Data Analysis

Data Analysis

Grade 10

Week 5

Objectives

PC 3.1 Identify the four types of data analysis and the relevant real-life applications for each.

PC3.2 Identify the two data collection methods: Primary and Secondary, and explore the related data collection techniques

PC3.3 Select appropriate representation of data(e.g charts, graphs, network diagrams, flowchart) and use computers to model and simulate different real-life processes and phenomena

Data Analysis

• Data Analysis is a vital part of running a success business. when data is used effectively, it leads to better understanding of a business previous performance and better decision making for its future activities. There are many ways that data can be utilized, at all levels of a company's operations.

• There are four types of data analysis that are in use across all industries While we separate these into categories, they are all linked together and build upon each other

Key Types of Data

1.1 Descriptive Analytics

¬Descriptive analytics is the simplest type of analytics and the foundation the other types are built on. it allows you to pull trends from raw data and succinctly describe what happened or is currently happening.

¬Descriptive analytics answers the question, “what happened”?

¬For example, imagine you’re analyzing your company’s data and find there’s a sesonal surge in sales for one of your products: a video game console. here, descriptive analytics can tell you, “This video game console experiences an increase in sales in October, November, and early December each year.

¬Data Visualization is a natural fit for communicating descriptive analysis because charts, graphs, and maps can show trends in data- as well as dips and spikes – in a clear, easily understandable way

Key Types of Data

1.2 Diagnostic Analytics

¬Diagnostics analytics address the next logical question, “Whydid this happen”?

¬Taking the analysis a step further, this type includes comparing coexisting trends or movement, uncovering correlations between  variables, and determining casual relationships where possible.

¬continuing the aforementioned example, you may dig intovideo game console user’s demographics data and find that they’re bewteen the ages of eight and 18. the customers, however, tend to be between the ages of 35 and 55. analysis of cutomer survy data reveals that one primary motivator for cutomers to purchase the video game console is to gift it to their childern. The spike is sales in the fall and early winter months may be dur ot the holidays that include gift-giving.

¬Diagnostic analytics is useful for getting at the root of an organizational issue

Key Types of Data

1.3 predictive Analytics

¬predictive analytics is used to make predictins about futuretrends or events and answers the question. “What mighthappen in the future”?

¬By analyzing historical data in tandem with industry trends, you can make informed predictions about what the future could hold for your company.

¬For intance, knowing that video game console sales havespiked in October, November, and early December everyyear for the past decade provides you with ample data topredict that the same trend will occur next year. backed byupward trends in the video game industry as a whole, this isa reasonable prediction to make.

¬making prediction for the future can help your organization formulate strategies based on likely scenarios

​Key Types of Data

1.4 prescriptive analytics

¬prescriptive analytics answers the question, “What should we donext?”

¬prescriptive analytics takes into account all possible factors in a scenario and sugests actionable takeways. this type of analytics can be especially useful when making data-driven decisions.

¬Rounding out the video game example: What should your teamdecide to do given the predicted trend in seasonality due to winter gift-giving? perhaps you decide to run an A/B test with two ads: one that caters to product end-users(childern) and one targeted to customers(their parents). The data from that test can inform howcapitalize on the seasonal spike and its supposed cause evenfurther. Or, maybe you decide to increase marketing efforets inseptember with holiday-themed messaging to try to extend the spike into another month.

¬while manual prescriptive analysis is doable and accessible, machine-learning algorithms are often emplyed to help parse through large volumes of data to recommend the optimal next step.

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¬algorithms use “if” and “else” statements, which works as rules for parsing data, if a specific combination of requirements is met, an algorithm recommends a specific course of action. while there’s far more to machine learning algorithms than just those statements, they – along with mathematical equations – serve as a core component in algorithm training.

Data Collection Methods

¬Data collection breaks down into two methods: Primary and Secondary

2.1 Primary

¬As the name implies, this is original, first-hand collected by thedata researchers. This process is the initial information gathering step, performed before anyone carries out any further or related research. Primary data results are highly accurate provided the researcher collects the information. However, there’s a downside, as first-hand research is potentially time-consuming and expensive

2.2 Secondary

¬Secondary data is second-hand data collected by other parties andalready having undergone statistical analysis. This data is eitherinformation that the researcher has tasked other people to collect or information the researcher has looked up. Simply put, it’s second-hand information. Although it’s easier and cheaper to obtain than primary information, secondary information raises concers regarding accuracy and authenticity. Quantitative datamakes up a majority of secondary data.

Data Collection Methods

3.1 Primary Data collection techniques

¬Interviews

¬Projective Technique

¬Delphi Technique

¬Focus Group

¬Questionnaires

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3.2 Secondary Data Collection Techniques

¬Unlike primary data collection, there are no specific collection methods. instead, since the information has already been collected, the researcher consults various data sources, such as: 

¬Financial Statements

¬Sales Report

¬Retailer/Distributor/Deal Feedback

¬Customer Personal Information(e.g name, address, age, contact info)

¬Business Journals

¬Government Records(e.g. census, tax records, social security info)

¬trade/Business Magazines

The internet

Activity

Work in group of 2-3 learner to create a presentation that contains information about:

1. The four types of data analysis with an example of real-life application for each

2. Explain the two data collection methods: Primary and secondary

3. .Demonstrate differen types of visual data representation(e.g charts, graph, network diagrams, flowcharts) and specify when it is appropriate to use each