BA 311 Lecture 1.pdf

Introduction to Descriptive Analytics Module Outline Business Analysis Process vs Organization Decision Making Process Goal of Descriptive Analytics Types of Data and Scales of Measurement Elements and Variables Types of Data Data Sources Factors to consider in Data Gatherin...

Introduction to Descriptive Analytics Module Outline Business Analysis Process vs Organization Decision Making Process Goal of Descriptive Analytics Types of Data and Scales of Measurement Elements and Variables Types of Data Data Sources Factors to consider in Data Gathering Data Processing in Spreadsheets Descriptive Analytics Descriptive Analytics or Exploratory Data Analysis is a type of Business Analytics application where data is described and summarized using basic statistical tools and graphs to produce reports and dashboards for decision making. It: Tell Me What has Happened and Why Tell Me What is Happening Right Now Traditional ODMP introduced by Elbing (1970) BAP vs ODMP There is a very close similarity between BAP and ODMP BAP is a data driven process ODMP on the other hand is descriptive in nature and maybe considered to be more subjective in its analysis BAP emphasizes on objectivity and fact-based Descriptive Analytics Describes any past phenomenon in terms of graphs, pictures, symbols and application of statistical tools Deals with what is contained with in the data set or database It’s purpose is to identify possible trends, get a broad picture of what generally the data looks like, what actually happened. Using Descriptive Analytics we try to find patterns and meanings Describes data sets/bases using measures of central tendencies, measures of dispersions, frequency distributions and probability and its distribution and sampling methods Important Terms to remember Data are numerical quantitative figures as well as qualitative facts. Data are facts and figures collected, tabulated, summarized and analyzed for presentation and interpretation Variable is a characteristic of an element Element is a unit of which data are collected Measurements of a variable provide data. Measurements obtained for a particular element is called an observation Element, Variable and Observation Element Variable Observation Role of Descriptive Analytics To collect and analyze data to gain a better understanding of variation and its impact to the business To learn more about the values (high and low) To learn about how values varies on certain scenarios or factors To maximize opportunity and control effects to the business Type of variables Decision Variables are values of some variables are under direct control of the decision maker. Random variable/uncertain variable are quantities whose values are not known with certainty, may fluctuate with uncertainty due to factors outside the direct control of the decision makers.  FR Types of Data Population Data Sample Data A population includes all of the A sample consists one or more elements from a set of data. observations drawn from the population. https://web.uri.edu/assessment/sample-size/  FR Types of Data Quantitative Data Categorical Data Can be characterized as measureable Can be characterized as countable data data Qualitative data, attribute data If numeric and arithmetic operation can Can be summarized by counting the be performed number of observations or computing the proportions of observations in each category https://www.tes.com/en-us/teaching-resource/quantitative-vs-qualitative-observations-7195170  FR Types of Categorical Data Nominal Scale Variable Ordinal Scale Variable Number or codes given to objects or events or naming or classifying only Classify data into categories and order Numbers used as codes have no true operational meaning Ordinal measurement require transitivity Nominal scale divide data into categories which are assumptions: mutually exclusives and collectively exhaustive If A is preferred to B, and B is preferred to C, A data point is classified only under 1 and only 1 category then A is preferred to C and all other data will be grouped somewhere else in the scale Example of ordinal scale: Example of nominal scale: Gender classification Ranking of Royalties (1-5), 1 – most stylish 1 – men and 5 least stylish 2 - women Categorical and Quantitative Data Categorical Data Quantitative Data  FR Types of Data Cross Sectional Data Time Series Data Cross-sectional data are observations Time-series data is a set of observations that come from different individuals or collected at usually discrete and equally groups at a single point in time. spaced time intervals. https://analystnotes.com/cfa-study-notes-distinguish-between-time-series-and-cross-sectional-data.html  FR Source of Data Experimental Data Observational Data A variable of interest is identified A variable is measured without trying to One or more variables are identified and change or affect the values controlled and manipulated so that the Example: data can be obtained about how they Survey influence the variable of interest Example: Effect of varied temperature level to drinking consumption Factors to consider in Data Gathering The time and cost of collecting data should be a factor Use of existing data sources is desirable when data must be obtained in a relatively short period of time If data is not available, time and cost can increase and must be taken into consideration. Cost of data acquisition and analysis should not exceed the savings generated by using the information to make a better decision. Use of basic Spreadsheet tools Formula/Functions for computation Difference/Percentage Total/Sum/Average Count If Sorting Filtering Conditional Formatting Formula and Function - Difference/Percentage Comparison/Difference/Percentage % Change for Honda Accord Sales = (D2-E2)/E2 found in F2 Sales INCREASED by 15.4% from 2010 to 2011 Formula and Function - Sum/Average Sum/Average Data Data 10 15 32 10 15 32 7 7 9 9 27 27 2 2 Formula Description Result Formula Description Result =AVERAGE(A2:A6) Average of the numbers in 11 cells A2 through A6. =SUM(A2:A6) Sum of the 55 numbers in cells =AVERAGE(A2:C2) Average of the numbers in 19 A2 through A6. cells A2 through C2. Formula and Function – Count / Counta The COUNT function counts the number of cells that contain numbers, dates and times. The COUNTA function counts the number of cells that are not empty in a range. The COUNTIF function count the number of cells that meet a criterion https://www.techonthenet.com/excel/formulas/counta.php Formula and Function IF function The IF function allows you to make logical comparisons between a value and what you expect. So an IF statement can have two results. The first result is if your comparison is True, the second if your comparison is False. If you are going to use text in formulas, you need to wrap the text in quotes (e.g. “Text”). The only exception to that is using TRUE or FALSE. For example, =IF(C2=”Yes”,1,2) says IF(C2 = Yes, then return a 1, otherwise return a 2). https://support.office.com/en-us/article/if-function-69aed7c9-4e8a-4755-a9bc-aa8bbff73be2 Formula and Function IF function https://support.office.com/en-us/article/if-function-69aed7c9-4e8a-4755-a9bc-aa8bbff73be2  Use of basic Spreadsheet tools – Sorting Sorting one column 2. To sort in ascending order, on the Data tab, in the Sort & Filter group, click AZ. 1. Click any cell in the column you want to sort Result https://www.excel-easy.com/data-analysis/sort.html  Use of basic Spreadsheet tools – Sorting Sorting multiple columns 3. Select Last Name from the 'Sort by' drop- down list. Click on Add Level. 1. Click any cell in the 4. Select Sales column you want to sort from the 'Then by' drop-down list Result. Records are sorted by Last Name first and Sales second. 2. On the Data tab, in the Sort & Filter group, click Sort. https://www.excel-easy.com/data-analysis/sort.html  Use of basic Spreadsheet tools - Filtering Filtering 5. Click the arrow next to Quarter. 6. Click on Select All to clear all the check boxes, and click the 1. Click any single cell check box next inside a data set. to Qtr 4. Click 2. On the Data tab, in OK. the Sort & Filter group, click Filter. 3. Arrows in the column headers appear. Click the arrow next to Result. Excel only displays Country. the sales in the USA in Qtr 4. 4. Click on Select All to clear all the check boxes, and click the check box next to USA. Click OK https://www.excel-easy.com/data-analysis/filter.html Use of basic Spreadsheet tools - Conditional Formatting 3. Click Highlight Cells Rules, Greater Than. 4. Enter the value 80 and select a formatting style. 5. Click OK. 1. Select the range A1:A10. Result. Excel highlights the cells that are 2. On the Home tab, in the greater than 80. Styles group, click Conditional Formatting. https://www.excel-easy.com/data-analysis/conditional-formatting.html Use of basic Spreadsheet tools - Conditional Formatting 3. Click Top/Bottom Rules, Above Average. 4. Select a formatting style. 5. Click OK. 1. Select the range A1:A10. Result. Excel calculates the average (42.5) and 2. On the Home tab, in the formats the cells that Styles group, click are above this average. Conditional Formatting. https://www.excel-easy.com/data-analysis/conditional-formatting.html Use of basic Spreadsheet tools - Conditional Formatting 1. Select a range. 2. On the Home tab, in the Styles group, click Conditional Formatting. 3. Click Data Bars and click a subtype. Explanation: by default, the cell that holds the minimum value (0 if there are no negative values) has no data bar and the cell that holds the maximum value (95) has a data bar that fills the entire cell. All other cells are filled proportionally. https://www.excel-easy.com/examples/data-bars.html  Reference Course Notes from Eugene Rex L. Jalao, Ph.D. Business Intelligence Training of University of the Philippines National Engineering Center. https://www.excel-easy.com/data-analysis/filter.html https://www.excel-easy.com/data-analysis/sort.html https://support.office.com/en-us/article/if-function-69aed7c9-4e8a-4755-a9bc-aa8bbff73be2 https://www.techonthenet.com/excel/formulas/counta.php https://www.excel-easy.com/examples/data-bars.html Turban, E; Sharda, R.; Delen, D.; King, D. (2010) Business Intelligence: A Managerial Approach Albright, S. Christian; Winston, Wayne L.; (2015) Business analytics: Data analysis and decision making Evans, James R. (2017) Business Analytics : methods, models, and decisions Camm, Jeffrey D.; Ohlmann, Jeffrey W.; Fry, Michael J.; Anderson, David; Cochran,, James J. (2015) Essentials of Business Analytics Ahmad, Rafi & Khan, Rafi & Nadeem, Adnan & Ali, Arshad. (2019). Business Analytics: A Framework. 10.