UDAU

• Data


  A collection of discrete values that convey information, describing quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted
• Datum


  A singular piece of data
• Internal sources of data

  • Reports and records of the organization itself
• External sources of data


  • Outside the organization
• Secondary Data


  these are not originally collected rather obtained from

  already published/unpublished sources
• Primary Data


  It is collected for the first time. It is original and more

  reliable.
• Nominal


  level of measurement classifies data into mutually exclusive

  (nonoverlapping) categories in which no order or ranking can be imposed on the

  data.
• Ordianal


  level of measurement classifies data into categories that can be ranked; however, precise differences between the ranks do not exist.
• Interval


  level of measurement ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero.
• Ratio


  level of measurement possesses all the characteristics of interval measurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population.
• Interval


  What level of measurement is SAT score?
• Ratio


  What level of measurement is salary?
• Nominal


  What level of measurement is ZIP code?
• Ordinal


  What level of measurement is rating scale?
• ordinal


  What level of measurement is small, medium, large?
• data


  unorganized raw facts that need processing without which it is

  seemingly random and useless to humans.
• information


  a group of data that collectively carry a logical meaning
• data


  measured in bits and bytes
• quantitative data collection


  measurable type of data collection method
• 7 characteristics that define data quality

  Accuracy and Precision

  • Legitimacy and Validity

  • Reliability and Consistency

  • Timeliness and Relevance

  • Completeness and Comprehensiveness

  • Availability and Accessibility

  • Granularity and Uniqueness
• Data cleansing or data cleaning

  is the process of identifying and removing (or correcting) inaccurate records from a dataset,
• true


  true or false.

  After cleaning, a dataset should be uniform with other related datasets in the operation.
• Exploratory data analysis, or EDA, is a (mainly) visual approach and philosophy that focuses on the initial ways by which one should explore a data set or experiment
• Exploratory data analysis


  Provides a variety of tools for quickly summarizing and gaining insight into a set of data
• Main aspects of EDA

  • Openness - A person exploring the data should be open to all possibilities prior to its exploration
  • Skepticism - One must ensure that the obvious story the data tells is not misleading
• General purpose of EDA

  To take a general view of some given data without making any assumptions about it, and to get a feel for the data and what it might mean as opposed to reject or accept some sort of premise around it before we begin its exploration
• EDA


  Lets the data speak for itself instead of trying to force the data into some sort of pre-determined model
• Uses of EDA


  • Catching mistakes and anomalies
  • Gaining new insights into data
  • Detecting outliers in data
  • Testing assumptions
  • Identifying important factors in the data
  • Understanding relationships
  • Helping figure out our next steps with respect to the data
• Methods to summarize data

  • Numerical summarization
  • Data visualization

  
• Numerical summarization


  Calculating measures like mean, median, mode, variance, and standard deviation
• Numerical summarization example


  • Given the data: 7, 1, 8, 6, 5, 4, 4, 8, 8, 1
  • Find the mean, median, and mode
• Finding the mean


  Add up all the values

  1. Divide the sum by the number of values
• Finding the median (even-numbered situation)

  Arrange the values in ascending order

  1. Identify the middle numbers
  2. Get the average of the middle numbers
• Finding the median (odd-numbered situation)

  Arrange the values in ascending order

  1. Identify the middle number
• Mode


  The value that occurs most frequently in the data set
• Finding the population variance

  Get the mean

  1. Subtract each number from the mean
  2. Square the values from step 2
  3. Add all the squared values
  4. Divide the sum by the number of values
• Standard deviation


  The square root of the variance
• Data visualization


  A graphical representation of information and data that reveals hidden information through simple charts and diagrams

  
• Common general types of data visualization

  • Charts
  • Tables
  • Graphs
  • Maps
  • Infographics
  • Dashboards

  
• Boxplot


  A graph of a data set obtained by drawing a horizontal line from the minimum data value to Q1, drawing a horizontal line from Q3 to the maximum data value, and drawing a box whose vertical sides pass through Q1 and Q3 with a vertical line inside the box passing through the median or Q2