Data Scientist

What is data science?

Data science is the field of study that involves the extraction of insights and knowledge from large, complex, and varied data sets using scientific and computational methods. With mathematical, statistical, and computational techniques, data scientists can discover patterns, trends, and insights that inform decision-making and drive innovation.

Companies of every size and industry need data to make informed business decisions. Doing so requires people with knowledge of statistics and data modeling to unlock the value in unprecedented amounts of raw data.

Let’s say a retail company collects data on customer purchases, including demographics, product preferences, and transaction history. By using data science techniques, such as clustering analysis, the company can group similar customers together based on their purchasing behavior, then tailor marketing campaigns to specific groups of customers. Additionally, by using predictive modeling, the company can forecast future sales and optimize inventory levels to avoid stockouts and overstocking.

Overall, data science has become increasingly essential in many fields, including finance, marketing, healthcare, and technology, where it is used to drive decision-making, optimize operations, and develop new products and services.

What does a data scientist do?

Data scientists use statistics, data analysis, and computer science to transmute unprocessed data into actionable insights.

On a more technical level, the core job responsibilities of data scientists include:

• Using database tools and programming languages to obtain, manipulate, and analyze data
• Building natural language processing applications
• Creating machine learning algorithms across traditional and deep learning
• Analyzing historical data to identify trends and support optimal decision-making
• Communicating with both technical and non-technical stakeholders 
• Keeping up-to-date with advancements in technology

What kinds of companies hire data scientists?

Any company that’s looking to collect, manage, and interpret data to make business decisions will need to hire data scientists. With companies in every industry becoming increasingly data driven, the demand and opportunities for data scientists is endless. To name a few, these industries and applications include:

• Finance. Applications include risk management, fraud detection, algorithmic trading, and consumer analytics
• Healthcare. Applications include medical imaging, gene sequencing, predictive analytics, patient monitoring, and disease prevention 
• Insurance. Applications include risk pricing, customer profiling, call center optimization, and fraud detection
• Pharmaceuticals. Applications include drug development, patient selection, safety assurance, and targeted marketing and sales
• Retail. Applications include fraud detection, inventory management, product recommendations, price optimization, trend prediction, and sentiment analysis
• Supply chain. Applications include distribution, pricing, sourcing/procurement, and demand forecasting
• Telecommunications. Applications include network optimization, service personalization, sentiment analysis, and some customer experience

Data scientist salary and job outlook

Data scientists tend to receive a salary significantly higher than the national average in their country.

On average, data scientists receive highly competitive compensation packages. However, data sources on technical salaries often present vastly different, and at times conflicting, numbers at both a regional and global level. Estimates of average base salary for data scientists in the U.S. range from $126,000 to $159,000. Data scientist salaries vary depending on experience, skills, industry, location, and company size.

The job outlook for data scientists is equally promising. As the quantity of data the world produces accelerates, so too will the demand for scientists to analyze that data. From 2020 to 2030, the U.S. Bureau of Labor Statistics projects the number of employed computer and information research scientists in the U.S. to grow by 22 percent — almost triple the 8 percent average growth rate for all occupations. 

As data science is still a maturing field, the role of data scientists will continue to evolve. Data scientists will play a critical role in the development of the world’s most promising technologies, including, artificial intelligence, deep learning, natural language processing, robotics, and self-driving vehicles.

Data scientist skills & qualifications

Technical skills

Data scientists primary skills include those directly related to data, including data processing, cleansing, management, analysis, wrangling, and visualization.

They use a range of technologies to perform these actions. Those technologies include, to name a few:

• SQL
• NoSQL databases
• Python
• Ruby
• MATLAB
• Julia
• R
• Scala
• Go

Many data science roles also require knowledge of at least one general-purpose programming language, such as C, C++, C#, or Java. And skills for cloud technologies such as AWS, Azure, and GCP can be necessary for certain projects or roles. Data scientists also might use data visualization tools such as Tableau or front-end languages such as JavaScript to create visuals.

Data scientists will often have proficiency in popular artificial intelligence and big data frameworks. These include:

• Apache Spark (data processing)
• Hadoop (big data processing)
• Hive (data warehousing)
• Keras (neural networks)
• Pig (data analytics)
• PyTorch (natural language processing)
• TensorFlow (neural networks)

It’s worth noting that there’s a degree of fluidity to the technologies that data scientists use. A framework that’s in demand today might be outdated a year from now.

Statistics and mathematics

One skill emphasis that makes data scientists unique is mathematics. While a strong background in math is important to any programmer, it’s essential to data scientists. Data science is equal parts statistics and computer engineering, so while the job description might not mention it, competency in the following subjects is vital:

• Linear Algebra
• Calculus
• Statistics
• Probability theory
• Classification 
• Regression
• Clustering

Soft skills

Technical competency alone isn’t enough to succeed in a data science role. Mathematical, analytical, and problem-solving skills are a must in any technical role. Employers often look for data scientists with strong soft skills, such as:

• Time management
• Communication
• Project management
• Problem solving

Communication skills, in particular, are critical to data science. One of a data scientist’s main responsibilities is to communicate complex information to nontechnical stakeholders in other departments. The ability to translate technical subject matter into digestible, actionable information that anyone can understand is highly valuable to data scientists — and the teams that employ them.

Experience & education

After competency, the most important qualification for data scientists is experience. On-the-job experience and training is a critical requirement for many employers.

Then there’s education. While a university education is common in technical professions (about 75 percent of developers have a bachelor’s degree or higher), the field of data science tends to place a greater emphasis on postgraduate education. One study found that 88% of data scientists have a master’s degree or higher. Doctorate degrees are also common — and sometimes required. Recruiters and hiring teams working on data science roles should anticipate that many candidates will have a postgraduate degree and some employers will require a degree. 

Data science trends

In the coming decade, data science will transform entire societies, governments, and global economies. Even the next evolution of humanity is in the works. Here are the eight data science trends that will drive that transformation in 2023.

Automated data cleansing

Before businesses can make data-driven decisions, data scientists have to detect, correct, and remove flawed data. This process is both time and labor intensive, which drives up costs and delays decision making. Automated data cleansing is emerging as an efficient and scalable way for data scientists to outsource labor-intensive work to AI-based platforms. This will give data scientists more time and resources to focus on higher-impact actions, like interpreting data and building machine learning (ML) models.

AutoML

Automated machine learning (AutoML) is the process of “automating the time-consuming, iterative tasks of machine learning.” With AutoML, data scientists are able to build machine learning models in a development process that’s less labor- and resource-intensive. Efficient, sustainable, and scalable, AutoML also has the potential to increase the production of data science teams and make machine learning more cost-effective for businesses. Tools like Azure Machine Learning and DataRobot are even making it possible for users with limited coding experience to work with ML.

Customer personalization

Have you ever received an ad for a product right after you thought about it? It wasn’t a coincidence. And brands aren’t able to read a consumer’s mind (yet). It turns out that data science is to blame. 

Data scientists are using artificial intelligence and machine learning to make recommendation systems so effective that they can accurately predict consumer behaviors. And it turns out that consumers are surprisingly excited about this new approach. 52% of consumers expect offers from brands to always be personalized. And 76% get frustrated when it doesn’t happen. To deliver on these expectations, companies need to collect, store, secure, and interpret huge quantities of product and consumer data. With the skills to analyze customer behavior, data scientists will be at the forefront of this effort.

Data Science in the blockchain

By 2024, corporations are projected to spend $20 billion per year on blockchain technical services. So, it shouldn’t come as a surprise that data science is poised to help companies make sense of the blockchain. Data scientists will soon be able to generate insights from the massive quantities of data on the blockchain.

Machine Learning as a Service

Machine Learning as a Service (MLaaS) is a cloud-based model where technical teams outsource machine learning work to an external service provider. Using MLaaS, companies are able to implement ML without a significant upfront investment of budget and labor. With such a low cost of entry, machine learning will spread to industries and companies that would otherwise not be able to implement it. 

Use cases for MLaaS include:

• Analyzing product reviews
• Powering self-driving cars
• Designing chatbots or virtual assistants
• Performing predictive analytics
• Improving manufacturing quality
• Automating natural language processing
• Building recommendation engines

Leading MLaaS providers include AWS Machine Learning, Google Cloud Machine Learning, Microsoft Azure Machine Learning, and IBM Watson Machine Learning Studio.

Natural language processing

Natural language processing (NLP) is the branch of AI focused on training computers to understand language the way human beings do. Because NLP requires massive quantities of data, data scientists play a significant role in this advancing field. 

There are a variety of use cases for natural language processing, including: 

• Credit score analysis
• Customer service chatbots
• Cyberbullying prevention
• Fake news detection
• Language translation
• Speech and voice recognition
• Stock market prediction

With so many potential applications, NLP is among the most promising trends in data science.

TinyML

TinyML is the implementation of machine learning on small, low-powered devices.  Instead of running on consumer CPUs or GPUs, TinyML devices can run microcontrollers which consume 1,000x less power. With such a high cost-efficiency, TinyML provides the benefits of machine learning while avoiding.

Synthetic data

In 2021, GPU Manufacturer Nvidia predicted that data would be the “oil” that drives the age of artificial intelligence. And with 912.5 quintillion bytes of data generated each year, it might seem like the supply to drive this revolution is endless. But what if you could make your own oil? 

Much like natural resources, access to data isn’t distributed evenly. Many companies don’t have access to the huge quantities of data they need to drive AI, machine learning, and deep learning initiatives. 

That’s where synthetic data can help. Data scientists use algorithms to generate synthetic data that mirrors the statistical properties of the dataset it’s based on. 

Unsurprisingly, the potential use cases for synthetic data are as limitless as the data it creates: 

• Autonomous robots
• DevOps
• Fraud detection
• Medical training resources
• Privacy protection
• Product development and testing
• Self-driving cars
• Video surveillance

But there’s one effect of synthetic data that’s a guarantee: more data. With synthetic data, the world’s data generation will accelerate to a rate the human mind can’t begin to fathom.