Building Data Analysis

Introduction

This week, Hilary Parker and we started the "Book club" with exceptional abnormalities where we will discuss Nigel Cross’s book Designing: Understand how creatives think and work. We will talk about how the work of the designer is equated with the science of science and the principles of the design of the text that best reflects the data analysis. Although the perception of the information is always evident in the design, we believe that many aspects of data analysis can benefit from the design of the design. However, I think this is probably something to be discussed between accountants and analysts.

One of the first things I have done recently is to find out whether the data analysis is not a natural phenomenon. Will you meet your data analysis when travelling to the field? Data analysis is to create and build people.

One way to analyze data analysis is to consider how the item is intended. Data analysis is not a technical exercise. The aim is not to present something new about the world or to get the truth (although knowledge and truth can be important). The purpose of the data analysis is to produce something useful. It is useful for scientists, useful for product owners, beneficial for executives or beneficiaries of the policymaker. From the point of view, the data analysis is a fair process.

Produce useful products requires consideration for who will use it. Good data analysis can be useful for everyone. The fact that many different people using data analysis is not the right news, but something new is getting access to general information.

If we consider the data analysis as planned, this will give us a roadmap on how to proceed.

Questioning the Question 

A quote quotes from John Tukey, legendary statistician and analyst Princeton, is

It is a very good answer to the estimation of the correct question, which is often unclear, according to the actual answer to the wrong question, which can always be done.

What do these words mean in terms of data analysis? Data analysis, often we start with a data or question. But analysts do not solve the problem they have given them. The reason is not necessary. Often the problem, as previously mentioned, is an early attempt. That's fine

Good data analyst recognizes that the problem itself needs to be investigated. For example, someone may ask: "Is the weather a bad air for your health?" This is a big question, and criticism of government policies, but it's hard to make a map of specific data. There are many types of dirty air and there are many health concerns that are worrying us. Priority and review of the root cause is an important step in data analysis. In my culture, this process brings in many important questions and the answer can lead to a clear action.

The first job of the data analyst is to know the real problem. The fact that the problem we are failing is not the problem we started out, it's not a particular mistake. It's just natural. Often, the science bureau will come to my office to talk only. They come up with a clear answer, but when you vote and ask questions: "What information is available?"; "What kind of action can lead to answering this question?"; "What resources are available to make this analysis?"; "Is it possible to collect new data?": The question can change and change. Good collaborations are not overrun in this process, but they are satisfied with how to clean up their thinking.

Bad cooperation is one of the purposes of submitting the question and waiting for the solution to appear. I saw my respectful view of these and almost never worked, except for the least problems. The process of making good data analysis cannot be changed here in order to clean up the question, data, analysis and outcomes, anyone who is engaged in his or her career with others. One would like to say it was so, because it would be easy to do, but would like to do so. The initial analysis on how to solve the problem is an important part of the design of the data analysis. But if he has a discussion talk, yet he does not complete the question.

Sometimes, problems are not clear unless we try to solve them. The business analyst's job is to present problems to the problem to explore the problem. For example, in the case of air pollution, we can demonstrate an interest in protecting airborne particulate matter. But from the data available, we see that there is a lot of value missing to make an effective analysis. So, we can change that we look at the ozone, which is more important than the health perspective.

At this time, it is important that you are not involved in the problem where a lot of time or resources should be invested. Making the first attempt to deal with the disadvantages that might have been incompatible with something other than downloading a website, but allowed us to explore the borders as much as possible. Sometimes proposed solutions "work only" but often collect new questions and force the analyst to repeat the problem. The first attempt to resolve the problem should be "famine" or a dimensional model to see if the work will work. Introductory information found in this diagram can be valuable to prioritize possible solutions and in a final way.

The beginning of the question of the question may be dissatisfied with some, especially those who turn to the data analyst to do something. In most cases, the employer feels that the analyst is questioning his or her knowledge of the topic and repeats the previously existing problems. The data analyst must be sensitive to this concern and explain why it is analyzing this. The analyst should also understand that the employer is an expert on his farm and may know what he is talking about. The best way for the analyst to be in shape

The solution to the problem

When we clean the question we ask and carefully describe the scope of the problem we are dealing with, we can continue to design the solution. In the same way, this will be a process, but we will deal with different things. At the moment, we need a reasonable explanation of the problem to use tools and methods that can be used in a variety of problems. In addition, it is necessary to develop the work that allows all parties interested in participating in the analysis and conducting their work properly.

The analyst should design the task of analysis into compliance with the different needs and capacities of the partners. Each project may have a different career pathway, especially if each project has a range of collaborative groups. This does not just comment about the tools for managing the flow of work, but overall how the information is transferred to and forwarded to people. Sometimes, the analyst is a "central centre" that provides all the information and sometimes "a lot of free", where everyone speaks to others. There is no right way, but it is important that everyone understands how to use them.

The analyst is also responsible for selecting methods, conflicts and summarizing the information. It may need to be upgraded to access the data to store or retrieve data. Mathematics methods can be a test compared to two groups or a regression model to maintain a variety of relationships. Selection of the tools and methods will be improved in part of the definition of the problem, as well as accessible sources and audiences that receive the analysis. Recognize the best way to handle the issues that have been addressed in the problem, is the analysis of the person's expertise. The analyst may need to assign specific tasks based on the team experience.

Group agreement

Analyzing exciting or complex information is likely to involve people from different perspectives. The academy, you may work with biology, engineer, computer lab and doctor at the same time. In the trade, you may need to integrate finance, marketing, production and engineering in the analysis provided. The hard task of analyzing the work is to manage all of the interests of these people in the same way as they embody the last analysis.

The challenge facing the analyst is that every process thinks their interests are ahead of others. In addition, collaborators think that disciplinary problems are the most important problems that need to be addressed. However, the analyst can not accept that any process is "linked to the first place"; Priority must be constructed and evaluated positively with regard to the problems that need to be addressed first, second, and so forth. This is a challenging task as part of an analyst and it works well with open communication and good communication with partners.

Priority challenge is also why it can be very difficult to organize data analysis. If the individual-person analysis is passed, each person will attempt to take a moral view of the problem and ignore the others. This is a loose and natural, and analyst to analyst looga prevent falls, so is not fit or focused on one side. Finally, the analyst must take responsibility for seeing "a great picture of the analysis, think of any of the ideas of each employer that chooses an acceptable way.

To getting expert-level training for Data Science Training in your location –Data Science Training in Chennai | Data Science Training in Bangalore | Data Science Training in Pune | Data Science Training in Kalyan nagar | Data Science Training in marathahalli| Data Science Training in Anna Nagar | Data Science Training in OMR | Data Science Training in rajaji nagar | Data Science Training in btm| Data Science with Python training in Chennai | Data Science with R Training in Chennai| Data Science with SAS Training in Chennai| Data science Training in velachery | Data science Training in tambaram | Data Science Training in Pune| Data Science Training in Kalyan nagar | Data Science Training in Chennai |Data Science training in Bangalore | Data Science training in Chennai| For getting online training | Data Science online Training