Data Science Life Cycle Process  

Introduction 

Data science life cycle is the combination of two fields – Data and Science. Data is any actual or imaginary thing, and science is nothing but the systematic study of the world, both physical and natural. Data Science is nothing but the systematic analysis of data and derivation of knowledge using testable methods to make predictions about the Universe. Simply put, it applies science to data science projects of any size and from any source. Data has become a new oil that is driving businesses today.

What is a Data Science Life Cycle? 

A data science lifecycle shows the initial steps to build, deliver, and maintain any data science product. But All data science projects are not made the same, so their life cycle also differs. Still, we can picture a general lifecycle that involves some of the most common data science projects. A general data science lifecycle process uses machine learning algorithms and statistical practices, resulting in better prediction models. Some of the most common data science projects involved in the process are data extraction, preparation, cleansing, modelling, evaluation, etc. The world of data science refers to this general process as the “Cross Industry Standard Process for Data Mining.

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The steps involved in the data science life cycle :

1. Understanding the Business Problem

To build successful data science projects, it’s very crucial to first know the business problem the client is facing. For example, he wants to predict the customer churn rate of his retail business. You may first want to understand his business, his needs, and what he wants to achieve from the prediction. In such cases, it is significant to consult domain experts and finally understand the underlying problems in the system. A Business Analyst is mainly responsible for gathering the client’s required details and forwarding the data to the data scientist team for further speculation. Even a minute error to define the problem and understand the need may be most important for the data science project; however, mu must do it with maximum precision. After asking the company stakeholders or clients the needed questions, we move to the following process, data collection.

2. Data Collection

After clarifying the problem statement, we need to collect relevant data for breaking the problem into small components.

The data science project starts with identifying many data sources, which may involve web server logs, social media posts, data from digital libraries, data accessed through various sources on the internet via APIs, web scraping, or information that is already present in an excel spreadsheet. Data collection entails obtaining information from known internal and external sources to address the business issue.

Usually, the data analyst team is responsible for gathering the data for the data science project. They must determine the best ways to source and collect data for the desired results.

There are two ways to source the data:

• Through web scraping with Python
• Extracting Data with the use of third-party APIs

3. Data Preparation

After data gathering from relevant sources, we must proceed to data preparation. This stage assists us in gaining a better point of view of the data and prepares it for further evaluation.

Additionally, this stage is referred to as Data Cleaning or Data Wrangling. It includes steps such as selecting relevant data, combining it by mixing data sets, cleaning it, dealing with missing values by either removing them or blaming them with relevant data, dealing with incorrect data by removing it, and checking for and dealing with outliers. You can create new data using feature engineering and extract unique features from existing ones. Format the data according to the desired structure and delete unnecessary columns or functions. Data preparation is the most time-consuming process, accounting for up to 90% of the total project duration, and this is the most crucial step throughout the entire life cycle.

Exploratory Data Analysis (EDA) is significant at this point because summarising clean data identifies the Data’s structure, outliers, anomalies, and trends. These insights can aid in determining the optimal set of features, an algorithm for model creation, and model construction.

4. Data Modeling

Data modelling is regarded as the core process throughout most data analysis cases. In this data modelling process, we take the data science project’s prepared data as the input; we try to prepare the wanted output.

Initially, we tend to select the suitable model that would implemented to acquire results, whether the problem is a regression problem or classification, or clustering-based. Depending on the type of data received, we choose the appropriate machine learning algorithm that is best suited for the model. Once this is done, we ought to tune the hyperparameters of the selected models to get a favourable outcome.

 5. Model Deployment

Before the model is deployed for the data science project, we must ensure that we have picked the correct solution after a rigorous evaluation. Later on, it is then deployed in the desired channel and format. This is naturally the last step in the life cycle of data science projects. Please take extra caution before executing each step in the life cycle to avoid unwanted errors. For example, choose the wrong machine learning algorithm for data modelling. You will not achieve the desired accuracy, and getting the project’s approval from the stakeholders will be challenging. If your Data is not cleaned correctly, you will have to handle missing values or the noise in the dataset later. Hence, you will have to do rigorous testing at every step to ensure that the model is deployed correctly and accepted in the real world as an optimal use case.

You may also like to read: How Data Science is Useful for Businesses

Frequently Asked Questions(FAQs) 

1. Is data science a safe career?

With the advancements in Machine Learning and Deep Learning, data science has gained popularity because of its use in various domains of applications. Data Science has helped grow many businesses by giving proper insights. There are multiple roles available to pursue a career in data science. With digital transformations, the availability of Data is enormous and easy. Someone has rightly said that Data is oil for the new century and is very valuable. 

2. Who can study data science?

The person should have the mathematical background to study data science. Many statistical methods are used aggressively in data science projects. Knowledge of programming language is also necessary to learn data science.  

3. Which tools help in various stages of data science?

At many stages, various tools are helpful in data science. Tools like PowerBI and Tableau are useful for Analysis and Visualization. Programming languages like Python and R are also useful for modelling and visualization. Spark and Hadoop are helpful when it comes to processing streaming data and big data.