The Data Science Lifecycle involves using machine learning and different analytical methods to understand information and make predictions that help a business achieve its goals. This process includes steps like cleaning and preparing data, creating models, and evaluating them. It can take several months to complete, so having a structured approach is crucial. The widely recognized framework used for solving analytical problems is known as the Cross Industry Standard Process for Data Mining or CRISP-DM framework. This blog will explore “What is the Lifecycle of Data Science?”. Looking to learn data science in chennai? Consider checking out our Data Science Course in Chennai, get in touch with our professionals they will help you to clear up all your doubts.
Why Data Science?
In the past, we had less data that was well-organized and could be easily managed in Excel sheets or with Business Intelligence tools. But now, we deal with huge amounts of data, approximately 3.0 quintals bytes every day. This massive surge in data has become a challenge for organizations. Research suggests that around 1.9 MB of data is generated every second by just one person.
Dealing with this enormous data requires powerful and complex algorithms and technologies, which is where Data Science becomes crucial.
Below are some key reasons for the adoption of Data Science technology:
- Transformation of large amounts of unprocessed and unorganized data into valuable insights.
- Facilitating accurate predictions, such as forecasting survey results, election outcomes, and more.
- Contributing to the automation of transportation, including the development of self-driving cars, which is considered the future of transportation.
- Companies, such as Amazon and Netflix, dealing with substantial data, are incorporating Data Science algorithms to enhance customer experiences.
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The Lifecycle of Data Science
Understanding the Business
The entire process centers around the business objective. Without a clear problem to solve, what would be the purpose of your analysis? It is crucial to grasp the business objective comprehensively as it will be the ultimate goal of your analysis. Only with a good understanding can you establish the specific goal of the analysis that aligns with the business objective. You must determine whether the client aims to minimize financial losses or predict the price of a commodity, among other possibilities.
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Understanding the Data
Following business understanding, the next step is comprehending the data. This involves gathering all available data. You should collaborate closely with the business team as they are well aware of the data present, what data is required for the business problem, and other related information. This step consists of describing the data, understanding its structure, relevance, and data type. It’s essential to explore the data using graphical plots, essentially extracting as much information as possible through data exploration.
Data Preparation
The next stage is the preparation of data. This involves selecting relevant data, integrating data by merging datasets, cleaning it, handling missing values by either eliminating or imputing them, dealing with inaccurate data by removing them, and also identifying outliers using box plots and addressing them. Creating new data involves deriving new elements from existing ones. Formatting the data into the desired structure and removing unnecessary columns and features are also part of this stage. Data preparation is the most time-consuming but arguably the most critical step in the entire lifecycle. The accuracy of your model heavily relies on the quality of your data.
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Exploratory Data Analysis
This stage involves gaining an understanding of the target and the factors influencing it before constructing the actual model. The distribution of data in different variables is explored graphically using bar graphs, and relationships between different aspects are captured through graphical representations such as scatter plots and heat maps. Several data visualization techniques are extensively employed to explore each feature individually and in conjunction with other features.
Data Modeling
Data modeling forms the core of data analysis. A model takes structured data as input and produces the desired output. This step involves selecting the appropriate type of model, whether it is for classification, regression, or clustering. After selecting the model family, from among the various algorithms within that family, we carefully choose the algorithms to implement. Tuning the hyperparameters of each model is necessary to achieve the desired performance. It is also important to ensure a proper balance between performance and generalizability. We don’t want the model to overfit the data and perform poorly on new data.
Model Evaluation
In this stage, the model is assessed to determine if it is ready to be deployed. The model is tested on unseen data and evaluated using a carefully selected set of assessment metrics. It is crucial to ensure that the model aligns with real-world scenarios. If the evaluation does not yield satisfactory results, the entire modeling process needs to be reiterated until the desired level of metrics is achieved. Similar to human learning, any data science solution or machine learning model must evolve and improve itself with new data, adapting to new evaluation metrics. While multiple models can be built for a specific phenomenon, many may be imperfect. Model evaluation assists in selecting and constructing an ideal model.
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Model Deployment
After undergoing thorough assessment, the model is finally deployed in the intended framework and channel. This marks the culmination of the data science life cycle. Each step outlined above must be meticulously executed. Any misstep in a particular stage can impact subsequent steps, rendering the entire effort futile. For instance, if data is not collected accurately, crucial information may be lost, compromising the development of an ideal model. Similarly, if data is not cleaned properly, the model’s functionality will be impaired. Inadequate evaluation can lead to failure in real-world applications. From business understanding to model deployment, each step demands appropriate attention, time, and effort.
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