Work on data science projects in such a way that a valuable and profitable result is achieved. To achieve this important and efficient principle, you first need to know the things that lead to it.
In the modern world, we are facing the introduction of various branches of information technology, including data science and its effective influence on various industries. Earn more.
The following are 5 important principles in the implementation of data science projects that by implementing them properly and professionally, we can hope to have the greatest impact on the successful implementation of projects.
1) Set up a mental scenario for data science projects!
Be transparent! Before starting and starting any project, first, clearly determine and record the output and result of the work, and while going through the steps and look at it, you may not believe, but this is the first and most important thing because the mind It frees you from confusion and focuses on the desired result.
One of the problems that are emerging in the world of data science is the existence of people with good skills in artificial intelligence and modeling capabilities using deep machine learning techniques who have sufficient knowledge but a specific goal in the implementation of projects. They do not pursue data science, which has drastically reduced the usefulness of this group.
Focusing on the desired result leads to the emergence of ideas and the use of combined and sometimes unique patterns in problem-solving. Having different solutions to a problem is just the result of gaining different experiences.
Once we know the end goal, we can be sure that we have selected the right data set, and selected the appropriate algorithms to process them. Therefore, when doing a data science job or project, you need to ask yourself:
A) What is the ultimate goal and what are we trying to achieve? B) Do we have a plan? C) How can it be decided whether the results obtained are desirable?
Setting final goals with measurable criteria, creating appropriate data, helps us achieve successful implementation and delivery of data science projects.
2) Collect your data and get ready for data science!
With the increasing popularity of data science, more and more companies are hiring data scientists today. Most of the time, however, these companies are not prepared to implement data science and do not have the basic infrastructure needed to implement data science algorithms and operations.
Companies need to be aware that machine learning takes place at a later stage after data storage and collection. Before any data mining is performed, the information is first reliably collected, converted, stored, secure, and then used for analysis, artificial intelligence, and machine data science modeling.
This means that if your company is not yet ready to accept data science, it needs to focus on building the core infrastructure first and hiring an experienced data specialist without the right tracking system and database can be catastrophic for both the company and its employees. , Because then only energy and time are lost.
3) You will love your data!
The data is not uniform and this makes them special. Most people think that good data is perfectly clean and evenly distributed. However, in reality, real data is neither clean nor evenly distributed, and that is, in fact, the great thing about data.
You may be surprised, but many times, these asymmetries and anomalies lead us to discover interesting facts about the field under study. For example, when segmenting and working on information, a lot of data may be deleted or ignored, assuming there is noise or anomaly in the data, but if a completely new and useful pattern of information Removed by those people, what?
The ability to discover unexpected features of data is what makes data science innovative and interesting. If you want to make the most of your raw data, spend some time with them, which may come as a surprise. You can also use some non-parametric statistical tests to open up a new world of data-driven discovery.
Following a chosen path usually does not make a difference. Explore the unknown and because of their diversity and creativity, you will fall in love with your data and the truth of data science technology and its impact.
4) Work on projects that add value to the business
It is important to ask the right questions when choosing data science projects for work, regardless of industry. This is mainly due to two factors:
A) The long-term schedule and the high cost of machine learning projects leave room for the wrong project and may even outweigh the benefits!
B) Calculating this opportunity and its potential impact on the business will help you determine if it is worth the project. Always, without fail, start projects whose output directly affects business leverage. In other words, it is essential that the results of data science projects be directly applicable.
The general formula for calculating the number of business opportunities:
Number of customers affected × Impact of calculation = Estimation of the actual impact of the project
For example, a real estate company may ask its data science team to provide an approximate estimate of the number of people looking to buy an apartment in a particular area. Merely having a number of potential buyers does not affect any business leverage because you do not know their preferences. You are not aware of their choices or budgets, and it is not enough to just identify a potential customer, finding their specific needs is something that changes everything.
5) The repetitive nature of modeling in data science projects
The winners of most machine learning competitions follow a repetitive approach. This means that you have to start with a simple model and then repeat it. An iterative approach in data science to quickly achieve the “first working model” is a focused and useful way to gradually begin to build and implement a model with a variety of variables and features and your own. When the first original model is built, features are added as a focus on continuous improvement.
To take advantage of the experimental nature of machine learning, try testing a specific model on more data so that you can develop it. For example, you can perform test A on a specific model for clients from a specific geography, and you can repeat it for several other geographies before testing for international clients.
Data scientists use model error analysis to find the weaknesses of the model and often seek the opinions of other experts in the field in areas that need improvement. Typically, a choice of iterative approach takes much less time and ends when the model is sufficiently improved to meet the target business requirements.
Understanding the principles of data science above is simple and easy to follow. Focusing on the end result will help you determine your success. Ensuring that your company is ready to adopt artificial intelligence and store data is essential for implementing data science projects.
You can cross boundaries and make amazing discoveries with a deep and accurate understanding of available data. Also, spend enough energy and time doing the right questions by asking the right questions, and ultimately help reduce costs and avoid recurrence by following a certain approach, and minimize the possibility of achieving unusual results.
To understand these principles and design some specific models, having 360-degree knowledge of data science is important and the best approach is to follow up and stay up to date in finding specialized resources in this field. Simulatoran, as one of them, is trying to take a step in introducing and effectively developing data science projects for industries and business owners, as well as providing specialized, useful, and up-to-date content in this field.
An interested and active person in the field of data science and molecular dynamics simulation