Data science is the study of data. Every day, companies collect vast amounts of data, and with data science, businesses can leverage information to make data-driven decisions. The field of data science exists at an intersection of business, science, statistics, and technology and encompasses careers that range from building artificial intelligence (AI) models to crafting marketing campaigns.
Data Science Definition
Data science is the analysis of data to find meaningful insights that can be used to inform business decisions. For example, a marketing analyst can use customer transaction data to determine which products customers in one demographic or geographic region often buy and adjust marketing campaigns accordingly.
Data science is an interdisciplinary field that relies on statistics, scientific computing, algorithms, programming, AI, machine learning, and scientific methods. Careers in data science vary greatly because the study and analysis of data is such a broad field. Some positions focus on data storage and maintenance, while others are more closely related to journalism, reporting findings from data analysis.
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Data Science Lifecycle
Data science follows a process or lifecycle involving five key stages, each including different data-focused professionals and specific methods. The five stages are capture, maintain, process, analyze, and communicate.
The capture stage is where data is collected. Data collection can be through manual entry (like a visitor to a website filling out a contact form) or through less structured means, like log files of every visit (human or otherwise) to a website.
The types of data collected ultimately depend on the company and the intended purpose of the data. Commonly collected types of data include:
- Customer information
- Transaction history
- Video files
- Social media activity
- Internet traffic data
Maintaining data involves storage and security. Often, a data engineer is responsible for watching data storage systems and ensuring they function correctly and securely. This includes the upkeep of Extract, Transform, Load (ETL) pipelines (which carry data throughout the data science lifecycle).
A data engineer or architect may also need to build proprietary data storage systems like warehouses, databases, or data lakes. The storage and maintenance processes differ from company to company since it depends on how a business plans to use the data and how much needs to be stored.
Processing collected and stored data often means cleaning and sorting the data to make it more accessible for people who use it, like analysts. In processing, data professionals remove duplicates and clean data sets to ensure they are only using high-quality data for analysis.
Engineers may also transform the data into different formats depending on business needs. Files and data sets are labeled, tagged, and sorted, making the data easy to navigate during analysis.
Data analysis involves a variety of methods. Analysts search for patterns, biases, and anomalies and may employ predictive analytics, machine learning, and deep learning tools to make analysis faster, more efficient, and more accurate.
While exploring data, analysts create hypotheses and test them on the data using various approaches, like:
- A/B testing
- Regression analysis
- Standard deviation
- Sample size determination
Data analysts need to share the information they find through analysis, typically with relevant teams or stakeholders. However, some data science efforts provide insights to the public through white papers or articles.
Data analysts and reporters use visualizations when communicating findings since visual aids can highlight important takeaways to a broader audience. Professionals create these visualizations using programming languages R or Python and can include bar charts, maps, and graphs.
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Why Data Science Matters
The primary goal of data science is to help companies make data-driven decisions. Data is ever-growing, and companies need ways to more accurately understand their market and customer bases. As such, the role of data science in business is also growing. In fact, according to the U.S. Bureau of Labor Statistics, the employment of data scientists is projected to grow 35% from 2022 to 2032.
Some key ways businesses use data science are:
- Optimization: Companies use data science to optimize processes so they can adapt to changes faster and quickly determine areas of the business that need improvement.
- Innovation: Data science helps companies craft novel approaches to problems and create new and better business products, processes, and services.
- Discovery: With the help of data science, companies can discover new markets or gaps in product offerings. Data science can also highlight previously unknown problems or redundancies, making companies more efficient.
- Prevention: By tracking trends and patterns, companies can avoid future problems. Companies use clean and accurate data to prevent minor issues from growing into ruinous dilemmas. Additionally, data science equips businesses with the tools to adapt to changes quickly and potentially prevent obstacles down the road.
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Data Science Industries
Certain companies seem like obvious hotbeds for data science — technology companies take in massive amounts of data from the internet and rely on good data to keep ahead of the competition. However, companies that sell goods through brick-and-mortar storefronts or e-commerce also take in a lot of data every day: credit card details, customer information, sales metrics, and transaction histories, among others. Using data science, these companies can apply these massive data sets to drive business decisions.
Ultimately, “every industry is getting impacted [by data science] due to data growth, processing speeds, and amazing algorithms,” says Dushyant Sengar, director of data science at BDO USA.
Some ways various industries use data science include:
- Health care: Predict illness, improve diagnostics, and minimize human error in testing and analysis
- Petroleum: Improve transportation and safety procedures, estimate where oil pockets may be located, and determine optimal conditions for drilling
- Telecommunications: Improve customer service, predict issues like outages, and determine customer wants and needs
- Banking and finance: Create financial models, predict market activity, and detect fraud
- Insurance: Assess risk, determine rates for customers, and flag potentially fraudulent claims
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Types of Careers in Data Science
Data scientists are analytics specialists who analyze and interpret vast amounts of data to find business solutions.
A data scientist is also “an expert in problem-solving and can break down business problems into granular tasks that can be solved using various data science techniques.” says Sengar.
Some data science techniques include creating statistical models, using software engineering to automate tasks, and working with engineers and business leaders to align company data needs.
Similar to data scientists, analysts interpret data to find meaningful insights. However, a data analyst is likely earlier in their career than a data scientist and focuses more on strictly analyzing and reporting.
>>MORE: Learn more about the difference between data analysts and data scientists.
Data engineers build and maintain data storage systems and pipelines. The storage systems data engineers make include warehouses, databases, data lakes, and the ETL pipeline.
However, “data science cannot work on bad data,” says Sengar.
So, data engineers are also responsible for ensuring good data — data that is collected accurately and efficiently and stored in ways that analysts and scientists can easily access.
Machine Learning Engineer
Machine learning engineers design, build, and maintain AI systems. They are often responsible for ensuring that software applications integrated with machine learning models function properly end to end. Often, this role involves creating models to train the AI programs and testing the quality of outcomes.
>>MORE: Learn how to become a machine learning engineer.
Marketing analysts apply findings from data analysis to marketing decisions. A marketing analyst is responsible for figuring out which products specific markets prefer and tracking the efficacy of different campaigns.
Data reporters play a significant role in the communication stage of the data science lifecycle. Sitting at a crossroad between journalism and data science, data reporters use data and analytics to find meaningful patterns and share them with a larger audience through articles and news stories.
“Data reporters build stories by analyzing data to uncover insights from data sets,” says Jenna Bellassai, lead data reporter at Forage. “They need to be able to vet data sources, clean messy data, manipulate data using languages like SQL and Python, and create data visualizations.”
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How to Get Into Data Science
Those interested in working in data science should focus on degrees in quantitative fields, like math, statistics, computer science, physics, or information technology. You can often specialize in an area of data science by taking specific courses in college. For instance, business and marketing courses can help if you want to be a marketing analyst and a foundation in journalism and writing-intensive subjects can help you break into data reporting.
Since data science exists in every industry, having a specialized understanding of an industry you enjoy can give you a great foundation to work from. For example, a background in finance is ideal if you want to work in data science at an investment bank.
Advanced degrees can be useful for upward mobility, especially when moving into more business-focused roles (as opposed to focusing on analysis or engineering). Data scientists often pursue master of business administration (MBA) degrees to better understand the business side of their work.
Data Science Certifications and Certificates
Data science certifications are typically exam-based programs that prove a specific skill set. Certificates are more like micro-degrees and may take a few months to complete.
Some common data science certificates and certifications include:
- Certified analytics professional (CAP) certification: Demonstrates technical proficiency in analytics and data science
- Senior Data Scientist certification: From the Data Science Council of America (DASCA); shows technical expertise and leadership skills
- Certified Big Data Professional: From the SAS (Statistical Analysis System) Institute; displays ability to work with massive data sets using open source tools and SAS
- Oracle Business Intelligence: Proves proficiency in using Oracle’s Business Intelligence program for modeling, analysis, forecasting, and reporting
- MongoDB Certified Associate Developer: Certifies ability to build modern applications using MongoDB databases
- IBM Data Science Professional Certificate: Demonstrates high-level skills in data science, ranging from data visualization to constructing machine learning models
- Google’s Data Analytics Professional Certificate: Shows data analysis skills taught by a leading tech company
You can also use SQL-focused data science bootcamps or coding bootcamps to learn skills that can land you jobs or help you transition into other areas of data science. For example, a data analyst can improve their coding and warehouse architecture skills to make a move into data engineering.
Regardless of the role in data science, certain hard skills are useful or necessary, including:
- Programming skills in languages like Python, R, and SQL (structured query language)
- Tableau and PowerBI for data visualization
- Applied mathematics
Data science also relies on many interpersonal and soft skills, though, such as
Remember that data science doesn’t have to be the end of your career journey, either.
“Since the data science field is an amalgamation of so many skills, it is easy to venture out into so many different directions if you have the right learning attitude, communication, and analytical skills,” notes Sengar.
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