Data science is the study of data — every day, companies collect vast amounts of data, and data science allows them to leverage this 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.
In this guide, we’ll go over:
- Data Science Definition
- Why Data Science Matters
- Types of Careers in Data Science
- Data Science Industries
- How to Get Into Data Science
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 buy often and adjust marketing campaigns to target those findings.
Data science is an interdisciplinary field that relies on statistics, scientific computing, algorithms, programming, AI, machine learning, and scientific methods. Because the study and analysis of data are so broad, careers in data science vary greatly. For example, some positions focus on data storage and maintenance, while others are more closely related to journalism, reporting findings from data analysis.
>>MORE: See how businesses leverage data science in the real world with BCG’s Data Science and Analytics Virtual Experience Program.
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, and 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 xtract, 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 will differ for certain companies since it depends on how a company 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, duplicates are removed, and data sets are cleaned to ensure only high-quality data is used 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. An analyst may also use other analytical methods, such as regression analysis, standard deviation, mean, and sample size determination.
Findings from data analysis need to be shared. The results are often shared internally with relevant teams or stakeholders. However, some data science is meant to 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. These visualizations are typically created using programming languages R or Python and can include bar charts, maps, and graphs.
>>MORE: Learn to visualize data and communicate findings with Accenture’s Data Analytics Virtual Experience Program.
Why Data Science Matters
Ultimately, the primary goal of data science is to help companies make data-driven decisions. Data is ever-growing, and companies need ways to understand their market and customer bases more accurately. As such, the role of data science in business is growing. For example, according to the U.S. Bureau of Labor Statistics, the employment of data scientists is projected to grow 36% from 2021 to 2031.
Some key ways businesses use data science are:
- Optimization: Data science allows companies to optimize processes to adapt to changes faster and quickly determine areas of the business that need improvement or are not working as efficiently as they could. Companies also use findings to optimize profitability and marketing campaigns.
- Innovation: Data science can help companies craft novel approaches to problems and create new and better business processes.
- 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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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 Dushyant Sengar, director of data science at BDO USA.
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. Learn more about the difference between data analysts and data scientists.
Data engineers build and maintain data storage systems and pipelines. The storage systems made by data engineers include warehouses, databases, and data lakes, and the ETL pipeline. This pipeline takes data from a capture point (someone making a purchase on a website), transforms it into useful forms, and loads it into a storage system.
However, “data science cannot work on bad data,” says Sengar.
So, data engineers are 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 Software Engineer
Machine learning software engineers design, build, and maintain AI systems used to improve the effectiveness of data analysis. 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 marketing campaigns.
Some marketing analysts work in a specific section of a marketing team. For example, digital marketing analysts focus entirely on online marketing efforts.
Data reporters play a significant role in the communication stage of the data science lifecycle. Sitting at a cross-road 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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Data Science Industries
Certain companies seem like obvious hotbeds for data science. For example, technology companies take in massive amounts of data from the internet and rely on good data to keep ahead of the competition. Additionally, any company that sells goods, be it in brick-and-mortar storefronts or through e-commerce, takes in a lot of data every day: credit card details, customer information, sales metrics, and transaction histories. 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 Sengar.
Some industries that rely on data science every day include:
- Health care: Uses it to predict illness, improve diagnostics, and minimize human error in testing and analysis
- Petroleum: Uses it to improve transportation and safety procedures, estimate where oil pockets may be located, and determine optimal conditions for drilling
- Telecommunications: Uses it to improve customer service, predict issues like outages, and determine customer wants and needs
- Banking and finance: Uses it to create financial models, predict market activity, and detect fraud
- Insurance: Uses it to assess risk, determine rates for customers, and flag potentially fraudulent claims
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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. For certain roles, there is a way to specialize while in school. For example, taking business and marketing courses in school can help if you want to be a marketing analyst. Or, if you want to go into data reporting, having some foundation in journalism and writing-intensive subjects is useful.
What courses you take or major you choose can also depend on the industry you want to work in. 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, having a background in finance or at least a good understanding of how banks work can help you 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 analysis or engineering). Data scientists may pursue a master of business administration (MBA) degree, for example, to better understand the business side of their work.
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 certifications and certificates for working in data science 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 coding bootcamps (many of which offer certificates for completion) and online courses 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 languages like Python and R
- 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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