What is Data Science Life cycle Data Sci

What is Data Science Life cycle Data Science Process Explained

What is Data Science Life cycle? - Data Science Process Explained


Data Science is considered one of the most valuable skills to have in the tech world right now. However, it can be a little difficult to understand how to build a solution using Data Science. Keeping that in mind, in this blog, we will discuss the Data Science Life Cycle that you can use in your next Data Science project.


Data Science are often leveraged by different fields. However, it are often overwhelming skills to start out and set about creating a knowledge Science project. Questions like the way to begin, what steps to follow, etc. are often very difficult to answer, especially for a beginner. Therefore, during this blog, we'll cover a knowledge Science process, which you'll use to create your next project. These steps will assist you build a knowledge Science project from start to end completely from scratch.


Introduction to Data Science Life Cycle


Data Science may be a confluence of computing and arithmetic. It deals with extracting information out of huge volumes of knowledge. Data Science has completely changed the way we solve problems using computer applications. Before Data Science, organizations had to handle giant volumes of knowledge but were only ready to extract a touch information out of them, which might be considered useful. Because of this, many companies were forced to make decisions based on this little information they had extracted and the trends that they had predicted. When Data Science became more prevalent, more and more people started using it. Nowadays, most of the businesses are ready to make use of the massive volumes of knowledge that they need accumulated from their customers, which helps them make more informed decisions about the services that they provide. Data Science has also helped in making models that allow them to form predictions, like expected sales turnover, or to classify information, like if a customer will upgrade to the newest plan or leave the service. These new abilities became so important to several companies that there has been a rapid demand for skilled Data Science professionals during this decade. Since Data Science is so popular then in demand, the professionals who are handling Data Science projects have come up with a process which will be used to solve Data Science problems. This process has distinct steps. We will discuss them right away.


Framing the Problem

Whenever we try to unravel a knowledge Science problem, we must first understand the scope and depth of the matter that we try to unravel. If we make an error during this step, then we find yourself solving a drag that we didn't got to solve, and that we find yourself spending tons of your time and resources on a project which will not yield the specified effect. For example, if the management of a corporation needs you to create a recommendation engine for his or her movie streaming service, and you begin the project without understanding the matter , then you'll find yourself building a system that generates a couple of recommendations as and when users tell the system about their likes and dislikes. Meanwhile, what the corporate officials actually wanted could be to create recommendation feeds which will even be sent via emails to entice customers to spend longer on their platform. During this case, your effort on the project will go vain.


To make sure that we are solving the proper problem, the foremost important thing is to ask as many questions as possible to urge a transparent sense of what the stakeholders wish from the merchandise or service. For instance, when building a movie recommendation engine, we will begin the work by asking questions such as:

  • What kind of a system would the company like to build?

  • What kind of data is available for us to use?

  • How many movies are there in the library?

  • How many movies should be there in a recommendation?

  • How are these recommendations going to be used?

Collecting Data


After specifying the matter we try to unravel, we've to gather the info which will be utilized in subsequent steps of solving the matter. Data collection may be a vital step within the entire Data Science life cycle. It is crucial because, in Data Science, all decisions are made using data. Hence, if the info that we get isn't good, then our solution won't be good also.

The data we collect may have several issues with it, like being faulty, incorrect, or just being insufficient to unravel the matter at hand. These sorts of problems may arise due to the info being gathered from multiple sources. As these sources are often very diverse and different from one another, we can also have problems with combining the info from these sources into one giant collection of knowledge. Also, the info we collect must be from reliable sources. If the source isn't reliable, then it could mean that the info isn't reliable, and this will lead us to finish up with an answer that's not very fruitful. There are several measures we will fancy make sure that the info we get is of top quality and is straightforward to form use of. First, we'd like to collect data directly from customers with their knowledge. For example, if we wish to form sure that the business decisions being taken are having an honest impact on users, then we should always collect data regarding the user experience from the users themselves by asking them questions about several aspects of the service—such as if the service is up to the mark if the changes made or the new features added are helpful, etc. This will ensure that the data is of good quality. We can also get data from sources like websites using web scraping, which can extract data from sites. Once the info is collected and if it's of excellent quality, then we will advance to subsequent steps.


Processing the Data


After gathering quality data from reliable sources, we'd like to process it. Processing is completed to make sure that any issues that the collected data are addressed before moving onto subsequent steps. Without this step, we'd find yourself producing errors or incorrect results. There might be several issues with the info that's collected. For instance, the info could have tons of missing values in several rows or columns. It could have many outliers, incorrect values, or values in timestamps with different time zones, etc. the info could even have issues associated with date ranges. For instance, in many countries, the date is formatted as DD/MM/YYYY, and in other countries, it's formatted as MM/DD/YYYY. Many issues could also arise within the data collection process, e.g., if the info is collected from multiple thermometers and any of these are faulty, then the info may need to be discarded or recollected. All these issues with the info got to be resolved during this step. A number of these can have multiple solutions like if the info contains missing values, we will either fill them with zero or fill them with the typical of all the values of the column. Also, if the column is missing tons of values, it's going to be better to drop the column entirely since there's so little data in it that it can't be of any use to us in solving our problem using our data science process. Now, in cases where the time zones are all involved, unless we will determine the time zones that are utilized in the given timestamps, we cannot use the info in those columns and should need to drop them. However, if we do have the time zones during which each timestamp is collected, then we will convert all timestamp values to a specific time zone. Like this, there are several ways to affect issues that would be present within the collected data.


Exploring the Data


Data exploration is one among the foremost important and time-consuming steps within the life cycle of knowledge Science. We could also be spending anywhere from each day to multiple weeks to explore data. The info exploration step is completed to form sure that we will extract some patterns from our data, which may lead us to unravel our problem. For example, imagine we are analyzing data from an e-commerce platform to assist devise a meaningful strategy to draw in more customers to every product. To unravel this problem, we will start by analyzing the age distribution of consumers of a specific product. By doing this, we may realize that the actual product is employed more by people that are young, especially aged between 20 and 40, than people that are old or are above the mentioned age bracket. This might help us devise a marketing strategy that focuses on younger customers to attach them with the merchandise. This kind of exploration are often performed using the visualizations and therefore the numerical summaries of the info and its columns. Using this, we will get a good little bit of the surface level understanding of the info that we are using within the stages of our data science process. However, we will get a way deeper understanding of our collected data within the next step.

Analyzing the Data


In this step, we attempt to get a deeper understanding of the info we've collected and processed. We use statistical and numerical methods to draw inferences about the info and to spot the connection between multiple columns in our dataset. We will also use visualizations to raised understand and summarize the info using images, graphs, charts, plots, etc. These tools allow us to create a model which will make predictions or perform classification on a given dataset. We get to find out the way to better understand our data and patterns underlying it to convert them into useful information. Using these tools, we will also determine how different columns are associated with one another by checking out their correlation. Using these insights, we will determine the way to solve the various problems that we are tackling. For example, if we are taking a glance at the correlation between columns and that we understand that some columns are highly correlated, then we will draw an insight that a rise during a value in one column will cause a rise during a value of another. Although, here, it's to be noted that correlation doesn't mean equal causation, i.e., simply because two columns are correlated doesn't mean that an increase in one value of the primary column always causes the increase during a value of the opposite.


Consolidating Results


Using the insights gained from all the previous steps of our data science process, we now need to be ready to consolidate the results in order that they will be analyzed and understood by stakeholders. That is, once we've created visualizations, analyzed the info, and have drawn conclusions, we'd like to make documents that justify our conclusions by describing the insights and visualizations.

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