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Most Popular Data Science Methodologies

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25th Apr, 2024
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    Most Popular Data Science Methodologies

    Every aspiring data scientist asks one of the questions: What methodology does an experienced data scientist use to solve a variety of real-world business problems? Here we will help you think like an experienced data scientist, including tackling a data science problem and applying the methodologies to interesting, real-world examples. Mentioned data science methodology will guide you in forming a business problem while keeping value addition in mind, collecting and analyzing the data, developing an analytical model, model deployment, and model monitoring or feedback analysis. 

    In this article, you will learn how to move from a problem to an approach, including understanding the question, the business goals and objectives, and how to pick the most effective method to answer the question. 

    Furthermore, you will learn systematic methods of working with data, such as determining data requirements, gathering appropriate data, understanding the data, and how to model the data using the proper analytical technique considering steps like business objectives and data requirements. Once the model is selected, we will cover the steps involved in evaluating and deploying the model, getting feedback, and implementing that feedback to improve the model. Know more about applications of artificial intelligence in healthcare. 

    You can start with a data science coding bootcamp  to learn about solving data science problems leveraging python and obtain a basic understanding of data science methodology. 

    10 Steps of Data Science Methodology

    1. Business Understanding

    Before solving any problem in the Business domain, it needs to be adequately understood. Business understanding forms a concrete base, leading to easy query resolution and clarity of the exact problem we will solve. Identifying and stating the business problem clearly is the most crucial step in any Data Science project. This step sets objectives and guides the rest of your data science project and team. 

    To enhance your business understanding better, data scientists must ask what problem you are trying to solve and how will it impact business objectives? 

    Some of the steps to ensure that: 

    1. Establishing a clearly defined data science problem by asking clearly defined questions to stakeholders or business leaders to understand the business objective and value creation. 
    2. People with both business and data understanding should be involved in the problem definition framework. 
    3. Leadership should allow time for a rigorous definition of the problem 
    4. Analyze the problem in terms of data complexity, data availability, and data liability. 
    5. The goal should be to define the problem clearly and to solve it in a way that will benefit the business.  

    2. Analytics Approach

    Once you get familiar with business understanding, you now know what kind of problem you are trying to solve. The analytics approach is a step where you get the answer using the data to all those questions you got familiar with in the previous step.

    Based on your business understanding, there is generally four types of analytics approaches that can be utilized. 

    1. Descriptive approach: to show current status based on the retrospective information, statistical analysis to show the relationship, track specific key performance indicators using business intelligence tool. 
    2.  Predictive approach: If the question is to determine the probabilities of action in the future based on the retrospective information. 
    3. Prescriptive approach: If the question is to determine an optimal course of action using the data. 
    4. Diagnostic approach: If the question is to understand why a particular change or event happened in the data? The diagnostics analytics approach generally uses data discovery, drill-down, mining, and correlation techniques. For example, Diagnostic analytics can help companies answer questions such as:
      • Why did our company sales decrease compared to the previous year?
      • Why is the user engagement down compared to the previous month?
      • Why is a specific product category demand increased compared to the previous year?
    5. Cognitive approach: You can think of the cognitive analytics approach as analytics with human-like intelligence. Cognitive analytics reveals specific patterns and connections that simple analytics cannot. This approach can include understanding the context and meaning of a sentence or visual recognition of an image or video provided a large amount of data. Cognitive analytics reveals specific patterns and connections that simple analytics cannot. 

    3.  Data Requirements

    You can’t get good results in data science without good-quality data. Getting the right data quality from multiple sources is crucial in data science. 

    The analytical method gathers suitable data sources, formats, and volumes. To understand data requirements in detail, one must answer the following questions before moving to the data collection methodology : 

    1. Which type of data is required. 
    2. How to identify the suitable source or collect them. 
    3. How to explore the data or work with them, and 
    4. How to prepare the data to meet the desired outcome. 

    Data requirement methodology includes identifying the necessary data content, formats, and sources for initial data collection. 

    4. Data Collection 

    The information gathered can be accessed in any random format. As a result, the data obtained should be validated according to the technique chosen, and the output approved. As a result, if necessary, additional data may be gathered, or unnecessary data can be discarded. 

    The data needs are reviewed throughout this phase, and choices are made regarding whether the collection requires more or less data. After gathering the data components, the data scientist will know what they will be working on throughout the data collecting stage. 

    Descriptive statistics and visualization techniques can be applied to the data collection to examine the data's content, quality, and early insights. Data gaps will be detected, and preparations will need to be established to fill them or make alternatives. 

    5.  Data Understanding

    Data understanding methodology responds to the question, Is the collected data reflective of the problem to be solved? Descriptive statistics computes the measurements applied to data to determine the content and quality of matter. This step may need a return to the previous action for adjustment. 

    The data understanding component of the data science approach essentially addresses the question: 

    •  Is the data you obtained reflective of the problem to be solved? 

    6. Data Preparation

    Data preparation is the most time-consuming phase of a data science project, with data collection and understanding typically taking 70-80% of the overall data science project time. 

    Automating some data collecting and preparation procedures in the database can cut this time in half. This time savings translates into more time for data scientists to spend on model creation. 

    Data preparation is the process of making sure that raw data is correct and consistent before processing and analyzing so that the output of BI and analytics apps will be valid. The data preparation step of the data science methodology, in particular, answers the question: How is data prepared? 

    It must be prepared to be free of missing or incorrect values and duplicates and adequately structured to work effectively with data. Data preparation includes feature engineering. It is the process of leveraging data domain knowledge to produce characteristics that allow machine learning algorithms to function. A feature is a property that can be useful in problem-solving. Data features are vital to predictive models and will impact the results you aim to attain. When using machine learning methods to evaluate data, feature engineering is essential. 

    The data preparation phase lays the groundwork for the subsequent stages in answering the issue. While this step may take some time, the outcomes will benefit the project if done correctly. If this step is skipped, the end result will be subpar, and you may have to start over. 

    If you want to dive deeper into Data Science and know the top data science courses in India, please refer to this article's data science courses in India. 

    7. Modeling

    Modeling determines if the data is suitable for processing or if extra finishing and preprocessing are required. This phase focuses on developing predictive/descriptive/prescriptive models. 

    “Data modeling is mainly concerned with creating either descriptive or predictive models.” 

    A descriptive model could investigate questions such as: what are the top ten selling products in a category? And A predictive model is a mathematical process used to predict future events/outcomes by analyzing patterns in a given set of input data, for example, to predict yes/no or multi-class outcomes. These models are dependent on the analytics technique used, which might be statistically or machine learning-driven. 

    The data scientist will use a training set for predictive modeling. A training set is a collection of data with known outcomes. The data scientist will experiment with various techniques to confirm the necessary variables. 

    The effectiveness of data gathering, preparation, and modeling depend on a thorough grasp of the situation at hand and a suitable analytical methodology.

    8. Evaluation

    Model assessment occurs during the model creation process. This determines the model's quality, fits the business needs, and goes through the diagnostic measure phase and statistical significance testing. 

    Model assessment may be divided into two stages. 

    • The diagnostic measures phase is used to confirm the model is functioning correctly. For Example, A decision tree may determine whether the model's output is consistent with the initial design if the model is predictive. If the model is descriptive, testing set with known results may be used, and the model can be adjusted. 
    • Statistical significance testing is a possible second phase of review. This form of assessment may ensure that the data is handled and processed correctly inside the model. This is done to avoid excessive second-guessing after the solution is revealed. 

    Ten standard predictive model evaluation metrics in data science :

    1. Mean Squared Error(MSE): The most used and simplistic evaluation metric for the regression model represents the squared distance between actual and predicted values.
    2. Root Mean Square Error (RMSE): An Evaluation metric for the regression model is the square root of mean squared error(MSE). The output value is in the same unit as the output variable, making interpretation of error easy.
    3. Precision: An evaluation metric for the classification model is a ratio that measures what proportion of predicted positives is truly positive?
    4. Recall: An evaluation metric for the classification model is a ratio that measures what proportion of actual Positives is correctly classified?
    5. F1 score: An evaluation metric for the classification model is the harmonic mean of precision and recall.F1 Score maintains a balance between precision and recall.
    6. AUC ROC: An evaluation metric for the classification model which indicates how well the probabilities from the positive classes segregate themselves from the negative classes.
    7. Log loss/Binary Cross entropy: When the output of a classifier is prediction probabilities. Log Loss considers the uncertainty of your prediction based on how much it varies from the actual label.
    8. Categorical Cross entropy: Generalized log loss to the multi-class classification problem. 
    9. Average Precision(AP): It is an essential Object Detection Evaluation Metric, and it summarizes the weighted mean of precisions for each threshold with the increase in recall. This metric makes model comparison easier.
    10. Mean Average Precision (mAP): Mean average precision is an extension of Average precision. In Average precision, we only calculate individual objects, but in mAP, it gives the precision for the entire model. 

    You can start with a knowledgehut data science coding bootcamp to learn about solving data science problems leveraging python and obtain a basic understanding of data science methodologies like data preparation, modeling, and evaluation. 

    9. Deployment

    If you have reached this stage, the model has been thoroughly assessed and is ready for implementation in the production environment. This is the ultimate test for the model to determine how well it performs on external data and how scalable it is. Depending on the model's goal, it may be pushed out to a small set of users or a test environment to gain confidence in implementing the results across the board or customer production environment. 

    10. Feedback

    Feedback is essential for production model performance monitoring. It also helps data scientists understand model robustness, for example, how well the model will perform in the long term? One of the significant purposes of this methodology is that it helps in refining the model and accessing its performance and impact.

    Feedback steps include defining the review procedure, tracking the record (data drift), measuring efficacy, and reviewing and improving. 

    Once you deploy the model in production, the predictions will be correct till data submitted to the model in production mimics. If it doesn’t, we call it a data drift.

    A variation in the production data from the data used to test and validate the model before deploying it in production is known as data drift. 

    Data drift can be for multiple reasons, like a significant time gap (weeks to months to years) between the time data is gathered, and the model deployed, which is used to predict with actual data depending on the complexity of the problem, errors in data collection, seasonality for example, if the data is collected before covid and model, is deployed post covid this will automatically cause data to drift. You can identify data drift using sequential analysis methods, model-based methods, and time distribution-based methods. For more information about data drift you can start here. 

    There are multiple steps to handle data drifts : 

    1. Check the data quality and compare it with current data and reference data to get the idea what changed. 
    2. Investigate the drift to understand where does the drift come from? 
    3. You can live with the drift provided it does not impact the business objectives. 
    4. Retrain the model with the current data which will refresh the model. 
    5. Calibrating or rebuilding the model means you can make more changes to the training pipeline like changing the prediction target, applying domain adaptation strategies, identifying new segments where the model fails, and reweighing samples in the training data. 
    6. Pause the model or scrap the model, in this case you can have fallback strategy like changing the nature of the solution. 
    7. You can tune the model and apply business logic on top of the model to get the relevant solutions. 

    Conclusion

    Data science Methodologies we have gone through in the article can be treated as an agile methodology as it allows data scientists or data teams to prioritize data and models according to their business goals and requirements of the project. Ultimately it helps data scientists give non-technical stakeholders a brief overview of each goal.

    Since data science procedures are iterative, reproducibility is critical to success; that’s where these methodologies can ensure a data science project achieves the same.

    The model should not be left untreated after completing these ten stages; instead, an appropriate update should be performed based on feedback and deployment. New patterns should be examined as new technologies emerge to ensure that the model continues to add value to solutions.

    Frequently Asked Questions (FAQs)

    1. How do you write a data science methodology?

    Data science methodologies are guidelines for ensuring that standard data science model development practices are followed to create a successful real-world data science model. 

    2. What are the three most popular data science methodologies?

    The three most popular data science methodologies are Data Collection, Data Preparation, and Data Modeling. 

    3. Which are phases of the data science methodology?

    Data science methodology is divided into ten phases, each outlining the steps involved in developing a standard data science model.

    4. What is the first stage of data science methodology?

    The first stage of data science methodology is Business Understanding which helps a data scientist establish a clearly defined business problem by asking clearly defined questions. This starts with understanding the objective of the data science problem by asking the relevant questions to stakeholders or business leaders. 

    5. Why do we need a methodology for data science?

    Data Science methodology helps :

    • Forming a concrete business or research problem 
    • Collecting and analyzing data 
    • Building a model and understanding the feedback after model deployment. 
    6. Which topic did you choose to apply the data science methodology to?

    You can apply data science methodology in predictive, predictive, diagnostic, cognitive or prescriptive model development.

    Profile

    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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