Is it necessary for you to know how big data will impact your business? This is a course you ought to take. You will learn about the insights that big data may offer by utilizing the same tools and processes that big data scientists and engineers do. To apply, you don’t need any past programming experience! Among the tools you’ll learn about in this course are MapReduce, Spark, Pig, and Hive. You may observe how graph analytics and predictive modeling can be utilized to solve problems by using the provided code. This is the right course if you want to learn more about data science or just have a better grasp of how it works. Your newly acquired knowledge will be put to the test in a capstone project created in collaboration with Splunk, a provider of data analytics tools.
The Specialization Techniques
Enroll in Classes
It’s a set of classes meant to assist you in becoming an expert in a certain field. Enroll in the Specialization first, or browse its courses and choose the one you wish to take first. The whole Specialization’s worth of courses are immediately included with your subscription. You are under no obligation to complete all of the courses; you are free to pause your subscription or stop learning at any time. Go to your learner dashboard to monitor your course enrollments and progress.
Practical Project
Every Specialization includes a practical project. A certificate of completion will be given to you after your project or projects are finished. You have to finish all of the other courses in the Specialization before you can start working on the practical project.
Acquire a Certificate
After completing all of the courses and the practical project, you will receive a Certificate that you can share with prospective employers and your professional network.
There are six courses in this specialization in total.
the fundamentals of big data.
Are you trying to learn more about the Big Data landscape? This course is intended to assist individuals who are new to data science and wish to comprehend the reasons for the emergence of the Big Data Era. It is intended for those who wish to understand the jargon and underlying concepts of big data applications, systems, and difficulties. This course is designed to begin people thinking about how Big Data might help their business or profession. This course introduces Hadoop, one of the most popular frameworks for big data processing. This raises the possibility that data will change the world!
After completing this course, you will be able to name the three primary sources of big data—people, organizations, and sensors—and define the big data landscape, including instances of big data problems that arise in the real world.
- Differentiate between and explain how each of the “Big Data V’s” (Volume, Velocity, Variability, Truth, Value, and Value) influences the gathering, storing, processing, and generating of reports.
- To make the most of your Big Data analysis, follow this 5-step method.
- Recognize what problems belong in the big data domain and which ones don’t, and then reframe the issue as a data science inquiry.
Describe the architectural elements and programming models of big data analysis. - Explain the main elements of the Hadoop stack, such as MapReduce, HDFS, and YARN. Is there a way to begin using Hadoop?
This is a course that everybody new to data science should take. Students must be able to install software and operate a virtual machine in order to complete the hands-on assignments. Hardware requirements: This application requires at least a Quad Core processor, 8 GB of RAM, and at least 20 GB of free disk space. Here’s how to access your hardware’s details: (Windows): Choose Properties by right-clicking on Computer; (Mac): Click the Apple menu, choose System Preferences, and open System. The Overview menu under the “About This Mac” section will display. Numerous computers with 8 GB of RAM that can meet the basic criteria are available on the market. Owing to the enormous files you intend to download, a high-speed internet connection is required.
Specifications for software:
This course makes use of a number of open-source software tools, including Apache Hadoop. To get started, there are no costs involved. The following applications and operating systems must be installed: VirtualBox 5, Mac OS X 10.10 or Ubuntu 14.04, Windows 7.
Big Data Modeling and Management
Once a big data problem has been discovered, how can you use Big Data solutions to collect, store, and manage your data? You will learn about several data kinds in this course, along with the proper management tools for each. You may better understand why there are so many new big data platforms available by utilizing big data management systems and analytical tools. You will get practical application knowledge by working with samples of semi-structured and real-time data. Among the systems and tools listed are AsterixDB, HP Vertica, Impala, Neo4j, Redis, and SparkSQL. This course shows you where to look for and how to use hitherto unexplored sources of information to get the most out of your data.
Upon finishing this course, you will be able to: * Explain to your team the need for an information system design and a big data infrastructure plan. Determine the regular procedures needed for different kinds of data. Choose a data model that best fits the properties of your data. Utilize methods to manage data streaming. Distinguish between a Big Data management system and a typical database management system. Recognize the rationale behind the abundance of data management systems.
This is a course that everybody new to data science should take. Before enrolling in this course, you must complete Intro to Big Data. To finish the practical projects, students need to be able to install software and operate a virtual system. See the specification technical requirements for an exhaustive list of hardware and software standards.
Specifications for Hardware:
To execute this application, you must have at least a Quad Core processor, 8 GB of RAM, and at least 20 GB of free storage space. Here’s how to access your hardware’s details: (Windows): Choose Properties by doing a right-click on the computer; (Mac): Click the Apple menu, choose System Preferences, and open System. The Overview menu under the “About This Mac” section will display. Numerous computers with 8 GB of RAM that can meet the basic criteria are available on the market. Owing to the enormous files you intend to download, a high-speed internet connection is required.
Specifications for software:
This course makes use of a number of open-source software tools, including Apache Hadoop. All required software can be downloaded and installed for free (your internet provider may charge for data usage). The following applications and operating systems must be installed: VirtualBox 5, Mac OS X 10.10 or Ubuntu 14.04, Windows 7.
Big Data Integration and Processing - Retrieve information from exemplar databases and big data management systems * Identify when data integration is necessary for a big data problem; * Describe the relationships between big data processing patterns and data management procedures that are necessary to employ them in large-scale analytical applications; * Carry out basic big data processing and integration on Hadoop and Spark platforms.
This is a data science beginner’s guide. Before continuing, you must first learn about big data. No prior programming expertise is necessary, however the hands-on assignments do require the ability to install software and use a virtual machine. The technical requirements for the specialization contain comprehensive specifications for both software and hardware.
You will require a 64-bit operating system, 8 GB of RAM, and 20 GB of free disk space if your processor is quad-core. Here’s where to find the hardware information you require: Click Start, right-click Computer, then choose Properties to open System in Windows; choose Overview from the Apple menu in Mac OS X, then click “About This Mac.” Within the last three years, computers with 8 GB of RAM should be able to meet the minimum requirements. The files can be as large as 4 Gb, thus you will need a fast internet connection.
This course requires a number of open-source software technologies, such as Apache Hadoop. All required software can be downloaded and installed for free (your internet provider may charge for data usage). It is necessary to have Windows 7, Mac OS X 10.10, Ubuntu 14.04, CentOS 6+, or VirtualBox 5.
Big Data and Machine Learning Applications
Are you having trouble sorting through and interpreting all of the information you have gathered? Do you have to use data when making decisions? In this course, you will study data exploration, analysis, and exploitation from a machine learning perspective. Utilizing the tools and algorithms you’ll learn about, develop data-driven machine learning models and apply them to large data issues.
You will be able to: • Create a strategy to leverage data through the machine learning process after completing this course.
Investigate and get ready data for modeling using machine learning methods.
You need to identify the type of problem you are trying to solve before you can apply the appropriate set of machine learning techniques.
Use open source software that is publicly available to implement data-driven models.
Scalable machine learning methods in Spark enable the analysis of big datasets.
specifications for the software
Platforms: KVM, Spark, and KNIME
Graph-Based Data Mining
How can you effectively identify the components that make up your data network? Would you like to know how to locate groups of related nodes in a graph? Do you want to know more about graph analytics? Have you heard of it before? You will learn about graph analytics and gain new insights into modeling, storing, retrieving, and analyzing graph-structured data in this course.
After taking this course, you will be able to represent an issue into a graph database and execute scalable analytical activities on the graph. Better still, you will be able to apply these techniques to your own projects to determine the value of your own data sets.
Big Data for the Capstone Project
Welcome to the Capstone Project on Big Data! For your final project, you will build a big data ecosystem using the resources and techniques you have studied in this specialization. You are going to examine a hypothetical game that many people are playing called “Catch the Pink Flamingo”. You will learn how to gather, investigate, prepare, analyze, and report on large data sets during the five-week Capstone Project. We’ll start by introducing you to the data set and you how to perform some exploratory analysis using Splunk and Open Office. Next, we’ll tackle more challenging large data issues that call for more sophisticated tools like as Gephi, Spark’s MLLib, and KNIME. We’ll finally demonstrate how to bring everything together to produce compelling and interesting reports and slide shows during the fifth and final week. We’ve collaborated with Splunk, a software business that specializes in the analysis of machine-generated big data, to provide our top students the opportunity to present their ideas to engineering leaders and recruiters at Splunk.