Data science is a vital technology field that businesses have come to rely on in this digital era. The growing demand for data science processes isn’t going away anytime soon. In fact, the US Bureau of Labor Statistics projects that there will be a 22 percent surge in the demand for data scientists between 2020 and 2030.
If you’re considering a data science career path and want to learn more about how data science processes work, this article is for you. In it, we’ll go over the components of data science processes, the steps of data science processes, and break down each of the data science processes.
What Is Data Science?
Data science is a broad field of study that deals with the manipulation of data through scientific processes. While goals may vary across data science sub-disciplines, it always revolves around extracting valuable insight from data. Data-driven organizations employ data scientists to collect and review complex business data and use science algorithms to extract quantifiable outcomes.
There are currently five main types of data science. These are machine learning, data analysis, predictive analysis, data mining, and data engineering. They are all in high demand across many industries. If you want to become a data scientist and you already have a background in business, you may want to work toward a career as a business analyst or machine learning engineer.
What Are the 4 Components of Data Science?
There are several components of data science that are consistent in all subsectors. Think of these components as essential features of all data science projects. Below, we’ll look at data engineering, data strategy, data visualization, and data analysis in detail.
Data engineering is a component of data science that deals with creating software to acquire and manipulate data. These software are the tools that data scientists use daily to process large volumes of data. In most cases, data engineering requires a combination of computer-based systems and a data engineer to oversee the creation and function of these systems.
Data engineering and data strategy are like two sides of the same coin. Data engineering has to do with using data science tools and data strategy to do research. It starts with deciding which data collection or manipulation strategy will be most suitable for helping a business meet its goals.
The data scientist must help the organization determine which data is worth collecting and applying to machine learning models or data science projects.
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This is arguably the most critical component of data science, especially from the business perspective. It involves analyzing collected data and converting it into valuable insights and predictive models. Data analysis is both a component and a process of data science. The accurate insight derived from predictive analytics programs is then used by the business to make predictions.
It is not enough to perform in-depth analysis. The data analyst or scientist also has to make sure the data they have collected is easy to understand and can be integrated to improve business. To do this, data is usually visualized before it is shown to stakeholders and decision-makers within a company.
What Are Data Science Processes?
Data science processes are a set of steps followed by data scientists as they collect, analyze, model, and visualize large volumes of data. The process covers everything from data collection to presenting visualized data and insights to the business stakeholders.
Each stage of the data science process must be followed precisely. During the data science process, scientists will take advantage of artificial intelligence, or any other technology that allows them to draw actionable insights. The data science process also involves identifying patterns that may be otherwise difficult to spot with traditional means.
What Are Data Science Processes Good For?
Data science processes are the proven way to interpret large sets of information and use it to generate actionable outputs. They help businesses make predictions about profitability in the short and long term. They also aid marketing and sales teams. Most importantly, they help businesses in the decision-making process.
- Predictions. At the beginning of every year, companies will establish short-term business goals and objectives. The data provided by data scientists plays a key part in setting these goals and subsequent predictions. At the end of each quarter, data scientists also provide new data that will be used for an annual report.
- Supporting the marketing and sales team. How does the marketing team know which campaign is yielding better results? And how does the sales team know the best ways to channel its efforts? They use the actionable insights provided by the data project team.
- Business decision-making. Data scientists provide valuable insights that help businesses determine if a particular venture is worth the risk. Examining this information in the evaluation phase may also help a company choose which products to continue working on. This explains why data scientists earn an average of $119,413 per year. Businesses are willing to pay a high price for their services.
What Are the 6 Steps of the Data Science Process?
Now that you know what data science processes are and the role they play in achieving business goals, it’s time to find out more about these stages and what they involve. Traditionally, there are six data science processes in the data science life cycle.
Data discovery involves collecting data from multiple sources and consolidating it into a single source. In this first stage of the data science process, the data scientist sorts and prepares the data for thorough analysis. In business intelligence, data discovery is a vital step that simplifies the subsequent processes and makes it easier to identify trends.
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Manual data discovery and smart data discovery are the two forms at play here. The manual data discovery method involves a hands-on approach, while the smart data discovery method involves the use of automated tools.
This is the second stage of the data science process. It involves cleaning the discovered data and making it ready for analysis. Raw and undefined data has to be cleaned and sorted to make sure that only the most meaningful data is pushed forward to the next stage of the data science process.
The four stages of data preparation are normalization, conversion, imputing missing values, and resampling the data. These stages ensure that the data is in the right form for the exploratory data analysis software to process and analyze it.
After the data has been discovered and prepared, the data science team must come together to determine the best strategy for data modeling. It is at this stage that they choose the software, hardware, modeling techniques, and workflow that will be used in the data modeling stage.
Among the model planning strategies that can be used are the issue-based strategic planning model, the basic strategic planning process model, the organic strategic planning model, the alignment strategic model, and scenario strategic planning. The most common method when dealing with huge amounts of data is the basic strategic planning model.
After choosing the modeling techniques that will be used, the scientists will start modeling the data. Simply put, data modeling is the process of classifying data in diagrams that show the relationship between multiple datasets. It is an excellent reporting tool that also helps data scientists determine the most efficient method for storing the data.
There are different forms of data modeling, but some of the most popular are physical data models, conceptual data models, and logical data models. These models reflect how data is stored on an organization’s database.
With the data analyzed and modeled, the next stage involves deploying the dataset into the organization’s real-time production environment. At this point, what started out as raw and undefined data has now become defined and actionable insight that can be used to answer core business questions.
Actionable Result Communication
This is the final step of the data science process. The stakeholders or other decision-makers in the organization will hold a meeting with the data scientist to see how the new information can be used in their business strategy. The goal for the data analyst is to summarize and communicate the insight to help set new business success criteria.
How Can I Learn Data Science Processes in 2022?
You can learn data science processes at a data science bootcamp, a university, or through online courses. Choose the data science study method based on how you like to learn. If you have already done some learning but want to further your education with an advanced degree, you should consider getting a Master’s Degree in Data Science.
If you’re interested in studying artificial intelligence, machine learning, or data science in a fast-paced and affordable way, you should consider attending a bootcamp. A bootcamp such as Flatiron School offers hands-on learning that will give students a deeper understanding of data science.
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Data Science Processes FAQ
Probability theory and descriptive analysis are two core concepts you need to understand thoroughly before learning data science. Statistics concepts like regression, probability distribution, statistical significance, and hypothesis testing are also vital data science skills.
Yes, if you learn machine learning, it will be easier for you to understand the needs of data scientists. With machine learning, you will be able to build more accurate data pipelines and get data models into production as quickly as possible.
Yes, data engineers code just like software engineers. However, in small organizations that do not have specific roles that define both jobs separately, a software engineer may complete all the responsibilities of a data engineer. In larger organizations, data engineers will only need to handle a few coding tasks.
Yes, data scientists need to study cloud computing since most organizations are migrating their data warehouses to the cloud. Due to its safety and cost-saving benefits, cloud computing has become an integral piece of technology at data-centric companies.
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