Data Science emerges from the convergence of two domains – Data and Science. Here, data encompasses both tangible and conceptual elements, while science signifies the methodical exploration of the physical and natural world. Therefore, Data Science involves the structured analysis of data and the extraction of knowledge using testable methods to make predictions about the Universe. Simply put, it’s the application of scientific principles to data of any size and origin. Since data has become the driving force for businesses, comparable to oil, a crucial aspect is understanding the data science project life cycle. Whether you’re a Data Scientist, Machine Learning Engineer, or Project Manager, awareness of these essential steps is imperative.
A career in data science is dynamic and impactful, offering opportunities to extract insights from vast datasets. Pursuing a Master’s in Data Science in India equips you with advanced skills and knowledge, providing a deep understanding of the dynamic data science landscape. This program hones analytical abilities, machine learning expertise, and statistical proficiency, making you adept at tackling real-world challenges. It ensures hands-on experience with industry-relevant tools and techniques, fostering adaptability in an evolving field. With a Master’s in Data Science, you gain a competitive edge, preparing you to navigate and excel in the diverse and dynamic realms of data-driven decision-making.
What is the Data Science Lifecycle?
The data science lifecycle outlines the iterative stages in creating, delivering, and maintaining data science products. While project specifics vary, a general lifecycle involves common steps such as data extraction, preparation, cleansing, modeling, and evaluation. Leveraging machine learning algorithms and statistical practices enhances prediction models. This process aligns with the “Cross Industry Standard Process for Data Mining” in data science. Subsequent sections will delve into each step, exploring how businesses execute them. Before that, an overview of the data science professionals engaged in projects will be discussed.
Data Science Lifecycle
The primary stages in the lifecycle of a Data Science project include:
Identifying the Problem
The initial and pivotal phase in a Data Science project involves comprehending the utility of Data Science in a specific domain. This step entails pinpointing relevant tasks crucial to the domain. The collaborative efforts of domain experts and Data Scientists play a vital role in problem identification. Domain experts contribute profound insights into the application domain, specifying the challenges to be addressed. Meanwhile, Data Scientists leverage their domain understanding to identify problems and propose potential solutions, creating a synergistic partnership to lay the foundation for the project.
Business Understanding
Business Understanding involves deciphering the customer’s business needs and goals. Whether it’s predicting outcomes, enhancing sales, minimizing losses, or optimizing specific processes, these objectives form the foundation of business goals. Two crucial steps during this phase are:
Key Performance Indicators (KPI): For any data science project, KPIs delineate its success. Agreement between the customer and the data science team on business-related indicators and project goals is essential. Business indicators are crafted based on the business need, guiding the team in defining goals. For instance, if the goal is to optimize company spending, the data science objective might be to manage double the clients with existing resources. Precise KPIs are vital, influencing the cost of solutions for different goals.
Service Level Agreement (SLA): Finalizing the SLA becomes crucial once performance indicators are set. SLA terms are determined based on business goals. For example, an airline reservation system may require simultaneous processing for 1000 users. Ensuring the product meets these service requirements is integral to the SLA.
Upon agreement on performance indicators and completion of the SLA, the project advances to the next critical phase.
Collecting Data
Data Collection stands as a pivotal step, forming the essential foundation to achieve targeted business goals. The flow of data into the system is diverse.
Surveys serve as a fundamental method for basic data collection, offering valuable insights. Data is often gathered from various enterprise processes recorded at different stages in the software systems used throughout product development, deployment, and delivery. Historical data from archives contributes to a comprehensive understanding of the business. Daily transactional data holds significance, undergoing statistical methods to extract crucial information related to the business. In a data science project, where the role of data is paramount, employing proper data collection methods is imperative.
Data Pre-processing
Amassing extensive data from archives, daily transactions, and intermediate records results in various formats and forms, some even in hard copy. This scattered data, spread across various servers, is meticulously extracted, converted into a unified format, and processed. A data warehouse is typically constructed during this stage, wherein the critical Extract, Transform, and Load (ETL) operations occur. The ETL operation holds paramount significance in data science projects. This stage necessitates the pivotal role of a data architect, responsible for determining the data warehouse structure and executing the essential steps of ETL operations.
Analyzing Data
Once the data is available in the required format, the next crucial step involves a comprehensive understanding through in-depth analysis. This analysis, known as Exploratory Data Analysis (EDA), employs various statistical tools. A data engineer plays an important role in examining the data by formulating statistical functions and identifying dependent and independent variables. Careful analysis unveils the significance and distribution of data features. Visualization tools like Tableau and PowerBI are popular for this purpose, emphasizing the importance of proficiency in Data Science using Python and R for effective EDA on diverse datasets.
Data Modeling
Following data analysis and visualization, data modeling becomes crucial. Essential components are retained in the dataset, refining it further. The focus now shifts to deciding how to model the data and determining the tasks suitable for modeling, such as classification or regression, based on the desired business value. Multiple modeling approaches are available, and Machine Learning engineers apply various algorithms to generate outputs. During the modeling process, it’s common to test models on dummy data resembling the actual dataset for validation.
Model Evaluation/Monitoring
Deciding on the most effective data modeling approach is critical, leading to the crucial phases of model evaluation and monitoring. Continuous monitoring becomes essential for improvement during model testing with actual data, especially when dealing with limited data. Two vital aspects during model evaluation are:
Data Drift Analysis: Analyzing changes in input data, known as data drift, is crucial for model accuracy. The model’s effectiveness in handling these data changes impacts its overall performance.
Model Drift Analysis: Utilizing machine learning techniques, such as Adaptive Windowing and Page Hinkley, helps identify and address model drift caused by constant changes. Incremental learning can also be effective, gradually exposing the model to new data.
Conclusion
Understanding the Data Science Lifecycle is pivotal for harnessing the power of data-driven insights. From problem identification to model training, each phase plays a crucial role in shaping informed decisions. A Master’s in Data Science in India not only delves into these intricacies but also equips individuals with advanced skills, making them adept at navigating the evolving landscape. With a comprehensive grasp of the lifecycle and hands-on expertise gained through a Master’s program, one can forge a futuristic career in Data Science, contributing significantly to innovation and leveraging data’s transformative potential across diverse industries.