While data scientists may argue regarding the implications of a given data set, almost all data science experts agree on the need to adhere to the data science process, which is a methodical approach to completing a data science project. Numerous frameworks are available, and some are more suited to business use cases than others.

This article will discuss the most popular data science process frameworks, which are ideal for each use case, and the key aspects of each one.

 

So let’s get started with the fundamentals. 

 

What Is the Data Science Methodology?

 

The data science process is a methodical approach to issue-solving with data. It gives you a disciplined framework for articulating your problem as a question, selecting how to answer it, and then delivering your solution to stakeholders.

 

The data science life cycle is another title for the data science process. Both phrases can be used interchangeably to describe a workflow process that begins with data collection and concludes with deploying a model that should answer your questions. The steps are as follows:

 

1. Framing the Issue – The first step in the data science life cycle is understanding and framing the problem. This framework will help you create a successful model that will benefit your company.

 

1. Data Collection – The following step is to collect the essential data. High-quality, focused data and the procedures for collecting it are critical for achieving meaningful results. Because much of the 2.5 quintillion bytes of data created every day is in unstructured formats, you'll almost certainly need to extract the data and export it into a CSV or JSON file are examples of useful formats.

 

1. Data Cleaning – Most of the information gathered during the gathering phase will be unstructured, irrelevant, and unfiltered. Because bad data yields bad results, the accuracy and efficacy of your analysis will be strongly dependent on the quality of your data.

Duplicate and null values, corrupt data, mismatched data types, invalid entries, missing data, and poor formatting are all eliminated when data is cleaned.

 

This is the most time-consuming step, but detecting and correcting errors in your data is critical for constructing effective models.

 

What Is the Difference Between Data Science and Data Analytics?

 

• Data Science:

 

Data scientists use programming, arithmetic, and statistics to gain insights and drive corporate strategy. Data modeling, machine learning, AI and the use of ML algorithms to automate operations are all skills that data scientists possess. Because useful data is field-specific, data scientists must also have domain expertise, or a thorough grasp of their sector or firm, to give context to the data they work with.

 

• Data Analytics:

 

Data analytics experts are in charge of data collection, organization, and maintenance, as well as gaining insights from data using statistics, programming, and other approaches. The role of a data analyst is to spot trends and assist in problem solutions. Order tracking, recommendation features, and store location identification are all examples of data analytics in retail.

 

Data analysts respond to decision-makers demands rather than driving the decision-making process. Using the data analytics course, you can also master analytics tools and become an IBM-certified data analyst.

 

Steps in the Data Science Process

 

You should be familiar with various different data science workflow frameworks. While they all seek to lead you through an efficient workflow, some techniques are better suited to specific use situations.

 

What is CRISP-DM?

 

CRISP-DM is an abbreviation for Cross Industry Standard Process for Data Mining. It is a common industry-standard technique and process model because it is versatile and adjustable. It's also a tried-and-true strategy for guiding data mining operations. In the data process life cycle, the CRISP-DM model includes six phases. These are the 6 phases:

 

Step 1: Understanding of Business

 

The first phase in the CRISP-DM process is to define the business's goals and bring the data science project into focus. Clearly, describing the aim should include more than just identifying the statistic that has to be changed. No amount of analysis, no matter how thorough, can impact measurements unless action is taken.

 

Data scientists consult with stakeholders, subject matter experts, and anyone who can contribute insights into the topic to understand the business better. They may also conduct a preliminary study to learn how others have approached comparable situations. Finally, they'll have a well-defined problem and a plan to resolve it.

 

Step 2: Data comprehension

 

Understanding your data is the next stage in CRISP-DM. In this step, you'll figure out what data you have, where you can acquire more of it, what your data contains, and how good it is. You'll also need to pick which data-gathering tools to utilize and how to acquire your initial data. Then you'll explain the basic data features, such as the format, number, and records or fields in your data sets.

 

After you have collected and described your data, you can begin studying it. Then, ask data science questions that can be answered through queries, visualization, or reporting to develop your initial hypothesis. Finally, you'll check the quality of your data to see if there are any errors or missing values.

 

Step 3: Data Preparation 

 

Data preparation is frequently the most time-consuming phase, and you may need to return to it several times throughout the project.

 

Data originates from various sources and is typically worthless in its raw form due to faulty and missing properties, contradictory values, and outliers. These flaws are resolved, and the quality of your data is improved, allowing it to be used effectively in the modeling step. Look at the data science certification course, to gain insight into this step. 

 

Step 4: Modeling

 

There are numerous data modeling options. You'll decide which choice is ideal based on the company's goals, the variables involved, and the resources available.

 

When deciding on a modeling technique, you will create two reports. The first will specify the modeling technique you will employ. The second will document the assumptions used in your modeling report—for example, if your model demands a specific type of data distribution.

 

After you've decided on a modeling technique, you'll create tests to see how well your model works. Your deliverable for this step will be your test design. This may include separating your data into training and testing sets to minimize overfitting. This occurs when you create a model that works wonderfully with one piece of data but not with others. It is critical to avoid introducing bias into your data during this stage.

 

Sep 5: Evaluation

 

During the evaluation phase, you will assess the model against your company's objectives. Then, you'll review your work method, explain how your model will benefit the company, summarize your findings, and make any necessary changes.

 

Step 6: Model Deployment

 

While deployment is the final stage of the CRISP-DM approach, it is not the conclusion of your project. You will plan and describe how you want to implement the model and how the results will be delivered and presented during the deployment phase. During the deployment phase, you must monitor the findings and maintain the model.

 

What Is the Importance of the Data Science Process?

 

Following the data science method provides organization and order to your work. If you stick to a tried-and-true method, your workflow will run smoothly, and you won't forget anything. Because it has been demonstrated to deliver the most accurate outcomes, a good data science approach gives you confidence in your results.

 

The data science approach you select will walk you through the processes required to acquire data, transform it into high-quality input, create and evaluate models, and understand and share your results. If you're prepared for a lucrative data science career, enroll in a data science course with placement to exhibit your skills.

Publication: 10/02/2023 06:50