This Intellipaat Training Course that includes the complete training in Hadoop Developer Hadoop Analyst Hadoop Administration Hadoop Testing and Data Science. The major topics covered are Hadoop and its ecosystem understanding of MapReduce and HDFS working with Hadoop clusters HBase Hadoop Administration along with introduction to Data Science various methods for data acquisition project lifecycle machine learning and statistical techniques. Get Big Data and Data Science Certification Training Here - https://intellipaat.com/data-science-with-hadoop-training/ Course Overview :- 1. Master fundamentals of Hadoop 2.7 and YARN 2. Setting up Pseudo node and Multi node cluster on Amazon EC2 3. Master HDFS MapReduce Hive Pig HBase etc. 4. Learn Spark Spark RDD Graphx MLlib writing Spark applications 5. Learn Data Science in real world Project Life cycle and Data Acquisition 6. Study tools and techniques of Experimentation Evaluation and Project Deployment 7. Prediction and Analysis Segmentation through Clustering 8. Big Data and ways to integrate R with Hadoop Click on the time-stamp below to move directly to the points you want to know- 0:24 Introduction Big Data 0:33 Introduction Data Science 0:48 Demand of Profession 1:11 Why Intellipaat 1:48 Course content 2:10 Job assistance Check our complete playlist here: https://www.youtube.com/user/intellipaaat/playlists?sort=dd view=50 shelf_id=3 https://www.youtube.com/user/intellipaaat/playlists?sort=dd view=50 shelf_id=5 Subscribe to our channel to get video updates. Hit the subscribe button above. Who should go for this course? ● Programming Developers and System Administrators ● Experienced working professionals Project managers ● Big Data Hadoop Developers ● Mainframe Professionals Architects Testing Professionals ● Business Intelligence Data warehousing and Analytics Professionals ● Big Data Specialists ● Business Analysts and Business Intelligence professionals ● Statisticians Developers ● Information Architects Read more about Big Data and Data Science from our free tutorials - https://intellipaat.com/tutorial/data-science-tutorial/ https://intellipaat.com/tutorial/big-data-and-hadoop-tutorial/ Read latest questions asked in Interview: https://intellipaat.com/interview-question/big-data-hadoop-interview-questions/ https://intellipaat.com/interview-question/data-science-interview-questions/ For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/intellipaatonline/ - Twitter: https://twitter.com/intellipaat For more Details please visit - http://intellipaat.com/ Or drop an email to - firstname.lastname@example.org Call us at - +91-7847955955
In this Intellipaat big data vs data science vs data analytics video you will understand the basic difference between these technologies. A lot of people generally gets confused between these and often believes they are all one and the same. Watch this video to find out the difference in them. Also subscribe to Intellipaat channel to get regular updates on them: https://goo.gl/hhsGWb Intellipaat Big Data Data Science Masters Course Training: https://goo.gl/iYrwgC Are you interested to learn big data data science or data analytics to get high paying jobs? Enroll in our Intellipaat course become a certified Professional (https://goo.gl/iYrwgC). All Intellipaat trainings are provided by Industry experts and is completely aligned with industry standards and certification bodies. If you’ve enjoyed this data science vs machine learning or big data analytics or data analytics video Like us and Subscribe to our channel for more informative tutorials. Got any questions? Ask us in the comment section below. ---------------------------- Intellipaat Edge 1. 24*7 Life time Access Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs 5. Industry Oriented Course ware 6. Life time free Course Upgrade BigDatavsDataSciencevsDataAnalytics DataSciencevsMachineLearning BigDatavsDataAnalytics ------------------------------ For more Information: Please write us to email@example.com or call us at: +91- 7847955955 Website: https://goo.gl/iYrwgC Facebook: https://www.facebook.com/intellipaatonline LinkedIn: https://www.linkedin.com/in/intellipaat/ Twitter: https://twitter.com/Intellipaat
Manufacturing innovation trends - how manufacturing is changing. Lessons from Toyota assembly lines for fork lift trucks. How to optimise factory production with just-in-time and hundreds of small enhancements with huge total impact using predictive analytics and Big Data. How to harness human genius in the workforce to make things faster and more reliably. Key manufacturing trends presented by Futurist keynote speaker Patrick Dixon - tour of Toyota factory site in Mjölby Sweden.
Download all files to follow along with video and do homework is zipped folder here: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/EDAB/DownloadFilesForEDAB08.zip To download individual files visit class web site here: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/EDAB/EDAB.htm This video is an introduction to Data Modeling in Excel Power Pivot to create reports and Dashboards. Learn how to use Power Query to import SQL Big Data into the Data Model. Learn how to build DAX Formulas. Lean how to create an entire Data Model and then build Data Model PivotTables and Data Model Charts. This class : Data Analysis Business Intelligence Made Easy with Excel Power Tools - Excel Data Analysis Basics = E-DAB Class – Sponsored by YouTube and taught by Mike Girvin Highline College Instructor Microsoft Excel MVP and founder of the excelisfun channel at YouTube. This is a free educational resource for people how want to learn about the Basics of Data Analysis and Business Intelligence using Microsoft Power Tools such as PivotTables Power Query Power Pivot Power BI Desktop and more. Topics: 1. (00:15) Introduction Over view of entire Big Data DAX Project. 2. (02:20) Download Files. 3. (02:57) What is a Data Model PivotTable and why Power Pivot is called “Power Pivot” 4. (04:03) Differences Between Standard PivotTable Data Model PivotTable 5. (06:20) Average Daily Reve Example and why DAX is Easier than Standard PivotTable. 6. (07:33) Choice between: Standard PivotTable Data Model PivotTable 7. (08:18) What is a SQL Database? 8. (09:06) Import Big Data from SQL Database using Power Query 9. (11:10) Load SQL Tables into Columnar Database in Power Pivot’s Data Model 10. (11:38) How to get into the Data Model. Into to Data Model. 11. (13:00) Create Relationships in Design View 12. (14:17) What is a Date Table? Create Date Table. 13. (18:40) Hide Column From Client Tool 14. (19:30) Overview of DAX Formulas 15. (20:58) DAX Calculated Column for Line Revenue 16. (23:42) Row Context 17. (25:55) DAX Measure to add Calculated Column using SUM 18. (27:07) SUMX DAX Measure to calculate Total Revenue without a Calculated Column. 19. (29:37) Compare and contrast Calculated Column and Measures. 20. (31:18) Create Data Model PivotTable 21. (33:22) Filter Context 22. (35:03) Create More DAX Formulas: Total COGS Gross Profit and Percent Gross Profit. 23. (37:54) Create Dashboard for Gross Profit. 24. (40:20) Create Running Total and Average Daily Revenue DAX Measures and Final Data Model PivotTable. 25. (43:40) Summary
“Kettle” is an extremely popular open source integration platform providing a codeless workflow-driven environment to graphically integrate transform and cleanse relational flat file stream data lake and other sources to accelerate Neo4j deployment and simplify maintenance. In this session learn best practices for using Neo4j and Kettle. Matt Casters Neo4j BigData Neo4j GraphConnect
Topic: Spark Mesos Akka Cassandra and Kafka (SMACK) Stack Real Time Big Data Speaker: Rohit Bharadwaj Event: Great Indian Developer Summit 2017 Day 3 - GIDS.Data / Cloud (Thu 27 April 2017)
Aditya Shivam recently got a new job as a Consultant at Atos. He was earlier working as a Senior Software Engineer at Nucleus Software where he wanted to move away from Oracle and SQL and get on to the latest trends in the industry i.e. Machine Learning and Big Data. I felt that I had the pre-requisites to join these two fields. This video is an upskilling success story of Aditya Shivam where he shares how he got to know about Simplilearn his experience with Simplilearn how the course helped him learn the basics of Machine Learning and helped him to get a new job as a Consultant the field of AI Machine Learning and RPA. Aditya had first thought of taking up just one course on Big Data. It was then he came to know about the Big Data Master’s course and that made him consider it. He went back and consulted with a few of his mentors and then decided to go ahead with the master’s course. He took up the Big Data Master’s course from Simplilearn. It started with Big Data Hadoop and Spark Developer course and covered MongoDB Big Data and Hadoop Administrator Storm Kafka and Cassandra. He also got complimentary courses on Data Science with R and Data Science with SAS courses. After finishing the classes he took some time out to explore the tools on my own. He prepared myself for 4-5 months and then he started giving interviews in this domain. After he did the course he got to attend a lot of interviews. The companies he interviewed for were happy to know that he knew Big Data Python ML and Data Science. Once he started interviewing with companies he realized that the training by Simplilearn has set up a base for him and now everything will move uphill from here. He then got a new job offer for the role of Consultant at Atos for AI ML and RPA with over 40% hike. He has plans to continue his career in AI Data Science and Robotics. Because all of these domains are still in their infant states and evolving and they will be in the market at least for the next 10-15 years. Begin your Big Data journey with us today!
This video was recorded at Scala Days Berlin 2016 follow us on Twitter @ScalaDays or visit our website for more information http://scaladays.org Abstract: Spark is implemented in Scala and its user-facing Scala API is very similar to Scala s own Collections API. The power and concision of this API are bringing many developers to Scala. The core abstractions in Spark have created a flexible extensible platform for applications like streaming SQL queries machine learning and more. Scala s uptake reflects the following advantages over Java: A pragmatic balance of object-oriented and functional programming. An interpreter mode which allows the same sort of exploratory programming that Data Scientists have enjoyed with Python and other languages. Scala-centric Notebooks are also now available. A rich Collections library that enables composition of operations for concise powerful code. Tuples are naturally expressed in Scala and very convenient for working with data. Pattern Matching makes data deconstruction fast and intuitive. Type inference provides safety feedback to the developer yet minimal manual typing of actual type signatures. Scala idioms lend themselves to the construction of small domain specific languages which are useful for building libraries that are concise and intuitive for domain experts. There are disadvantages too which we ll discuss. Spark like almost all open-source Big Data tools leverages the JVM which is an excellent general-purpose platform for scalable computing. However its management of objects is suboptimal for high-performance data crunching. The way objects are organized in memory and the subsequent impact that has on garbage collection can be improved for the special case of Big Data. Hence the Spark project has recently started a project called Tungsten to build internal optimizations using the following techniques: * Custom data layouts that use memory very efficiently with cache-awareness. * Manual memory management both on-heap and off-heap to minimize garbage and GC pressure. * Code generation to create optimal implementations of certain heavily-used expressions from user code. This talk discusses the strengths and weaknesses of Scala and the JVM for Big Data Spark in particular and how we might improve both to make them better tools for our needs.
Users of Big (and not so Big) Data roughly divide into three groups developers like us traditional data analysts and a hybrid called data scientists. The analysis prefer SQL SAS and similar traditional tools. The scientists (mostly statisticians really) prefer Python and R with Julia emerging. The Developers started with Java but they are being seduced by Scala because it offers ideal tools for data-centric applications. This talk explains why data-centric applications are driving Scala adoption. Scala already provides these essentials: Expressive DSLsThe JVMActors for distributed scalingPrimitive handlingFunctional combinatorsWe ll examine code examples from the following tools: Scalding and Summingbird - for Hadoop and StormSpark and H2O - the Next Generation...Spire and Algebird - MathematicsFinally we ll discuss what s missing and what s ahead. Author: Dean Wampler Dean Wampler is the Big Data Architect at Typesafe and specializes in the application of Functional Programming principles to Big Data applications using Hadoop and alternative technologies.
** Hadoop Training: https://www.edureka.co/big-data-hadoop-training-certification ** This Edureka video on Data Science vs Big Data vs Data Analytics will explain you the similarities and differences between them. Also you will get a complete insight of the skills required to become a Data Scientist Big Data Professional and Data Analyst. Below topics are covered in this video: 1. What is Data Science Big Data Data Analytics? 2. Roles and Responsibilities of Data Scientist Big Data Professional and Data Analyst 3. Required Skill set. 4. Understanding how data science big data and data analytics is used to drive the success of Netflix. Check our complete Hadoop playlist here: https://goo.gl/hzUO0m Do subscribe to our channel and hit the bell icon to never miss an update from us in the future. PG in Artificial Intelligence and Machine Learning with NIT Warangal : https://www.edureka.co/post-graduate/machine-learning-and-ai Post Graduate Certification in Data Science with IIT Guwahati - https://www.edureka.co/post-graduate/data-science-program (450+ Hrs || 9 Months || 20+ Projects 100+ Case studies) ----------------------------------------------------------------- How does it work? 1. This is a 5 Week Instructor-led Online Course 40 hours of assignment and 30 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! -------------------------------------------------------------------- About The Course Edureka’s Big Data and Hadoop online training is designed to help you become a top Hadoop developer. During this course our expert Hadoop instructors will help you: 1. Master the concepts of HDFS and MapReduce framework 2. Understand Hadoop 2.x Architecture 3. Setup Hadoop Cluster and write Complex MapReduce programs 4. Learn data loading techniques using Sqoop and Flume 5. Perform data analytics using Pig Hive and YARN 6. Implement HBase and MapReduce integration 7. Implement Advanced Usage and Indexing 8. Schedule jobs using Oozie 9. Implement best practices for Hadoop development 10. Work on a real life Project on Big Data Analytics 11. Understand Spark and its Ecosystem 12. Learn how to work in RDD in Spark --------------------------------------------------------------------- Who should go for this course? If you belong to any of the following groups knowledge of Big Data and Hadoop is crucial for you if you want to progress in your career: 1. Analytics professionals 2. BI /ETL/DW professionals 3. Project managers 4. Testing professionals 5. Mainframe professionals 6. Software developers and architects 7. Recent graduates passionate about building a successful career in Big Data --------------------------------------------------------------------- Why Learn Hadoop? Big Data! A Worldwide Problem? According to Wikipedia Big data is collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. In simpler terms Big Data is a term given to large volumes of data that organizations store and process. However it is becoming very difficult for companies to store retrieve and process the ever-increasing data. If any company gets hold on managing its data well nothing can stop it from becoming the next BIG success! The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago with increasing amount and complexity of data these are soon becoming obsolete. The good news is - Hadoop has become an integral part for storing handling evaluating and retrieving hundreds of terabytes and even petabytes of data. --------------------------------------------------------------------- Opportunities for Hadoopers! Opportunities for Hadoopers are infinite - from a Hadoop Developer to a Hadoop Tester or a Hadoop Architect and so on. If cracking and managing BIG Data is your passion in life then think no more and Join Edureka s Hadoop Online course and carve a niche for yourself! Please write back to us at firstname.lastname@example.org or call us at +91 88808 62004 for more information. --------------------------------------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information Please write back to us at email@example.com or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
This video is a webinar recording of Rajib Layek [ Business Analyst Expert ] talking about Data Science Hype vs Reality Industrial Revolution 4.0 Data Science Application Machine Learning vs AI What is Big Data Big Data Analysis Pipeline Fraud Detection Case Study Data Science Jobs Data Science Roles Data Science Companies If you dream to have a career in data science you can trust us to help you realize your dream. Check out our PG Diploma in Data Science program - Now with placement guarantee http://bit.ly/2W7G5kk About the program Manipal ProLearn’s PG Diploma in Data Science program is designed to provide you with a broad understanding of the basic and advanced concepts of Data Science. The Data Science training will enable you to implement Big Data techniques using tools using R Excel Tableau SQL NoSQL Hadoop Pig Hive Apache Spark and Storm. After completing the Data Science diploma you’ll be considered as a strong and competent data scientist. The course will help you to: Perform data analysis modelling predictive analysis and storytelling through data visualization which is crucial to business decision-making. Analyze data sets to summarize their main characteristics often with visual methods with Exploratory Data Analysis Understand and use Big Data technologies as enablers to deploy enterprise information management and solve business problems Learn artificial intelligence and neural network that emphasizes the creation of intelligent machines. Apply the methods tools and techniques to real-world problems by leveraging technologies such as R Python Excel SQL NoSQL Tableau Hadoop Pig Hive Apache. Spark and Storm and other open source and proprietary products as well. Communicate analytics problems methods and findings effectively verbally visually and in writing. Become more accurate in predicting outcomes without Machine Learning. Cleaning and unify messy and complex data set with Data Scrapping and Data Wrangling Help Companies make critical decisions through analysis modeling visualization etc. Learn the emerging data science of unstructured data analysis and robotic process automation by choosing an elective based on your area of interest. Term 1: Programming for Data Science Data Scrapping and Data Wrangling Statistical techniques for Data Science Machine Learning Data Analysis and Visualisation Big Data Technologies Term 2: Artificial Intelligence Elective 1 (Banking Analytics or Marketing Analytics) Elective 2 (Unstructured Data Analysis/Robotic Process Automation) Project/Internship Manipal Placement Guarantee Manipal ProLearn promises that every student successfully completing the academic requirements of the PG Diploma in Data Science from Manipal Academy of Higher Education (MAHE) and conforming to the program’s disciplinary norms will be placed by the end of the program. A student is considered to be placed when he/she receives an offer letter for a paid position from a company. ---------------------------------------------------------------------------------------------------------------------------------- Subscribe to our channel to get video updates. Hit the subscribe button above https://www.youtube.com/channel/UCllnb6S5fPzpVYcV8KYzhnA?view_as=subscriber?sub_confirmation=1 Also follow us on other channels: Facebook: https://www.facebook.com/manipalprolearn/ Twitter: https://twitter.com/manipalprolearn LinkedIn: https://www.linkedin.com/company/manipal-prolearn/
Alexander Hendorf is known for his expertise in Big Data Data Mining Machine Learning and Artificial Intelligence. For sharing his knowledge he has become a popular speaker at international conferences such as PyData and PyCon events around the world. On top of being a Fellow with the Python Software Foundation he was recently elected Chairman of the German Python Association. Furthermore he was the longtime Program Chair of the EuroPython conference and is currently chairing the EuroSciPy and PyConDE Pydata Berlin conferences. Besides being a data science practitioner he also has an entrepreneurial track record. He is the founder of an independent music label and has begun developing trading platforms and databases for the music industry. Now he offers his vast digitization knowledge as a consultant. Websites linked to Alexander Hendorf: (https://www.koenigsweg.com/koenigsweg/berater/ahendorf/index_ger.html) Königsweg (https://www.koenigsweg.com) LinkedIn (https://www.linkedin.com/in/hendorf/) Twitter (https://twitter.com/hendorf) PyData Südwest (https://www.meetup.com/PyData-Suedwest/) Python Software Verband e.V. (https://python-verband.org/) Visit us at https://www.whu.edu Facebook: https://www.facebook.com/visit.WHU Twitter: https://twitter.com/whu_edu Instagram: https://www.instagram.com/whu_official/ Xing: https://www.xing.com/companies/whu LinkedIn: https://www.linkedin.com/edu/school?id=11993
Oleksandr Konduforov Data Science Competence Leader at AltexSoft discusses the differences between data science machine learning artificial intelligence and big data. How data science makes use of data? What is the difference between traditional programming and machine learning? How to identify strong and weak types of artificial intelligence? Who are big data engineers? And how is this all connected to fake reviews? Watch the video to find out more! Read more in our blog: https://www.altexsoft.com/blog/data-science-artificial-intelligence-machine-learning-deep-learning-data-mining/ Learn more about AltexSoft: https://www.altexsoft.com Follow us on LinkedIn: https://www.linkedin.com/company/altexsoft/ Follow us on Facebook: https://www.facebook.com/altexsoft/ Follow us on Twitter: https://twitter.com/AltexSoft DataScience MachineLearning BigData
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