to export data from Hadoop to MySQL
  • Use Sqoop to export data from Hadoop to MySQL
  • Why NoSQL?
  • What is HBase
  • Import movie ratings into HBase
  • Use HBase with Pig to import data at scale.
  • Cassandra overview
  • If you have trouble installing Cassandra
  • Installing Cassandra
  • Write Spark output into Cassandra
  • MongoDB Overview
  • Install MongoDB, and integrate Spark with MongoDB
  • Using the MongoDB shell
  • Choose a database for a given problem
  • Overview of Drill
  • Setting up Drill
  • Overview of Phoenix
  • Install Phoenix and query HBase with it
  • Integrate Phoenix with Pig
  • Overview of Presto
  • Install Presto, and query Hive with it.
  • Tez explained
  • Use Hive on Tez and measure the performance benefit
  • Mesos explained
  • ZooKeeper explained
  • Simulating a failing master with ZooKeeper
  • Oozie explained
  • import setup step for Oozie on HDP 2.6.5!
  • Set up a simple Oozie workflow
  • Use Zeppelin to analyze movie ratings, part 1
  • Use Zeppelin to analyze movie ratings, part
  • Hue overview
  • Other technologies worth mentioning
  • Spark Streaming: Introduction
  • Kafka explained
  • Setting up Kafka, and publishing some data.
  • Publishing web logs with Kafka
  • Flume explained
  • Set up Flume and publish logs with it.
  • Kafka explained
  • Setting up Kafka, and publishing some data.
  • Publishing web logs with Kafka
  • Flume explained
  • Set up Flume and publish logs with it.
  • Spark Streaming: Introduction
  • Analyze web logs published with Flume using Spark Streaming
  • Monitor Flume-published logs for errors in real time
  • Exercise solution: Aggregating HTTP access codes with Spark Streaming
  • Apache Storm: Introduction
  • Count words with Storm

Data Pre Processing Spark Hadoop

  • Using labs for preparation
  • Setup Development Environment (Windows 10) - Introduction
  • Setup Development Environment - Python and Spark - Prerequisites
  • Setup Development Environment - Python Setup on Windows
  • Setup Development Environment - Configure Environment Variables
  • Setup Development Environment - Setup PyCharm for developing Python application
  • Setup Development Environment - Pass run time arguments or parameters
  • Setup Development Environment - Download Spark compressed tar ball
  • Setup Development Environment - Install 7z for uncompress and untar on windows
  • Setup Development Environment - Setup Spark
  • Setup Development Environment - Install JDK
  • Setup Development Environment - Configure environment variables for Spark
  • Setup Development Environment - Install WinUtils - integrate Windows and HDFS
  • Setup Development Environment - Integrate PyCharm and Spark on Windows
  • Introduction and Setting up Python
  • Basic Programming Construct
  • Functions in Python
  • Python Collections
  • Map Reduce operations on Python Collections
  • Setting up Data Sets for Basic I/O Operations
  • Basic I/O operations and processing data using Collections
  • Get revenue for given order id - as application
  • Setup Environment - Options
  • Setup Environment - Locally
  • Setup Environment - using Cloudera Quickstart VM
  • Using Itversity platforms - Big Data Developer labs and forum
  • Using itversity's big data lab
  • Using Windows - Putty and WinSCP
  • Using Windows - Cygwin
  • HDFS Quick Preview
  • YARN Quick Preview
  • Setup Data Sets
  • Introduction
  • Introduction to Spark
  • Setup Spark on Windows
  • Quick overview about Spark documentation
  • Connecting to the environment
  • Initializing Spark job using pyspark
  • Create RDD from HDFS files
  • Create RDD from collection - using parallelize
  • Read data from different file formats - using sqlContext
  • Row level transformations - String Manipulation
  • Row Level Transformations - map
  • Row Level Transformations - flatMap
  • Filtering data using filter
  • Joining Data Sets - Introduction
  • Joining Data Sets - Inner Join
  • Joining Data Sets - Outer Join
  • Aggregations - Introduction
  • Aggregations - count and reduce - Get revenue for order id
  • Aggregations - reduce - Get order item with minimum subtotal for order id
  • Aggregations - countByKey - Get order count by status
  • Aggregations - understanding combiner
  • Aggregations - groupByKey - Get revenue for each order id
  • groupByKey - Get order items sorted by order_item_subtotal for each order id
  • Aggregations - reduceByKey - Get revenue for each order id
  • Aggregations - aggregateByKey - Get revenue and count of items for each order id
  • Sorting - sortByKey - Sort data by product price
  • Sorting - sortByKey - Sort data by category id and then by price descending
  • Ranking - Introduction
  • Ranking - Global Ranking using sortByKey and take
  • Ranking - Global using takeOrdered or top
  • Ranking - By Key - Get top N products by price per category - Introduction
  • Ranking - By Key - Get top N products by price per category - Python collection
  • Ranking - By Key - Get top N products by price per category - using flatMap
  • Ranking - By Key - Get top N priced products - Introduction
  • Ranking - By Key - Get top N priced products - using Python collections API
  • Ranking - By Key - Get top N priced products - Create Function
  • Ranking - By Key - Get top N priced products - integrate with flatMap
  • Set Operations - Introduction
  • Set Operations - Prepare data
  • Set Operations - union and distinct
  • Set Operations - intersect and minus
  • Saving data into HDFS - text file format
  • Saving data into HDFS - text file format with compression
  • Saving data into HDFS using Data Frames - json
  • Problem Statement
  • Launching pyspark
  • Reading data from HDFS and filtering
  • Joining orders and order_item
  • Aggregate to get daily revenue per product id
  • Load products and convert into RDD
  • Join and sort the data
  • Save to HDFS and validate in text file format
  • Saving data in avro file format
  • Get data to local file system using get or copyToLocal
  • Develop as application to get daily revenue per product
  • Run as application on the cluster
  • Different interfaces to run SQL - Hive, Spark SQL
  • Create database and tables of text file format - orders and order_items
  • Create database and tables of ORC file format - orders and order_items
  • Running SQL/Hive Commands using pyspark
  • Functions - Getting Started
  • Functions - String Manipulation
  • Functions - Date Manipulation
  • Functions - Aggregate Functions in brief
  • Functions - case and nvl
  • Row level transformations
  • Joining data between multiple tables
  • Group by and aggregation
  • Sorting the data
  • Set operations - union and union all
  • Analytics functions - aggregations
  • Analytics functions - ranking
  • Windowing functions
  • Creating Data Frames and register as temp tables
  • Write Spark Application - Processing Data using Spark SQL
  • Write Spark Application - Saving Data Frame to Hive tables
  • Data Frame Operations
  • Introduction to Setting up Environment for Practice
  • Overview of ITVersity Boxes GitHub Repository
  • Creating Virtual Machine
  • Starting HDFS and YARN
  • Gracefully Stopping Virtual Machine
  • Understanding Datasets provided in Virtual Machine
  • Using GitHub Content for the practice
  • Using Resources for Practice

Apache Spark 3.x -Data Processing -Getting Started

  • Introduction
  • Review of Setup Steps for Spark Environment
  • Using ITVersity labs
  • Apache Spark Official Documentation (Very Important)
  • Quick Review of Spark APIs
  • Spark Modules
  • Spark Data Structures - RDDs and Data Frames
  • Develop Simple Application
  • Apache Spark - Framework

Apache Spark 3.x -Data Frame and Predefined Functions

  • Introduction
  • Data Frames - Overview
  • Create Data Frames from Text Files
  • Create Data Frames from Hive Tables
  • Create Data Frames using JDBC
  • Data Frame Operations - Overview
  • Spark SQL - Overview
  • Overview of Functions to manipulate data in Data Frame fields or columns

Apache Spark 3.x -Processing Data using Data Frames-Basic Transformations

  • Define Problem Statement - Get Daily Product Revenue
  • Selection or Projection of Data in Data Frames
  • Filtering Data from Data Frames
  • Joining multiple Data Frames
  • Perform Aggregations using Data Frames
  • Sorting Data in Data Frames
  • Development Life Cycle using Data Frames
  • Run applications using Spark Submit

Machine learning with pyspark

  • DataGeneration
  • Spark
  • Spark Core
  • Spark Components
  • Setting Up Environment
  • Windows
  • Anaconda Installation
  • Java Installation
  • Spark Installation
  • IOS
  • Docker
  • Databricks
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Load and Read Data
  • Adding a New Column
  • Filtering Data
  • Condition 1
  • Condition 2
  • Distinct Values in Column
  • Grouping Data
  • Aggregations
  • User-Defined Functions (UDFs)
  • Traditional Python Function
  • Using Lambda Function
  • Pandas UDF (Vectorized UDF)
  • Pandas UDF (Multiple Columns)
  • Drop Duplicate Values
  • Delete Column
  • Writing Data
  • CSV
  • Parquet

Linear Regression

Variables

Theory

Interpretation

Evaluation

Code

  • Create the SparkSession Object Step
  • Read the Dataset Step
  • Exploratory Data Analysis Step
  • Feature Engineering Step
  • Splitting the Dataset Step
  • Build and Train Linear Regression Model Step
  • Evaluate Linear Regression Model on Test Data
  • Probability
  • Using Linear Regression
  • Using Logit
  • Interpretation (Coefficients)
  • Dummy Variables
  • Model Evaluation
  • True Positives
  • True Negatives
  • False Positives
  • False Negatives
  • Accuracy
  • Recall
  • Precision
  • F1 Score
  • Cut Off /Threshold Probability
  • ROC Curve
  • Logistic Regression Code
  • Data Info
  • Step 1: Create the Spark Session Object
  • Step 2: Read the Dataset
  • Step 3: Exploratory Data Analysis
  • Step 4: Feature Engineering
  • Step 5: Splitting the Dataset
  • Step 6: Build and Train Logistic Regression Model
  • Training Results
  • Step 7: Evaluate Linear Regression Model on Test Data
  • Confusion Matrix
  • Decision Tree
  • Entropy
  • Information Gain
  • Random Forests
  • Code
  • Data Info
  • Step 1: Create the SparkSession Object
  • Step 2: Read the Dataset
  • Step 3: Exploratory Data Analysis
  • Step 4: Feature Engineering
  • Step 5: Splitting the Dataset
  • Step 6: Build and Train Random Forest Model
  • Step 7: Evaluation on Test Data
  • Accuracy
  • Precision
  • AUC
  • Step 8: Saving the Model
  • Recommendations
  • Popularity Based RS
  • Content Based RS
  • Collaborative Filtering Based RS
  • Hybrid Recommender Systems
  • Code
  • Data Info
  • Step 1: Create the SparkSession Object
  • Step 2: Read the Dataset
  • Step 3: Exploratory Data Analysis
  • Step 4: Feature Engineering
  • Step 5: Splitting the Dataset
  • Step 6: Build and Train Recommender Model
  • Step 7: Predictions and Evaluation on Test Data
  • Step 8: Recommend Top Movies That Active User Might Like
  • Starting with Clustering
  • Applications
  • K-Means
  • Hierarchical Clustering
  • Code
  • Data Info
  • Step 1: Create the SparkSession Object
  • Step 2: Read the Dataset
  • Step 3: Exploratory Data Analysis
  • Step 4: Feature Engineering
  • Step 5: Build K-Means Clustering Model
  • Step 6: Visualization of Clusters
  • Introduction
  • Steps Involved in NLP
  • Corpus
  • Tokenize
  • Stopwords Removal
  • Bag of Words
  • Count Vectorizer
  • TF-IDF
  • Text Classification Using Machine Learning
  • Sequence Embeddings
  • Embeddings
450+
Hours of Content
12
Case Study & Projects
35+
Live Sessions
11
Coding Assignments
10
Capstone Projects to Choose From
20
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

Building a Data Warehouse using Spark on Hive

In this hive project , we will build a Hive data warehouse from a raw dataset stored in HDFS and present the data in a relational structure so that querying the data will be natural

Airline Dataset Analysis using Hadoop, Hive, Pig and Impala

Performing basic big data analysis on airline dataset using big data tools -Pig, Hive and Impala

Process a Million Song Dataset to Predict Song Preferences

In this big data project, we will discover songs for those artists that are associated with the different cultures across the globe

Certification

Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

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What do you benefit from this programs
  • Candidate will gain a deeper knowledge of various Big Data frameworks
  • Hands-on learning on Big data Analytics with Hadoop
  • Projects related to banking, governmental sectors, e-commerce websites, etc
  • Learn to extract information with Hadoop MapReduce using HDFS, Pig, Hive, etc

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