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Data Science Process: A Beginner’s Guide in Plain English

9 minute read | May 16, 2022
Sakshi Gupta

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Sakshi Gupta

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While data scientists often disagree about the implications of a given data set, virtually all data science professionals agree on the need to follow the data science process, which is a structured framework used to complete a data science project. There are many different frameworks, and some are better for business use cases, while others work best for research use cases.

This post will explain the most popular data science process frameworks, which ones are the best for each use case, and the fundamental elements comprising each one. 

What Is the Data Science Process?

The data science process is a systematic approach to solving a data problem. It provides a structured framework for articulating your problem as a question, deciding how to solve it, and then presenting the solution to stakeholders. 

Data Science Life Cycle

Source: Towards Data Science

Another term for the data science process is the data science life cycle. The terms can be used interchangeably, and both describe a workflow process that begins with collecting data, and ends with deploying a model that will hopefully answer your questions. The steps include: 

Framing the Problem

Understanding and framing the problem is the first step of the data science life cycle. This framing will help you build an effective model that will have a positive impact on your organization. 

Collecting Data

The next step is to collect the right set of data. High-quality, targeted data—and the mechanisms to collect them—are crucial to obtaining meaningful results. Since much of the roughly 2.5 quintillion bytes of data created every day come in unstructured formats, you’ll likely need to extract the data and export it into a usable format, such as a CSV or JSON file. 

Cleaning Data

data science process: Cleaning Data

Most of the data you collect during the collection phase will be unstructured, irrelevant, and unfiltered. Bad data produces bad results, so the accuracy and efficacy of your analysis will depend heavily on the quality of your data. 

Cleaning data eliminates duplicate and null values, corrupt data, inconsistent data types, invalid entries, missing data, and improper formatting. 

This step is the most time-intensive process, but finding and resolving flaws in your data is essential to building effective models. 

Exploratory Data Analysis (EDA)

Now that you have a large amount of organized, high-quality data, you can begin conducting an exploratory data analysis (EDA). Effective EDA lets you uncover valuable insights that will be useful in the next phase of the data science lifecycle. 

Model Building and Deployment

Next, you’ll do the actual data modeling. This is where you’ll use machine learning, statistical models, and algorithms to extract high-value insights and predictions. 

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Communicating Your Results

Lastly, you’ll communicate your findings to stakeholders. Every data scientist needs to build their repertoire of visualization skills to do this. 

Your stakeholders are mainly interested in what your results mean for their organization, and often won’t care about the complex back-end work that was used to build your model. Communicate your findings in a clear, engaging way that highlights their value in strategic business planning and operation.  

Data Science Process Steps and Framework 

There are several different data science process frameworks that you should know. While they all aim to guide you through an effective workflow, some methodologies are better for certain use cases. 

CRISP-DM

CRISP-DM stands for Cross Industry Standard Process for Data Mining. It’s an industry-standard methodology and process model that’s popular because it’s flexible and customizable. It’s also a proven method to guide data mining projects. The CRISP-DM model includes six phases in the data process life cycle. Those six phases are: 

1. Business Understanding

data science process: Business Understanding

The first step in the CRISP-DM process is to clarify the business’s goals and bring focus to the data science project. Clearly defining the goal should go beyond simply identifying the metric you want to change. Analysis, no matter how comprehensive, can’t change metrics without action. 

To better understand the business, data scientists meet with stakeholders, subject matter experts, and others who can offer insights into the problem at hand. They may also do preliminary research to see how others have tried to solve similar problems. Ultimately, they’ll have a clearly defined problem and a roadmap to solving it. 

2. Data Understanding

The next step in CRISP-DM is understanding your data. In this phase, you’ll determine what data you have, where you can get more of it, what your data includes, and its quality. You’ll also decide what data collection tools you’ll use and how you’ll collect your initial data. Then you’ll describe the properties of your initial data, such as the format, the quantity, and the records or fields of your data sets. 

Collecting and describing your data will allow you to begin exploring it. You can then formulate your first hypothesis by asking data science questions that can be answered through queries, visualization, or reporting. Finally, you’ll verify the quality of your data by determining if there are errors or missing values. 

3. Data Preparation

data science process: Data Preparation

Data preparation is often the most time-consuming phase, and you may need to revisit this phase multiple times throughout your project. 

Data comes from various sources and is usually unusable in its raw state, as it often has corrupt and missing attributes, conflicting values, and outliers. Data preparation resolves these issues and improves the quality of your data, allowing it to be used effectively in the modeling stage. 

Data preparation involves many activities that can be performed in different ways. The main activities of data preparation are: 

  • Data cleaning: fixing incomplete or erroneous data
  • Data integration: unifying data from different sources
  • Data transformation: formatting the data
  • Data reduction: reducing data to its simplest form
  • Data discretization: reducing the number of values to make data management easier
  • Feature engineering: selecting and transforming variables to work better with machine learning

4. Modeling

There are multiple options for data modeling. You’ll choose which option is best based on the goals of the business, the variables involved, and the tools available. 

When selecting your modeling technique, you’ll produce two reports. The first one will specify the modeling technique you’re going to use. The second will document the assumptions your modeling report uses—if your model requires a specific type of data distribution, for example. 

Once you’ve chosen your modeling technique, you’ll design tests to determine how well your model works. For this step, your deliverable will be your test design. This may entail splitting your data into training data and testing data to avoid overfitting, which happens when you design a model that perfectly fits one set of data but doesn’t work with others. It is essential to avoid introducing any bias into your data in this phase. 

Next, you’ll build your model to address your specific business goals. When you do this, you’ll deliver three items: 

  1. A list of parameter settings 
  2. A description of the models 
  3. The models themselves

The final step in the modeling phase is to assess your models. You’ll review them from both technical and business standpoints. Subject matter experts on your project team may also participate in reviewing your models. 

A model assessment will summarize the conclusions of your model review, including a ranking of models if you’ve developed more than one. At this time, you may revise your parameters and conduct another round of modeling.  

5. Evaluation

data science process: Evaluation

In the evaluation phase, you’ll evaluate the model based on the goals of your business. Then, you’ll review your work process and explain how your model will help the business, summarize your findings, and make any corrections. 

Finally, you’ll determine your next steps. Is your model ready for deployment? Does it need a new iteration or another dependency project? 

6. Deployment

While deployment is the last phase in the CRISP-DM methodology, it’s not necessarily the end of your project. During the deployment phase, you’ll plan and document how you intend to deploy the model and how the results will be delivered and presented. You’ll also need to monitor the results and maintain the model during the deployment phase. 

You’ll conclude your project by creating a summary report and presentation. Afterward, plan to review the entire process to determine what worked and what needs improvement.  

OSEMN

Unlike CRISP-DM, OSEMN is not an iterative process. It’s not as widely used as CRISP-DM, but it’s more valuable for certain use cases. 

Because it omits business-oriented phases, it’s ideal for projects focusing on exploratory research, and is often used by research institutions and public health organizations. With OSEMN, you’re more interested in what the data have to say, as opposed to asking specific questions.

Obtain Data 

data science process: Obtain Data 

Because OSEMN is not goal-driven, the first phase is to obtain data. As with other frameworks, you can use several tools—including web scraping, APIs, or SQL—to obtain your data.  

Scrub Data

Regardless of what you plan to do with your data, it must be scrubbed before it has any value. Even clean data might need to be reformatted to make it usable. This is the most important step in any data project. 

Explore Data 

Exploring your data helps you develop a better understanding of any preliminary trends and correlations you see. You aren’t testing any hypotheses or evaluating any predictions yet. Some methods you can use to explore your data in this phase include: 

  • Command-line tools 
  • Histograms to summarize data attributes
  • Pairwise histograms to discover relationships and highlight outliers 
  • Dimensionality reduction methods
  • Clustering to uncover groupings 

Model Data

The ultimate measure of a successful model is how accurate it is. Usually, the most predictive model is the best model. You can measure your model’s predictive accuracy by how well it performs on novel data. 

Data modeling is accomplished with machine-learning techniques using supervised or unsupervised algorithms. Supervised learning uses labeled datasets designed to train the model to make predictions or accurately classify data. 

Unsupervised learning uses algorithms to cluster and analyze unlabeled datasets. Unsupervised learning models are helpful for uncovering hidden patterns and correlations. 

Although both types of algorithms may be used, unsupervised models are particularly useful in the OSEMN process because they can help you find patterns you may not be aware of when you start your project. 

Interpret Results

The final step in the OSEMN model is to interpret your results. At this point, it’s important to note that a model’s predictive and interpretive powers don’t always coincide and may even conflict. A highly predictive model may make it harder to interpret the results, not easier. 

A model’s ability to predict relies on its ability to generalize, while a model’s interpretative power can provide insights into a problem or domain. You may want to find a model with a good balance of predictive and interpretive capabilities, or you may want to focus on one over the other.  It all depends on your project goals. 

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What Is the Significance of the Data Science Process?

data science process: Interpret Results

Following the data science process gives your work structure and order. If you follow a proven formula, your workflow can proceed smoothly, and you can be sure that you aren’t forgetting something. A good data science process gives you confidence in your results because it’s proven to produce the most accurate results. 

The data science process you choose will guide you through the necessary steps for collecting data, transforming it into a high-quality input, building and evaluating models, and interpreting and sharing your results. If you’re interviewing for a data science job, showcase projects that follow the data science process to demonstrate your knowledge. 

FAQs About the Data Science Process

Which Is the Most Crucial Step in the Data Science Process?

The most crucial step in the data science process is data cleaning. The computer programming adage “garbage in, garbage out” also applies here. If your data is inaccurate, irrelevant, incomplete, or contains errors, your results will be worthless. 

Most data scientists spend most of their time on a project in the data processing stage because the quality of the data is directly proportional to the quality of the results. No model, no matter how elegant or complex, can overcome noisy or junky data. 

Is the Data Science Process Hard To Learn?

The data science process itself is not hard to learn. However, some steps in the data science process can be difficult to learn. Building machine learning models requires specialized math and programming knowledge. 

Are You Required To Use the Data Science Process?

You aren’t required to use a specific data science process, but it is important to follow a data science process framework. There are many to choose from, so find the one that works best for your project. Data science processes may vary, but they all contain the essential elements for doing good work. Following an established process also makes it easier for other data scientists to understand your work.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.