One of the primary purposes of data science as a field and as a component of multiple industries is to carry out some form of research and analysis. In contrast to data analytics, data science is focused on standardizing the collection of information and data in order to produce solutions and a greater understanding of a particular topic or problem. Consequently, whether you are a beginner in data science or a data science professional, it is important to know and remember the data science life cycle. Combining the scientific method with the components of project management, the data science life cycle offers a systematic approach to successfully completing a data science project from start to finish.
What is the Data Science Life Cycle?
The data science life cycle is a process that can be used to produce a solution, finding, or product in response to a problem or hypothesis. The data science lifecycle is utilized within multiple fields and industries. You will commonly see this process employed within business strategy, scientific research, product development, advertising and marketing, and any other space which requires a systematic approach to data collection and analysis in order to deliver key insights and findings. There are multiple names and descriptions of each step in the process, depending on the intended outcome for the information and data being produced. These five steps offer a general overview of how the data science life cycle can be used by students and professionals in the field of data science.
1. Identifying the Problem
Similar to the scientific method and other standardized procedures in completing a research project, the first step of the data science life cycle is focused on understanding and identifying the problem, as well as the intended outcome and objectives of the project. For most data science professionals, the problem will be presented to you by a client or company. In the case of more academic or scientific research, the problem can be discovered through a review of the literature and prior research on a specific topic. Once you know what problem needs to be solved, you must also think about the intended outcome of solving that problem. While some clients, companies, or researchers intend to solve their problem through presenting the findings from their data collection and analysis, others will solve the problem through the creation and presentation of project deliverables, such as a product, service, or prototype.
When working on a project in collaboration with others, data science professionals should also use this initial step of the data science life cycle in order to establish the expectations for their involvement. Some questions that are useful to ask in order to identify the problem and project intentions are as follows:
- What is the problem? Can this problem be solved?
- In what ways have other individuals or teams solved, or tried to solve, this problem?
- Based on what is known about the problem and potential solutions, how can the problem be solved for this project?
- What are the expected outcomes or deliverables for this project i.e. what is the best method and outcome for solving this problem?
These questions will also begin a dialogue amongst the various stakeholders and contributors to the data science project, making it much easier to work through the following steps in the data science life cycle.
2. Data Collection and Exploration
After the problem is identified and the project deliverable(s) has been established, the next step in the data science life cycle is the process of data collection. Depending on the problem being addressed, the intended deliverable of the project, and the field or industry, there are multiple ways to engage in data collection: the process of going out into the world to gather resources about the problem, or using digital tools and technologies to capture data that corresponds to the problem. Data collection is divided into different methods of collecting data—quantitative methods and qualitative methods. A combination of the two can be described as mixed methods.
Quantitative methods include the collection of data that is more static or numerical and offers answers to a problem that is focused on questions around what something is or how many there is of something. For example, quantitative data collection may include compiling statistical information from a survey or scraping data with R or Python. In contrast, qualitative methods focus on the collection of data that is more dynamic and focused on qualities, characteristics, or contributions. Qualitative methods usually include the collection of data from interviews and observations such as focus groups and written responses to a product, service, or experience. As a happy medium between the two, mixed methods research has seen increased popularity within the world of data science and includes a combination of surveys, trends, and data collection which draws from both methods in order to create a more holistic understanding of a problem.
3. Data Cleaning and Organization
Following the process of data collection, data is usually stored in a database or system, from which a data science professional or practitioner can take on the process of data cleaning, or organizing and preparing the data. Data cleaning and data organization describe the process of formatting and categorizing data in such a way that is made legible enough to be analyzed. Data cleaning includes removing data that is not relevant to the initial problem or overall project, as well as creating metadata and types for each piece of data that has been collected. This metadata acts as descriptors that can later be used to sort through data and make comparisons or correlations by identifying the relationships within a dataset.
Especially when completing a large-scale data analysis project, the amount of data that is collected in order to address the problem also influences the methods of data cleaning and organization. While smaller-scale data can be easily cleaned using spreadsheet programs or software, big data projects require some knowledge of coding or technologically advanced programs in order to organize the data. Statistical analysis software, database design software, and most programming languages are equipped with the necessary data science tools needed to properly store, clean, and organize a collection of data in advance of the process of data analysis.
4. Data Analysis and Modeling
Considered to be one of the most important steps in the data science life cycle, the process of data analysis and modeling is where much of what we hear about data science happens. Similar to data cleaning and organization, data analysis requires some knowledge of data science tools and technologies that can be used to gather findings from the data that has been collected and organized. Some of the most popular tools for data science analysis include statistical analysis software, programming languages, and various database-specific tools. By using these data science tools, you can uncover information and findings within the data that offer potential solutions to the problem established in the beginning of the data science life cycle.
After the data has been analyzed, it is important to create a model or representation of that data. Depending on the type of information that comes from the process of the analysis, data modeling can include the creation of charts, graphs, tables, or diagrams that communicate the data findings as a system or process. Data modeling is especially useful for data scientists within the world of engineering, business, finance, and other industries that rely on strategic and solution-oriented planning. Through the creation of a model based on data analysis, potential solutions and outcomes can be presented in a way that makes it easier for audience members to utilize the findings in practical ways.
5. Data Visualization and Deliverables
Once the data has been collected, cleaned, and analyzed, it is important to present the data findings or deliverables to an audience. Depending on the outline and objectives that were established during step one of the data science life cycle, the audience is composed of the client or company that presented the initial problem to the data science professional or team. However, in the case of creating a product/deliverable or doing academic research, the audience for a completed data analysis project can be significantly larger. While data science researchers present their findings and deliverables to an entire field of students and researchers, creating a product or service means that the outcome of the data analysis project will be presented to an entire consumer base or even a test market.
There are also multiple examples of the type of visualizations or deliverables that come from the process of data analysis. Presentations and portfolios often demonstrate the findings of the project in a way that presents the problem as a hypothesis which is then refuted or confirmed through the process of data analysis. For projects that include a product deliverable, this final step might include the creation of a prototype based on the data analysis and modeling step. Within the world of business and financing, this final stage might also include a step-by-step business plan or breakdown of strategy based on the findings.
Need more experience with the data science life cycle?
As one of the fastest-growing fields of the 21st century, there are many ways that you can learn more about data science or update your skills in the industry. Noble Desktop offers several data science classes and a Data Science Certificate that teach how to collect, analyze, and visualize data through hands-on and interactive exercises and portfolio projects. There are also dozens of live online data science classes which take a variety of approaches to the data science lifecycle. You can find in-person data science classes near you for a more traditional classroom experience.