ADET LESSON 2

• Data Science

  • combines multiple fields including statistics, scientific methods, anddata analysis.
• History of Data Science - In 2001, William S. Cleveland wanted to bring data mining to anotherlevel.
• COMPUTER SCIENCE + DATA MINING = DATA SCIENCE
• Data Mining

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

  • stands for human activities aimed at gaining some insight on a dataset.
• Three Types Of Data Analysis

  • Predictive (forecasting)
  • Descriptive (business intelligence and data mining)
  • Prescriptive (optimization and simulation)
• Predictive (forecasting)

  • turns data into valuable, actionable information
• Descriptive (business intelligence and data mining)

  • looks at data and analyzes past events for insight as to how toapproach the future.
• Prescriptive (optimization and simulation)

  • automatically synthesizes big data, mathematical sciences, businessrules.
• How Data Mining Can Help a Business Improve Competitiveness (4)

  • Sales Forecasting
  • Database Marketing
  • Market Segmentation
  • E-commerce Basket Analysis
• 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 therequired purpose.
• 6 There are many ways to collect data such as:

  • ONLINE SURVEY
  • OBSERVATION
  • INTERVIEW
  • CASE STUDY METHODS
  • QUESTIONNAIRE
  • GOOGLE FORMS
• 3 Data preprocessing techniques includes:

  • Formatting of data
  • Cleaning of data
  • Sampling of data
• Data Aggregation

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

  • process of changing or converting information into meaningful.
• 6 Data Processing Cycle

  • Data Collection
  • Data Preparation
  • Data Input
  • Processing
  • Data Output
  • Data Storage
• 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 toprocess using traditional methods.
• Three V’s:

  Volume

  Velocity

  Variety
• Volume

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

  • With the growth in the Internet of Things, data streams in tobusinesses.
• 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.
• 3 Apache Hadoop offers below modules:

  • Hadoop Common
  • Hadoop Distributed File System (HDFS)
  • Hadoop YARN
• 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.