Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. To uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
The term “data scientist” was coined when companies first realized the need for data professionals skilled in organizing and analyzing massive amounts of data. Ten years after the widespread business adoption of the internet, Hal Varian, Google’s chief economist, first dean of the UC Berkeley School of Information (I School), and UC Berkeley emeritus professor of information sciences, business, and economics, predicted the importance of adapting to technology’s influence and reconfiguration of different industries.
“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
– Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics1
Today, effective data scientists masterfully identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are now required in almost all industries, which means data scientists have become increasingly valuable to companies.
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Data scientists have become assets across the globe and are present in almost all organizations. These professionals are well-rounded, analytical individuals with high-level technical skills who can build complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organizations. They also have the communication and leadership experience to deliver tangible results to various stakeholders across an organization or business.
Data scientists are typically curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.
They also use key technical tools and skills, including:
Apache Pig
Tableau
iPython notebooks
GitHub
As increasing amounts of data become more accessible, large tech companies are no longer the only ones in need of data scientists. There’s now a demand for qualified data science professionals across organizations, big and small.
With the power to shape decisions, solve real-world challenges, and make a meaningful impact in diverse sectors, data science professionals have the opportunity to pursue various career paths.
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Data is everywhere and expansive. Various terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but the roles typically involve different skill sets. The complexity of the data analyzed also differs.
Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Data scientists also leverage machine learning techniques to model information and interpret results effectively, a skill that differentiates them from data analysts. Results are then synthesized and communicated to key stakeholders to drive strategic decision making in the organization.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning
Data analysts bridge the gap between data scientists and business analysts. They’re provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization
Data engineers manage exponentially growing and rapidly changing data. They focus on developing, deploying, managing, and optimizing data pipelines and infrastructure to transform and transfer data to data scientists and data analysts for querying.
Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)
Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. Data science professionals with the appropriate experience and education have the opportunity to make their mark in some of the most forward-thinking companies in the world.
Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts use high-level programming skills to create algorithms that continuously gather data and adjust their learning to improve prediction performance.
Data science is the practice of using computational and statistical methods to find valuable insights and patterns hidden in complex data. It brings together skills from various fields like statistics, programming, and business knowledge to help organizations make better, data-driven decisions. Think of a data scientist as a detective, using data as clues to solve a mystery for a company.
A data scientist’s primary role is to transform raw data into a narrative that can be used to solve business problems. This involves a full cycle of activities, from data collection and cleaning to building predictive models using machine learning, and finally, communicating the findings clearly to non-technical stakeholders. They’re part-analyst, part-engineer, and part-storyteller, all focused on unlocking the potential of data.
Yes, data science is built on a strong foundation of math, particularly statistics, probability, and linear algebra. However, you don’t need to be a theoretical mathematician. The goal isn’t to solve complex equations by hand, but rather to understand the underlying principles of the algorithms you’re using. Many modern tools handle the heavy computations, so a practical understanding of how and why these mathematical concepts work is more crucial than deep, theoretical knowledge.
A data science degree provides a multidisciplinary education that combines the technical skills and theoretical knowledge needed for the field. The curriculum typically includes coursework in statistics and probability, computer science (programming, algorithms, databases), and machine learning. Additionally, a strong program will emphasize communication skills and domain-specific knowledge to help you apply your technical skills to real-world problems.
Yes, Artificial Intelligence (AI) and Machine Learning (ML) are considered key components and powerful tools within the broader field of data science. Data science provides the framework for collecting, processing, and analyzing data, which is then used to train and develop AI systems. While data science is about extracting insights from data, AI is about building intelligent systems that can use those insights to make decisions or perform tasks. It’s a symbiotic relationship.
A data analyst focuses on analyzing historical data to identify trends and create reports. A data scientist uses more advanced techniques, like machine learning, to build predictive models and solve complex problems.
Big data refers to the massive, complex datasets themselves. Data science is the field that uses scientific methods and tools to extract insights and knowledge from that data.
The most common languages are Python, popular for its ease of use and extensive libraries, and R, which is widely used for statistical analysis. SQL is also a key skill for managing and querying data in databases.
The interdisciplinary nature of data science, which combines skills from statistics, computer science, and specific subject matter expertise, allows professionals to solve real-world problems more effectively. By bridging these different areas, a data scientist can not only analyze data but also understand its context and communicate its business value, making them a more well-rounded and impactful professional.
Stay up-to-date in data science by following industry blogs and publications – many highlight new research and tools in plain language. Join online communities or competitions to connect with practitioners and see emerging skills in action. Conferences and webinars – virtual or in person – also provide expert insights and networking opportunities. Applying new methods through personal projects helps solidify what’s most relevant in practice.
The best approach to learning data visualization is to begin with the fundamentals: knowing your audience, choosing chart types that fit the story, and focusing on clarity. After that, practice with widely used tools or programming libraries to build hands-on skills. Reviewing strong examples from books, case studies, and public dashboards can spark inspiration and highlight best practices. Over time, refining your own projects and seeking feedback will strengthen both your technical skills and design sense.
1Hal Varian on How the Web Challenges Managers. (2009). Mckinsey. Retrieved December 2023.arrow_upward
