Drive better business decisions with an overview of how big data is organized, analyzed, and interpreted. Apply your insights to real-world problems and questions.
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Do you need to understand big data and how it will impact your business? This Specialization is for you. You will gain an understanding of what insights big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Previous programming experience is not required! You will be guided through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. By following along with provided code, you will experience how one can perform predictive modeling and leverage graph analytics to model problems. This specialization will prepare you to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets. In the final Capstone Project, developed in partnership with data software company Splunk, you’ll apply the skills you learned to do basic analyses of big data.
専門講座のしくみ
コースを受講しましょう。
Coursera（コーセラ）の専門講座は、一連のコース群であり、技術を身に付ける手助けとなります。開始するには、専門講座に直接登録するか、コースを確認して受講したいコースを選択してください。専門講座の一部であるコースにサブスクライブすると、自動的にすべての専門講座にサブスクライブされます。1つのコースを修了するだけでも結構です。いつでも、学習を一時停止したり、サブスクリプションを終了することができます。コースの登録状況や進捗を追跡するには、受講生のダッシュボードにアクセスしてください。
実践型プロジェクト
すべての専門講座には、実践型プロジェクトが含まれています。専門講座を完了して修了証を獲得するには、成功裏にプロジェクトを終了させる必要があります。専門講座に実践型プロジェクトに関する別のコースが含まれている場合、専門講座を開始するには、それら他のコースをそれぞれ終了させる必要があります。
修了証を取得
すべてのコースを終了し、実践型プロジェクトを完了すると、修了証を獲得します。この修了証は、今後採用企業やあなたの職業ネットワークと共有できます。
この専門講座には6コースあります。
コース1
Introduction to Big Data
4.6
4,929件の評価
Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world!
At the end of this course, you will be able to:
* Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors.
* Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting.
* Get value out of Big Data by using a 5-step process to structure your analysis.
* Identify what are and what are not big data problems and be able to recast big data problems as data science questions.
* Provide an explanation of the architectural components and programming models used for scalable big data analysis.
* Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model.
* Install and run a program using Hadoop!
This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments.
Hardware Requirements:
(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size.
Software Requirements:
This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
コース2
Big Data Modeling and Management Systems
4.3
1,954件の評価
Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools. Through guided hands-on tutorials, you will become familiar with techniques using real-time and semi-structured data examples. Systems and tools discussed include: AsterixDB, HP Vertica, Impala, Neo4j, Redis, SparkSQL. This course provides techniques to extract value from existing untapped data sources and discovering new data sources.
At the end of this course, you will be able to:
* Recognize different data elements in your own work and in everyday life problems
* Explain why your team needs to design a Big Data Infrastructure Plan and Information System Design
* Identify the frequent data 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
* Appreciate why there are so many data management systems
* Design a big data information system for an online game company
This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.
Hardware Requirements:
(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size.
Software Requirements:
This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
コース3
Big Data Integration and Processing
4.4
1,395件の評価
At the end of the course, you will be able to:
*Retrieve data from example database and big data management systems
*Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications
*Identify when a big data problem needs data integration
*Execute simple big data integration and processing on Hadoop and Spark platforms
This course is for those new to data science. Completion of Intro to Big Data is recommended. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Refer to the specialization technical requirements for complete hardware and software specifications.
Hardware Requirements:
(A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size.
Software Requirements:
This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.
コース4
Machine Learning With Big Data
4.6
1,172件の評価
Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
At the end of the course, you will be able to:
• Design an approach to leverage data using the steps in the machine learning process.
• Apply machine learning techniques to explore and prepare data for modeling.
• Identify the type of machine learning problem in order to apply the appropriate set of techniques.
• Construct models that learn from data using widely available open source tools.
• Analyze big data problems using scalable machine learning algorithms on Spark.
Software Requirements:
Cloudera VM, KNIME, Spark
講師
Ilkay Altintas
Chief Data Science OfficerSan Diego Supercomputer Center
Amarnath Gupta
Director, Advanced Query Processing LabSan Diego Supercomputer Center (SDSC)
Mai Nguyen
Lead for Data AnalyticsSan Diego Supercomputer Center
業界パートナー
カリフォルニア大学サンディエゴ校について
UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.
よくある質問
返金ポリシーについて教えてください。
1つのコースだけに登録することは可能ですか？
はい。まず始めに興味のあるコースカードをクリックして登録します。コースに登録して修了することによって、共有できる修了証を取得するか、無料でコースを聴講してコースの教材を確認することができます。専門講座の一部であるコースにサブスクライブすると、専門講座全体に自動的にサブスクライブされます。進捗を追跡するには、受講生のダッシュボードにアクセスしてください。
学資援助はありますか？
無料でコースを受講できますか？
このコースは100%オンラインで提供されますか？実際に出席する必要のあるクラスはありますか？
このコースは完全にオンラインで提供されているため、実際に教室に出席する必要はありません。Webまたはモバイル機器からいつでもどこからでも講義、学習用教材、課題にアクセスできます。
専門講座を修了するのにどのくらいの期間かかりますか？
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in about 7 months.
What background knowledge is necessary?
This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments.
What are the software requirements?
This specialization relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge (except for data charges from your internet provider). Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+
Do I need to take the courses in a specific order?
We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.
専門講座を修了することで大学の単位は付与されますか？
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
What will I be able to do upon completing the Specialization?
You will be able to process, analyze, and interpret massive and complex data using current big data technologies. You will have the basic skills to model, manage and process big data of various sources and formats.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
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