Big data is a term defined for data sets that are large or complex that traditional data processing applications are inadequate. Big Data basically consists of analysis zing, capturing the data, data creation, searching, sharing, storage capacity, transfer, visualization, and querying and information privacy. BIG DATA is an evolving term that describes a large volume of data ( structured, unstructured and semi-structured) that has the potential to be mined for information and used in Machine Learning projects and other advanced analytical applications.
TABLE OF CONTENTS
Big data is a term defined for data sets that are large or complex that traditional data processing applications are inadequate. Big Data basically consists of analysis zing, capturing the data, data creation, searching, sharing, storage capacity, transfer, visualization, and querying and information privacy.
Who should study this course? (Big Data)
- Beneficial to recent graduates looking to get a foothold in the IT Industry.
- IT managers looking to better manage data analysis.
- Businesses looking to organize and analyze large amounts of vital data in order to improve business insights.
- Managers wanting to reach business goals and improve agility.
- IT professionals looking to implement new data analysis tools.
BEFORE YOU START
- It would be better if you have a basic knowledge of Java. …
- To work on Big Data, you should have a better understanding of SQL.
About the Course
This introductory course will begin discussions on defining, understanding and using data. The succeeding modules will discuss the facts, capabilities, and benefits of Big Data; the 3V’s of Big Data and Big Data Analytics. It will also present implementing data, Big Data Management and Big Data in the real world.
WHAT YOU’LL LEARN
- Be introduced to Data and its aspects.
- Be aware of the facts, capabilities, and benefits of Big Data.
- Learn the elements (3V’s) of Big Data and their characteristics.
- Study Big Data analytics and how it works.
- Learn about implementing Big Data.
- Understand Big Data management and its technologies.
- Find out about the myths and challenges
- Cost Savings
- Time Reductions
- New Product Development
- Understand the market conditions
- Control online reputation
According to IDC, the Big Data market would be worth $46.34 billion by 2018, its’ technology and associated services market is likely to grow at a compound annual growth rate (CAGR) of 23.1% from 2014 to 2019.
Real Career Impact
1. Huge Demand for Big Data Professionals
2. The Shortage of Big Data Talent
3. Wide Choice of Job Types and Technologies
4. Lucrative Salary Offers
Data analysts Data scientists Data architects
Database managers Big data engineers
- Big Data Using Hadoop and Python
- Digital Marketing
- Marketing Analytics
- Strategic Marketing
Basic/Nano Degree Certificate
Individual Certificates for each course
BIG DATA – ANALYTICS
Big Data is the Future
“ Information is the oil of the twenty-first century and Analytics is the combustion engine”
BIG DATA is an evolving term that describes a large volume of data ( structured, unstructured and semi-structured) that has the potential to be mined for information and used in Machine Learning projects and other advanced analytical applications.
In 2001 Gartner analyst Doug Laney identified the 3 Vs by which Big Data was often characterized. The extreme ‘volume’ of data, the wide ‘variety’ of data types and the ‘velocity’ at which data must be processed. More recently, 3 more Vs have been added to descriptions of Big Data –veracity, value and variability. Big Data volume is not measured in any way other than being described as Terabytes, petabytes, and exabytes……. as captured over time.
Big Data is a collection of data from various sources ranging from well-defined to loosely defined, derived from human or machine sources.
Big Data encompasses a wide variety of data types, including structured data in SQL databases and data warehouses; unstructured data such as text and document files held in Hadoop clusters; or No SQL systems; and semi-structured data, such as web server logs or streaming data from sensors. Further Big Data, includes multiple, simultaneous data sources, which may not otherwise be integrated. For example, a Big Data analytics project may attempt to gauge a product’s success and future sales by correlating past sales data, return data and online buyer review data for that product.
‘Voluminous’ data can come from innumerable different sources, such as business transaction systems, customer databases, medical records, internet clickstream logs, mobile applications, social networks, the collected results of scientific experiments, machine-generated data and real-time data sensors used in Internet of Things( IoT) environments. Data may also be left in its raw form or pre-processes condition using data mining tools or data preparation software before it’s analyzed.
‘Velocity’ refers to the speed at which big data is generated and must be processed and analyzed. Big data analytics projects ingest, correlate and analyze the incoming data, and then render an answer or result based on an overarching query. Velocity is also important as big data analysis expands into fields like machine learning and artificial intelligence(AI) where analytical processes automatically find patterns in the collateral data and use them to generate insights.
Bad data leads to inaccurate analysis and may undermine the value of business analytics because it can cause executives to mistrust data as a whole. The amount of uncertain data in an organization must be accounted for before it is used in big data analytics applications. IT and analytics teams also need to ensure that they have enough accurate data available to produce valid results.
As data collection and use has increased, so has data misuse. Concerned citizens who have experienced the mishandling of their data or been victims of a data breach are calling for laws around data collection transparency and consumer data privacy.
The basics of Hadoop is the most important tool in Big Data. Hadoop is an open source software framework used for storing and processing big data in a distributed manner on large clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks. Hadoop is used to store data in blocks in different machines and then merge them on demand. It’s programmed in Java and distributed by Apache Foundation.
Some of the common Big Data terms are: Big Data Analytics, Big Data Management, Graph database, Data Architecture, Data modeling, DW software, Map Reduce, Hadoop and No SQL
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