Nearly ten years ago, Harvard Business Review named data science the sexiest job of the 21st century based on a few factors. Fast forward a decade; data scientists are still one of the most sought-after professionals. They help make sense of the predicted 120 zettabytes of data generated by the end of 2023. Big data, as it's called, is worth trillions. Why? Big data is information that companies use to make financial decisions, hospitals and research centers rely on to create cures and save lives, and governments implement to protect citizens.
Data scientists skillfully transform raw and processed data into digestible, bite-sized insights. The field of data science encompasses numerous specializations, each offering a unique skill set and commanding six-figure salaries. Let's delve deeper into this.
What Does a Data Scientist Do?
Data scientists use various tools and techniques to collect, clean, analyze, structure, store and present or visualize all data types, depending on the company. The goal is to turn raw data or information into actionable insights or revenue-generating goals. Data scientists rely on creativity and critical thinking to analyze data sets, identify relationships that may not be obvious and use machine learning models to help them classify and structure data and make predictions. They also develop algorithms that can be scaled up and integrated into existing systems.
Skills Every Data Scientist Should Know
Data scientists must possess key technical and non-technical skills. Technical or hard skills are essential for executing the Data Science Method (DSM) using predictive tools and presenting key findings to teams, upper management and stakeholders.
Programming - Data scientists need to understand, write and execute code to do their job. For example, they'll use programming languages like Python to create machine learning algorithms, SQL (structured query language) to comb databases and R for statistical analysis
Statistical Analysis - Statistics are data sets. Data scientists must take complex data sets, break them down into manageable bits and use them to help predict future actions.
Machine Learning - Machine learning is a subset of Artificial Intelligence (AI) that relies on instruction from algorithms to operate. Data scientists write the code for these algorithms and then clean and collect uncovered insights. They also use deep learning and natural language processing to analyze and organize data.
Data Visualizations - Data scientists use visualization tools to present ideas, evidence and findings to fellow teammates to help instruct them on the next steps and demonstrate results to upper management or stakeholders unfamiliar with many scientific concepts.
Non-technical, or soft skills, are also vital to this role. It's one thing to break down data, but you must also work with others daily and explain or present findings. Here are some must-have soft skills:
Problem-Solving - Much like putting a puzzle together, data scientists must have a passion and a knack for finding solutions to complex problems while wading through massive amounts of data pieces.
Communication - Data scientists work with teams that can include engineers, developers, designers and junior and senior data scientists. Even during the presentation stage, data scientists must explain complex issues to executives or stakeholders who may not have a background in this field.
Adaptability - Data is constantly changing, as are teams, timelines, deadlines, tools and other aspects of technology. A great data scientist can adapt quickly and be flexible throughout the project.
Creativity - Who says data science can’t be fun? With the sheer amount of data they deal with, getting creative with AI or other data science tools can help data scientists stay engaged and on their toes.
What Does A Data Scientist Do On A Daily Basis?
The day-to-day duties of a data scientist can vary depending on the organization they work for, the industry they're in and the specialized nature of their job title and description. Regardless, every data scientist will perform the Data Science Method for every project, a process that consists of collecting data, organizing data, analyzing data, creating data models for evaluation and deploying results. Below is an expanded explanation of these core responsibilities.
1. Collecting and Cleaning Data: Whether already in databases or raw, data scientists gather the data into sets based on the project, organize it based on project parameters and clean it to make sure there are no inaccuracies, errors or data that doesn't belong in the set.
2. Data Analysis: The data is analyzed via statistics or machine learning techniques to help identify customer behaviors, buying trends, study results, goals achieved and other patterns and insights to help create actionable predictive outcomes.
3. Artificial Intelligence: Data scientists design, create and execute machine learning algorithms and build models for deep learning to help organize data sets, identify images and patterns through voice and image recognition and run possible future actions through scenarios.
4. Data Visualization: In its raw or cleaned state, data can look like a jumble of words, numbers and articles. Once data sets have been analyzed and actionable insights uncovered, they are ready for presentation. Data scientists build visuals like charts, graphs and more to make it easier for non-technical people, like sales teams or C-suite executives, to grasp.
5. Documentation: Once all steps have been taken, data scientists will document the process from start to finish, including models, machine learning algorithms, data analytics and the final results so that the project can be accessed by anyone at any point for further review and research.
Data Scientist Vs. Data Analyst
The terms data scientists and data analysts are often interchanged, just like software engineer and software developers. Though some job duties and skills overlap, the roles are vastly different. According to Springboard, data analysts typically look for answers within the data, whereas data scientists use machine learning and other tools to predict outcomes based on the data. Data analysts' work tends to be more fast-paced, while data scientists have projects that can last months or years.
What Jobs Are Available For Data Scientists
Data science is a vast field, but there are specialized roles that prioritize specific skills. Data science is also a career with tons of upskill potential, which also means increasing your salary. Here are six popular data science specializations and the average annual salary:
Business Analyst - $83,645 per ZipRecruiter.
Natural Language Processing Engineer - $101,704 per Glassdoor.
Big Data Engineer - $130,384 per ZipRecruiter.
Senior Data Scientist - $143,524 per ZipRecruiter.
Machine Learning Engineer - $152,309 per Indeed.
Data Architect - $164,892 per GlassDoor.
Start Your Career In Data Science
The Bureau of Labor Statistics predicts a 36% growth in data science jobs through 2031. For perspective, most jobs have a 5% growth rate prediction. Because of this, you’re more likely to land a job. Many technology jobs do not require a bachelor's or master's degree, but formal training at a university or college is one way to learn. Thanks to online videos, coding camps and other resources, self-taught data scientists can find success as junior or level one data scientists.
A cost-effect way to become job-ready and certified is through online bootcamps. The Stony Brook University Data Science Bootcamp has developed a curriculum designed to grow your skills and offers the opportunity to build real-world capstone projects for your portfolio within nine months. Since the bootcamp is fully online, you don’t have to quit your job in order to learn new skills. You can explore the program or apply online for more information.