What is the Data Science Life Cycle? | Everything you need to know (2022)

The term “Data science” was first coined by Dhanurjay Patil, who served as the Chief Data Scientist of the United States Office of Science and Technology Policy. Jeff Hammerbacher, another fellow data and computer scientist, in 2008. Since the acceptance of data science as an area of specialization that requires more research, data science has been rapidly adopted for further specialized studies to assist a more efficient, fluid, automated, and smoother technology experience. A data science life cycle refers to the established phases a data science project goes through during its existence. These steps or phases in a data science project are specified by the data science life cycle. It is beneficial to use a well-defined data science life cycle model, which offers a map and clear understanding of the work that has to be done in a data science project. This article will discuss this process and data science life cycle details.

Table of Contents

What Is a Data Science Life Cycle?

From its creation for a study to its distribution and reuse, the data science life cycle refers to all the phases of data during its existence. The lifecycle of data starts with a researcher or a team creating a concept for a study, and the data for that study is then collected once a study concept is established. After data is obtained, it is prepared for distribution to be archived and used by other researchers at a future stage. When data enters the distribution point of the life cycle, it is contained in a location where other researchers can then discover it.

The following illustration describes an example of the Data Science Life Cycle at NASA. Understandably, for beginners, this may be overwhelming at the moment, and you will read more through this blog; different pieces will fall into place.

(Video) Data Science Life Cycle | Life Cycle Of A Data Science Project | Data Science Tutorial | Simplilearn

What is the Data Science Life Cycle? | Everything you need to know (1)

What Is the Five-stage Life Cycle in Data Science?

The OSEMN framework is a great data science life cycle example to refer to. This framework covers the five stages of a data science life cycle. These are essentially 5 phases a data science project goes through to be successful.

What is the Data Science Life Cycle? | Everything you need to know (2)

The five stages are as follows:

  1. Obtaining the Data: This stage involves using technical knowledge like MySQL to process and generate the data. It can even be in simpler file formats such as Microsoft Excel. Some examples like Python and R even directly import the datasets into a data science program.
  2. Scrubbing the Data: This stage involves cleaning raw data to retain only the relevant part of the processed data. The noise is also scrubbed off, and the data is refined, converted, and consolidated.
  3. Exploring the Data: This stage consists of examining the generated data. The data and its properties are inspected since different data types demand specific treatments. Descriptive statistics are then computed to extract the features and test the significant variables.
  4. Modeling the Data: The dataset is refined further, and only the essential components are kept. Only relevant values are kept and tested to predict accurate results.
  5. Interpreting the Data: At this stage, the final product is interpreted for the client or business to analyze if it meets the requirement or answers a business question. The insights are shared with everyone, and the results of the final stage are visualized.

What Are the Key Steps of a Data Science Project?

What is the Data Science Life Cycle? | Everything you need to know (3)

A data science project has a few fundamental milestones which need to be met as the project moves forward. Here are the key steps of a data science project.

Business Requirement and Understanding: Understanding the needs of the business or client and getting an idea of the problem

(Video) Complete Life Cycle of a Data Science Project

The problem or requirement is properly understood and the specifics are discussed.

Data Generation and Understanding: The available data which can be used and the data which needs to be generated is analyzed and discussed. This is one of the fundamental data science life cycle steps as it deals with understanding the data requirement and gathering the data.

Data Preparation: This part of the process deals with preparing the raw data by cutting out the noise and irrelevant information. This is a time-consuming process because it deals with the cleaning and fine-tuning of data from datasets that are relevant and won’t lead to the corruption of the model.

Modeling of Project: The project is modeled, and different variations are tried out before deciding upon the final one with statistical and analytical means.

Evaluation of Model: This stage deals with finding out if the model is good enough before deployment. It is checked if the model can tackle a business problem or serve the business requirement.

Deployment of Model and Communication: The model is deployed and monitored. Basic communication is done regarding the model in regards to optimization and maintenance.

(Video) W004-Data Science Life Cycle

Which Is the Most Important Thing in Data Science?

The most important thing in Data Science to understand the business context and organizational needs for which Data Science is put to use. Often, professionals are too focused on the technicalities and fancy algorithms and lose the focus on the actual business outcome or organizational objectives, without achieving any Data Science project has almost no purpose. So it becomes imperative for any Data Science professional to keep the end objective and business questions in consideration right from the beginning.

The other very important thing in data science would be a good grasp of the mathematical and fundamentals of statistics for Data Science. Mathematical concepts such as linear algebra, distributions, and probabilities are important for data science and help to work on Data Science projects in a more meaningful way. . Similarly, a solid foundation in statistical concepts such as inferential and descriptive statistics is highly recommended. Many programming languages can be used for data science, but a good knowledge of prominent tools like Python, SQL, and R helps immensely.

Related: How to Become A Data Scientist – Step By Step Guide

What Is the Data Science Process?

The Data Science process consists of all the key steps involved in a Data Science project. A Data Science process from a traditional data science life cycle example would consist of framing the problem or requirement and then collecting the raw data required. The data is then processed for analysis, and the data are explored. In-depth analysis and testing with statistical tools are then performed to conclude the project. The results are then shared with the concerned entities.

You may also like to read: What Is Data Science Process, Steps Involved, and Their Significance?

Conclusion

With the advent of Deep Learning, AI, Complex Data requirements, and more efficiency, there has never been more importance put into Data Science. The Data science life cycle is one of the basic concepts that should be covered and studied to understand the different phases of a data science project successfully.

(Video) The Data Science Life Cycle Explained

FAQs – Frequently Asked Questions

Q1. What is data science methodology?

Data Science Methodology is a systematic series of techniques that guides data scientists through a specified sequence of steps to the ideal approach to solving data science problems.

Q2. How is data science used in healthcare?

Medical imaging is one of the most powerful applications of data science in healthcare. Computers learn how to view X-rays, mammography, MRIs, and other image forms, recognize data patterns and detect tumors, stenosis of the artery, abnormalities of the organ, and more. It is possible to detect a health problem by taking past historical data from other patients, a patient’s trends, and genetic details into consideration before it gets out of control. This assists doctors and patients both to detect issues with a patient’s bodies beforehand. Big data helps scientists simulate a drug’s reaction to body proteins and various cell types and conditions to have a higher chance of being effective hence highly supporting drug discoveries. In hospitals, predictive analytics may make scheduling more efficient and tell hospital workers which beds should be cleaned first and which patients during the discharge process can face difficulties.

Q3. At what stage of the Data Science life cycle do you optimize the parameters?

Parameters are optimized in the last stage of the implementation of a data science project. This phase is known as the monitoring or closure phase. This is fundamentally the end-point of a typical Data Science project. Every day, vast quantities of data are analyzed and generated. Therefore there is a definite need for the models to keep learning and getting trained. The models of Data Science need to adjust to this fresh information. This is different from retraining or remaking since this stage is nothing but preserving the model’s efficiency by taking appropriate steps. This also prevents any data loss or a future system malfunction. This process is referred to as optimization within the data science life cycle steps.

Q4. What are tools used for Data Science?

Many tools are used for data science. For AI and machine learning, Python, R, and Apache Spark are most preferred. Microsoft Excel and SQL are preferred as well due to their simplicity. There are many other tools like Tableau, Alteryx, MATLAB etc. which are also very popular.

AnalytixLabs offers a wide range of Data Analytics Training Courses to prepare you for a successful career in data science and machine learning. AnalytixLabs methodically creates every course and maps it in accordance with job roles in Data Engineering, AI, and Data Science.

With the increasing need for data science, we need to be more familiar with data science life cycle details and data science tools. Please drop a comment below if you want to hear back from us or in the case of any inquiry. We would love to hear your opinions and answer your queries!

(Video) Data Science Life Cycle

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FAQs

What is the life cycle of data science? ›

A general data science lifecycle process includes the use of machine learning algorithms and statistical practices that result in better prediction models. Some of the most common data science steps involved in the entire process are data extraction, preparation, cleansing, modelling, and evaluation etc.

How many steps are there in data science life cycle? ›

Right from the first step of obtaining data to analysis and result presentation, a Data Science Life Cycle is a definite procedure that has five important steps.

What is data science explain about life cycle of data science What are the prerequisites of data science? ›

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

What are the 6 phases of data lifecycle? ›

While there is no industry standard for enterprise DLM, most experts agree that the data lifecycle includes these six stages: creation, storage, use, sharing, archiving, and destruction.

What are the 5 stages of life cycle? ›

Key Takeaways

There are five steps in a life cycle—product development, market introduction, growth, maturity, and decline/stability. Other types of cycles in business that follow a life cycle type trajectory include business, economic, and inventory cycles.

What are the 4 stages of data cycle? ›

Data Creation

Data Acquisition: acquiring already existing data which has been produced outside the organisation. Data Entry: manual self-service entry of new data by personnel within the organization. Data Capture: data generated by devices used in various processes in the organization.

What are the 3 main concepts of data science? ›

Statistics, Visualization, Deep Learning, Machine Learning are important Data Science concepts.

What are the 4 life cycles? ›

A product's life cycle is usually broken down into four stages; introduction, growth, maturity, and decline.

Why do we need to define the life cycle of a data science project? ›

Why do we need to define the Life Cycle of a data science project? In a normal case, a Data Science project contains data as its main element. Without any data, we won't be able to do any analysis or predict any outcome as we are looking at something unknown.

What is the data life cycle in simple words? ›

The data life cycle is the sequence of stages that a particular unit of data goes through from its initial generation or capture to its eventual archival and/or deletion at the end of its useful life.

What are the 7 stages of the life cycle? ›

Life Cycle of Human
  • 1) Foetus: The sperm from the adult male human and the egg from the adult female human form a zygote inside the uterus of the female. ...
  • 2) Infancy: ...
  • 3) Toddler years: ...
  • 4) Childhood: ...
  • 5) Adolescence: ...
  • 6) Adulthood: ...
  • 7) Middle age: ...
  • 8) Old age:

What are the 8 stages of data analysis? ›

data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating ...

What are the 14 steps in data processing? ›

Data Processing Cycle
  1. Data Collection. The primary stage in the data processing cycle involves collecting raw data acquired from standard sources such as data warehouses and data lakes. ...
  2. Data Preparation. ...
  3. Data Input. ...
  4. Data Processing. ...
  5. Output. ...
  6. Storage. ...
  7. Manual Data Processing. ...
  8. Mechanical Data Processing.
Mar 20, 2022

What is an example of a life cycle? ›

What is an example of a life cycle? A life cycle is the series of stages of life for an organism, beginning with life and ending with death. An example would be the life cycle of a bird. A bird's life cycle consists of four main stages, which include 1) egg, 2) hatchling, 3) fledgling, and 4) adult.

What are the 10 life stages? ›

The major stages of the human lifecycle include pregnancy, infancy, the toddler years, childhood, puberty, older adolescence, adulthood, middle age, and the senior years.

Why is life cycle important? ›

A life cycle approach can help us make choices. It implies that everyone in the whole chain of a product's life cycle, from cradle to grave, has a responsibility and a role to play, taking into account all the relevant impacts on the economy, the environment and the society.

What are the three 3 data processing cycle? ›

Collection of data. Preparation of the data into a format suitable for data entry, as well as error checking. Entry of the data into the system, which may involve manual data entry, scanning, machine encoding, and so forth. Processing of the data with computer programs.

What are the 3 processes of data processing cycle? ›

Data procesing refers to the transformating raw data into meaningful output. Data Input- the collected data is converted into machine-readable form by an input device, and send into the machine. Output is the production of the required information, which may be input in future.

What are the 5 P's of data science? ›

The 5 Ps of product, price, promotion, place, and people are the holy grail of business for retailers and consumer packaged goods (CPG) enterprises. Data scientists are now simplifying and creating the optimal mix of these 5 Ps for enterprises, using the massive amount of data they generate.

What are basics of data science? ›

Data science is the multidisciplinary field that focuses on finding actionable information in large, raw or structured data sets to identify patterns and uncover other insights. The field primarily seeks to discover answers for areas that are unknown and unexpected.

What are the three types of life cycles? ›

There are three types of life cycles: Haplontic life cycle, Diplontic life cycle and Haplodiplontic life cycle.

How many types of life cycle do we have? ›

In regard to changes of ploidy, there are three types of cycles: haplontic life cycle — the haploid stage is multicellular and the diploid stage is a single cell, meiosis is "zygotic". diplontic life cycle — the diploid stage is multicellular and haploid gametes are formed, meiosis is "gametic".

What are the 4 stages of data cycle? ›

Data Creation

Data Acquisition: acquiring already existing data which has been produced outside the organisation. Data Entry: manual self-service entry of new data by personnel within the organization. Data Capture: data generated by devices used in various processes in the organization.

What are the 4 stages of data processing cycle? ›

The sequence of events in processing information, which includes (1) input, (2) processing, (3) storage and (4) output. The input stage can be further broken down into acquisition, data entry and validation.

What are the 4 steps of the data cycle? ›

The four stages of the data cycle are: collection, processing, analysis, and presentation.

What are the four stages of data science? ›

But it's not just access to data that helps you make smarter decisions, it's the way you analyze it. That's why it's important to understand the four levels of analytics: descriptive, diagnostic, predictive and prescriptive.

What are the three 3 data processing cycle? ›

Collection of data. Preparation of the data into a format suitable for data entry, as well as error checking. Entry of the data into the system, which may involve manual data entry, scanning, machine encoding, and so forth. Processing of the data with computer programs.

What are the 3 processes of data processing cycle? ›

Data procesing refers to the transformating raw data into meaningful output. Data Input- the collected data is converted into machine-readable form by an input device, and send into the machine. Output is the production of the required information, which may be input in future.

What are the 5 parts of data processing? ›

Data Processing refers to converting raw data into meaningful information, and these data are machine-readable as well. Thus, data processing involves collecting, Recording, Organizing, Storing, and adapting or altering to convert the raw data into useful information.

What are the 8 data processing process? ›

Common data processing operations include validation, sorting, classification, calculation, interpretation, organization and transformation of data.

What are the 5 P's of data science? ›

The 5 Ps of product, price, promotion, place, and people are the holy grail of business for retailers and consumer packaged goods (CPG) enterprises. Data scientists are now simplifying and creating the optimal mix of these 5 Ps for enterprises, using the massive amount of data they generate.

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