Data science is evolving to be one of the booming fields in the technology industry. With robust improvements in computational performance that allow for the analysis of massive datasets, we can uncover patterns and insights about user behavior and world trends to an unprecedented extent.
The data science life cycle commences with diagnosing a problem or issue and presenting a solution to those problems. Generally, data scientists set up a process to collect and analyze data on an ongoing basis.
In business, data science projects focus on business analytics and on producing sales forecasts or creating a machine learning system that lets HR managers automatically select job applications that meet their requirements. Whether in the corporate world, healthcare, entertainment, or manufacturing and logistics, data science is an indispensable component of our day-to-day operations.
Though data science projects can be intricate, and the specifications can vary depending on each project’s subject and goals, most data scientists follow the same fundamental framework. They follow a similar trajectory and will have at least some steps in common with other data science efforts.
Data science is comparatively a new domain that requires advanced qualifications for real-world employment and applications. The steps taken by data scientists can vary depending on each project’s purpose, the availability of usable data, the skills, and people’s knowledge involved in the project.
Every data science project begins with the same step: identifying problems and establishing clear goals. To set a goal for a project, you need to understand what hindrance you are trying to solve.
In some cases, identifying the problem is effortless. At other times, it may be an ambiguous inquiry or to address a very general issue.
The first and foremost task is to come up with tangible problems and well-defined goals. These goals will provide the base for the next steps in the data science life cycle.
Picking the correct representative sample for your data science project is imperative, especially for big data plans. This step entails figuring out which data sets are necessary to answer the questions and solve the problems at your project’s essence.
You select a representative sample by determining which variables you need to answer the question or solve the problem posed in your project.
Also known as data mining, collects the necessary data for the project. Some data scientists write their programs or work with data engineers and design applications that mine the required data.
Many tools are available for scraping data off websites, collecting it from mobile apps, or sourcing it from third-party data warehouses. Data scientists need to identify where to source their data, and the collection methods depend on the location and approachability.
In this step, the data science team also needs to establish databases to store their information for the project’s duration. This task usually falls to a data engineer who designs the database to meet the data scientists’ needs.
It involves transforming the data you collect into a convenient form and ensuring that it applies within the representative sample. At first, the data scientist needs to ensure they are using quality data, then finding and tossing out data that does not meet their standards.
They also need to be sure the complete data is in the same format. This step may necessitate making calculations or administering the data through algorithmic functions, converting it, and matching the other variables you have collected.
Data that isn’t clean may lead to unsatisfactory results. You will not get the correct results if the variables are incorrect, even if the algorithms for analyzing the data are flawless.
A data model selects the data and organizes it according to the project’s needs and parameters — developing a data model is the step most people associate with data science.
The development of the data model will depend on what the data scientist wants to achieve. Despite the different data model choices, the intention is to generate results you can analyze and utilize to solve the initial problem and meet the project goal.
It aims to discover patterns, trends, and connections that can help create hypotheses and gain insights. It may help you find answers to the questions and problems you identified at the beginning of the project. You can also find a ground for outliers or anomalies.
The central aspect of data analysis is feature engineering. Features can refer to measurable attributes — they provide quantifiable results scientists can use for predictive modeling or machine learning. Data scientists often use multiple features to arrive at the most accurate predictions possible.
It is helpful on a couple of levels; data visualization involves creating graphs, tables, or charts explaining the results of your data study. It can assist data scientists and analysts as they scan for patterns related to their data. It can also help them interpret complex results to people who do not have the same academic background.
Data scientists need knowledge of the software that can produce graphs and tables. However, the most significant data visualization aspect is to ensure that the visual elements accurately represent the data analysis results.
Data scientists need to test the system to ensure it provides accurate results or performs the required tasks accurately because deployment involves using the results of the analysis to perform predictive interpretation, launch a program for machine learning, or contribute insights to decision-makers in the organization using tools that were a result of the study.
Some data science projects are continuous. Variables may change over time and require extended data mining, data cleaning, and analysis. Data scientists and engineers need to create systems that regularly mine and produce new data sets in these instances.
Re-evaluation may occur in several forms. During a project, data scientists may evaluate each step to decide if corrections can be made to improve the project or the deployed models or algorithms before stepping on to the next phase of the lifecycle.
In review, these are the ten essential steps of a Data Science Life Cycle, which every learner of data science should be accustomed to. However, it is not merely the necessary data skills that get the job done. A crucial skill set to have is providing a lucid and pragmatic narrative.
The presentation of the data acquired and converted must be concise and understandable enough for the audience. Information is the key to success here, and the heart of the Data Science Life Cycle integrates the existing goals, data content, and analytical method.
If you are keen to learn about data science, check out our 11-month in-person Postgraduate Diploma in Data Science (PGD-DS), ranked #2 among the ‘Top 10 Full-Time Data Science Courses in India.’ The curriculum of this comprehensive program is supported by an industry-recognized certification from the Manipal Academy of Higher Education (MAHE) and offers a guaranteed placement* after successful completion.