9 Data Science Skills for Beginners

The past two decades have seen a proliferation of data generation and collection. This trend has been driven by several developments, including the emergence of social media, e-commerce, smartphones, wearable technology, and the internet of things (IoT).

To the untrained eye, much of this data may appear as white noise, but in truth, it can be a valuable source of insight. Businesses that invest in data generation, collection, and analysis are often able to leverage it to inform decision-making and strategic initiatives. This has made data science skills extremely valuable for professionals looking to advance in their careers.

Below is a look at why data science is important to modern business, who should prioritize developing data science skills, and a list of skills that those new to data science should gain.

Who Needs Data Science Skills?

Data science skills are most important for professionals who directly work with data and need to strongly understand it to do their jobs (for example, data scientists, data engineers, and analysts).

Other professionals, however, can benefit from developing data science skills. Whether you’re an individual contributor, manager, or business leader, building your data science skills can empower you to:

• Find and evaluate data that may be relevant to your job, even if you don’t typically use data
• Become more data-driven in your decision-making
• Better communicate with others in your organization (especially those on the data team), as well as executives and members of the C-suite
• Tie your work back to its business case by understanding the key metrics executives care about, along with your contributions to those metrics
• Change your career to a more data-focused role

Regardless of how often you interact with data, a firm understanding of data science can be an asset to your career, especially as small- and mid-sized businesses embark on the data-driven path blazed by larger companies.

Data Science Skills for Beginners

1. Basic Data Literacy

To interact with data and those who work with it, you need to understand its key terms, concepts, and language.

This understanding is commonly known as data literacy. By developing your data literacy, you can effectively discuss different types of data, data sources, analysis, data hygiene, along with key tools, techniques, and frameworks. You can also leverage the steps in the data life cycle—which underlies most data projects—and elements of the data ecosystem.

Without basic data literacy, you’ll likely find it difficult to talk about or use data, making it one of the most important data science skills to develop as a beginner.

2. Domain Fluency

To effectively leverage data, you must first have a solid understanding of your domain: the trends, developments, challenges, opportunities, and other factors that not only affect your industry and organization, but also the work you perform.

While domain expertise isn’t a data science skill in and of itself, it can be difficult to know which data points are relevant to your work and industry without it. This, in turn, can make it challenging to generate, collect, evaluate, and analyze data. Domain fluency enables you to cut through the noise and identify the metrics and data points that are most useful to you.

3. Data Generation and Collection

Before data is manipulated and analyzed so you can glean insights from it, it must first be generated and collected. As such, data generation and data collection are the earliest—and arguably most important—steps in the data life cycle.

Depending on your role, you may not be in a position to generate or collect data. Still, it’s important to understand the different ways it can be generated and collected, such as surveys or questionnaires. Once you know what’s possible, you can more easily communicate with those who are responsible for data generation and collection.

4. Data Manipulation

Data is rarely useful in its raw state. This is because nearly every dataset includes errors, gaps, or information that’s unrelated to the business question at hand. For data to be analyzed, it must first be manipulated and transformed into something that can be more readily used.

Data can be manipulated in several ways. Data wrangling includes cleaning a dataset by removing errors and filling gaps. Data encryption makes a dataset more secure. Data compression makes it easier to store and query a set of data. While you don’t need to know how to perform all of these activities, understanding what goes into each can give you the vocabulary to speak about them and understand how they impact your project.

5. Analytical Skills

Analytical skills are ultimately what allow you to dig into a dataset and come away with insights that inform business strategy and other decisions. With that in mind, data analysis typically involves searching for and identifying trends, patterns, or outliers in data. Developing an analytical mindset that’s capable and comfortable working with numbers can prove critical if you hope to leverage data in your role.

Data analysis can be conducted in a variety of ways. Statistical modeling, data mining, artificial intelligence, machine learning, and algorithms are all powerful tools you can leverage to quickly digest large amounts of information.

6. Data Ethics

Organizations that collect, store, and analyze data are responsible for protecting that data from misuse. This includes sensitive data, such as medical information, and seemingly less sensitive data, such as purchase histories. Organizations that fail to protect data from unauthorized access or use it inappropriately risk losing consumer trust and can also face monetary fines and legal penalties.

With this in mind, it’s a good idea to understand the laws, rules, and regulations that dictate how data is used within your industry and organization. Familiarizing yourself with the concepts and core tenets of data ethics, data privacy, and data governance is also recommended.

7. Critical Thinking Abilities

Critical thinking is perhaps the most essential skill in this list. It’s by leveraging critical thinking skills that you can:

• Identify the metrics that matter most to your project and should be collected
• Evaluate a dataset for completeness and accuracy
• Identify trends and patterns, and seek to understand the “why” behind them
• Recognize potential biases or errors in a dataset or analysis
• Extrapolate insights you can use to inform business strategy

Without robust critical thinking skills, it’s more difficult to perform all of these activities. There’s also an increased risk that you may accept bad data or faulty analysis.

8. Communication Skills

Though data is a powerful tool, if you’re unable to communicate your analysis effectively, it can be difficult to turn insights into action. That’s why learning how to tell a story with data is crucial.

This often means adopting different communication practices, depending on your audience. For example, when you’re communicating with someone on the data team or another professional who’s familiar and comfortable with data, it’s important to use proper terminology so you don’t come across as uninformed.

On the other hand, if you’re communicating with someone who’s less data literate, you may need to simplify the message. Data visualization can be an especially powerful tool in such a case. By leveraging data visualization tools and techniques, you can generate easy-to-digest graphics and charts, which can be particularly useful when communicating with those who are uncomfortable with data.

9. Mathematical and Programming Skills

When it comes to working with large datasets and performing complex forms of analysis, having strong mathematical and programming skills can make the task much easier. These are among the more technical data science skills. Though they’re important for data scientists and analysts, they’re less of a priority for beginners learning data basics.

Some of the most important mathematical skills for data science include statistics, probability, algebra, and multivariate calculus. Some of the most valuable programming and coding skills for data science include programming languages R, Python, and SQL. In addition, it’s critical to understand how machine learning and artificial intelligence work.

Developing Your Data Science Skills

When learning data science and developing your skills, there are several routes you can take. Reading data science material and watching videos can be an excellent way of exposing yourself to the subject. While it’s possible to self-teach, many find that they learn more quickly and easily when material is presented in a structured way, such as through a course or workshop.

Are you interested in furthering your data literacy? Explore our online business essentials courses, and download our free data and analytics e-book to learn how you can use data for professional and organizational success.