Important data science skills for data professionals

The data science discipline requires a mix of data science skills — mathematics, programming knowledge, data/technical skills — and soft skills. Additionally, data scientists should be familiar with their organization’s business and domain.

Mathematics

Mathematics — statistics, probability, and linear algebra — enable data scientists to identify patterns in data. For example, the engine of a machine learning application is mathematics. Specifically, data scientists use their mathematics skills to create algorithms that can extract value from large data sets and enable machine learning applications to learn from the data and make predictions.

Programming/coding knowledge

Programming languages and coding can help data scientists carry out operations in databases and perform analytical functions. Python, Java, Perl, and C/C++ are common languages used in data science because they can be used to work with various formats of data, including both structured data, which is typically stored in databases, and unstructured data, which is not predefined through data models or structures but may contain valuable information, such as numbers and facts.

Data/technical skills

The following are the most commonly sought-after data and technical skills in data science candidates:

●  Data wrangling: Data wrangling describes the multistep process of transforming raw data into a format that is usable for analytics.
●  Data manipulation/analysis: In the data manipulation/analysis process, data is modified and structured to make it easier for humans to understand, read, and sort through, for example, data listed in alphabetical order.
●  Data visualization: Visual formats such as charts, graphs, and maps can make it easier to find trends and patterns in data that may reveal valuable information.
●  Model building: A data model is a collection of data sets used for testing, training, and production. A data scientist can use a model to find an answer in the data to a business question or achieve an objective. Model types include statistical, mathematical, and simulation.
●  SQL: SQL stands for Structured Query Language. It allows data scientists to access and manipulate databases such as relational databases. SQL offers data scientists the ability to query large sets of structured data.
●  Machine learning: Machine learning is a subset of data science. It does not require programming to run a machine learning application. Instead, algorithms that data scientists have created power machine learning applications. The algorithms enable the machine learning application to learn from the data to make predictions and identify trends.

Nontechnical/soft skills

Data science has a heavy technical component. However, soft skills are foundational competencies for individuals to succeed in the role.

●  Problem-solving: Data scientists benefit from their ability to solve problems to overcome barriers in working with various types of data and analytics tools.
●  Communication: After using scientific, mathematical, and technical methods to find value in data, data scientists use their communication skills to present their findings to business decision-makers.
●  Curiosity: Data scientists have an innate intellectual curiosity to search deeply for answers to challenging questions.
●  Storytelling: Data scientists have the know-how to translate the data into stories to allow nontechnical individuals to understand its value.
●  Structured thinking: Data scientists must be able to frame questions, see all angles of a problem, and understand results.

Business/domain knowledge

Data scientists apply their skills in working with data and numbers to solve problems unique to their industries. Therefore, it is important that in navigating through the data science and analysis process, they understand how the data applies to their businesses and domain areas. With this knowledge, data scientists arm themselves with a holistic view of the business problem they’re trying to solve and how data can support business growth.

Resources: Essential data science skills

The following resources offer additional insights into the many data science skills individuals interested in data science careers should pursue:

●  KDnuggets, “Top 5 Must-Have Data Science Skills for 2020”: This resource discusses skills such as Python, SQL, and machine learning that are typically associated with data science roles, as well as competencies vital for individuals to remain competitive in the job market.
●  QuantHub, “Data Science Skills — A Brief Guide”: This resource highlights core data science competencies individuals should look to acquire to enter the data science field, build data science teams, or enhance their data science skill sets.
●  Tableau, 10 Skill Sets Every Data Scientist Should Have: This resource discusses the nontechnical skills that are as important as the technical skills needed in data science careers.
●  TechTarget, “14 Most In-Demand Data Science Skills You Need to Succeed”: This resource discusses how technical and soft skills, such as critical thinking, communication, and business knowledge, can prepare individuals for success in data science careers.
●  The Balance Careers, “Important Job Skills for Data Scientists”: This career resource discusses the data science skills that employers are seeking and how to make these skills stand out in resumes and cover letters.

In the data science field, various data roles are often mentioned together. Three of the most commonly referenced job titles are data scientist, data analyst, and data engineer. However, data science skills are important in other careers as well. Some of the activities of these roles overlap; for example, statistics and data science share similar responsibilities, such as transforming quantitative data into qualitative data.

Consider various examples of data roles that use data science skills.

Data scientist

Data scientists work toward finding answers to questions using their advanced knowledge of mathematical concepts, such as statistics. Their responsibilities include designing data models, including predictive models, and creating algorithms for machine learning applications. A data scientist’s primary aim is to leverage data to produce reliable predictions for a business or industry.

Data analyst

Data analysts get a big-picture view of data and use their findings as an engine for decision-making. Typical data analyst responsibilities include mining data from various sources, spotting trends and patterns, and developing reports and stories that communicate the value of data and enable business leaders to make data-driven business decisions.

Data architect

Data comes in various formats — unstructured and structured — and from different sources. It is the data architect's job to identify potential data sources that will add value to the business. The data architect then builds the framework for a secure data management system that integrates, centralizes, and maintains the data, and enables business stakeholders to access data when they need to.

Data engineer

Data engineers are responsible for developing and maintaining the analytics infrastructure for data processing, analysis, modeling, and calculations. They make raw data usable for their businesses and ensure that individuals who work with data, including data scientists and data analysts, can reliably access the data to find insights.

Statistician

Statistics, a traditional method of getting information from numerical or quantitative data, falls in the data science field. Statisticians use logic to harvest data and turn it into knowledge. A statistician’s responsibilities can range from designing surveys to collect market study research to creating statistical theories and methodologies.

Database administrator

Database administrators are IT professionals with the primary responsibility of maintaining, protecting, and troubleshooting issues with systems that store and provide access to data. A database administrator’s responsibilities include installing network servers, upgrading database software, determining hardware requirements, configuring databases, and migrating data between databases.

Business analyst

A business analyst’s role is a mix of business, data analytics, and IT. Business analysts use data analytics to deliver data-driven recommendations to business stakeholders. The data insights revealed in business analysis can help organizations identify areas for improvement and improve operational processes.

Data analytics manager

Data analytics managers possess various data science skills. However, their primary role is to develop data analytics strategies to support business goals, drive data analysis projects throughout their lifecycles, and manage teams of data analysts and other data professionals.

Resources: Data science and data professional careers

Use the following resources to get further insights about data science and data professional careers:

●  Chartio, “Distinguishing Data Roles: Engineers, Analysts, and Scientists”: Many roles have “data” in the job title. This resource highlights the responsibilities of some of these roles, including data engineers, analysts, and scientists.
●  TechTarget, “Data Scientist”: The skills required for data science professionals vary considerably depending on the role. This resource expands on the core skill sets, essential technical knowledge, and mindset required for data professionals; it also includes a discussion of data scientist vs. data analyst roles.
●  Robert Half, Want to Be a Data Scientist? Here’s What You Need to Know: This resource from a leading recruiting organization explores the core skills needed to become a data scientist and discusses the responsibilities and compensation for the role.