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All About Big Data Specialization, Your Skills

Writer : Mis. Rossela Ilkan

Do you need to know how big data will affect your company? You should take this course. By working with the tools and systems that big data scientists and engineers use, you will gain an understanding of the insights that big data can provide. You don't need any prior programming experience to apply! MapReduce, Spark, Pig, and Hive are just some of the tools you'll learn about in this course. Using the code provided, you can see how predictive modeling and graph analytics can be used to solve problems. If you're interested in becoming a data scientist or just getting a better understanding of how data works, this is the course for you. You'll put your newfound knowledge to the test in a Capstone Project developed in conjunction with the data analytics software company Splunk.

 

The Methods of Specialization

 

Take Courses

It's a series of courses designed to help you master a particular skill. Start by signing up for the Specialization or looking through its courses and selecting the one you want to begin with. All of the courses that make up the entire Specialization are automatically included in your subscription. You don't have to finish all of the courses; you can stop your learning at any time or pause your subscription. To keep track of your course enrollments and progress, log in to your learner dashboard.

Hands-on Project

A hands-on project is part of every Specialization. Once you've completed your project(s), you'll be awarded a certificate of completion. To begin working on the hands-on project, you must complete all of the other courses in the Specialization first.

Earn a Certificate

This program culminates in a Certificate that can be shared with potential employers as well as your professional network once you've completed all the courses and the hands-on project.

This Specialization has a total of six courses.

 

The basics of Big Data.

Looking to gain a better understanding of the Big Data environment? To help those who are new to data science and want to understand why the Big Data Era has come to be, this course is designed for you. It's for people who want to learn the terminology and fundamental ideas behind big data problems, applications, and systems. To get people thinking about how Big Data can benefit their business or career, this course is for you. Hadoop, one of the most widely used frameworks for big data analysis, is introduced in this course. This increases the potential for data to transform our world!

After taking this course, you'll be able to describe the Big Data landscape, including examples of real-world big data problems, and identify the three main sources of Big Data: people, organizations, and sensors.

* Distinguish between and why each of the "Big Data V's" (Volume/Velocity/Variability/Truth/Value/Value) affects data collection, monitoring, storage, analysis, and report generation.

* Structure your analysis of Big Data using a 5-step process to get the most out of it.

* Be able to tell the difference between what constitutes a big data problem and what does not, and then reframe the problem as a data science question.

Give an explanation of big data analysis's architectural components and programming models.

* Describe the key components of the Hadoop stack, including YARN, HDFS, and MapReduce.

* Is there any way to get started with Hadoop?

Anyone new to data science should take this course. In order to complete the hands-on assignments, students must have the ability to install applications and use a virtual machine.

Requirements for Hardware:

At least a Quad Core processor, 8 GB of RAM, and a free disk space of at least 20 GB are required to run this application. Here's how to get at the details of your hardware: (Windows): Right-click Computer and select Properties; (Mac): Open System by clicking the Apple menu and selecting System Preferences. Apple menu > "About This Mac" > Overview will appear. There are plenty of computers on the market with 8 GB of RAM that will be able to meet the minimum requirements. Due to the large files that you will be downloading, you will need a high-speed internet connection.

Requirements for software:

Several open-source software tools, such as Apache Hadoop, are used in this course. You don't have to pay for anything to get started. Requirements for operating systems and software include: Windows 7, Mac OS X 10.10 or Ubuntu 14.04 and VirtualBox 5.

 

 

Modeling and Management of Big Data

 

How do you collect, store, and organize your data using Big Data solutions once you've identified a big data problem? In this course, you'll learn about a variety of data types and the appropriate management tools for each. Big data management systems and analytic tools will help you understand why there are so many new big data platforms in existence. Using real-time and semi-structured data examples, you will learn how to apply the techniques in a hands-on manner. AsterixDB, HP Vertica, Impala, Neo4j, Redis, and SparkSQL are a few of the systems and tools mentioned. To maximize the value of your data, this course teaches you how to find and exploit previously untapped sources of information.

After completing this course, you will be able to:

* Show your team why a Big Data Infrastructure Plan and Information System Design is necessary. Identify the frequent operations required for various types of data. Select a data model to suit the characteristics of your data. Apply techniques to handle streaming data. Differentiate between a traditional database management system and a Big Data management system. Understand why there are many data management systems.

Anyone new to data science should take this course. Intro to Big Data is required prior to taking this course. In order to complete the hands-on assignments, students must have the ability to install applications and use a virtual machine. For a complete list of hardware and software specifications, refer to the specification technical requirements.

Requirements for Hardware:

At least a Quad Core processor, 8 GB of RAM, and a free disk space of at least 20 GB are required to run this application. Here's how to get at the details of your hardware: (Windows): Right-click Computer and select Properties; (Mac): Open System by clicking the Apple menu and selecting System Preferences. Apple menu > "About This Mac" > Overview will appear. There are plenty of computers on the market with 8 GB of RAM that will be able to meet the minimum requirements. Due to the large files that you will be downloading, you will need a high-speed internet connection.

Requirements for software:

Several open-source software tools, such as Apache Hadoop, are used in this course. Download and install all necessary software for free (except for data charges from your internet provider). Requirements for operating systems and software include: Windows 7, Mac OS X 10.10 or Ubuntu 14.04 and VirtualBox 5.

 

 

Integration and Processing of Big Data

 

You will be able to: *Retrieve data from example databases and big data management systems *Describe the connections between data management operations and the big data processing patterns needed to use them in large-scale analytical applications *Identify when a big data problem requires data integration *Execute simple big data integration and processing on Hadoop and Spark platforms

This is a beginner's guide to data science. Intro to Big Data is required before moving on. The hands-on assignments require the ability to install applications and use a virtual machine, but no prior programming experience is required. Detailed specifications for hardware and software can be found in the specialization technical requirements.

If you have a quad-core processor, you'll need a 64-bit operating system, 8 GB of RAM, and 20 GB of free disk space. Here's how to locate the data you need about your hardware: Open System in Windows by clicking Start, right-clicking Computer, and selecting Properties; in Mac OS X, select Overview from the Apple menu and then select "About This Mac." Computers with 8 GB of RAM that were purchased within the last three years should be able to meet the minimum specifications. A high-speed internet connection is required because the files will be up to 4 Gb in size.

Several open-source software tools, including Apache Hadoop, are required for this course. It is possible to download and install all of the necessary software for free (except for data charges from your internet provider). Windows 7, Mac OS X 10.10, Ubuntu 14.04, or CentOS 6+ VirtualBox 5 are all required.

 

The use of Big Data and Machine Learning

Looking for a way to organize and make sense of all the data you've collected? Do you need to make decisions based on data? You'll learn about data exploration, analysis, and exploitation through the lens of machine learning in this course. Learn how to build data-driven machine learning models and apply them to big data problems using the tools and algorithms you'll learn about.

After taking this course, you will be able to: • Design an approach to leverage data using the machine learning process.

Explore and prepare data for modeling with machine learning techniques.

In order to use the correct set of machine learning techniques, you must first determine what kind of problem you're trying to solve.

Implement data-driven models using freely available open source software.

Spark can be used to analyze large datasets using scalable machine learning algorithms.

Requirements for the software

Platforms: KNIME, KVM, and Spark

 

Data Mining with Graphs

What's the best way to figure out what makes your data network tick? Do you want to learn how to identify clusters of closely connected nodes in a graph? Have you heard of graph analytics and are interested in learning more about it? This course introduces you to the field of graph analytics and teaches you how to model, store, retrieve, and analyze graph-structured data in new ways.

You will be able to model a problem into a graph database and perform analytical tasks on the graph in a scalable manner after completing this course. Even better, you'll be able to put these methods to use in your own projects to figure out the significance of your own data sets.

 

The Capstone Project - Big Data

Welcome to the Big Data Capstone Project! You'll use the tools and methods you've learned in this specialization to create a big data ecosystem for your final project. What you're going to do is look at an imaginary game called "Catch the Pink Flamingo" that has a large number of people playing it. During the five-week Capstone Project, you will learn how to acquire, explore, prepare, analyze, and report on big data sets. To begin, we'll introduce you to the data set and show you how to use Splunk and Open Office to conduct some exploratory analysis. Then we'll move on to more difficult big data problems that require more advanced tools like KNIME, Spark's MLLib, and Gephi. Finally, in the fifth and final week, we'll show you how to put it all together to create reports and slide presentations that are both engaging and persuasive. Splunk, a software company specializing in the analysis of machine-generated big data, has partnered with us to allow our best students to present their projects to Splunk recruiters and engineering leaders.

 

Read more:

Big Data Course

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