Predictive Analytics

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Govt. Certified Predictive Analytics Professional

Predictive Analytics & Big Data

  • Predictive Analytics and Big Data
  • Evolution of Predictive Analytics

Process & Application

  • Applications of Predictive Analytics
  • Predictive Analytics Process and Technology
  • Practice: Identifying the Type of Analytics

Key Statistical Concepts

  • Importance of Statistics in Analytics
  • Overview of Probability Theory
  • Using Statistics in Predictive Analytics

Correlation & Regression

  • Introduction
  • Practice: Applying Statistical Approaches

Data Collection & Exploration

  • Data Needs and Sources
  • Collection of Data and Exploration

Data Mining, Data Distributions, & Hypothesis Testing

  • Data Mining and Analytics
  • Hypothesis Testing and Data Distributions
  • Practice: Data Mining Methods

Data Preprocessing

  • Pre-processing Data

Data Reduction & Exploratory Data Analysis (EDA)

  • Data Reduction with PCA and Factor Analysis
  • Tools for Exploratory Data Analysis (EDA)
  • Practice: Using PCA for Feature Selection

A/B Testing, Bayesian Networks, and Support Vector Machine

  • A/B Testing
  • Naïve Bayes and Bayesian Belief Networks
  • Support Vector Machines
  • Practice: Applying Predictive Approaches

Artificial Neural Networks

  • K-Nearest Neighbor (k-NN)
  • K-Nearest Neighbor (k-NN) & Artificial Neural Networks

Clustering Techniques

  • Using Clustering Techniques
  • K-Means Clustering
  • Hierarchical Clustering and DBSCAN

Linear and Logical Regression

  • Linear Regression
  • Logistic Regression
  • Practice: Linear Regression Inference

Text Mining & Social Network Analysis

  • Text Mining
  • Social Network and Media Analytics

Time Series Modeling

  • Introduction to Time Series
  • Time Series Forecasting Models
  • Practice: Time Series Modeling Concepts

Machine Learning, Propensity Score, & Segmentation Modeling

  • Machine Learning
  • Propensity Score and Segmentation Modeling

Random Forests & Uplift Models

  • Random Forests
  • Uplift Models
  • Practice: Advanced Predictive Tools

Model Life Cycle Management

  • Introducing Model Life Cycle Management

Model Development, Validation, & Evaluation

  • Model Building
  • Validation and Model Considerations
  • Model Evaluation
  • Practice: Classification Model Performance

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Sources of Big Data

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Big Data and Apache Hadoop Developer

Sources of Big Data

Big data can be classified as

  • Social Networks (or human-sourced information): this information is the record of human experiences, previously recorded in books and works of art, and later in photographs, audio and video. Human-sourced information is now almost entirely digitized and stored everywhere from personal computers to social networks. Data are loosely structured and often ungoverned.
  • Internet of Things (or machine-generated data): derived from the phenomenal growth in the number of sensors and machines used to measure and record the events and situations in the physical world. The output of these sensors is machine-generated data, and from simple sensor records to complex computer logs, it is well structured. As sensors proliferate and data volumes grow, it is becoming an increasingly important component of the information stored and processed by many businesses. Its well-structured nature is suitable for computer processing, but its size and speed is beyond traditional approaches.

Current Situation – Big Data

Certify and Increase Opportunity.
Big Data and Apache Hadoop Developer

Current Situation - Big Data

Current Situation – Big Data

Big data is a large volume unstructured data which can not be handled by standard database management systems like DBMS, RDBMS or ORDBMS. Big Data is very large, loosely structured data set that defies traditional storage. Few examples are as


  • Facebook : has 40 PB of data and captures 100 TB / day
  • Yahoo : 60 PB of data
  • Twitter : 8 TB / day
  • EBay : 40 PB of data, captures 50 TB / day


Big Data and Apache Hadoop Developer Tutorials | Vskills

Certify and Increase Opportunity.
Big Data and Apache Hadoop Developer

Big Data and Apache Hadoop Developer
Module 1: Introduction to Big Data and Hadoop

1. Today’s Market
2. Current Situation
3. Introduction to Big Data
4. Sources of Big Data
5. Technical & Business Drivers
6. Big Data Use Cases – Banking, Healthcare, Agriculture
7. Traditional DBMS & their Limitations
8. Introduction to Hadoop
9. Hadoop Usage
10. Real-Time Use Cases – Retail, Farming

Module 2: Getting started with Hadoop
1. Hadoop History
2. Hadoop v/s RDBMS
3. Hadoop Architecture
4. Hadoop Ecosystem components
5. Hadoop Storage – HDFS
6. Hadoop Processor – MapReduce
6. Hadoop Server Roles: NameNode, Secondary NameNode, DataNode
7. Anatomy of File Write and Read

Module 3: Hadoop Distributed File System
1. HDFS Architecture
2. HDFS internals and use cases
3. HDFS Daemons
4. Files and blocks
5. NameNode memory concerns
6. Secondary NameNode
7. HDFS access options

Module 4: MapReduce
1. Use cases of MapReduce
2. MapReduce Architecture
3. Understand the concept of Mappers, Reducers
4. Anatomy of MapReduce Program
5. MapReduce Components – Mapper Class, Reducer Class, Driver code
6. Splits and Blocks
7. Understand Combiner and Partitioner
8. Write your own Partitioner
9. Joins – Map Side, Distributed, Distributed Cache, Reduce Side Join
10. Counters
11. Map Reduce API & Data Types

Module 5: Pig
1. Introduction to Apache Pig
2. Pig Data Types
3. Operators in Pig
4. Pig program structure and execution process
5. Joins & filtering using Pig
6. Group & co-group
7. Schema merging and redefining functions
8. Pig functions

Module 6: Hive
1. Understanding Hive
2. Hive Architecture & Components
3. Using Hive command line interface
4. Data types and file formats
5. Hive DDL & DML operations
6. Hive vs. RDBMS

Module 7: HBase
1. What is HBase
2. HBase architecture
3. HBase in Hadoop Ecosystem
4. HBase vs. HDFS
5. HBase Data model
6. Physical Model in HBase
7. Components of HBase
8. Managing large data sets with HBase
9. Using HBase in Hadoop applications
Module 8: Sqoop
1. Introducing Sqoop
2. The principles of Sqoop Design
3. Connectors and Drivers
4. Importing Data with Sqoop
5. Exporting Data with Sqoop

Module 9: ZooKeeper
1. Overview of Zookeeper
2. How ZooKeeper Works
3. The ZooKeeper CLI
4. Reading and Writing Data
5. Sequential and Ephemeral znodes
6. Watches
7. Versioning and ACLs
8. Zookeeper use cases

Module 10: Flume
1. Flume Overview
2. Channels
3. Sinks and Sink Processors
4. Sources and Channel Selectors
5. Interceptors, ETL, and Routing
6. Monitoring Flume

Module 11: Oozie
1. Introduction to Oozie
2. Oozie – Simple/Complex Flow
3. Oozie – Components
4. Oozie Service/ Scheduler
5. Use Cases – Time and Data triggers
6. Running/Debugging a Coordinator Job
7. Bundle

Module 12: Yarn
1. History of Yarn
2. Core Components
3. YARN Administration
4. Capacity Scheduler
5. YARN Distributed-shell

Module 13: Troubleshooting, Administering and Optimizing Hadoop
1. Planning a Hadoop Cluster
2. Identity, Authentication and Authorization
3. Resource Management
4. Cluster Maintenance
5. Troubleshooting
6. Monitoring
7. Backup and Recovery

Module 14: Real-Time Projects
1. Twitter Data Analysis
2. Stack Exchange Ranking and Percentile data-set
3. Loan Dataset
4. Data-sets by Government
5. Machine Learning Dataset like Badges datasets
6. NYC Data Set
7. Weather Dataset

Apply for Apache Big Data and Apache Hadoop Developer Certification Now!!