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
  1. Problem Identification
  2. Collecting Data
  3. Processing the Data
  4. Exploring the Data
  5. Analyzing the Data
  6. Consolidating Result
• Professionals Involved in the Data Science Lifecycle Process
• Conclusion
• FAQs

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Meaning of Data Science Lifecycle

Previously, businesses struggled to extract useful information from their data, leading to decisions based on limited insights and predictions. To address the challenges faced during data processing, professionals follow a systematic approach known as data science lifecycle processes. 

The data science lifecycle is an iterative set of steps that describes how machine learning can be used to generate insights and conduct a complete analysis of data to achieve the business goal. In the data science world, we refer to this process as the “Cross Industry Standard Process for Data Mining” (CRISP-DM). 

Phases of the Data Science Lifecycle

The data science lifecycle includes these six phases: problem identification, data collection, processing the data, exploring the data, analyzing the data, and consolidating results. 

Every step of the data science lifecycle process needs to be performed properly, any small mistake can impact the subsequent step, ultimately altering 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

Whenever data scientists try to solve a data science problem, they must first understand the scope and depth of the problem. In the first stage, they spend a significant amount of time understanding and clarifying the problem at hand before diving into solution development.

In this phase, data scientists analyze the different case studies and inspect the business trends. It is important to understand the business requirements because that is the ultimate goal of analysis. After identifying and evaluating all the aspects, the data science team will prepare a hypothesis based on the current scenario to tackle or solve the problem.

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Key Questions That Must Be Asked in Framing the Problem

To make sure that data scientists are solving the right problem, the most important thing is to ask as many questions as possible to get a clear sense of what the stakeholders wish for the product or service. For example, when building a movie recommendation engine, we can begin the work by 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.

Go through our What is Data Ingestion blog to learn about it in detail.

2. Collecting Data

Data collection is a vital step in the data science life cycle, as all decisions are based on the data we have. It is crucial to ensure that the collected data is of high quality and sufficient for solving the problem at hand. The format of the captured data is not fixed, as it can be in structured or unstructured form. 

Data can be collected from multiple sources, such as social media platforms, online repositories, streaming data, historical data from archives, Excel sheets, etc. Issues like data faults, inaccuracies, or insufficiencies can arise when gathering data from diverse sources. Combining data from these sources into a single dataset can also be challenging.

To ensure the data’s quality, various strategies have to be implemented. One option is to engage customers directly in the data collection process, obtaining their input through surveys or interviews to gain valuable insights. Another technique involves web scraping, which involves extracting data from websites as a supplementary source of information. These measures contribute to obtaining reliable and comprehensive data for analysis and decision-making purposes.

Key Points to Keep in Mind While Collecting Data

• Ensure data is gathered from reliable sources to maintain data integrity.
• Always select the data related to the problem.
• Verify the accuracy and completeness of the collected data to avoid making decisions based on faulty information.
• Collect data directly from relevant stakeholders, such as customers or users, to obtain firsthand insights.
• Implement proper data governance and data management practices to maintain data quality and consistency.
• Consider the legal and ethical aspects of data collection while respecting privacy and data protection regulations.

3. Processing the Data

Data processing is a crucial step in the data science life cycle, as 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.
• Integrating data collected from various sources is an important and tedious task, as the data we collect comes in different structures or formats. This data processing step helps us integrate the diverse data, ensuring compatibility and coherence in the final dataset.
• During data processing, we do data normalization and convert timestamps to a consistent time zone, and handle categorical variables to enhance the quality of the data.

B. Common Data Processing Issues and Solutions

• We handle 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 the Date formats is very crucial, like converting the date into a common format. Based on the time zone information, you can adjust the timestamps.
• Sometimes data sources are identified as faulty; in that case, we discard the data and search for other reliable sources.

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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: In the financial industry, exploring data plays a vital role in fraud detection. Analysts examine transactional data, including transaction amounts, timestamps, geographical locations, and user behavior patterns. By exploring the data, they can identify abnormal patterns, suspicious activities, or anomalies that may indicate fraudulent behavior. These insights help in developing effective fraud detection algorithms and systems to mitigate risks and protect customers’ financial interests.
• E-commerce Customer Segmentation: In the data exploration phase of an e-commerce company, analysts delve into customer data to discover patterns and segments. They examine diverse customer characteristics, including demographics, purchase history, browsing habits, and engagement metrics. This exploration process enables them to extract valuable insights such as identifying lucrative customer segments, understanding buying preferences, and customizing marketing tactics to better cater to individual customer needs.

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5. Analyzing the Data

In this step of the data science life cycle, 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 (EDA). We select the features for our model. Also, we look for the correlation between multiple columns in our dataset to determine how they differ from each other. One thing that you need to remember is that the data you input determines 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 analyze social media data to understand user engagement and preferences. Using charts and graphs, they can 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.

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B. Customer Satisfaction Analysis

In the data exploration stage, analysts may analyze customer feedback data to assess satisfaction levels. Charts and graphs can provide visual representations of the findings. 

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.

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.

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