A Step-by-Step Guide to the Life Cycle of Data Science

The life cycle of a data science project starts with the definition of a problem or issue and ends with the presentation of a solution to those problems. There can be many steps along the way and, in some cases, data scientists set up a system to collect and analyze data on an ongoing basis. 

Data science has a wide range of applications. For example, data scientists can use data and modeling to define crime hotspots and predict law enforcement needs in a city, or they can use user-generated reviews to provide quality ratings for local businesses. 

In business, data science projects can focus on business analytics and on providing sales forecasts or creating a machine learning system that allows HR managers to automatically select job applications or resumes that meet their hiring requirements. 

Whether in the corporate world, health care, entertainment or manufacturing and logistics, data science is a vital component of day-to-day operations. 

Though data science projects can be complicated, and the details can vary depending on the subject and goals of each project, most data scientists follow the same fundamental framework. 

To fully understand the data science process, you need to grasp some terms data scientists use regularly. These definitions help describe the different steps data scientists take. 

Data science is a relatively new field that often requires advanced degrees for real-world employment and applications. The steps data scientists take can vary depending on the purpose of each project, the availability of usable data, and the skills and knowledge of the people involved in the project. 

Though no two data scientists will come up with precisely the same steps for their work, most data science projects follow a similar trajectory and will have at least some steps in common with other data science efforts. 

All data science projects begin with the same steps: identifying problems and setting goals. To set goals for a project, you need to understand what problem you are trying to solve or what question you are trying to answer. 

In some instances, identifying the problem is straightforward. At other times, the client or employer may have an ambiguous request or a very general issue they want you to address. 

The first task, in these cases, is to come up with concrete problems and well-defined goals. These goals will provide the basis for the next steps in the data science life cycle. 

Choosing the right representative sample for your data science project is essential, especially for plans involving big data. This step in the process requires figuring out which data sets are necessary for answering the questions and solving the problems that are at the heart of your project. 

You need a sample that is large enough that you can see things such as regression and diverse enough to account for all possible variables. At the same time, the results of your analysis might get skewed if you use data that does fit within the parameters of your project. 

You select a representative sample by deciding which variables you need to answer the question or solve the problem posed in your project. 

For example, for a project involving a mobile shopping app, you would collect data from people who owned a smartphone and used it for shopping online. To get a fully representative sample, a data scientist might collect data from people of different ages and groups, in different locations, and of varying income levels. 

Data gathering, which is also known as data mining, is the process of collecting the necessary data for the project. Some data scientists write their own programs or work with data engineers to design or customize applications that mine the necessary data. 

Many existing tools are available for scraping data off of websites, collecting it from mobile apps, or sourcing it from third-party data warehouses. Data scientists need to figure out where to source their data, and the collection methods will depend on the location and accessibility. 

During this step, the data science team also needs to build databases where they can store their information for the duration of the project. This step usually falls to a data engineer who designs the database to meet the needs of the data scientists. 

Data cleaning involves converting the data you collect into a usable form and ensuring that it fits within the representative sample. 

First, a data scientist needs to ensure they are using quality data and locating and throwing out data that does not meet their standards. Then, they need to be sure the data is all in the same format. This step may require making calculations or running the data through algorithmic functions to convert it so it matches other variables you have collected. 

Data that isn’t clean may lead to flawed results. Even if the algorithms for analyzing the data are flawless, if the variables are incorrect, you will not get the correct results.  

Developing a data model is the step of the data science life cycle that most people associate with data science. A data model selects the data and organizes it according to the needs and parameters of the project. 

A data model can organize data on a conceptual level, a physical level, or a logical level. The type of data model will depend on what the data science professional wants to accomplish. Despite the different data model choices, the goal is always to produce results you can analyze and use to solve your initial problem and meet your goal. 

The goal of data analytics is to find patterns, trends and relationships that can help you create hypotheses and gain insights. These ideas may help you find answers to the questions and problems you identified at the beginning of the project. You can also find a reason for outliers or anomalies. 

One of the most important aspects of data analysis is feature engineering. Features are measurable attributes or outcomes related to the questions or problems at the heart of a data science project. They provide measurable results scientists can use for things like predictive modeling or machine learning. Often, data scientists use multiple features to arrive at the most accurate predictions possible. 

Some data science degree programs focus on analysis because many entry-level data science positions are for analysts. 

Data visualization involves creating graphs, tables or charts that explain the results of your data study. 

Data visualization is helpful on a couple of levels. It can aid data scientists and analysts as they look for patterns related to their data. Also, it can help them explain complex results to people who do not have the same scientific and mathematical background. 

Data scientists may need knowledge of the software that can produce graphs and tables. However, the most crucial aspect of data visualization is to ensure that the visual elements accurately represent the results of your data analysis. 

Not only does the data visualization need to represent your data and analysis accurately, but you need to make sure that it answers the questions or explains solutions to the problems you defined at the start of the study. 

Deployment involves using the results of your analysis to perform predictive analysis, launch a machine learning program, or continue to provide insights to decision-makers in your company or organization using data analysis tools that were a result of your study. 

Before deployment, you need to test your system to ensure it provides accurate results or performs the required tasks accurately. 

Some data science projects are ongoing. Variables may change over time and require continued data mining, data cleaning and analysis. In these instances, data scientists and engineers need to create systems that continuously mine and produce new data sets. 

Re-evaluation can take several forms. During a project, you may wish to evaluate each step to decide if you can make improvements before moving on to the next part of the life cycle. 

You can also re-evaluate your process at the end of the project and decide if there are any corrections you can make to improve the project or the deployed models or algorithms. If possible, you can make improvements to the existing project. If not, you can use the insights from re-evaluation to improve future projects.