What Is Data Science and How Can You Advance Your Career in It?

Organizations in all industries use insights from data science to improve operations and solve problems. For example, hospitals analyze patient wait times and employee productivity metrics to optimize staff scheduling.¹ Similarly, banks use data science to evaluate financial statements and detect fraud.² These applications let businesses make strategic decisions and uncover patterns that might otherwise go unnoticed.

As more companies embrace data science, the number of job opportunities in this field has skyrocketed. The U.S. Bureau of Labor Statistics predicts that the employment of data scientists will increase by 35% between 2022 and 2032.³ These professionals earn a median wage of $108,020, but top earners can make over $184,000.⁴

If you’re exploring career paths, you might wonder, “What is data science, and how can I advance in this field?” Read on for an in-depth overview of data science, including core concepts, applications, and tools.

What Is Data Science?

Data science is “an interdisciplinary field which uses statistics, computer science, programming, and domain knowledge to collect, process, and analyze data for the purpose of acquiring knowledge or solving a problem.”⁵

Data scientists analyze information to identify hidden patterns and relationships. They use these insights to help businesses make strategic decisions and gain a competitive edge. For example, a fashion retailer could analyze customer shopping habits and social media posts to predict seasonal trends. They can then use their findings to anticipate future demands and develop trendy products that appeal to their target audience.

The field of data science emerged in the 1960s and 1970s when computer scientists created algorithms–or computational procedures–to process and analyze data. In the 1980s, technological advancements allowed computers to gather and interpret “big data,” which refers to complex and vast datasets that grow exponentially.⁶

Today, data science has become a key driver of business success. According to a recent Harvard Business Review study for Google Cloud, companies that invest in data analytics navigate disruptions and challenges more successfully than competitors. Additionally, data leaders outperform peers on a variety of key metrics, including revenue, operational efficiency, customer loyalty and retention, employee satisfaction, and IT cost predictability.⁷

What Does a Data Scientist Do?

A data scientist gathers and analyzes data to answer questions and develop data-driven recommendations. Their responsibilities vary by position but often include these tasks:⁸

• Select relevant data sources, such as customer reviews, electronic health records, surveys, and website traffic
• Collect raw data using questionnaires, web-scraping software, and other tools
• Categorize and organize data into usable formats
• Develop and train machine learning algorithms to process and analyze data
• Use statistical methods to interpret large sets of structured and unstructured data
• Develop machine learning models to predict future trends and events
• Visualize data as charts, graphs, and other images
• Present findings to clients, managers, and other stakeholders
• Use actionable insights to help business leaders make informed decisions

Key Concepts in Data Science

The process of managing and analyzing data involves these basic steps:⁹

• Goal formulation: Define the business problem to solve or the question to answer
• Data collection: Gather relevant data from existing databases or primary sources, such as financial records and focus groups
• Data validation: Evaluate the accuracy, quality, and scope of the data
• Data cleaning: Detect and remove corrupt, inaccurate, or anomalous data
• Data analysis: Use statistical methods and software to interpret data and identify trends
• Visualization: Use software to transform the data into accessible graphics that may reveal new correlations and patterns
• Results interpretation: Analyze the findings to see if they answer the original question or open additional paths of inquiry
• Data reporting: Compile the results into a detailed report to share with stakeholders

You may see this process referred to as the data science lifecycle, which refers to a set of generalizable, iterative steps that all data science projects go through. A high-level model of the data science process begins with the problem definition. After initial exploration of the data, an analysis model is developed, then tested and refined, before being put into full-scale operation. ¹⁰

Data scientists also need a strong foundation in artificial intelligence (AI) and machine learning. These sophisticated technologies allow them to build algorithms that collect and analyze data without human intervention.¹¹ Additionally, many data scientists use data mining techniques to extract information and patterns from datasets.¹²

Tools and Technologies Used in Data Science

Skilled data scientists often work with enormous datasets that would take many years to gather and analyze manually. They can use specialized tools and technology to speed up this process and gain deeper insights.

Most data scientists use at least one programming language to handle and interpret data. Popular languages include:¹³

• Python: Analyze data and develop machine learning programs 
• R: Perform complex statistical analyses and visualize data 
• SQL: Build, manage, and manipulate structured databases 

Many professionals also use data visualization tools to translate information into graphic representations. For instance, Tableau allows users to create interactive dashboards and easy-to-understand visualizations. Similarly, Microsoft Power BI–short for Business Intelligence–processes and visualizes data.¹⁴

Advanced Topics in Data Science

Data scientists can use deep learning to process unstructured data like photos and videos. This advanced variety of AI has neural networks that imitate the structures in the human brain. These networks can recognize subtle patterns in datasets for more nuanced insights.¹⁵

Professionals should continue to follow industry best practices for ethical data usage. For example, they can anonymize data to protect customer privacy and ask for consent before using information.¹⁶

Using Data Science in Different Industries

Data science professionals work in industries, helping organizations use data science tools to analyze their customers, performance, and internal operations. Here are a few examples of how organizations benefit from the expertise of data scientists:

• Healthcare organizations use data science to predict disease outbreaks and track adverse drug reactions¹⁷ 
• Financial institutions analyze big data to improve risk management and create individualized investment portfolios¹⁸ 
• Digital marketing companies rely on data science to understand consumer behavior, preferences, and needs¹⁹ 
• Retailers use data science and machine learning to forecast sales, improve products, and streamline supply chains²⁰

How to Advance Your Career in Data Science

In the dynamic field of data science, you have to be proactive to advance your career and keep your skills current. Networking, pursuing a mentor relationship, building a portfolio, earning certifications, and obtaining a master's degree are all good ways to cultivate career growth.

Build Your Professional Network to Build Your Career

As in any career, networking and mentorship can provide significant professional benefits. Networking plays a vital role in advancing your career in data science. By networking with data professionals in different roles and various industries, you gain exposure to different perspectives and career opportunities. Engaging in online forums, attending conferences, and joining professional organizations are effective avenues for networking, allowing you to stay updated on industry trends and build relationships that can lead to job referrals and collaborations.

A mentor can provide valuable guidance and support for your career progression. An experienced professional, perhaps one of your data science team members or data science manager, can offer insights into the data science industry, help you navigate challenges, and provide feedback to improve your skills.

You don't have to restrict your search for a mentor to your current organization, but can seek help through online networking sites and professional organizations. This relationship can significantly accelerate your career growth and professional development.

The Importance of a Data Analysis Portfolio

Practical experience is an important method of advancing your career as a data scientist, as it bridges the gap between theoretical knowledge and real-world application. Build a robust portfolio showcasing personal projects, internships, and contributions to data science competitions to enhance your professional credibility. This hands-on experience demonstrates your ability to use data science techniques to tackle complex problems and deliver actionable insights

Practical experience helps you develop skills across the data science lifecycle ranging from data mining, cleaning, and exploration to statistical analysis, interpreting data, data visualization, and machine learning modeling. Highlighting these skills in your portfolio demonstrates your proficiency in applying theoretical knowledge to real-world challenges and effectively communicating your findings.

The Career Value of Earning a Data Science Certificate or Master's Degree

Data science certifications offer a focused approach to skill development and validation. Depending on the scope of the certificate, it can take from a few weeks to several months to earn one. They frequently focus on helping you gain proficiency in specific areas like statistical foundations, programming languages, machine learning, or data visualization. These certifications can accelerate career advancement by demonstrating your expertise, enhancing your credibility, and providing a quicker path to acquiring new skills.

A master's degree in data science offers comprehensive educational benefits, providing in-depth knowledge and a strong theoretical foundation. With a typical timeline of one to two years, this advanced degree is often essential for senior roles and leadership positions, enhancing career advancement opportunities and offering valuable networking with faculty and peers.

In a master's program, you can build advanced skills in statistical modeling, machine learning theory, and data engineering concepts. The curriculum also hones problem-solving, critical thinking, and communication abilities, equipping graduates to tackle complex data challenges and lead data-driven projects effectively.

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