Weekly Summary 1.1

University of the Potomac

CBSC520 Data Analytics

Abstract

This paper is a concise rundown of what we realized in the First week of our course just as blueprints what we comprehended. In the main week we were acquainted with the Business Analytics: Data Analysis and Decision making, where we found out about Data Driven Decision making, dispersion of a solitary variable, Probability and Probability disseminations, Normal Distribution. Today we found out about the Descriptive measures for the absolute factors with the assistance of the case of retail deals. We likewise found out about the inspecting, covariance and coefficient. Assortment, Organization, portrayal of information, count and translation of coefficients have a place with Descriptive Statistics, while investigation and understanding of information, related with an edge of vulnerability, is the duty of the Inductive or Inferential Statistics, additionally called As the proportion of vulnerability or techniques that depend on likelihood hypothesis. We discovered that the utilization of tables and graphical showings are visit in Statistics. The table serve to compose and arrange the information, and the designs pass on the data with clearness and straightforwardness, adding to target perusing.

Weekly Summary 1.1

This paper describes the weekly summary on the First week of our study on Business Analytics. We were provided an overview on data analysis by discussing models, simulations, data sets, types of variables, data collection and organization of the data, which can be further utilized for different purposes such as accounting, finance, marketing etc. We learned about using the excel to compute the sample variance, standard Deviation and co-efficient of variation with the example of the monthly salary of the employees.

Tremendous data examination is the path toward breaking down far reaching and moved data sets – i.e., immense data – to uncover covered precedents, darken connections, grandstand designs, customer tendencies and other profitable information that can empower relationship to settle on increasingly instructed business decisions.

Normal Distribution

Normal Distribution also known as Gaussian distribution, is not only an important and most common type of distribution used by the statisticians to analyze the data such as stock market analysis, mutual funds, measurements etc. The normal distribution is symmetrical and its mean, median and mode are equal, and is also best described by a bell curve. The standard normal distribution has two parameters: the mean and the standard deviation.

The standard deviation is of utmost importance in the normally distributed data, which can be used to determine the proportion of the values that are within a specified number of standard deviation from mean (Frost, 2016). This is also known as Empirical Rule, which describes the percentage of the data that fall within specific numbers of standard deviations from the mean for bell-shaped curves.

A value on the standard normal distribution is known as a standard score or a Z-score. A standard score represents the number of standard deviations above or below the mean that a specific observation falls. For example, a standard score of 1.5 indicates that the observation is 1.5 standard deviations above the mean. On the other hand, a negative score represents a value below the average. The mean has a Z-score of 0.

Example of Normal Distribution:

Figure 1 Distribution of birth weight in 3,226 newborn babies (data from O’ Cathain et al 2002)

One such example is the histogram of the birth weight (in kilograms) of the 3,226 new born babies shown in Figure 1. The histogram of the sample data is an estimate of the population distribution of birth weights in new born babies. This population distribution can be described by the `bell-shaped’ curve or `Normal’ distribution as shown in Figure 1. We presume that if we were able to look at the entire population of new born babies then the distribution of birth weight would have exactly the Normal shape. We often infer, from a sample whose histogram has the approximate Normal shape, that the population will have exactly, or as near as makes no practical difference, that Normal shape.

Benefits of the Big Data Analytics

Driven by specific examination systems and programming, gigantic data examination can be viewed in the manner which distinctively exhibits business benefits, including new salary openings, progressive promotion, improved customer advantage, upgraded operational adequacy and high grounds over adversaries.

Colossal data examination applications empower information scientists, perceptive modelers, investigators and distinctive examination specialists to separate creating volumes of sorted out trade data, notwithstanding extraordinary kinds of data that are frequently left unfamiliar by standard business knowledge (BI) and examination programs. That incorporates a mix of semi-organized and unstructured data – for example, web stream information, web server logs, web based systems administration content, content from customer messages and survey responses, mobile phone call-detail records and machine data gotten by sensors related with the web of things.

Big data analytics technologies and tools:

While taking a glimpse at a specific populace, choosing tests to make surmising’s, we have to record our perceptions or the attributes of the information we are contemplating. A variable is the term used to record a specific normal for the populace we are contemplating.

For example, if our populace comprises of pictures taken from Mars, we may utilize the accompanying factors to catch different attributes of our populace:

quality of a picture

title of a picture

latitude and longitude of the center of a picture

date a picture was taken

and so on. It is useful to put variables into different categories, as different statistical procedures apply to different types of variables. Variables can be categorized into two broad categories, numerical and categorical:

Categorical Variables are factors that have a set number of unmistakable qualities or classes. They are some of the time called discrete factors.

Numeric Variables refer to characteristics that have a numeric values. They are usually continuous variables, i.e. all values in an interval are possible.

Categorical variables again split up into two groups, ordinal and nominal variables:

Ordinal variables represent categories with some intrinsic order (e.g., low, medium, high; strongly agree, agree, disagree, strongly disagree). Ordinal variables could consist of numeric values that represent distinct categories (e.g., 1=low, 2=medium, 3=high). To best remember this type of variable, think of “ordinal” containing the word “order”.

Nominal variables represent categories with no intrinsic order (e.g., job category or company division). Nominal variables could also consist of numeric values that represent distinct categories (e.g., 1=Male, 2=Female).

Conclusion

I learned about the Data Sets, Variables, and observations and also about the Minimum, maximum percentiles, and Quartiles, Incur a reasonability to use software such as excel to solve the business problems. I learned about the collection of data and I learned to solve the problems and come up with the solution from the book. We discussed the graphical models to describe the analysis, which is useful from the business point of view.

References:

Albright, S. & Winston, W. (2017). Business Analytics – Data Analysis and Decision Making: Cengage Learning,

Frost, J. (2016, March 16). Making Statics intuitive. Retrieved from https://statisticsbyjim.com/basics/normal-distribution/.

Web reference:

http://www.swlearning.com/decision_sciences/albright/ch_01

https://quizlet.com/258855066/chapter-2-describing-the-distribution-of-a-single-variable-flash-cards/