Data Science Project Lifecycle

:bulb: Data Science projects in the industry are usually followed as a well-defined lifecycle which adds structure to the project & defines clear goals for each step.:zap:

There are many such methodologies available like CRISP-DM, OSEMN, etc. One such process defined by Microsoft’s Team Data Science Process (TDSP) lifecycle.

It states below the main steps::arrow_heading_down:

  1. Business Understanding: The first and utmost crucial step is to define the business problem and translate that into a Data Science solution - How & what Data will be, the target variable(s), metrics for success, team structure, etc.:bulb:

  2. Data Acquisition & Understanding: Once we know what and where the data will come from, we need to build pipelines to fetch and ingest it. The data then needs to be cleaned and made ready to model.:chart_with_upwards_trend::scissors:

  3. Modelling: With the cleaned data, models need to prepared by selecting and creating the best features. Training and evaluation of multiple models is done.:bar_chart::fast_forward:

  4. Deployment: Pipelines are then built to deploy and operationalize the models by adding APIs/frontend. Retraining continues with new data.:rocket::repeat_one:

  5. Customer Acceptance: Validation from a client that the pipeline works up to expectation in production and serves their purpose.:dart::white_check_mark:

The Data Science Lifecycle is centered on the application of machine learning and various analytical methodologies to extract insights and predictions from data in order to achieve a commercial company goal. A lot of processes are included in the complete method, including data cleaning, preparation, modeling, model evaluation, and so on. It is a time-consuming technique that could take many months to finish. As a result, having a generic structure to follow for each and every problem is critical.

The lifecycle of Data Science

  1. Business Understanding: The enterprise aim is at the center of the entire cycle. What will you do if you no longer have a specific issue to solve? Because the final purpose of the study will be to comprehend the business goal thoroughly, it is really important to do so. Only after we have a desirable perception can we design a precise evaluation goal that is in line with the enterprise goal. You must determine whether the consumer wants to reduce savings loss or prefers to estimate the rate of a commodity, for example.

  2. Data Understanding: After gaining an understanding of the enterprise, the next stage is to gain a comprehension of the data. This is a list of all the data that can be accessed. Here, you must work closely with the business group, since they are well aware of what information is available, what facts should be used for this business challenge, and other relevant information. This stage entails characterizing the data, its structure, its significance, and the type of records it contains. Graphical charts can be used to explore the data. Basically, extracting any facts about the information that you can obtain by simply browsing the data.

  3. Preparation of Data: Following that is the data preparation stage. Selecting relevant data, integrating it by merging data sets, cleaning it, resolving missing values by removing or imputing them, treating erroneous data by eliminating it, and testing for outliers with box plots and dealing with them are all part of this process. Making new data and extracting new elements from old data. Remove any superfluous columns and features from the data and format it according to your preferences. Data preparation is the most time-consuming, but possibly most significant, step in the entire existence cycle. The accuracy of your model will be determined by the data you submit.

  4. Exploratory Data Analysis: Before building the true model, this step entails acquiring a general idea of the response and the factors that influence it. The distribution of data within various variables of a character is graphically investigated using bar graphs, and the relationships between various features are represented using graphical representations such as scatter plots and warmth maps. Many data visualization methodologies are widely utilized to identify each and every characteristic separately and in combination with other characteristics.

  5. Data Modeling: Data modeling is the beating heart of data analysis. A model takes structured data as input and produces the intended outcome. This stage comprises selecting the proper model, whether the task is a classification difficulty, a regression challenge, or a clustering problem. After agreeing on the model family and the number of algorithms inside that family, we must carefully select the algorithms to implement and enforce. To get the best results, we need to fine-tune each model’s hyperparameters. We must also strike a proper balance between overall performance and generalizability. We don’t want the model to spend any more time studying the data and performing poorly on new data.

  6. Model Evaluation: This is where the model is put to the test to check if it’s ready for deployment. The model is evaluated using a carefully established set of assessment measures and tested using previously unreported data. Furthermore, we must check that the model is correct. If the evaluation does not give a suitable result, the entire modeling method must be repeated until the appropriate level of metrics is achieved. Like a human, every data science solution, such as a machine learning model, must evolve, be able to improve with new data, and adapt to a new evaluation measure. For any given event, we can develop several models, but many of them will be flawed. The model evaluation aids in the selection and construction of the ideal model.

  7. Model Deployment: After a thorough evaluation, the model is finally implemented in the selected structure and channel. The data science life cycle comes to a close with this step. Each phase in the above-mentioned data science life cycle must be carefully considered. If one step is done incorrectly, it will have an effect on the next stage, and the entire effort will be wasted. For example, if data isn’t adequately gathered, you’ll lose records and won’t be able to design an ideal model. The model will no longer work if the data is not adequately cleansed. If the model is not correctly evaluated, it will fail in the real world. From business perception to model deployment, each stage requires careful consideration, time, and effort.