- Information
- AI Chat
DATA Analysis Notes- Bachelors notes ky
Accounting and finance
Kyambogo University
Recommended for you
Preview text
DATA ANALYSIS
Definition of key terms Data: Raw materials which do not carry any specific meaning. It means facts and statistics collected together for reference or analysis. It refers to facts, figures, statistics, details, particulars, specifics, or features. Data are a set of values of qualitative or quantitative variables about one or more persons or objects. Data can be texts or numbers written on papers. Data are plain facts. The word “data” is plural for “datum.” When data are processed, organized, structured or presented in a given context so as to make them useful, they are called Information. It is not enough to have data (such as statistics on the economy). Data refers to factual information (such as numbers, words, measurements, or statistics) used as a basis for reasoning, discussion, or calculation. Data themselves are fairly useless, but when these data are interpreted and processed to determine its true meaning, they become useful and can be named as Information. It can be anything like name of a person or a place or a number etc. Data is the name given to basic facts and entities such as names and numbers. The main examples of data are weights, prices, costs, numbers of items sold, employee names, product names, addresses, tax codes, registration marks etc. Data is the raw material that can be processed by any computing machine. Data can be represented in the form of numbers and words which can be stored in computer’s language Information: This is data that has been processed in such a way as to be meaningful to the person who receives it. it is anything that is communicated. Information is data that has been converted into a more useful or intelligible form. It is the set of data that has been organized for direct utilization of mankind, as information helps human beings in their decision making process. Examples are: Time Table, Merit List, Report card, printed documents, pay slips, receipts, reports etc. The information is obtained by assembling items of data into a meaningful form. For example, marks obtained by students and their roll numbers form data, the report card/sheet is the .information. Other forms of information are pay-slips, schedules, reports, worksheet, bar charts, invoices and account returns etc. It may be noted that information may further be processed and/or manipulated to form knowledge. Information containing wisdom is known as knowledge.
Information is needed to:
- To gain knowledge about the surroundings, and whatever is happening in the society and universe.
- To keep the system up to date.
- To know about the rules and regulations and bye laws of society, local government, provincial and central government, associations, clients etc. as ignorance is no bliss.
- Based on above three, to arrive at a particular decision for planning current
and prospective actions in process of forming, running and protecting a
process or system
Types of data
- Quantitative data: This is data that can be measured. It’s numbers or something you can count. Because it’s countable it can be reliable evidence. E. Number of participants, ages of the participants. Interval data and ratio data come under quantitative data. Interval data: Interval Data are measured and ordered with the nearest items but have no meaningful zero. The central point of an Interval scale is that the word 'Interval' signifies 'space in between', which is the significant thing to recall, interval scales not only educate us about the order but additionally about the value between every item. Interval data can be negative, though ratio data can't. Examples are: Time interval on a 12-hour clock (6 am, 6 pm). Ratio data: Ratio Data are measured and ordered with equidistant items and a meaningful zero and never be negative like interval data. An outstanding example of ratio data is the measurement of heights. It could be measured in centimeters, inches, meters, or feet and it is not practicable to have a negative height. Ratio data enlightens us regarding the order for variables, the contrasts among them, and they have absolutely zero. It permits a wide range of estimations to be performed and drawn. Distance (measured with a ruler or any other assessing device).
- Qualitative data: This is data about qualities, you can’t count it. That is, it’s information about how people feel about something. E. causes of war, signs of malaria. Under this subdivision, nominal data and ordinal data come under qualitative data.
based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your incomes, or tackle uncommon situations before they become problems. Like this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software, present the information in a professional and interactive way to different stakeholders. 2. Reduce costs: Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 3. Data helps you recognize problems: The truth is that every business has challenges and inefficiencies. Because of the ever- changing complexity of the business world and culture as a whole, it isn’t easy to optimize how a business operates. This being said, access to useful data should ensure that you can identify potential issues early on and take steps to address them. 4. Data lets you understand the performance: Similar to how data helps you recognize short-term issues, it also offers the resources required to build more reliable long-term hypotheses. Data can be thought of as building blocks needed to create coherent models that allow you to imagine what’s going on in various parts of your organization. To fully implement meaningful strategies, you need to grasp what’s going on at various facilities and in multiple departments. The data will help you to do this. 5. Data will back up the claims: Nowadays, everyone appears to have an opinion on everything these days. Making a real positive change to happen within a business can be tough, as it is tough to make everyone agree outside their comfort zone. Fortunately, if you have a backup of sound data on your hand, you’re going to be in a much better place to move your ideas forward. 6. Data makes your approach strategic: The value of data collection is that it increases productivity while helping to remove doubts. The most successful business has both short-and long-term plans in place. Substantial field data collection and analysis ensures that you will still be able to position your precious resources where they are most needed. It is essential to understand what area needs to be prioritized to help you evolve and move forward.
The data shows you what you do well: In addition to finding challenges and inefficiencies, data also gives you the ability to see your strengths and apply the same strategies throughout the company. Being able to recognize the high-performers and understand what they’re doing better will give you the resources you need to implement plans and support initiatives in places that aren’t performing so well.
Data saves time: Getting a smart data collection system in place will save you precious time on the lane. Too many resources are being spent going back and forth to collect the same information. A smart device can capture and view data in a manner that is easy to access and navigate, ensuring that anyone who is part of the company will save time.
Data increases the asset return: More and more studies are showing the vital role that data plays in moving forward an organization and making the best of what the business already has at its fingertips. You’re currently still sitting on potential extra income, so you need a smart data collection program to tap into it. There is plenty of evidence to show these gatherable data, such as maintenance, schedules, and monthly inspections. This, in turn, can help you enhance the reliability of assets over time. What Is The Data Analysis Process? When we talk about analyzing data, there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. Here is a rundown of the five essential steps of data analysis. Identify the need for data: Before you get your hands dirty with data, you first need to identify why do you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. Collect the data needed: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of information you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, focus groups, among others. An important note here is that the way you collect the information will be different in a quantitative and qualitative scenario. Clean the data collected: Once you have the necessary data, it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of information in
Familiarizing with the data: Get a basic overview of the data and try spotting any details manually, if possible:
Defining objectives: Define your objectives and know what questions this data can answer:
Make your plan: Figure out the broad ideas and assign them labels to structure the data:
Find patterns: Start looking for patterns and connections in data using data analysis techniques. The qualitative data analysis techniques are as follows:
Narrative Analysis: If your research is based upon collecting some answers from people in interviews or other scenarios, this might be one of the best analysis techniques for you. The narrative analysis helps to analyze the narratives of various people, which is available in textual form. The stories, experiences, and other answers from respondents are used to power the analysis. The important thing to note here is that the data has to be available in the form of text only. Narrative analysis cannot be performed on other data types such as images. Content Analysis: Content analysis is amongst the most used methods in analyzing quantitative data. This method doesn’t put a restriction on the form of data. You can use any kind of data here, whether it’s in the form of images, text, or even real-life items. Here, an important application is when you know the questions you need to know the answers to. Upon getting the answers, you can perform this method to perform analysis to them, followed by extracting insights from it to be used in your research. It’s a full-fledged method and a lot of analytical studies are based solely on this. Grounded Theory: Grounded theory is used when the researchers want to know the reason behind the occurrence of a certain event. They may have to go through a lot of different use cases and comparing them to each other while following this approach. It’s an iterative approach and the explanations keep on being modified or re-created till the researchers end up on a suitable conclusion that satisfies their specific conditions. So, make sure you employ this method if you need to have certain qualitative data at hand and you need to know the reason why something happened, based on that data. Discourse Analysis: Discourse analysis is quite similar to narrative analysis in the sense that it also uses interactions with people for the analysis purpose. The only difference is that the focal point here is different. Instead of analyzing the narrative, the researchers focus on the context in which the conversation is happening. The complete background of the person being questioned, including his everyday environment, is used to perform the research. QUANTITATIVE ANALYSIS Quantitative analysis involves any kind of analysis that’s being done on numbers. From the most basic analysis techniques to the most advanced ones, quantitative analysis techniques comprise a huge range of techniques. No matter what level of research you need to do, if it’s based on numerical data, you’ll always have efficient analysis methods to use. There are two broad ways here; Descriptive statistics and inferential analysis.
Inferential Analysis Inferential statistics point towards the techniques used to predict future occurrences of data. These methods help to draw relationships between data and once it’s done, predicting future data becomes possible. 1. Correlation Correlation is the measure of the relationship between two numerical variables. It measures the degree of their relation, whether it is causal or not. For example, the age and height of a person are highly correlated. If the age of a person increases, height is also likely to increase. This is called a positive correlation. A negative correlation means that upon increasing one variable, the other one decreases. An example would be the relationship between the age and maturity of a random person. 2. Regression Regression aims to find the mathematical relationship between a set of variables. While the correlation was a statistical measure, regression is a mathematical measure that can be measured in the form of variables. Once the relationship between variables is formed, one variable can be used to predict the other variable. This method has a huge application when it comes to predicting future data. If your research is based upon calculating future occurrences of some data based on past data and then testing it, make sure you use this method.
DATA Analysis Notes- Bachelors notes ky
Course: Accounting and finance
University: Kyambogo University
- Discover more from:Accounting and financeKyambogo University88 Documents
- More from:Accounting and financeKyambogo University88 Documents
