A data scientist is a technical expert able to analyze and work with large data sets to solve complex business problems. They dig deep into massive amounts of information to identify what issues need to be addressed. Data scientists lean heavily on statistics, computer programming and business analysis skills to spot trends and opportunities for a company to improve efficiency or profitability and review go-to-market activities.
What is a data scientist? What do data scientists do? Keep reading to learn more about the career path of data scientists.
What Is a Data Scientist?
Data scientists play an important role in modern businesses. They find meaningful insights from large data sets. Using programming languages — like Python, R, SQL — data analysts have the know-how to spot trends, problem areas and opportunities for your business.
A data scientist may find insights and potential improvements that can boost efficiency and operating margins — two major focuses for most businesses. Just as many large companies employ financial analysts to work with accounting and financial data, businesses employ data scientists to be intellectually curious about the vast quantities of data your company is producing. Often they’re not held on a tight leash. Instead, they’re left to explore data sets, connect them and clean and organize the information to gather meaningful insight.
- Data scientists use statistics, programming and analysis skills to find meaningful trends and opportunities from large data sets.
- Financial and operating data are helpful tools for any business looking to improve efficiency or profitability.
- Large database systems empower data scientists to build valuable models and projections.
- Data scientists are highly educated experts in a wide range of essential business skills.
Data Scientist Explained
Data scientists may use internal business data, public databases from government organizations and industry groups, statistical analysis, machine learning and the ability to explain what they found to business decision-makers.
One example of successful implementation of data science is ridesharing company Uber. The key to success for Uber has been its masterful use of data. What may seem like an easy request from your home to the airport actually taps into monumental stores of company, consumer and public data. Data includes your own rating, your driver’s rating, algorithms monitoring traffic to better price the journey and much more all coming together into a seamless experience for driver and rider alike. Data scientists have built the infrastructure to automate the flow of all this information and keep it up to date.
Why Is Data Science Important?
The adage “You don’t know what you don’t know” holds quite true in most businesses. And data scientists are there to help you address that specific issue. They bring useful insights to your company and ways to address shortcomings.
The data scientist role is growing quickly in many industries. And while the concept of combing through data and helping structure it in a way that brings to light helpful information applies to most areas, the role accomplishes very different things depending on the company, industry and other factors. For example, a data scientist for a health system might work on incorporating third party patient information such as from wearable devices capable of monitoring health indicators into existing electronic medical records. While a data scientist working for a retail outlet may use her skills and knowledge to better understand customer behavior and create recommendation engines to drive sales. Both teams of data scientists organize, structure and manage enormous amounts of data, but to different ends and with different challenges.
What Do Data Scientists Do?
Roles and responsibilities
While most employment includes "other duties as assigned," these are typical responsibilities of data scientists:
Data collection: Data analysis starts with asking the right question and finding the right data, whether that’s tracking public health records, ensuring operations efficiency or pricing software products. In some cases, business data can be found in enterprise resource planning (ERP) software. These tools organize information from the day-to-day activities of your business such as accounting, human capital management and supply chain operations. ERP software eliminates data duplication and improves data integrity with a single source of information by collecting and integrating your company’s transactional data from multiple sources. Data scientists use this and other data for their work.
Sanitize and store data: Once you have the right data, it's important to make sure it's free of errors and all components follow the same format. Data scientists also create secure backups that can be referenced again in the future.
Data analysis: Using models, algorithms and programming, this is the vital step of finding trends and information to help inform business decisions. Another term for data analysis is data intelligence, which refers to analysis activities that have a focus on resolving issues and making better informed business decisions.
Metric tracking: After identifying areas for possible improvement, setting goals with key performance indicators (KPIs) can help your company reach important milestones.
Internal consulting: If your company employs a full-time or a team of data scientists, any department could utilize the scientist’s skills. For example, a data scientist for a retailer may work on personalized marketing campaigns for the advertising department or a price optimization tool for sales. Adaptability in most roles is key.
Common Data Scientist Job Titles
Here are some common job titles held by those with data science backgrounds:
- Data scientist
- Data analyst
- Data engineer
- Business intelligence analyst
- Data architect
- Financial analyst
- Business analyst
Depending on location and experience, data science roles typically pay around $85,000 to $125,000 per year. Senior roles in data science fields can earn upwards of $150,000.
How to Become a Data Scientist
If you’re excited about the field of data science, there are a few potential routes to a career.
Many universities and colleges, as well as technical schools, are creating data science programs. One of the most common routes is to finish an undergraduate degree in math, statistics, business or related field and then pursue a master’s degree in data science. There are even many online and part-time programs for those who would like to get their degree while working full time.
Because it’s such a new field, many data scientists don't have a degree in that particular field. Instead, many have computer science, math, statistics or finance backgrounds and have learned with the industry as it’s grown.
Essential Data Science Skills
Data science work relies on a broad set of skills. Here's an expanded look at the most critical skills data scientists need to succeed.
Data analysis: What does analyzing data entail? It’s the process of cleansing, examining and modeling data to uncover insightful information to guide decisions in your business.
Data visualization: Helping people understand the insights data teams have uncovered is an important step. Visual representations of data with charts, graphs and other tools can help business leaders grasp concepts and insights quicker. It’s not enough just for the data team to understand the findings of their work, other business stakeholders have to be clued in and visualizations can be a useful tool.
Machine learning and AI: A subset of artificial intelligence (AI), machine learning involves systems finding patterns and making decisions based on data. A common example is Facebook. Machine learning is used to identify past browsing history, current friends, groups and interactions and then recommend more pages, people and groups you should connect with. Complex algorithms crunch this data for millions upon millions of users and no human intervention is necessary to create these suggestions. The broader field of AI is trying to teach computers to perform tasks that would normally require human-level intelligence such as detecting irregularities in spending to identify possible credit card fraud. Data scientists use both AI and machine learning to supercharge output and work with larger and more complex data.
Pattern recognition: Think about a child learning the ABCs for the first time. After hearing the sequence over and over, the child will eventually be able to recognize it and continue if you start saying “A, B, C,”. A function of machine learning, pattern recognition is similar. It’s used to match incoming data with similar information already in databases, and it’s common in a variety of industries, including health care to identify patterns in imaging and electronics and technology with speech recognition for smart devices. Pattern recognition allows data scientists to find meaningful insights that can translate to business planning and decision making.
Reporting: Data scientists often develop their own inquiries. But the findings still need to be shared with stakeholders. Data scientists often create visually driven, clear and concise reports to help various people from around the business understand the findings. This is often known as data storytelling. It refers to the narrative about findings and the visualizations that help illustrate the information and takeaways from those results of data intelligence.
Tools That Data Scientists Use
Data scientists often use specialized software to leverage statistics and modeling capabilities to process enormous amounts of data, as well as programming languages that allow them to interpret it, such as:
Python — Python is the most commonly used data science programming language in the field. It’s supplanted R and SQL as the tool of choice for data scientists because of its libraries for data manipulation and storage.
SQL — SQL is more or less the standard querying language for relationship databases, and it’s also the key API for many data platforms’ databases.
Apache Spark — A powerful analytics engine and data processing framework designed to handle stream and batch processing in very large data sets.
One of the most important tools is your company’s database system. For example, NetSuite includes modules that store operating, financial, human resources, supply chain and other essential data for most industries. These databases integrate with a spreadsheet, database analysis, and cloud-based analytics tools that can wrangle, maintain and analyze large amounts of data.
Industries That Use Data Scientists
Nearly any industry can use data scientists. Here are some examples of how data science can be applied across different industries:
Airlines: In aviation, data scientists may find ways to optimize routing, passenger loading, fuel purchasing and other aspects of keeping airlines profitable.
Construction: In construction, data science is being used to improve predictive analytics. More accurate cost estimates for the life of a project or a more efficient supply chain for materials are just some of the ways it’s impacting the industry.
Manufacturing: For manufacturing, data scientists identify ways to lower costs by improving production speed and optimizing delivery.
Telecommunication: From smart traffic routing to optimizing data center and colocation rent, data scientists are extremely valuable in this industry.
Retail: In retail, data science helps with pricing, ordering, distribution, ecommerce, marketing and floorplans.
Health care: From research on the performance of drugs against different diseases to mining the vast amounts of data collected on patients from health systems and other sources, data science is an important aspect of improving patient care.
When Is a Business Ready for a Data Scientist?
The first question you should ask about hiring a full-time data scientist is, how much data does your company have? Next, consider where you are in your data journey. Do you have already existing KPIs and understand what’s driving your business? If you don’t have that already in place, it may be difficult to measure the success of the new position. Data scientists are different than an analyst, and they excel when left to solve complex business goals. Finally, consider what the data scientist would accomplish and how you would measure success. If you’re not sure how to answer those last two questions, consider beginning with hiring a contractor or consultant to help you get started.
Data scientists are often naturally curious people and want to understand not just how things work, but also how to improve them. They come from a variety of backgrounds, including computer science, programing and statistics. The market for data scientists is rapidly growing as more and more industries find use for their abilities to solve business problems and generate more sales.
However, modern ERP software with advanced analytics for small- to mid-sized businesses is designed to give regular analysts powerful reporting at their fingertips without the need for advanced statistics and coding skills. Data science specialists often become necessary as businesses approach the enterprise level, but as your business grows, data science may become an essential part of your operation.
ERP systems can substitute for data scientists in many cases, and also aid data scientists with a consolidated data source, which often occurs within large companies. An ERP with embedded analytics enables the discovery of “hidden” information that can be used to make critical decisions and gain meaningful insight into company performance across multiple departments, teams and subsidiaries. With the right team — and the right software — data science can accelerate and build on profitability and success.
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Data Scientist FAQs
What is the salary range for data scientists?
Data scientists often earn around $85,000 to $125,000 per year. At the manager or director level, salaries grow to around $120,000 to $150,000 per year or more.
What skills do data scientists need?
You may ask yourself, “What skills are needed to be a data scientist?” Data scientists need excellent computer, math, analysis, reporting and communication skills.
What education is required to become a data scientist?
Data science is still a new field. And while there are now undergraduate and graduate programs in the subject, many data scientists have computer science, data analysis, statistics and financial analysis backgrounds.
What does a data scientist do?
Data scientists gather and analyze large data sets to find patterns and insights that can improve business results.
Do data scientists get paid well?
Yes, data scientists often command a six-figure salary with good experience.
Is data scientist a good career?
Yes, data science is an excellent career path. If you’re numbers, computer, tech or financially minded, you may see great success and happiness in a data science career.