BrainStation’s Data Scientist career guide will help you take the first steps toward a lucrative career in data. The guide provides an overview of what Data Scientists and data science are, how a Data Scientist compares to a Data Analyst, what a Data Scientist does on the job, and more.
What Is a Data Scientist?
Data Scientists gather, organize and analyze large sets of big data – structured and unstructured data – to create actionable data-driven business solutions and plans for companies and other organizations. Combining a sense for math, computer science, and business, Data Scientists need to possess both the technical skills to process and analyze big data and the business acumen to unearth insights hidden in that data.
Data Science vs. Data Mining
There are a few differences between data science and data mining. Let's take a closer look:
- Is a broad field that tends to include machine learning, artificial intelligence, predictive causal analytics, and prescriptive analytics
- Deals with all kinds of data, including both structured and unstructured data
- Aims to build data-centric products and make data-driven decisions
- Focuses on the scientific study of data and pattern Data Mining:
- Is a subset of Data Science that includes data cleaning, statistical analysis, and pattern recognition, and sometimes includes data visualization, machine learning, and data transformation
- Deals primarily with structured data, not unstructured data
- Aims to take data from various sources and make it usable
- Focuses on business practices Read more about the differences between data science and data mining. Link to Data Science vs. Data Mining
What Does a Data Scientist Do?
A Data Scientist analyzes big data sets to unearth patterns and trends that lead to actionable business insights and help organizations solve complicated problems or identify opportunities for revenue and growth. A Data Scientist can work in virtually every field and must be adept at handling structured and unstructured data sets. It’s a multi-disciplinary job and to become a Data Scientist, you must possess an understanding of math, computer science, business, and communication to perform your job effectively.
Although the specific job duties and responsibilities of a Data Scientist will vary greatly depending on the industry, position, and organization, most Data Scientist roles will include the following areas of responsibility:
A Data Scientist needs to understand the opportunities and pain points specific to both an industry and an individual company.
Before any valuable insights can be found, a Data Scientist must define which data sets are useful and relevant before collecting, extracting, cleaning, and applying structured and unstructured data from a variety of sources.
Creating models and algorithms
Using machine learning and artificial intelligence principles, a Data Scientist must be capable of creating and applying the algorithms necessary for implementing tools of automation.
It’s important for a Data Scientist to be able to quickly analyze their data to identify patterns, trends, and opportunities.
Visualization and Communication
A Data Scientist must be able to tell the stories discovered through data by creating and organizing aesthetically appealing dashboards and visualizations, while also possessing the communication skills to persuade stakeholders and other team members that the findings in the data are worth acting upon.
The most recent BrainStation’s Digital Skills Survey found that data professionals spent most of their time on “data wrangling and cleanup.” Respondents also concluded that their work’s objective most often is the optimization of an existing platform, product, or system (45 percent), or the development of new ones (42 percent).
Types of Data Science
The broader field of Data Science incorporates many different disciplines, including:
Designing, building, optimizing, maintaining, and managing the infrastructure that supports data as well as the flow of data throughout an organization.
Cleaning and transforming data.
Extracting (and sometimes cleaning and transforming) usable data from a larger data set.
Using data, algorithms, and machine learning techniques to analyze the likelihood of various possible future outcomes based on data analysis.
Automating analytical model building in the data analysis process to learn from data, discover patterns and empower systems to make decisions without much human intervention.
Using visual elements (including graphs, maps, and charts) to illustrate insights found in data in an accessible way so audiences can understand trends, outliers, and patterns found in data.
Benefits of Data Science
Companies in all industries in all parts of the globe are devoting more and more money, time, and attention to data science and looking to add a Data Scientist to their te. Research shows that companies that truly embrace data-driven decision-making are more productive, profitable, and efficient than the competition.
Data science is crucial to helping organizations identify the right problems and opportunities while helping to form a clear picture of customer and client behavior and needs, employee and product performance, and potential future issues.
Data science can help companies:
- Make better decisions
- Learn more about customers and clients
- Capitalize on trends
- Anticipate the future
How Can Data Science Improve Value for a Company?
Data science is such an increasingly popular investment for businesses because the potential ROI on unlocking the value of big data is huge. Data science is a worthy investment because:
It removes the guesswork and provides actionable insights. Companies make better decisions powered by data and quantifiable evidence.
Companies better understand their place in the market. Data science will help companies analyze the competition, explore historical examples, and make numbers-based recommendations.
It can be leveraged to identify top talent. Lurking in big data are lots of insights about productivity, employee efficiency, and overall performance. Data can also be used to recruit and train talent.
You’ll get to know everything about your target audience, client, or consumer. Everyone is generating and collecting data now, and companies that don’t properly invest in data science simply collect more data than they know what to do with. Insights into the behavior, priorities and preferences of past or potential customers or clients are invaluable, and they’re simply waiting for a qualified Data Scientist to discover.
Salaries for Data Scientists
While salaries for Data Scientists vary greatly by region and industry, the average salary for a Data Scientist in the U.S. is reported as being anywhere from $96,000 to $113,000, depending on the source. A Senior Data Scientist can bring in roughly $130,000 on average.
Demand for Data Scientists
Data Scientists are in high demand and short supply across virtually all industries. A report by Deloitte Access Economics found that 76 percent of businesses planned to increase spending over the next years on data analytic capabilities, while IBM predicted a 28 percent surge in data science demand at the beginning of the decade.
The U.S. Bureau of Labor Statistics has projected a 31 percent growth in data science over the next 10 years. Meanwhile, a Markets and Markets Report found that the global market for big data is predicted to grow to $229.4 billion by 2025, with the data science platform growing 30 percent by 2024.
Everywhere in the world, it seems, investments in data science are expected to rise and, with that, the demand for Data Scientists.
What Tools Do Data Scientists Use?
Data Scientists use a variety of different tools and programs for activities including data analysis, data cleaning, and creating visualizations.
Python is the top programming language for Data Scientists polled in the BrainStation Digital Skills Survey. A general-purpose programming language, Python is useful for National Language Processing applications and data analysis. R is also often used for data analysis and data mining. For heavier number-crunching, Hadoop-based tools like Hive are popular. For machine learning, Data Scientists might choose from a wide range of tools including h2o.ai, TensorFlow, Apache Mahout, and Accord.Net. Visualization tools are also an important part of a Data Scientist’s arsenal. Programs like Tableau, PowerBI, Bokeh, Plotly, and Infogram help Data Scientists create visually appealing diagrams, heat maps, graphics, scatter plots, and more.
Data Scientists should also be extremely comfortable with both SQL (used across a range of platforms, including MySQL, Microsoft SQL, and Oracle) and spreadsheet programs (typically Excel).
What Skills do Data Scientists Need?
There are a number of skills that all aspiring Data Scientists and should develop, including:
Excel. The most-used tool for 66 percent of data professionals polled in the BrainStation Digital Skills Survey, Excel is still crucial for Data Scientists.
SQL. This querying language is indispensable in database management and it’s used by roughly half of data respondents.
Statistical programming. Python and R are commonly used by Data Scientists to run tests, create models and conduct analyses of large data sets.
Data visualization. Tools like Tableau, Plotly, Bokeh, PowerBl, and Matplotlib help Data Scientists create compelling and accessible visual representations of their findings.
Read more about the skills Data Scientists need to excel at their work. Link to What Skills Do You Need to Be a Data Scientist?
Data Scientist Career paths
As a relatively new profession, Data Scientist career paths aren’t written in stone, and many people find their way to data science from backgrounds in computer science, IT, math, and business. But the four main axes for a Data Scientist's career path generally are data, engineering, business, and product. Many multi-disciplinary roles in data science require a mastery of several or all of those areas.
People working in data science are at the very forefront of the technological changes that will most impact the future. Because data science can contribute to advancements in virtually every other field, Data Scientists are in a position to further research in everything from finance and commerce to actuarial statistics, green energy, epidemiology, medicine and pharmaceuticals, telecommunications – the list is virtually endless. Every industry traffics in its own different types of data, leveraging it in different ways to reach different goals. Wherever that happens, Data Scientists can guide better decision-making, whether that’s in product development, market analysis, customer relationship management, human resources, or something else entirely.
Not only are the applications for data science broad, touching on many different sectors, but there are different types of data science as well. What all these activities share in common is that all of them try to turn data into knowledge. More precisely, Data Scientists use a methodical approach to organize and analyze raw data to identify patterns from which useful information can be identified or inferred.
Given the scope of their impact, it’s no wonder that Data Scientists occupy positions that are highly influential – and highly in demand. While the road to becoming a Data Scientist can be demanding, there are now more resources for aspiring Data Scientists than ever, and more opportunities for them to build the kind of career they want.
But for all the ways Data Scientists can contribute to different industries, and all the different career paths a Data Scientist can follow, the types of work they do can be broken down into a few main categories. Not all data science fits neatly into these groups, especially at the forefront of computer science, where new ground is continually being broken – but they will give you some idea of the ways Data Scientists turn data into insight.
At the heart of data science, statistics is the field of mathematics that describes the different characteristics of a data set, whether that’s numbers, words, images, or some other kind of measurable information. Much of statistics is concentrated on simply identifying and describing what’s there – especially with very large data sets, just knowing what the information does and does not include is a task unto itself. Within the field of data science, this is often called descriptive analytics. But statistics can go even further, testing to see if your assumptions about what’s in the data are correct, or, if it is correct, whether it’s significant or useful. This can involve not just examining the data, but also manipulating it to draw out its salient features. There are many different ways to do this – linear regression, logistic regression and discriminant analysis, different methods of sampling, and so on – but ultimately, each of these techniques is about understanding the features of a set of data, and how accurately these features reflect some meaningful truth about the world they correspond to.
While it’s built on the foundation of statistics, data analysis goes a little bit further, in terms of understanding causality, visualization, and communicating findings to others. If statistics sets out to define the “what” and “when” of a data set, data analysis tries to identify the “why” and “how.” Data Analysts do this by cleaning up the data, summarizing it, transforming it, modeling it, and testing it. As mentioned above, this analysis isn’t restricted to numbers alone. While much data analysis uses numerical data, it’s also possible to conduct analysis on other types of data as well – written customer feedback, for example, or social media posts, or even images, audio, and video.
One of Data Analysts’ main goals is to understand causality, which can then be used to understand and predict trends across a wide range of applications. In diagnostic analysis, Data Analysts look for correlations that suggest a cause and effect, which insight can in turn be used to help modify outcomes. Predictive analysis similarly looks for patterns, but then extends them further, extrapolating their trajectories beyond known data to help predict how unmeasured or hypothetical events – including future events – might play out. The most advanced forms of data analysis set out to provide guidance on specific decisions by modeling and predicting the outcomes of various choices to identify the most appropriate course of action.
Artificial Intelligence and Machine Learning
One of the great advancements currently taking place in data science – and one that’s poised to exert enormous influence in the future – is artificial intelligence, and more specifically, machine learning. In a nutshell, machine learning involves training a computer to perform tasks we’d typically think of as requiring some form of intelligence or judgment, such as being able to identify the objects in a photo. This is typically achieved by providing it with copious examples of the type of determination you’re training the network to make. As you’d imagine, this requires both reams of (usually structured) data and an ability to get a computer to make sense of that data. Strong statistics skills and programming skills are a must.
The beneficial effects of machine learning are virtually unlimited, but first and foremost is an ability to perform intricate or prolonged tasks more quickly than any human ever could, such as identifying a specific fingerprint from within a repository of millions of images, or cross-referencing dozens of variables in thousands of medical files to identify associations that might offer clues to what causes illness. With enough data, machine learning experts can even train neural networks to produce original images, extract meaningful insights from massive bodies of written text, make predictions about future spending trends or other market events, and allot resources that depend on highly complex distribution, like energy, with maximum efficiency. The benefit of using machine learning to perform these tasks, as opposed to other forms of automation, is that an unsupervised A.I. system can automatically learn and improve over time – even without new programming.
As you might have guessed from the earlier reference to market events, the world of business and finance is one of the places that machine learning has made one of its earliest and most profound impacts. Thanks to the enormous amount of numerical data available – marketing databases, surveys, banking information, sales figures, and so on, most of which is highly organized and relatively easy to work with – Data Scientists are able to use statistics, data analysis, and machine learning to extract insights about myriad aspects of the business world, guiding decision-making and optimizing outcomes, to the point that business intelligence has become a field of data science unto itself.
Quite often, Business Intelligence Developers aren’t simply looking at whatever data happens to be available to see what they can discover; they’re proactively pursuing data collection and developing techniques and products to answer specific questions and reach specific goals. In that sense, Business Intelligence Developers and Analysts are crucial to strategic development in the worlds of business and finance – helping leadership to make better decisions and make them faster, understand the marketplace to identify a business’s opportunities and challenges, and improve the overall efficiency of a business’s systems and operation, all with the overarching goal of achieving a competitive advantage and boosting profits.
The final major field of study that Data Scientists often work in comprises a whole range of different job titles – Data Engineer, Systems Architect, Applications Architect, Data Architect, Enterprise Architect, or Infrastructure Architect, to name just a few. Each of these roles has its own set of responsibilities, with some developing software, others designing IT systems, and still others aligning a company’s internal structure and processes with the technology it uses to pursue its business strategies. What links them all is that Data Scientists working in this field are applying data and information technology to create or improve systems with a specific function in mind.
An Applications Architect, for example, observes how a business or other enterprise uses specific technological solutions, then designs and develops applications (including software or IT infrastructure) for improved performance. A Data Architect similarly develops applications – in this case, solutions for data storage, administration, and analysis. An Infrastructure Architect might develop the overarching solutions a company uses to conduct daily business to ensure those solutions meet the company’s system requirements, whether that’s offline or in the cloud. Data Engineers, for their part, focus on data processing, conceiving and implementing the data pipelines that collect, organize, store, retrieve, and process an organization’s data. In other words, the defining feature of this broad category of data science is that it involves designing and building things: the systems, structures, and processes by which data science is carried out.
What Are the Most In-Demand Data Science Jobs?
Data science in general is a highly in-demand skill, so there are a great many opportunities to be found in every area and specialty of the field. In fact, in 2019, LinkedIn listed Data Scientist as the most promising job of the year, and QuantHub predicted an acute shortage of qualified Data Scientists in the year ahead.
The key word here is “qualified.” Often, the technical requirements a Data Scientist must meet are so specific that it can take a few years of experience working in the industry to build up the necessary range of competencies, beginning as a generalist, then slowly adding more and more aptitudes and abilities to their skill set.
These are just a few of the most common ways Data Scientists can do that – there are as many potential career paths as there are Data Scientists, but in every case, career advancement depends on gaining new skills and experience over time.
As the name suggests, Data Analysts analyze data – but that short title only captures a tiny part of what Data Analysts can actually achieve. For one thing, data seldom starts out in an easy-to-use form, and it’s typically Data Analysts who are responsible for identifying the kind of data needed, gathering and assembling it, and then cleaning and organizing it – converting it into a more useable form, determining what the data set actually contains, removing corrupted data, and evaluating its accuracy. Then there’s the analysis itself – using different techniques to examine and model data, look for patterns, extract meaning from those patterns, and extrapolate or model them. Finally, Data Analysts make their insights available to others by presenting the data in a dashboard or database that other people can access, and communicating their findings to others via presentations, written documents, and charts, graphs, and other visualizations.
Data Analyst Career Path
Data Analyst is an excellent entry point into the world of Data Science; it can be an entry-level position, depending on the level of expertise required. New Data Analysts typically enter the field straight out of school – with a degree in statistics, mathematics, computer science, or similar – or transition into data analysis from a related field like business, economics, or even the social sciences, typically by upgrading their skills mid-career through a data analysis bootcamp or similar certification program.
But whether they’re recent grads or seasoned professionals making a mid-career change, new Data Scientists typically start off by carrying out routine tasks like acquiring and manipulating data with a language like R or SQL, building databases, performing basic analysis, and generating visualizations using programs like Tableau. Not every Data Analyst will need to know how to do all of these things – there can be specialization, even in a junior position – but you should be able to perform all these tasks if you hope to progress in your career. Flexibility is a great asset at this early stage.
How you advance as a Data Analyst depends to some extent on the industry you’re working in – marketing, for instance, or finance. Depending on the sector and the type of work you’re doing, you may choose to specialize in programming in Python or R, become a pro at data cleaning, or concentrate solely on building complex statistical models or generating beautiful visuals; on the other hand, you may also choose to learn a little bit of everything, setting you up to take on a leadership position as you take on the title of Senior Data Analyst. With broad and deep enough experience, a Senior Data Analyst is poised to take on a leadership role overseeing a team of other Data Analysts, eventually becoming a department manager or director. With additional skills training, Data Analysts are also in a strong position to move into the more advanced position of Data Scientist.
Data Scientists proper can typically do all the things Data Analysts can do, plus a few more things besides – in fact, with the right training and experience, a Data Analyst may eventually advance to the position of Data Scientist. So yes, Data Scientists should be able to acquire, clean, manipulate, store and analyze data – but also to understand and work with different methods of machine learning, and be able to program in Python, R, or a similar statistical programming language to build and evaluate more advanced models.
Data Scientist Career Path
Many people enter the field as Data Analysts before gaining the experience and added skills required to call themselves Data Scientists. Then, from Junior Data Scientist, the next step is typically Senior Data Scientist – although that simple change in title belies the work it takes to make that transition; a Senior Data Scientist will either command a superior understanding of virtually all aspects of data science – A.I., data warehousing, datamining, cloud computing, and so on – in addition to their familiarity with an industry-specific field such as business strategy or healthcare analytics, or they will specialize in one of these areas with guru-level expertise.
It’s worth mentioning that while some Data Scientists begin their careers in analytics and work their way to more senior positions in specialized fields like psychology, marketing, economics, and so on, others begin as professionals in one of those different fields before transitioning into a data science role.
For many, Senior Data Scientist is the ultimate career goal; this is already such an advanced role to hold that, at least within the field of data science, it’s often the most senior position that can be attained – you simply become a better, more capable Senior Data Scientist with greater areas of specialization. For some, though, especially those who take a more generalist approach, it’s possible to make further advances into a managerial position like Lead Data Scientist, running a team or department, or even Chief Data Officer, leading an institution’s data strategy at the highest level and answering only to the CEO.
What distinguishes Data Engineers from other professionals working in the data field is the fact that they design and build entire systems – including the infrastructure and processes the company uses to make the most of that data. That is, Data Engineers are the people who determine the ways in which other Data Scientists can do their jobs. What forms of data can the company’s system accommodate? What methods are used to collect data from sales and marketing, or the results of a healthcare survey, and make it available for analysis? To do this, Data Engineers need to be very familiar with the types of work that other data science professionals do – Database Administrators, Data Analysts, Data Architects, and so on – to the point that Data Engineers can often perform each of these roles as well. But because they’re builders, Data Engineers usually spend more time working on development than other data science professionals – writing software programs, building relational databases, or developing tools that let companies share data between departments.
Data Engineer Career Path
Like other jobs working in data, the first step to becoming a Data Engineer is often a university degree (usually a bachelors or masters in engineering, computer science, or mathematics) – but not always. Someone with plenty of experience working in IT or software development may find they already have all the requisite skills to become a Data Engineer except for the data skills themselves, in which case some skills retraining, such as a data bootcamp, can help to bring them up to speed. Many of the skills a Data Engineer requires (like SQL, UNIX and Linux, ETL development, or configuring IT systems) can be developed by working in an adjacent field; others (like machine learning or building data pipelines) will require more focused learning.
That being said, most Data Engineers begin their careers working in some subfield of computer science before acquiring all the skills it takes to become a Junior Data Engineer – indeed, most job postings for Junior Data Engineers require between one and five years of work experience. From there, the next logical step is to Senior Data Engineer and Lead Data Engineer. But, with their command of so many aspects of IT, software engineering, and data science, there are plenty of other positions open to Data Engineers as well – including Data Architect, Solutions Architect, or Applications Architect. For someone looking to do less hands-on work and more employee management, other options include Product Development Manager – or, eventually, given the right people skills, even Chief Data Officer or Chief Information Officer.
Can Data Scientists Work From Home?
Like many jobs in the technology field, Data Scientist roles can often be done remotely—but this is ultimately dependent upon the company you work for and the kind of work you do.
When Can Data Scientists Work Remotely?
Data science positions that work with highly sensitive or confidential data and information (which includes a large number of them, even outside privacy-heavy fields like banking and healthcare, as proprietary data can be one of a large company’s most valuable assets) will find they face many more restrictions with regards to remote work. In these cases, it’s likely you will be required to work in-office during working hours.
Some other factors to consider:
How traditional your company is. Larger, older companies are not typically as remote-friendly—although COVID may have brought big changes in this area.
How easily you can work with other teammates and departments remotely. If your work is highly collaborative, it’s more likely you’ll be required to show up in person.
Data Scientists working on contract—or even on a consulting basis—may also have more flexibility to choose their own location.
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