Data Science is a field of research that entails using a variety of scientific techniques, algorithms, and processes to derive information from large quantities of data. It aids in the discovery of hidden trends in raw data. The evolution of statistical statistics, data mining, and big data have given rise to the word Data Science. In this article, we will discuss, what is data scientist, the data science process, what does a data scientist does, and the steps in the data processing.
In this article let us look at:
- Data Science Process
- Data Science Process Steps
1. Data Science Process
Every finished product or model that exists in Data Science has gone through a rigorous process that entails a wide range of specialized skills. Data Science is a multidisciplinary field that allows you to extract knowledge from both structured and unstructured data. You can use data science to turn a business problem into a research project, and then back into a practical solution. For today’s world, data is the oil. We can use data and turn it into a distinct business advantage with the right tools, technologies, and algorithms.
Using advanced machine learning algorithms, Data Science can assist you in detecting fraud. It assists you in avoiding significant financial losses. It enables machines to develop intellectual abilities. You can use sentiment analysis to determine whether or not a customer is loyal to a particular brand. It allows you to make better and faster decisions while also assisting you in recommending the right product to the right customer to grow your business.
So, what does a data scientist do? Data scientists appear to have a mystical quality about them. They’re thought to take a data set, work their magic on it, and out pop insights that will transform the company into a profit machine. As simple as that may appear, the process entails a great deal more effort.
2. Data Science Process Steps
The data science process is as follows:
- Business Understanding – We try to get a clearer understanding of what market needs we should be extracted from data in this first phase. What sorts of questions should we be posing to better advance the company and to help the business consider what steps it should take based on the data’s trends? This could be left open-ended, with you, the data scientist, asking questions about what you see and discover. That may even be a set of questions from your customer about something they’re curious about.
- Data Understanding – This is the process of gaining a business understanding of the data you have and deciphering what each piece of data means. This could entail determining exactly what data is required and the best methods for obtaining it. This also entails determining what each of the data points means in terms of the company. If you’re given a data set from a client, for example, you’ll need to know what each column and row represents. Is it true that each row represents a single customer? Is there a significant relationship between the data and this one column with a heading that appears to be an acronym? We can’t truly comprehend this unless we first comprehend what it means.
- Data Preparation – This is where you will spend the majority of your time during the process. Cleaning data is more of an art than a science because you must first determine if you have the correct data to proceed to a good model and then know how to clean it properly so that it does not corrupt your model. I would also consider having trustworthy data to be a part of this. “Garbage in, garbage out,” as the saying goes. If you feed your model bad data, it won’t be very effective.
- Modeling – This is where statistics and data analysis come into play to come up with a model that best fits the data. You may have to try on a few different models before finding the one that’s right for you. Going back to how the data was prepared may be necessary to accomplish this. Missing data can be cleaned in a variety of ways. Is it safe to remove the rows all at once? Is there a figure we can use as a benchmark? Depending on the industry, there may even be a better value in filling in the gaps. All of these can greatly improve the model.
- Evaluation – Before deploying or presenting your model, you must first evaluate it to see if it is fit for purpose. As shown in the diagram, this is also the stage in which you verify that the model answers the business questions you posed at the start of the process. It may lead to the discovery of new, more important questions.
- Deployment – This is where you share the results of your data analysis. This isn’t just about having an API that uses your model to call. It could be as simple as writing down your findings in an email, sharing a document, or giving a presentation to a group of executives. While it’s easy to get technical with your coworkers, the key to this step is relaying what you find in the data to a sales team or executives so they can act on it.
Since the data science method is recursive, a successful data scientist can go back to the beginning to fine-tune each step based on the knowledge they discovered. Data Science is a field of study that entails using a variety of scientific methods, algorithms, and processes to extract insights from massive amounts of data. Data Science concepts include statistics, visualization, deep learning, and machine learning. Discovery, Data Preparation, Model Planning, Model Construction, Operationalize and Communicate Results are all steps in the Data Science Process. This article summarises the data science process and outlines the key steps and actions you’ll take throughout a project.
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