How to Get Into Data Science: A Guide for Professionals

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How to Get Into Data Science: A Guide for Professionals

Companies across all industries have access to a wealth of data. It's used within corporations to set strategic plans, prepare marketing campaigns, define annual budgets and for countless other purposes. Until recently, many organizations used a very shallow—and very narrow—set of data to improve their operations.

 

But today, a revolution in business data is ongoing. Storage and processing power are cheaper, thanks to cloud computing. Unstructured data stores, data lakes and data mining technology are more widely available than ever before. The internet of things (IoT) has created a massive set of data points that can be collected and analyzed, and the potential of AI and machine learning continues to grow. Companies are increasingly aware that seemingly unrelated data can actually be used to provide even deeper insights and drive decision-making.

 

Yet with this increase in useful data, companies are realizing that they are short on employees with the key skills that would allow them to leverage those assets into new insights. They need team members that can look at information and extract actionable insights from it. Consequently, the demand for data scientists has spiked, and the future for those with the right skills looks bright.

 

Today, many professionals are asking themselves, “How do I get into data science?” It might seem straightforward to a student or entry-level employee, but for a seasoned technologist or IT professional, the path toward a new skill set and role is less clear.

 

Professionals interested in making a career move into data science (or simply those looking to better understand the field in order to build or lead a data team) need to understand the data scientist's role, essential traits and necessary skills needed to make the leap to this growing field.

The Discipline of Data Science

With all of the activity surrounding data in today’s organizations, it’s no surprise that even seasoned professionals need clarity in defining data science roles. For those looking to get into data science as a career, it’s crucial to understand what the role entails—and what it doesn’t.

 

First, it's important to clarify that a data scientist is neither a database engineer nor a data analyst. A database engineer works with the applications and systems to design and monitor databases. A data analyst works to understand what a company’s data looks like.

 

A data scientist draws on both of these disciplines but uses the output of their work to answer questions and solve problems. The work of a data scientist results in explanations and actionable insights, and the activities of a data scientist involves drawing complex and actionable conclusions that go beyond a basic reading of what the data says. For this reason, data science can be considered a multidisciplinary role.

How to Get Into Data Science: Traits Needed

Data scientists often require a greater range of professional traits than other roles. There are specific traits that those looking to get into data science should either already have or should work to cultivate.

 

Curiosity

Data science requires a sense of curiosity. The role requires the desire to ask “why” and “how” in a directed way. Also, as the landscape continues to evolve, those getting into data science must be eager to keep up with advances in the field.

 

Technical Aptitude

A data scientist uses technology on a daily basis to dive into data and extract usable insights from it. It’s important that those getting into data science are comfortable with technology and have an interest in continuing to learn about it. The tools are constantly evolving, so those working in data science must become lifelong learners to keep up.

 

Creativity

This trait might seem out of place in a highly technical field, but understanding what data is trying to say may mean looking at it in a number of different ways. While the processes of investigating a hypothesis should be structured, formulating transformative ideas requires thinking in new, creative ways.

 

Business Acumen

At its core, the role of a data scientist is to answer questions for the business and offer actionable insights based on what the data is saying. To do this, those getting into data science must understand the basics of business needs.

 

Soft Skills

Soft Skills like teamwork, communication and presentation are critical to the data scientist. Data scientists often collaborate with others (like database engineers and analysts), so teamwork is key. Data scientists must also be able to relay their findings persuasively to their company’s decision makers, so communication and presentation skills are also essential.

How to Get Into Data Science: Skills Needed

If you’re equipped with the right mindset and personality traits of a data scientist and looking to enter the field, you should seek out educational opportunities in the following professional skills.

 

Statistics

Statistical analysis is foundational to data science. It structures investigations into data through distribution theory, logistical regression and principles of estimation. Statistics is the framework that data scientists use to test their theories.

 

Understanding of Data

Understanding how data is related, how it is stored, and how it applies to business questions and strategies is core to the success of the data scientist. That does not mean data scientists must know how to create a database or allocate server space for big data. But they should know the difference between clean data, data quality and data maturity, as well as what relationships between these categories look like.

 

Programming

Fluency in programming languages allows the data scientist to build data models and explore their hypotheses. Ideally, a data scientist would understand how to structure complex SQL queries and leverage a language like Python or R to create datasets. Additionally, the ability to build NoSQL queries and use machine learning to explore predictive analytics is helpful.

 

Data Science Concepts: Big Data, AI, Data Mining, Predictive Analytics

The tools of the data scientist are evolving at a rapid pace. There is a growing set of technologies that those in the field should be aware of and understand, including big data, artificial intelligence, data mining and predictive analytics.

Start Getting Into Data Science With EmergingEd

Data science is a fascinating, in-demand and growing role in modern organizations. As companies continue to take advantage of highly accessible data storage and the newest tools for big data, data mining and machine learning, the need for qualified data scientists continues to grow.

 

If you’re a professional who wants to evolve and remain relevant, consider an online data science course from EmergingEd to help you make your next career move.