5.0 CHAPTER 5 - DATABASES AND DATA ANALYTICS 2023.pdf
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Chapter 5 Databases and Data Analytics By the end of this lecture, you will be able to: Discover the world of databases and data analytics. Learn about different types of databases, data analytics techniques, data warehousing, data visualization, and big data analytics. s s Part 1: Introduction t...
Chapter 5 Databases and Data Analytics By the end of this lecture, you will be able to: Discover the world of databases and data analytics. Learn about different types of databases, data analytics techniques, data warehousing, data visualization, and big data analytics. s s Part 1: Introduction to Databases Physical and logical views Characters, fields, records, tables, and databases Key fields Batch processing and real-time processing Database models Individual, company, distributed and commercial databases Database uses and security concerns Introduction Like a library, secondary storage is designed to store information and an organized collection of data A database is an electronic system that allows data to be easily accessed, manipulated and updated Data Examples of data include: • Facts or observations about people, places, things, and events • Audio, music, photographs, and video Type of data • Structured data • Semi structured data • Unstructured data Data Organization ▪ ▪ ▪ ▪ ▪ Character Field Record Table Database Key Field ▪ ▪ Unique identifier also known as primary key Common examples: • • • • • Social Security Numbers Student Identification Numbers Employee Identification Numbers Part Numbers Inventory Numbers Batch Processing ▪ Batch processing: ▪ Data is collected over a period of time and the processing happens later all at one time Real-time Processing ▪ Real-time processing: ▪ Also known as online processing because it happens immediately during the transaction Databases ▪ Collection of integrated data ▪ ▪ Logically related files and records Databases address data redundancy and data integrity Need for Databases ▪ ▪ ▪ ▪ Sharing Security Less data redundancy Data integrity Database Management ▪ ▪ ▪ DBMS engine Data definition subsystem Data dictionary or schema Database Management (Continue) • Data manipulation subsystem ❑ ❑ Query-by-example Structured Query Language (SQL) • Application generation subsystem • Data administration subsystem ❑ ❑ Database Administrators (DBAs) Processing rights DBMS Structure • Database model: ❑ DBMS programs work with data that is logically structured or arranged ❑ Model defined rules and standards for data in a database • Five common data models: ❑ ❑ ❑ ❑ ❑ Hierarchical database Network database Relational database Multidimensional database Object-oriented database Hierarchical Database • Fields or records structured in nodes • Nodes ❑ Points connected like branches of an upsidedown tree • One parent per node • Parent can have several child nodes ❑ One-to-many relationship Network Database • Hierarchical node arrangement • Each child node may have more than one parent node (many-to-many relationship) • Pointers ❑ Additional connections between parent and child ❑ Nodes can be reached through multiple paths Relational Database • • • • More flexible Data stored in table called a relation Tables consist of rows and columns Tables related via a common data item / key field Multidimensional Database • A variation and an extension of the relational model to include additional dimensions, sometimes called a data cube • Good for representing complex relationships • Advantages over relational ❑ Conceptualization ❑ Processing speed Object-oriented Database • Works with unstructured data ❑ ❑ ❑ Photographs Audio Video • Objects contain both data and instructions • Organize using objects, classes, entities, attributes, and methods Types of Databases • • • • Individual Company or shared Distributed Commercial Types of Databases (Continue) Relational Databases The most popular type of database, used for storing structured data. Examples include MySQL, Oracle, and Microsoft SQL Server. NoSQL Databases Used for storing semi-structured and unstructured data. Examples include MongoDB, Cassandra, and Amazon DynamoDB. Graph Databases Used for storing interconnected data, such as social networks, recommendation engines, and fraud detection systems. Examples include Neo4j and Amazon Neptune. Individual Databases • Also called a microcomputer database • Integrated file collection for one person usually under the person’s direct control • Generally stored on the user’s hard-disk drive or on a LAN file server Company or Shared Databases • Usually stored on a central database server and managed by a database administrator • Users throughout a company can access the database through the company’s networks Distributed Databases • Database is located in a place or places other than where users are located • Typically, database servers on a client/server network provide the link between users and the distant data Commercial Databases Enormous database developed by an organization to cover particular subjects • Access is offered to the public or selected individuals for a fee • Most designed for • organizational and individual use • Also referred to as information utilities or data banks • Database Uses and Issues • Strategic uses ❑ ❑ Special type of database called data warehouse Data mining is used to search databases for information and patterns • Security ❑ ❑ Databases are valuable Protection necessary Security: Electronic fingerprint scanner Careers in IT • Database administrators ❑ ❑ Determine the most efficient ways to organize and access a company’s data Responsible for database security and backing up the system • Employers look for ❑ ❑ Bachelors degree in Computer Science Technical experience • Database administrators can expect to earn $48,500 to $85,000 annually Part 2: Introduction to Data Analytics What is Data Analytics? Data Analysis vs. Data Analytics vs. Data Science Use of Big Data in Data Analytics Data Analytics Types Data Analytics Techniques Process of Data Analytics Data Visualization & Data Warehousing Role of Data Analyst in the Business Where are Big Data Analytics in IR4.0 Technologies? Source: https://aethon.com/mobile-robots-and-industry4-0/ What is Data Analytics? B IG DATA ANALYTICS WHAT IS Data Analytics? A series of techniques aimed at extracting relevant and valuable information from extensive and diverse sets of data gathered from different sources and varying in sizes 10100110100100001010100 11110111011011011010101 00001110010101100101010 01110101000101010001011 01011011011010001010111 00010101000101000101110 10110001001101001101001 00001010100111101110110 11011010101000011100101 01100101010011101010001 01010001011010110110110 1001 For examples: • content preferences • different types of interactions with certain kinds of content or ads • use of certain features in the applications • search requests • browsing activity • online purchases BIG DATA Source: https://theappsolutions.com/blog/develop ment/what-is-big-data-analytics/ What is Data Analytics? Data Analytics??? Big Data Analytics??? Big Data??? Data analytics is a process of analyzing raw datasets in order to derive a conclusion regarding the information they hold Data Analytics Data analytics processes and techniques may use applications operating on machine learning algorithms, simulation, and automated systems They help organizations understand their clients better, analyze their promotional campaigns, customize content, create content strategies, and develop products Source: https://corporatefinanceinstitute.com/resources/knowledge/other/data-analytics/ Data Analysis vs. Data Analytics vs. Data Science Data Analysis vs. Data Analytics vs. Data Science DATA ANALYSIS The data analysis primarily focuses on processes and functions DATA SCIENCE The data science includes data analysis but also has elements of data cleaning and preparation (for further investigation) DATA ANALYTICS The data analytics deal with information, dashboards, and reporting Big Data and Data Analytics 1 Introduction Big data refers to large volumes of structured and unstructured data that cannot be processed using traditional database and analytics tools. 2 Challenges and Opportunities Big data comes with challenges such as data quality, privacy, security, and scalability, but also provides opportunities for innovation and competitive 3 advantage. Technology and Tools Big data technologies and tools include Hadoop, Spark, NoSQL databases, data lakes, and cloud services such as AWS and Azure. Use of Big Data in Data Analytics Use of Big Data in Data Analytics Source: https://images.xenonstack.com/blog/10-vs-of-big-data.png Data Analytics Types Data Analytics Types Descriptive Analytics Predictive Analytics describes the happenings over time, such as whether the number of views increased or decreased and whether the current month’s sales are better than the last one focuses on the events that are expected to occur in the immediate future. Predictive analytics tries to find answers to questions like, what happened to the sales in the last hot summer season? How many weather forecasts expect this year’s hot summer? Diagnostic Analytics focuses on the reason for the occurrence of any event. It requires hypothesizing and involves a much diverse dataset. It examines data to answer questions, such as “Did the weather impact the selling of umbrella?” or “Did the new ad strategy affect sales?” Data Analytics Prescriptive Analytics indicates a plan of action. If the chance of a hot summer calculated as the average of the five weather models is above 58%, other than an umbrella, a rain coat should be considered to maximize the production Source: https://corporatefinanceinstitute.com/resources/knowledge/other/data-analytics/ Process of Data Analytics Process of Data Analytics Step 4 Cleaning the data The data is first cleaned up to ensure that there is no overlap or mistake. Then, it is reviewed to make sure that it is not incomplete. Cleaning the data helps to fix or eliminate any mistakes before the data goes to a data analyst for analysis Step 3 Step 2 Step 1 Determine the criteria for grouping the data Data can be divided by a range of different criteria such as age, population, income, or gender. The values of the data can be numerical or categorical data Collecting the data Data can be collected through several sources, including online sources, computers, personnel, and sources from the community Organizing the data The data must be organized after it is collected so that it can be examined. Data organization can take place on a spreadsheet or other type of software that is capable of taking statistical data Step By Step Source: https://corporatefinanceinstitute.com/resources/knowledge/other/data-analytics/ Data Visualization & Data Warehousing Data Visualization Importance Types of Tools Best Practices Data visualization helps turn Data visualization tools can Effective data visualization complex data into insights range from simple charting requires understanding your and communicate them libraries to more advanced audience, choosing the right effectively to stakeholders. tools that allow for type of visualization, using interactive dashboards and appropriate colors and storytelling. labels, and avoiding clutter and complexity. Data Warehousing Definition and Purpose Data warehousing is the process of storing and managing large volumes of data from different sources to support business decisionmaking. Extract, Transform, and Load (ETL) Process The ETL process involves extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse. Benefits and Challenges Data warehousing provides a centralized repository of information that can be used for analytics and reporting, but it also comes with challenges such as cost, complexity, and data integration. Role of Data Analyst in the Business Role of Data Analyst in the Business Role 1 Study the information Roles of Data Analyst Role 4 Develop the scenarios for automation and machine learning Role 2 Clean it from noise Role 5 Oversee the proceedings Role 3 Assess the quality of data and its sources Source: https://corporatefinanceinstitute.com/resources/knowledge/other/data-analytics/ Data Scientist vs Data Analyst Source: https://data-flair.training/blogs/data-scientist-vs-data-analyst/ Conclusion Key Points Importance Databases and data analytics are essential for The ability to effectively manage and analyze modern businesses to make informed data is critical for success in today's world of decisions. Data warehousing, visualization, and information overload. big data analytics are important components of data analytics.
