Data Science Life Cycle: A Complete Guide
Did you know that the amount of data created in the world doubles every two years? As the world moves towards technological advancement, it is also entering a world of massive databases. Hence, the need to store data has also grown. The data science life cycle is a process that helps in analyzing these big databases and producing meaningful results.
What is Data Science?
Data science is the method of interpreting large amounts of data to derive valuable insights from it. It helps analyze, clean, process, and interpret data in many ways and uses a variety of data science tools to support the process. It is a multidisciplinary field, i.e., a combination of various disciplines, such as statistics, mathematics, artificial intelligence, machine learning, and many more.
The volume of data has grown exponentially in the past years making data scientists an integral part of every company. To become a part of this growing data science community you can pursue a data science course with placement guarantee to enhance your knowledge.
Life Cycle of Data Science
The data science life cycle is a methodical way of processing and analyzing data to gain useful insights and make informed decisions. This process involves the following key stages:
1. Identifying Problems
In a data science project, the identification of problems is the key requirement to deliver the proposed solutions. Domain experts and data scientists work together to deal with the identified problems.
In the data science field, domain expertise is required in data mining processes. The domain expert’s role is to have a thorough knowledge of domain applications and identify the problems to be solved whereas the data scientist helps in identifying and finding possible solutions to the problems.
2. Knowing the Business
Understanding the business perspective is all about knowing what the client wants. Based on the kind of products and services that a business offers, clients desire a business strategy to make themselves profitable.
There are two important factors in knowing the business well. These are:
- Key Performance Indicator (KPI): The client and data science project team work together to decide on the performance indicators related to the data science project goals.
- Service Level Agreement (SLA): Once the performance indicators are decided, the service level agreement is finalized. The conditions of this essential agreement are selected in accordance with the business objectives.
3. Collection of Data
Once the key performance indicators and service level agreement are in place, the next step is to gather data to achieve the project’s targets. Data can be gathered from a variety of sources, such as internal databases, external databases, and third-party data suppliers.
4. Pre-Processing of Data
Data pre-processing entails cleaning and converting raw data into a format suitable for analysis. This includes removing duplicates, adding missing values, and formatting data consistently. To improve the accuracy of the analysis, data scientists may need to perform feature engineering, which involves creating new features from existing data.
The ETL (Extract, Transform, and Manipulate) process is also carried out on the data. The goal of data pre-processing is to ensure that the data is complete and ready for analysis.
5. Exploratory Data Analysis
After preparing the data, it is ready for in-depth analysis. Exploratory data analysis (EDA) is a crucial step in data analysis. It involves examining data using statistical functions and identifying dependent and independent variables.
It helps identify important features and data spread and uses visualization tools like Tableau and PowerBI, which are popular for EDA. Knowledge of data science with Python and R is essential for successful EDA on any data type.
6. Data Modeling
Once the data is analyzed and visualized, it is modeled and refined by retaining the important components. It is essential to determine how the data will be modeled.
Tasks like classification or regression are required depending on the business value. There are several modeling options such as applying various algorithms to the input to determine the results or test the model with a sample data that is similar to the actual data set.
7. Model Evaluation
This is the step where we take all the raw data and transform it into something usable by the algorithm. Using the organized data as input, the model gives the preferred output. This phase involves choosing the appropriate model type, depending on whether the issue is one of classification, regression, or clustering.
While modeling data, the models are first tested using dummy data that is similar to the actual data. The following two phases are important when modeling data.
- Data Drift Analysis: Here, “data drift” refers to changes in input data, and the analysis of this change is known as data drift analysis. The accuracy of the data model depends on this analysis.
- Model Drift Analysis: Here, “model drift” refers to the decay in the predictive power of the model. Methods such as adaptive windowing, Page Hinkley, and machine learning techniques can detect data drift, while incremental learning effectively exposes models to new data incrementally.
8. Model Training
The model is ready to train after the data model has been finalized and the data drift analysis process has been completed. The model is trained with actual data in phases so that the important parameters can be adjusted to achieve the required accuracy. During this process, the output is monitored carefully to identify any issues and maintain the quality of the model.
9. Model Deployment
In this step, the model is allowed to interact with real-time data that is flowing into the system, and the output is produced. The model can be implemented as a mobile application, a web service, or an embedded application.
10. Producing BI Reports and Providing Insights
Once the model is deployed, it is critical to test how it responds in a real-world situation. Insights from the model are used to guide strategic business decisions. Several reports are generated to assess the state of the business. These reports aid in determining whether or not key process indicators are met.
11. Decision-Making Based on Insights
Data science is crucial for organizations to make informed decisions and generate valuable insights. By following these steps carefully and accurately, organizations can make key decisions such as predicting raw material needs in advance. This can also aid in business growth and revenue generation.
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Different Roles Involved in the Data Science Life Cycle
Let’s have a look at some of the roles that are involved in the life cycle of data science projects.
- Domain Expert: A domain expert is a person who can define the framework for a data science project. As someone who is well-versed in the ins and outs of the domain, they would know what the current challenges are and how they must be answered.
- Business Analyst: A business analyst is an expert who analyzes business processes and data to identify individual client and business requirements. They work with structured data to lay out plans and develop actionable insights.
- Data Scientist: A data scientist is an expert who works with both unstructured and structured data and uses languages, such as Python, R, TensorFlow, etc., to analyze data for information and insights using statistical and computational techniques.
- Machine Learning Engineer: A machine learning engineer offers guidance on which model should be used to get the desired result and come up with a strategy to get the required output.
- Data Engineer and Architect: They help in maintaining the system for the large amounts of data and retrieving it. They also help in visualizing the data for better understanding.
Conclusion
The data science life cycle is an essential process that every data professional must be familiar with. From identifying problems to collecting data and conducting analysis to preparing reports, each step of this life cycle plays a crucial role in producing useful findings. Since data will continue to play an essential role in decision-making across industries, it is crucial to apply this process rigorously to drive insights and outcomes that are reliable and actionable.
Think of yourself in the role of a data scientist. Which of the steps in the data science life cycle do you think is the most important? Share your answers with us in the comments section below. Also, check out data science tools used by data scientists to accomplish their tasks.
