What Is Data Science Life Cycle?

The data science lifecycle consists of a series of iterative steps. To achieve a standardized result, teams follow this series of steps. In this blog, we will therefore discuss the steps of the data science life cycle that you can follow in your next data science project

Table of Contents

• Meaning of Data Science Lifecycle
• Six Phases of the Data Science Lifecycle
• Professionals Involved in the Data Science Lifecycle Process
• Conclusion
• FAQs

Meaning of Data Science Lifecycle

Earlier, businesses were not able to extract useful information from their data, and hence decisions were made based on limited insights and predictions. To overcome the challenges faced during data processing, professionals follow a systematic approach known as data science lifecycle processes

It refers to an iterative set of steps that describe how one could use machine learning for insight generation and complete analysis of the data to achieve business objectives. In the world of data science, this is referred to as “Cross Industry Standard Process for Data Mining” or simply known as CRISP-DM

Phases of the Data Science Lifecycle

This would include six stages in a data science lifecycle such as identifying problems, collecting data, processing, exploring, analysis, and the consolidation of results.

Every step of the data science lifecycle process needs to be done perfectly, any small mistake can affect the next step, which in turn changes the final output. However, the whole process is very time-consuming and might take several months to complete. With the help of the following image, we will understand how these steps are executed throughout the data science projects:

1. Problem Identification

As data scientists try to solve a problem in data science, the first thing they need to do is understand the scope and depth of the problem. At this initial stage, they spend a huge amount of time understanding and clarifying the problem at hand before finally diving into solution development.

This is the stage wherein data scientists look into different case studies and inspect the business trends. This is crucial to know because, after all, business requirements are the final motive for analysis. The team will, based on the scenario that has been found out, come up with a hypothesis on the current scenario to conquer or solve the problem after evaluating all aspects.

Learn about the difference between Data Science Vs Computer Science

Key Questions That Must Be Asked in Framing the Problem

In this case, to be certain that data scientists are actually solving the right problem, it is crucial to ask as many questions as one can in order to grasp the nature of what the stakeholders want from the product or service. For instance, if one were to be tasked with creating a movie recommendation engine, then work might begin with asking questions such as:

• What kind of 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 recommended?
• How are these recommendations going to be used?

We should only begin building the system after obtaining clear answers to these questions. This step will ensure that we are solving the same problem that the company wants us to solve.

2. Collecting Data

Data collection is one of the critical stages of the data science life cycle, as all the decisions are based on the data we have. All decisions are based on the collected data, and therefore it should be of good quality and sufficient to solve the problem at hand. Captured data does not take any fixed format as sometimes it may be structured or unstructured

There are many sources from which data can be collected, such as social media, online storage, streamed, historical data from archives, Excel sheets, etc. When collecting data from diversities of sources, there might be problems or mismatches concerning that data or its incompleteness. The integration of data from those sources into one data file might be a problem, too.

Developing good quality data requires diverse strategies, including engaging customers in the actual data collection process for acquisition of their contribution through valuable surveys or interviews. Other techniques include web scraping, where data is scraped from websites as a secondary source of information. All these safeguards ensure that meaningful and all-encompassing data are available for analysis and decision-making.

Key Points to Keep in Mind While Collecting Data

• Ensure data is collected from trusted sources to preserve data integrity.
• Always choose data concerning the problem.
• Always ensure the correctness and adequacy of the data to avoid decisions based on inaccurate information.
• Collect data from relevant sources directly from people such as customers or users for firsthand insight.
• Ensure good data governance and data management to preserve quality and uniformity in data.
• Consider the legal and ethical aspects of gathering data while respecting privacy and the protection of data

Transform Data into Insights

with Our Exclusive Certification Program

Explore Program

3. Processing the Data

Data processing is an important step in the data science life cycle because it ensures the quality and reliability of the collected data before further analysis. The data collected from the sources mentioned in the above stage may contain lots of impurities that can affect the final output, like inaccurate results and flawed decision-making. To make our prediction accurate, data scientists process the data to remove impurities.

A. Importance of Data Processing

• With the help of data processing, we resolve common errors such as outliers, missing values, incorrect values, and inconsistent date formats. This process ensures the reliability and suitability of the data for analysis.
• The procedure for integrating the data collected from different sources is an important and tedious task as the data we collect is in different structures or configurations. This is the process that integrates different data into one or other compatible forms or into coherent forms in the final dataset.
• We also undertake some of the data preprocessing activities like normalization of data, time zone conversion, and also handling categorical variables, that in total improve the quality of the data.

B. Common Data Processing Issues and Solutions

• We deal with missing values by filling them with mean, median, or regression imputation, or we sometimes drop the column if it contains too many missing values.
• Outliers are those observation values that lie at an abnormal distance from other values in the sample. We first identify them and decide whether to remove them or not. Sometimes we handle them using statistical techniques.
• Handling of the date format is very critical, just like changing to a more common format. Given the time zone information, one can align the timestamps accordingly.
• Sometimes, we determine the source of data to be defective and, therefore, eliminate it and seek other credible sources.

4. Exploring the Data

Data exploration is one of the most important and time-consuming steps in the life cycle of data science. 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 business problem.

For example,

• Fraud Detection in Financial Transactions: Data exploration in the financial industry can help detect fraudulent activities. Analysts study transactions-analyzed dollars and cents, value arranged on a timeline, a geography, and analysis patterns of users. Data exploration helps uncover abnormal patterns, suspicious activities, or anomalies related to fraud. They thus build sophisticated algorithms or systems that protect their clients against such fraud risks.
• E-commerce Customer Segmentation: In the e-commerce company data exploration phase, analysts go into customer information to find patterns and segments. They examine diverse aspects of customer characteristics, like demographics, purchase history, browsing habits, and so on. The process involved in this helps them extract valuable insights including identifying lucrative customer segments, understanding buying preferences, customizing marketing tactics to respond better to the needs of individual customers, and the like.

5. Analyzing the Data

In this step of the life cycle of data science, we try to get a deeper understanding of the data we have collected and processed. Here, a data engineer uses statistical and numerical methods to draw inferences about the data. This step is also known as exploratory data analysis, or EDA. We select features for our model. Also, we look for a correlation between multiple columns in our dataset to determine how they differ from each other. One thing you need to remember is the fact that the data you input dictates your output.

We normally use data statistics methods like mean, median, etc., to understand the data. We use visualizations to better understand the patterns and summarize the data using images, graphs, charts, plots, etc. These tools allow us to build a model that can make predictions or perform classification on a given dataset. This helps us better understand our data and the patterns underlying it to convert it into useful information. Using these insights, we can determine how to solve the different problems that we are tackling.

For example,

A. Social Media Engagement Analysis

During the data exploration stage, analysts may also analyze social media data so that they can understand user engagement and preferences. They may use charts and graphs to visualize key metrics and patterns

For example,

• A bar graph can show the number of likes, comments, and shares for different posts, identifying the most engaging content.
• A line chart can display the trend in followers’ growth over time, indicating the effectiveness of marketing campaigns.
• A pie chart can illustrate the distribution of audience demographics, allowing for targeted content creation.

B. Customer Satisfaction Analysis

Then, analyst shall investigate customer feedback data. They can do this as yet another route of exploration: discover the area of satisfaction levels. This is basically what charts and graphs present once data has been unearthed.

For instance,

• A stacked bar graph can show the percentage of positive, neutral, and negative customer reviews, providing an overall sentiment analysis.
• A line chart can display the average ratings over time, helping identify any fluctuations in customer satisfaction.
• A scatter plot can depict the relationship between customer satisfaction scores and specific product features, highlighting areas for improvement.

Shape your Career

with Our Premier Certification

Explore Program

6. Consolidating Results

Using the insights gained from all the previous steps of the data science life cycle, we have to consolidate the results. Later, stakeholders can analyze and understand the consolidated results. That is, once we have created visualizations, analyzed the data, and concluded, we need to create documents that justify our conclusions by describing the insights and visualizations. The result depends on how well we have performed the previous steps. If there are any mistakes in any of those steps, we might not achieve the desired goal. 

After completing this process, we can proceed to modeling. This means we can create our model since we have the processed data. Subsequently, we can deploy the model, and machine learning (ML) engineers will do the modeling. In this process, you will be selecting different ML algorithms based on the kind of data you have. Then, you will be training the model, testing it, and later deploying it.

Professionals Involved in the Data Science Lifecycle Process

In this section, we will see how different data science professionals are involved in the data science project lifecycle process:

1. Business Analyst

The business analyst plays a crucial role in understanding the business requirements and determining the project timeline. Additionally, they engage in communication with clients to ascertain and define project requirements.

2. Data Scientist

Data scientists have experience working with large data sets, and they use data to understand and explain any pattern, helping organizations make better decisions. They decide the questions they will be asking the clients and also find ways to answer the questions using the data, either by finding trends or patterns in it.

3. Data Analyst

A data analyst is a person who has expertise in gathering data and extracting insights from it to solve a specific problem. The problems can be related to finance, medical science, business, crime and justice, government, etc., where they might be asked to find patterns or unusual trends in the data. They are responsible for creating algorithms and data models to forecast results.

4. Data Engineer

Data engineers are experts in making models and creating pipelines. They are responsible for creating pipelines to collect data from multiple sources. They work with data scientists to integrate the data source with the model.

5. Machine Learning Engineer

A machine learning (ML) engineer designs and builds machine learning models for the prediction, forecasting, or automation of a process. They need massive amounts of data to build and train the model.

Conclusion

We hope this blog has helped you understand 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.

FAQs