Qualitative vs. Quantitative Data

• Data is information either transmitted or stored, available in numbers, text, or pictures
• Data is collected and translated for analysis, but lacks context for humans or computers

Types of Data

• The text refers to two forms of data, Qualitative and Quantitative data
• Qualitative data is descriptive such as, "What a nice day it is"
• Quantitative data is numerical information such as, "1" or "3.65."
• Quantitative data has discrete and continuous subtypes
• Discrete data is expressed as a specific, countable value
• Continuous data can take on any value in an interval and can be measured
• Examples of discrete data include the number of months in a year or the number of family members
• Examples of continuous data are the amount of oxygen in the atmosphere and the age of family members

Real World Data Examples

• Video platforms use data and data analysis to determine suggested videos based on a users preferences
• The way platforms determine videos is by storing and studying user preferences and the videos people usually play after watching a video and forming a algorithm
• Data analysis is applied in real life in the the entertainment industry

Benefits of Data in the Entertainment Industry

• Prediction of audience interests
• Optimized or on-demand scheduling of media streams in digital media distribution platforms
• Providing insights from customer reviews
• Effective targeting of the advertisements

What is Data Science

• Data science extracts meaningful interpretations from the data
• Tremendous data amounts generated from daily activities like; purchases or ATM transactions
• Social media activities that help give a clear idea of what can be offered to enrich daily life
• Useful in helping various industries cater and assist authorities to nab criminals and help learn cricket in a better way

Data Science Careers

• Data Scientists gather and analyze large sets of structured/unstructured data
• Data Scientists roles include; roles combine computer science, statistics, and mathematics
• Business Intelligence Analysts asses the market to find the latest industry trends to clear a companys strategy
• Data Engineer looks at data for their businesses and also the data of third parties, and creates robust data algorithms
• Data Architects creates a data management blueprint that data management systems use to centralize, integrate and maintain the data sources
• Senior Data Scientists design new data analyzing standards because they anticipate future business needs

What Data Science Achieves

• Data science assists in answering a lot of questions
• Questions that are divided into five common types
• The questions will result in a decision in A or B
• The mechanism is called binary classification in the case of only two choices
• Multiclass classification algorithms if we try to predict between more than two choices
• Another question is Is this an outlier, which aims to fin anomalies in data that is consistent
• Some questions that focus on anomaly detection include, 'Is this e-mail normal or spam?' Or 'You are checking your car tyre pressure. Is the reading normal?'
• Answering 'what will probably be the value of this variable?, requires predicting numerical values of continuous variables for data like rainfall amounts or a team score
• This type of algorithm is regression
• Next is 'How is the data grouped?' where data is separated into distinct groups based on chosen parameters, which is called clustering, a type of unsupervised machine learning
• Final question is 'what should be done now?' this is primarily for self driving cars or autonomous robots that are based on external changes, which is helped with machine learning in reinforcement learning

Data Visualization

• Data visualization represents data or information in graphs, charts, or other visual formats
• Provides a way to see and understand/communicate trends, outliers, and patterns
• Common types are charts, graphs, tables, maps, and histograms

Data Visualization Examples

• Using and reading pie charts and line graphs to determine food preference or number of students that week respectively effectively visualize data

Collecting the Right Data

• The quality, completeness, and format of data are key to understanding data collection best practices
• It is extremely important the analyst ask the correct and most poignant questions that are crucial to the analysis of that data
• When analyzing data make sure you consider the data as a whole
• Make sure to consider initial analysis questions by brainstorming and developing a draft guideline with the analysts specific questions they have
• The analyst should also determine which statistical techniques are applicable and who uses the final results

Statistical Analysis Techniques

• Regression Analysis finds relationships and correlations among the different variables in data
• Cohort Analysis compares how different customers/customer groups differ over time
• Predictive Analysis analyzes historical datasets to predict future possibilities
• Each can also be used for generating alternative risk assessments

Applications of Data Science

• Data Science is everywhere and is being used by many companies to grow their business
• Track searches and learn user preferences in digital advertising
• With speech recognition, take inputs from users and helping organizations make speech recognition a lot more accurate
• There have been four tasks to using text analytics technologies including; querying data, mining for data, and analyzing the data to get insights

Analytics on Image Data

• You can use Image recognition to process images to identify people, patterns, logos, objects, or faces
• Computer vision technologies achieve image recognition in mobile phone cameras
• Also used to check attendance, identify individuals, and search content

Overview of AI

• Aims to make computers behave like humans through goals
• Logical Reasoning is the AI that wants to achieve computers doing intelligent tasks
• Knowledge Representation is about making computers describe objects to describe a car violating traffic
• Planning and navigation would be getting computers to self drive a car
• Natural Language Processing would be to have computers process and understand language like a web translator
• Perception gives computers the ability to touch, smell, and observe the environment
• Emergent Intelligence makes computers exhibit non-explicit behavior and moral reasoning
• Artificial Intelligence and Machine Learning are subsets of each other while Machine Learning is the superset of Deep Learning