Big Data Reviews

  • What is Big Data? Big Data Explained (Hadoop & MapReduce)

    What exactly is Big Data? This video defines and explains Big Data as well as Hadoop and MapReduce in simple language.
  • Why Big Data Analytics is the Best Career Path? Become a big Data Engineer in 2018

    Check out my latest Video:
    Why Big Data Analytics is the Best Career Path? Become a big Data Engineer in 2018.

    Why Big Data Analytics is the Best Career Path?

    Hello guys my name is Daniel and you are watching beginner tuts and in this video, we are going to talk about why big data analytics is the best career path? If you’re looking for an amazing career option in information technology and don’t know anything about this industry, then this video can help you become a big data analytics engineer. The Average salary of a big data engineer is 100,000 annually… That’s right guys. 100 grand a year. Now you must be wondering, what the heck is big data??

    Big data is a term for data sets that are so large or complex that traditional data processing application software is too weak to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

    The Great White is considered to be the King of the Ocean. This is because the great White is on top of its game. Imagine if you could be on top of the game in the ocean of Big Data!

    Big Data is everywhere and there is almost an urgent need to collect and preserve whatever data is being generated, for the fear of missing out on something important. There is a huge amount of data floating around. What we do with it is all that matters right now. This is why Big Data Analytics is in the frontiers of IT. Big Data Analytics has become crucial as it aids in improving business, decision makings and providing the biggest edge over the competitors. This applies for organizations as well as professionals in the Analytics domain. For professionals, who are skilled in Big Data Analytics, there is an ocean of opportunities out there.


    Why Big Data Analytics is the Best Career move?
    If you are still not convinced by the fact that Big Data Analytics is one of the hottest skills, here are 5 more reasons for you to see the big picture.

    1. Soaring Demand for Analytics Professionals:
    Jeanne Harris, senior executive at Accenture Institute for High Performance, has stressed the significance of analytics professionals by saying, “…data is useless without the skill to analyze it.” There are more job opportunities in Big Data management and Analytics than there were last year and many IT professionals are prepared to invest time and money for the training.

    The job trend graph for Big Data Analytics, from, proves that there is a growing trend for it and as a result there is a steady increase in the number of job opportunities.

    The current demand for qualified data professionals is just the beginning. Srikanth, the Bangalore-based cofounder and CEO of CA headquartered Fractal Analytics states: “In the next few years, the size of the analytics market will evolve to at least one-thirds of the global IT market from the current one-tenths”.

    Technology professionals who are experienced in Analytics are in high demand as organizations are looking for ways to exploit the power of Big Data. The number of job postings related to Analytics in Indeed and Dice has increased substantially over the last 12 months. Other job sites are showing similar patterns as well. This apparent surge is due to the increased number of organizations implementing Analytics and thereby looking for Analytics professionals.

    In a study by QuinStreet Inc., it was found that the trend of implementing Big Data Analytics is zooming and is considered to be a high priority among U.S. businesses. A majority of the organizations are in the process of implementing it or actively planning to add this feature within the next two years.

    2. Huge Job Opportunities & Meeting the Skill Gap:
    The demand for Analytics skill is going up steadily but there is a huge deficit on the supply side. This is happening globally and is not restricted to any part of geography. In spite of Big Data Analytics being a ‘Hot’ job, there is still a large number of unfilled jobs across the globe due to shortage of required skill. A McKinsey Global Institute study states that the US will face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using Big Data by 2018.

    According to Srikanth, co-founder and CEO of Fractal Analytics, there are two types of talent deficits: Data Scientists, who can perform analytics and Analytics Consultant, who can understand and use data. The talent supply for these job title, especially Data Scientists is extremely scarce and the demand is huge.

    3. Salary Aspects:
    Strong demand for Data Analytics skills is boosting the wages for qualified professionals and making Big Data pay big bucks for the right skill. This phenomenon is being seen globally where countries like Australia and the U.K are witnessing this ‘Moolah Marathon’.
  • Science Documentary 2016 | Big Data

    With the rapid emergence of digital devices, an unstoppable, invisible force is changing human lives in incredible ways. Every two days the human race is now generating as much data as was generated from the dawn of humanity through the year 2003. The massive gathering and analyzing of data in real time is allowing us to address some of humanity's biggest challenges but as Edward Snowden and the release of NSA documents have shown, the accessibility of all this data comes at a steep price. This film captures the promise and peril of this extraordinary knowledge revolution.
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  • Лучшие в своём деле: Артур Хачуян | Большие данные — Big Data | ЛСД #7

    Разговоры про большие данные, защиту персональных данных и тревожное будущее с создателем главного российского BigData-алгоритма Артуром Хачуяном.

    Система искусственного интеллекта, которая может анализировать открытые источники, вычленять из них знания, собирать геоинформацию, обрабатывать чеки, банковские транзакции, знания о людях, как часто они путешествуют и даже какое порно смотрят. Будущее уже здесь и оно знает про вас всё.

    -=Уголок спонсора=-
    Открывай счет в «Точке» —

    Прошлые выпуски ЛСД:

    Лучшие в своём деле: Пётр Верзилов про акционизм и нападение | ЛСД #6

    Лучшие в своём деле: Митя Алешковский | ЛСД #5

    Лучшие в своём деле: Наталия Фишман | ЛСД #4

    Лучшие в своём деле: Евгений Чичваркин | ЛСД #3

    Лучшие в своём деле: хирург-онколог Андрей Павленко | ЛСД #2

    Лучшие в своём деле: Покрас Лампас | ЛСД #1

    Для тех, кто хочет помочь с субтитрами или переводом этого ролика:


    Предложения по поводу коллабораций, развития канала и сотрудничеству (кроме рекламы):
  • Big Data Analytics using Python and Apache Spark | Machine Learning Tutorial

    Apache Spark is the most active Apache project, and it is pushing back Map Reduce. It is fast, general purpose and supports multiple programming languages, data sources and management systems. More and more organizations are adapting Apache Spark to build big data solutions through batch, interactive and stream processing paradigms. The demand for trained professionals in Spark is going through the roof. Being a new technology, there aren't enough training sources to provide easy guidance on building end-to-end solutions.

    Section 1: Introduction
    Lecture 1
    About the course
    Lecture 2
    About V2 Maestros
    Lecture 3
    Resource Bundle
    Section 2: Overview
    Lecture 4
    Hadoop Overview
    Lecture 5
    HDFS Architecture
    Lecture 6
    Map Reduce - How it works
    Lecture 7
    Map Reduce - Example
    Lecture 8
    Hadoop Stack
    Lecture 9
    What is Spark?
    Lecture 10
    Spark Architecture - Part 1
    Lecture 11
    Spark Architecture - Part 2
    Lecture 12
    Installing Spark and Setting up for Python
    Quiz 1
    Hadoop and Spark Architecture
    5 questions
    Section 3: Programming with Spark
    Lecture 13
    Spark Transformations
    Lecture 14
    Spark Actions
    Lecture 15
    Advanced Spark Programming
    Lecture 16
    Python - Spark Programming examples 1
    Lecture 17
    Python - Spark Programming Examples 2
    Quiz 2
    Data Engineering with Spark
    5 questions
    Lecture 18
    PRACTICE Exercise : Spark Operations
    Section 4: Spark SQL
    Lecture 19
    Spark SQL Overview
    Lecture 20
    Python - Spark SQL Examples
    Quiz 3
    Spark SQL
    2 questions
    Lecture 21
    PRACTICE Exercise : Spark SQL
    Section 5: Spark Streaming
    Lecture 22
    Streaming with Apache Spark
    Lecture 23
    Python - Spark Streaming examples
    Quiz 4
    Spark Streaming
    3 questions
    Section 6: Real time Data Science
    Lecture 24
    Basic Elements of Data Science
    Lecture 25
    The Dataset
    Lecture 26
    Learning from relationships
    Lecture 27
    Modeling and Prediction
    Lecture 28
    Data Science Use Cases
    Lecture 29
    Types of Analytics
    Lecture 30
    Types of Learning
    Lecture 31
    Doing Data Science in real time with Spark
    Quiz 5
    Spark Data Science
    5 questions
    Section 7: Machine Learning with Spark
    Lecture 32
    Spark Machine Learning
    Lecture 33
    Analyzing Results and Errors
    Lecture 34
    Linear Regression
    Lecture 35
    Spark Use Case : Linear Regression
    Lecture 36
    Decision Trees
    Lecture 37
    Spark Use Case : Decision Trees Classification
    Lecture 38
    Principal Component Analysis
    Lecture 39
    Random Forests Classification
    Lecture 40
    Python Use Case : Random Forests & PCA
    Lecture 41
    Text Preprocessing with TF-IDF
    Lecture 42
    Naive Bayes Classification
    Lecture 43
    Spark Use Case : Naive Bayes & TF-IDF
    Lecture 44
    K-Means Clustering
    Lecture 45
    Spark Use Case : K-Means
    Lecture 46
    Recommendation Engines
    Lecture 47
    Spark Use Case : Collaborative Filtering
    Lecture 48
    Real Time Twitter Data Sentiment Analysis
    Quiz 6
    Spark Machine Learning Algorithms
    4 questions
    Lecture 49
    PRACTICE Exercise : Spark Clustering
    Lecture 50
    PRACTICE Exercise : Spark Classification
    Section 8: Conclusion
    Lecture 51
    Closing Remarks
    Lecture 52
    BONUS Lecture : Other courses you should check out
The Number One Menace to All Organizations

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