Data Science can be leveraged by different fields. However, it can be daunting to know how to start and go about creating a Data Science project. Questions such as how to begin, what steps to follow, etc. can be very difficult to answer, especially for a beginner. Therefore, in this blog, we will cover a Data Science L, which you can use to build your next project. These steps will help you build a Data Science project from start to finish completely from scratch.
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Introduction to Data Science LifeCycle
Data Science is a confluence of computer science and mathematics. It deals with extracting information out of large volumes of data. Data Science has completely changed the way we solve problems using computer applications. Before Data Science, organizations had to handle giant volumes of data but were only able to extract a little information out of them, which could 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 companies can make use of the large volumes of data that they have 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 make predictions, such as expected sales turnover, or to classify information, such as if a customer will upgrade to the latest plan or leave the service. These new abilities have become so important to many companies that there has been a rapid demand for skilled Data Science professionals in this decade.
Since Data Science is so popular and so in demand, the professionals who have been dealing with Data Science projects have come up with a process that can be used to solve Data Science problems. This process has distinct steps. We will discuss them right away.
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The Six Stages of the Data Science Life Cycle
Framing the Problem
Whenever we are trying to solve a Data Science problem, we must first understand the scope and depth of the problem that we are trying to solve. If we make a mistake in this step, then we end up solving a problem that we did not need to solve, and we end up spending a lot of time and resources on a project that will not yield the desired effect.
For example, if the management of an organization needs you to build a recommendation engine for their movie streaming service, and you start the project without understanding the problem, then you may end up building a system that generates a few recommendations as and when users tell the system about their likes and dislikes. Meanwhile, what the company officials wanted might be to build recommendation feeds that can also be sent via emails to entice customers to spend more time on their platform. In this case, your effort on the project will go vain.
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Key Questions must be asked in framing the problem:
To make sure that we are solving the right problem, the most important thing is to ask as many questions as possible to get a clear sense of what the stakeholders wish for the product or service. For example, when building a movie recommendation engine, we can 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?
Only after getting clear answers to these questions and more, we should begin building the system. This step will ensure that we are solving the same problem that the company wants us to solve.
After specifying the problem we are trying to solve, we have to collect the data that can be used in the next steps of solving the problem. Data collection is a very important step in the entire Data Science life cycle. It is crucial because, in Data Science, all decisions are made using data. Hence, if the data that we get is not good, then our solution will not be good as well.
The data we collect may have several issues with it, such as being faulty, incorrect, or simply being insufficient to solve the problem at hand. These kinds of problems may arise because of the data being gathered from multiple sources. As these sources can be very diverse and different from each other, we may also have problems with combining the data from these sources into a single giant collection of data. Also, the data we collect needs to be from reliable sources. If the source is not reliable, then it could mean that the data is not reliable, and this can lead us to end up with a solution that is not very fruitful.
There are several measures we can take to ensure that the data we get is of high quality and is easy to make use of. First, we need to gather data directly from customers with their knowledge. For example, if we wish to make sure that the business decisions being taken are having a good impact on users, then we should 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 such as websites using web scraping, which will extract data from web pages. Once the data is collected and if it is of good quality, then we can move on to the next steps.
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Processing the Data
After gathering quality data from reliable sources, we need to process it. Data processing is done to ensure that any issues that the collected data have been dealt with before moving on to the next steps. Without this step, we might end up producing errors or incorrect results.
There could be several issues with the data that is collected. For example, the data could have a lot 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 data could also have issues related to date ranges. For example, in many countries, the date is formatted as DD/MM/YYYY, and in other countries, it is formatted as MM/DD/YYYY. Many issues could also arise in the data collection process, e.g., if the data is collected from multiple thermometers and any of those are faulty, then the data might have to be discarded or recollected.
All these issues with the data need to be resolved in this step. Some of these can have multiple solutions such as if the data contains missing values, we can either fill them with zero or fill them with the average of all the values of the column. Also, if the column is missing a lot of values, it may be better to drop the column entirely since there is so little data in it that it cannot be of any use to us in solving our problem using our data science life cycle process.
Now, in cases where the time zones are all mixed up, unless we can determine the time zones that are used in the given timestamps, we cannot use the data in those columns and may have to drop them. However, if we do have the time zones in which each timestamp is collected, then we can convert all timestamp values to a particular timezone. Like this, there are several ways to deal with issues that could be present in the collected data.
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Exploring the Data
Data exploration is one of the most important and time-consuming steps in the life cycle of Data Science. We may be spending anywhere from a day to multiple weeks exploring data. The data exploration step is done to make sure that we can extract some patterns from our data, which can lead us to solve our problem.
For example, imagine we are analyzing data from an e-commerce platform to help devise a meaningful strategy to attract more customers to each product. To solve this problem, we can start by analyzing the age distribution of customers of a particular product. By doing this, we may realize that the particular product is used more by young people, especially of age between 20 and 40, than people who are old or are above the mentioned age group. This may help us devise a marketing strategy that focuses on younger customers to connect them with the product.
This kind of exploration can be performed using the visualizations and the numerical summaries of the data and its columns. Using this, we can get a fair bit of the surface-level understanding of the data that we are using in the stages of our data science process. However, we can get a much deeper understanding of our collected data in the next step.
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Analyzing the Data
In this step, we try and get a deeper understanding of the data we have collected and processed. We use statistical and numerical methods to draw inferences about the data and to identify the relationship between multiple columns in our dataset. We can also use visualizations to better understand and summarize the data using images, graphs, charts, plots, etc.
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These tools allow us to build a model that can make predictions or perform classification on a given dataset. We get to learn how to better understand our data and patterns underlying it to convert them into useful information. Using these tools, we can also determine how different columns are related to each other by finding out their correlation. Using these insights, we can determine how to solve the different problems that we are tackling.
For example, if we are taking a look at the correlation between columns and we understand that some columns are highly correlated, then we can draw an insight that an increase in a value in one column will cause an increase in the value of another. Although, here, it is to be noted that correlation does not mean equal causation, i.e., just because two columns are correlated does not mean that a rise in one value of the first column always causes the rise in a value of the other.
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Using the insights gained from all the previous steps of our data science process, we now have to be able to consolidate the results so that they can be analyzed and understood by stakeholders. That is, once we have created visualizations, analyzed the data, and have concluded, we need to create documents that justify our conclusions by describing the insights and visualizations.
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We hope this blog has helped demystify the phases of the Data Science life cycle, which can help you tackle your next Data Science project systematically. Here, we have discussed a standard process that is in use. However, you can create your processes by adding steps to this standard one or removing steps based on your problems and their requirements.
If you have any queries or questions you can write to them on our Data Science community forum.