DATA ANALYTICS HAND NOTES

DATA ANALYTICS​ HAND NOTES (586) What is Data Analytics - YouTube (586) Process of Data Analytics - YouTube What Is Data Analytics? Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms​ that work over raw data for human consumption. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system. Understanding Data Analytics Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. For example, ​manufacturing​ companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity. Data analytics can do much more than point out ​bottlenecks​ in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click. The process involved in data analysis involves several different steps:

1. The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or be divided by category. 2. The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel. 3. Once the data is collected, it must be organized so it can be analyzed. Organization may take place on a spreadsheet or other form of software that can take statistical data. 4. The data is then cleaned up before analysis. This means it is scrubbed and checked to ensure there is no duplication or error, and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed. [Important: Data analytics focuses on coming to conclusions based on what the analyst already knows.] Key Takeaways ● Data analytics is the s​ cience of analyzing raw data​ in order to make conclusions about that information. ● The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. ● Data analytics help a business optimize its performance. Why Data Analytics Matters Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.

Types of Data Analytics Data analytics is broken down into four basic types. 1. Descriptive analytics describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last? 2. Diagnostic analytics focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales? 3. Predictive analytics moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year? 4. Prescriptive analytics suggests a course of action. If the likelihood of a hot summer is measured as an average of these five weather models is above 58%, we should add an evening shift to the brewery and rent an additional tank to increase output. Special Considerations: Who's Using Data Analytics? Some of the ​sectors​ that have adopted the use of data analytics include the travel and hospitality industry, where turnarounds can be quick. This industry can collect customer data and figure out where the problems, if any, lie and how to fix them. Healthcare combines the use of high volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The information retailers collect and analyze can help them identify trends, recommend products, and increase profits. (586) Part 2 | Data Analytics for Beginners | Analytics Lifecycle - YouTube

4 Types of Data Analytics to Improve Business Decision Making [With Examples] | upGrad blog 1) Descriptive data analytics: Discerning the reality  Descriptive data analytics is all about using existing raw data to paint a clear picture of what exists. For example, data from the monthly profit and loss statements of an organization could be used to know more about its performance. And different measures and metrics about the business could be compiled to give a holistic view of its strengths and weaknesses. Descriptive analytics is also useful in presenting insights for further analysis. A statistical analysis of the demographic data of customers could reveal the percentage of people in a particular age group. Sales and pricing data could be consolidated and compared over the years or across departments. Data aggregation and data mining are some of the techniques used in this process. Analysts also use visualization tools to enhance the message.

2) Diagnostic data analytics: Figuring out the ‘why’  After the ‘what,’ comes the ‘why.’ And diagnostic data analytics facilitates this reasoning process. Analysts read, scan, filter, and extract useful data to find out why something is happening. As the name suggests, diagnostic analytics is about breaking down the available information and identifying the causes behind specific problems, events, and behaviors. For example, a large organization may want to gain meaningful insights into its complex workforce issues. With the help of data analytics, managers can search and create snapshots of employees working across multiple locations and divisions. They can also filter and compare their work attendance, performance, tenures, and succession metrics. Business Information or BI dashboards with interactive tools are especially useful in getting to the root-cause of problems in this manner. Drill-down, data discovery, data mining, and correlations are some of the popular techniques used in the diagnostic analysis.

3) Predictive data analytics: Getting an idea about the  future  Predictive analytics is one of the most exciting types of data analytics. It helps us in learning about the future! The world is full of uncertainty. And we can never fully know what will happen. But, we can try to predict future events and hence, make better decisions. Predictive data analytics can help us estimate the likelihood of an event, when something might happen, or the extent of an upcoming change. It analyzes past and present data to forecast the future. Will the sales increase or decrease? What will be the revenue situation in 2025? Analysts seek to make such projections with as much precision as they can. Data modeling and machine learning are some of the techniques that are increasingly gaining popularity in this area. Typically, they use variable data to predict otherwise unknown events. Let’s say that a predictive model churned out a statistic about a higher risk of heart attacks among older people. The prediction would be made after finding a linear relationship

between the variable data on age and frequency of heart attacks in a population. Such analysis can thus, improve patient care, reduce costs, and bring greater efficiencies to the healthcare industry.​ The financial services industry also uses predictive analytics for fraud detection, predictive investing, etc. 4) Prescriptive data analytics: Suggesting the way  forward  If predictive analytics is about forecasting, prescriptive analytics is about using those predictions to deliver value. It provides the key to the future by prescribing the best course of action out of the available alternatives. At this stage, analytics use the insights from the first three steps to determine the possible solution to a problem. And it is not just about picking any but comparing and selecting the most suitable recommendations for the given situation. For instance, a mobile application for road traffic can help you choose the best route to reach home from your current location.

The App would take into consideration the distance, speed, and traffic congestions to tell you the shortest or the most timely way to get there. Another example is a consulting agency using data analytics to suggest advantageous locations to roll out a new product. Conclusion  Today, data science is delivering tremendous value across industries. And all 4 types of data analytics mentioned above will continue contributing to the transformation in their own ways! (586) Levels of Analytics - YouTube

next →←​ prev Introduction Data mining is a significant method where previously unknown and potentially useful information is extracted from the vast amount of data. The data mining process involves several components, and these components constitute a data mining system architecture. Data Mining Architecture The significant components of data mining systems are a data source, data mining engine, data warehouse server, the pattern evaluation module, graphical user interface, and knowledge base. Data Source:

The actual source of data is the Database, data warehouse, World Wide Web (WWW), text files, and other documents. You need a huge amount of historical data for data mining to be successful. Organizations typically store data in databases or data warehouses. Data warehouses may comprise one or more databases, text files spreadsheets, or other repositories of data. Sometimes, even plain text files or spreadsheets may contain information. Another primary source of data is the World Wide Web or the internet. Different processes: Before passing the data to the database or data warehouse server, the data must be cleaned, integrated, and selected. As the information comes from various sources and in different formats, it can't be used directly for the data mining procedure because the data may not be complete and accurate. So, the first data requires to be cleaned and unified. More information than needed will be collected from various data sources, and only the data of interest will have to be selected and passed to the server. These procedures are not as easy as we think. Several methods may be performed on the data as part of selection, integration, and cleaning. Database or Data Warehouse Server: The database or data warehouse server consists of the original data that is ready to be processed. Hence, the server is cause for retrieving the relevant data that is based on data mining as per user request. Data Mining Engine: The data mining engine is a major component of any data mining system. It contains several modules for operating data mining tasks, including association, characterization, classification, clustering, prediction, time-series analysis, etc. In other words, we can say data mining is the root of our data mining architecture. It comprises instruments and software used to obtain insights and knowledge from data collected from various data sources and stored within the data warehouse. Pattern Evaluation Module:

The Pattern evaluation module is primarily responsible for the measure of investigation of the pattern by using a threshold value. It collaborates with the data mining engine to focus the search on exciting patterns. This segment commonly employs stake measures that cooperate with the data mining modules to focus the search towards fascinating patterns. It might utilize a stake threshold to filter out discovered patterns. On the other hand, the pattern evaluation module might be coordinated with the mining module, depending on the implementation of the data mining techniques used. For efficient data mining, it is abnormally suggested to push the evaluation of pattern stake as much as possible into the mining procedure to confine the search to only fascinating patterns. Graphical User Interface: The graphical user interface (GUI) module communicates between the data mining system and the user. This module helps the user to easily and efficiently use the system without knowing the complexity of the process. This module cooperates with the data mining system when the user specifies a query or a task and displays the results. Knowledge Base: The knowledge base is helpful in the entire process of data mining. It might be helpful to guide the search or evaluate the stake of the result patterns. The knowledge base may even contain user views and data from user experiences that might be helpful in the data mining process. The data mining engine may receive inputs from the knowledge base to make the result more accurate and reliable. The pattern assessment module regularly interacts with the knowledge base to get inputs, and also update it. (586) Introduction to data mining and architecture in hindi - YouTube Data Mining Architecture - Javatpoint (586) Data Preprocessing, Data Cleaning, Ways to handle missing data during cleaning - YouTube What is Data Preprocessing? - Definition from Techopedia

Data Mining — Handling Missing Values the Database | by Eran Kampf | DeveloperZen 6 Types of Regression Models in Machine Learning You Should Know About | upGrad blog The 5 Types of Data Processing | Xplenty What are the Sources of Data? Primary and Secondary Data (byjus.com) (586) (10) WHAT IS DATA ? TYPES OF DATA | SOURCES OF DATA COLLECTION { Ch.- 2 } - YouTube