UEE60420 Advanced Diploma of Computer Systems Engineering

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Computer Science and Engineering is a part of an academic curriculum in many colleges and universities that combines scientific and technological aspects of computing. Engineering informatics is explained by the term CSE quite often in Europe. The programs in the Academic curriculum differentiate between colleges. The Assignments combine the introduction part to programming, data structures, algorithms, networks, OS, embedded systems, analysis of circuit and logistics etc. It also includes core subjects like theory of computation, machine learning, deep learning, numerical methods, and paradigms. The modern curriculum includes emerging fields such as data science, artificial intelligence, robotics, image processing, bioinformatics, etc. Mathematical knowledge is mandatorily required in many of the cases above. one of the trending examples is Econometrics that includes statistical analysis. Econometrics is an application in statistics to provide information that is based on experience and has empirical data about it. The information which is obtained by any research and observation is known as empirical data. This process of collecting information is known as gathering empirical data. Econometrics requires conducting an analysis of large data sets for the identification of different financial indices. To perform the analysis various forms of data need to be understood to find the best appropriate formulas and calculations to avoid errors. The article will take you through different types of data that are used in the domain of econometrics for example; time-series data and pooled data.

What will you study (Example)

Types of data

There are three types of data followed:

Cross-sectional Data

This type of data is gathered within a single time point and is then categorized by a single unit for example; person, organization, countries, states, companies. Some other examples include; Marks of the students, housekeeping data, car maintenance data. The order of the data is not a matter to concern about in cross-sectional data type. We can order the data as per our choice in ascending, descending, and random order. This random or any other type does not affect the results. The following data are the example of cross-sectional data on the speed of cars and stopping distance.

Time-series Data

This type of is collected at specific time points. Examples: stock process, rate of interests, monthly rents, tax payments, GST, GDP, good and services process. The observation taken for this type of data is obtained in different frequencies such as in hours, daily basis, monthly basis, and annually, etc.

The order of the data is important in this type of data type. Since each point represents different values at a specific point in time. Chronological order type is followed in time series data type. The following sample data represent time-series data:

It has a set of observations on different values assigned to different points of time. This type of data is collected at different fixed intervals. For example; On a daily, weekly, monthly basis. The application of this type of series is found in financial economics and macroeconomics where it is used for cost analysis and currencies etc.

Panel/longitudinal type of data

This type of data is the combination of the above two types. The observation is carried out for similar individuals for example; Individual persons, firms, countries, states, houses, companies, etc. this type of observation which is obtained is noted for different points of time like, in days, yearly, prior, and post-treatment.

These data types allow us to have control of variables.

1. Factors of cultures
2. Different business styles and practices.

If the observation is taken for the same period for each unit then this type of data is known as the balanced panel.

What are the sources of data?

There are several sources to access and gather data to perform statistical analysis and study. This could be time-consuming to collect all the data. The research part and finding data can take more time than creating a report. Few sources are governmental agencies that have privacy issues for accessing it some have payment requirements. But there are many other web sources where you can freely access enough data and perform your analysis to create a report.

Data accuracy

It is found that the data gathered from social sciences are generated under some given conditions which results in having some unknown influences.

1. Scaling ratio: It refers to data in ratio format and comparison, form.
2. Scaling in intervals: It refers to data that has parameters and distances.
3. Scaling ordinally: It refers to an order which is not either qualitative or quantitative. It has a natural order and a series of various categories and information. For example Income classes which are categorized into a different level of cases. It can be measured in two statistical methods which are parametric and non-parametric.
4. Scaling nominally: Where no order is required or specified. For example Genders, materials comparison, etc. This type of data can only be measured in the non-parametric method of statistics.

Description of the Assignment

The given Assignment covers topics like designing, installing, evaluating and administering computer systems and other equipment, risk management, management and estimation of projects and at last providing technical advice.

There is no such requirement of licensing, certification or legislation applies to the given Assignment during the time of publication.

There are no requirements to make any type of entries for the given Assignment.

Contents in the Package

Many units ask for the pre-requisites requirements.

Core Units

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