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• Emerging Technologies 

  new or latest technology, the continuing development of the current technology.
• Technological Evolution and development

  theory of radical transformation or change of society.
• Technology

  • presently includes advancements in communication and how information is handled.
• Evolution

  • process of developing by gradual changes.
• Data

  • collection of facts,number,words,measurements,observations.
• Memory

  • storage space in the computer.
• Microprocessors

  • the brain of the Computer.
• Networks

  • allows your computer to connect to other computer.
• Logic Devices

  • process the boolean expression.
• Programmable Devices

  • used to build reconfigurable digital circuits.
• Human-computer interaction (HCI)

  • multidisciplinary field of study focusing on the design of computer technology.
• Data Science

  • combines multiple fields including statistics, scientific methods, and data analysis.
• History of Data Science

  • In 2001, William S. Cleveland wanted to bring data mining to another level.
• Data Mining

  • process of analysing data from different perspectives and summarising.
  • Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.
• Data Analysis

  • stands for human activities aimed at gaining some insight on a dataset.
• Predictive (forecasting)

  • turns data into valuable, actionable information.
• Descriptive

  • looks at data and analyzes past events for insight as to how to approach the future.
• Prescriptive

  • automatically synthesizes big data, mathematical sciences, business rules.
• Sales Forecasting

  • analysing when customers bought to predict when they will buy again.
• Database Marketing

  • marketing: examining customer purchasing patterns and looking.
• Market Segmentation

  • a classic use of data mining, using data to break down a market.
• E-commerce Basket Analysis

  - using mined data to predict future customer behavior
• Data Preprocessing

  part of data preparation method and a data mining technique.
• Data Collection

  • prepare machine learning models we need to collect data for the required purpose.
• Data Aggregation

  • type of data and information mining process where data is searched.
• Data Processing Cycle

  • process of changing or converting information into meaningful.
• Data Collection

  • the first and the most crucial stage the quality impacts
• Data Preparation

  • this stage,bad,incomplete or incorrect data will be eliminated.
• Data Input

  • cleaned data is entered through a computer  to process.
• Processing

  • data entered on the computer is processed.
• Data Output

  • processed data is now delivered to the user.
• Data Storage

  • last stage of the cycle where the data is stored for future.
• Big Data

  • data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods.
• Volume

  • Organizations collect data from a variety of sources, including business transactions, smart (IoT) devices.
• Velocity

  • With the growth in the Internet of Things, data streams in to businesses.
• Variety

  • Data comes in all types of formats – from structured, numeric data.
• Big Data with Hadoop

  • Tools to store and analyze data in Data Processing.
• Hadoop Common

  • module consists of the utilities to support other modules.
• Hadoop Distributed File System (HDFS)

  • High-throughput access to the application data.
• Hadoop YARN

  • cluster resource management and job scheduling are achieved.