IBM - Big Data Analytics Professional Program Online Course

IBM - Big Data Analytics Professional Program

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Course Description

In the age of information, data is the new oil. The ability to harness, analyze, and derive actionable insights from vast amounts of data is a critical skill in today’s digital economy. The Big Data Analytics Professional Program is designed to equip you with the knowledge and skills needed to thrive in this data-driven world. This IBM - Big Data Analytics Professional Program covers a broad ra...

This IBM - Big Data Analytics Professional Program covers a broad range of topics, from foundational concepts to advanced analytical techniques, ensuring that graduates are well-prepared to tackle real world challenges.

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What you’ll learn

Big Data 101 : Understand the fundamentals of big data, including its characteristics and significance.
Hadoop 101 : Learn the basics of Hadoop, its architecture, and its role in big data processing.
NoSQL - MongoDB : Explore NoSQL databases with a focus on MongoDB for handling unstructured data.
Introduction to Big Data, Hadoop & Ecosystem : Get an overview of big data concepts and the Hadoop ecosystem.
Spark Fundamentals I : Master the basics of Apache Spark for large-scale data processing.
Predictive Modeling Fundamentals I : Learn the fundamentals of predictive modeling and its applications.
Simplifying Data Pipelines with Apache Kafka : Understand how to use Apache Kafka for building data pipelines.
Solr 101 : Discover Apache Solr and its capabilities for enterprise search.
Developing Distributed Applications Using ZooKeeper : Learn how to use Apache ZooKeeper for managing distributed applications.

Covering Topics | Program Insights

Learning Mode : Online Self-Paced (Learn Anywhere & Anytime).

Program Duration : 6 Months.

Learning Hours : 60+ (Includes E-Courseware, Videos, Hands-on-Labs, Assignments, Quiz etc by IBM).

Support : Weekly Doubt Clearance Session.

Certification : Official IBM certifications that directly engage with your LinkedIn Profile.

Curriculum

  Course 1 : Big Data 101
Module 1: What is Big Data? 
Module 2: Big Data - Beyond the Hype
Module 3: The Big Data and Data Science
Module 4: BDUse Cases
Module 5: Processing Big Data

  Course 2 : Hadoop 101
Module 1: Introduction to Hadoop 
Module 2: Hadoop Architecture
Module 3: Hadoop Administration
Module 4: Hadoop Components

  Course 3 : NoSQL- MongoDB

Module 1: Overview of NOSQL 
Module 2: Introduction to MongoDB
Module 3: Basic MongoDB Operations
Module 4: Advanced MongoDB Operations

  Course 4 : Introduction to Big Data, Hadoop & Ecosystem4

Module 1: What is Big Data and Data Analytics
Module 2: Overview about HDP
Module 3: Introduction to Apache Ambari
Module 4: Hadoop and the Hadoop Distributed File System (HDFS)
Module 5: MapReduce and YARN
Module 6: Apache Spark
Module 7: Overview on Data File Formats, HBase, Pig, Hive, R and Python
Module 8: ZooKeeper, Slider, and Knox
Module 9: Flume and Sqoop
Module 10: DataPlane Service
Module 11: Stream Computing

  Course 5 : Spark Fundamentals  I

Module 1: Introduction to Spark - Getting started
Module 2: Resilient Distributed Dataset and DataFrames
Module 3: Spark application programming
Module 4: Introduction to Spark libraries
Module 5: Spark configuration, monitoring and tuning

  Course 6 : Predictive Modeling Fundamentals I

Module 1: Introduction to Data Mining
Module 2: The Data Mining Process
Module 3: Modeling Techniques
Module 4: Model Evaluation
Module 5: Deployment on IBM Bluemix

  Course 7 : Simplifying Data Pipelines with Apache Kafka

Module 1: Introduction to Apache Kafka
Module 2: Kafka Command Line
Module 3: Kafka Producer Java API
Module 4: Kafka Consumer Java API
Module 5: Kafka Connect and Spark Streaming

  Course 8 : Solr 101

Module 1: Search Engines
Module 2: Configure and Add Documents to Solr
Module 3: Analyzers and Queries
Module 4: SolrJ and Customization

  Course 9 : Developing Distributed Applications Using ZooKeeper

Module 1: Introduction to ZooKeeper
Module 2: The ZooKeeper Data Model
Module 3: Programming and Advanced Topics