6 Essential Skills for a Career in Data Science

No aspect of tech remains untouched by data. Professionals in data science, business intelligence, and similar fields are at the heart of successful strategic operations.

One of the most shocking findings from Skillsoft's C-Suite Perspective Report is that 30% of executives agree Analytics, Big Data, and Data Science are the most challenging skill areas to hire for. (See the data here.)

However, data is a broad field that requires a unique mixture of schooling, skills, and experience to excel. Finding the right path to a career in data science can be tricky, so we've compiled the top skills job seekers need to master for a successful career.

Exploring Roles in Data

To better understand what skills a data specialist requires, we'll first outline the most common roles and responsibilities a data pro can choose from. Data is a vast field with many career opportunities, each with its unique focus. The most common roles in data are:

1. Data Analysts
2. Data Scientists
3. Data Engineers
4. Data Architects

While the necessary skills for each of these vary, there are core skills that transfer down into what can only be deemed essential.

An essential entry position in data, data analysts interpret data sets using statistical techniques to draw conclusions and insights that aid business decision-making. They often create visual data representations and translate complex findings into understandable charts and graphs.

What Do Data Analysts Earn on Average?

According to Glassdoor, the average salary for a data analyst in the United States is approximately $77,568 annually. However, it's important to note that this figure can fluctuate depending on factors such as level of experience, education, and geographic location. In some tech hubs like San Francisco and New York, the average salary can be much higher.

Data scientists are the mathematicians of the data world, designing and constructing new data modeling and production processes. They use prototypes, algorithms, predictive models, and custom analysis. Their strong skills in statistics and algorithm development enable them to interpret data and use machine learning algorithms to predict future trends based on historical data.

What Do Data Scientists Earn on Average?

According to the U.S. Bureau of Labor Statistics, the average salary for data scientists in the United States is approximately $103,500 per year. However, it's important to note that this figure can vary depending on factors such as the level of experience, education, and geographic location. In some tech hub cities like San Francisco and New York, the average salary for Data Scientists can exceed $150,000 due to the high demand for their expertise.

Database programmers or engineers combine the skills and expertise of programming with the building and maintaining data pipelines. While data scientists use their advanced skills to model and predict future events, database programmers or engineers are the ones who implement and support systems for an efficient and secure data environment.

What Do Database Programmers Earn on Average?

The average salary for database programmers in the United States varies widely depending on factors such as years of experience, level of education, industry, and geographical location. According to Glassdoor, the average salary for a database programmer is approximately $87,944 per year. However, salaries can exceed six figures, reflecting the high demand and competitive nature of the field.

Data architects are responsible for designing, creating, deploying, and managing an organization's data architecture. They define how data will be stored, consumed, integrated, and managed by different data entities and IT systems, ensuring alignment with business objectives.

What Do Data Architects Earn on Average?

The average salary for data architects in the United States is quite competitive, reflecting the critical role they play in managing and leveraging an organization's data. According to the U.S. Bureau of Labor Statistics, the average salary for a data architect is approximately $112,000 per year. Notably, in major tech hubs, data architects can earn well over $150,000 due to the high demand for their skills.

Building these skills and fostering a mindset of continuous learning is paramount for long-term success in the data science field. As technology and data-driven business models evolve, data scientists must stay abreast of new tools, techniques, and industry trends to remain competitive and innovative.

The value of programming, statistical analysis and data visualization skills is well recognized. However, it's the consistent honing of these skills through practice, exploration, and learning that truly distinguishes successful data scientists.

Here are the skills every data scientist should have:

1. Statistical Knowledge

At the heart of data analysis is statistics. A robust statistical theory and application foundation is crucial for accurately interpreting data. An analyst must understand basic concepts such as mean, median, correlation, probability, standard deviation, and regression.

While certainly not required, data analysts find the CompTIA Data+ certification as a solid introduction to essential data analytics and business intelligence. This certification provides a reliable data foundation teaching about mining and manipulating data to visualize and apply basic and complex statistical methods.

2. Programming

A crucial skill for anyone interested in becoming a data programmer or engineer, knowing your programming languages — namely, Python, Pandas, R, or SQL — can go a long way in managing and manipulating large datasets. According to Codecademy, Python and SQL remain the among most popular programming languages in 2023.

Interest in learning more about Python? Check out our Python Courses

3. Data Wrangling

Real-world data is often messy and riddled with inconsistencies, missing values, and outliers. Data wrangling is crucial for driving insights from data and transforming raw, complex data into a structured, clean format for more accessible analysis. To be a data analyst, scientist, or architect, having the ability to clean and preprocess data for analysis is essential.

Businesses across all sectors benefit from data wrangling, enabling them to gain a competitive edge, improve operational efficiency, and make informed strategic decisions.

4. Data Visualization

Good data analysts don't just crunch numbers; they create beautiful displays and illustrations of data to tell a story.

Data visualization is a practical must for anyone working in the field. Proficiency in data visualization tools like Tableau or Power BI aid in creating compelling graphical representations of data that non-technical team members can easily understand.

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5. Critical Thinking

Ever read Sherlock Holmes? While Sherlock and Watson may not have been designing data structures, they were historically adept at processing and analyzing the clues (data). While data analysts today aren't solving fantastical crimes, they share common critical thinking and problem-solving skills.

Data analysts, scientists, and architects need to approach problems logically and develop solutions that make sense in the given context. Being adept at identifying trends, patterns, and outliers in datasets to draw meaningful conclusions from them is essential for a career in data.

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6. Power Skills

Data analysts must explain complex concepts and findings to stakeholders without a technical background. Practical communication skills enable data professionals to effectively convey their results clearly to all stakeholders.

Additionally, these skills foster collaboration within cross-functional teams, ensuring everyone understands and works toward the same goals. Delving further into power skills, data professionals who practice better communications can often better understand the needs of the business, customer perspectives, and market trends while translating them into actions.

With the constant evolution of technology, possessing a habit of continuous learning is essential for data scientists to stay ahead of the curve. Continuous learning allows data scientists to not only adapt to industry changes but also anticipate and lead these changes.

The data field is complex and diverse. Each position has unique responsibilities and requires particular skills. While these essential skills are sure to ground a data pro with a solid foundation, the path to a career in data is in the hands of those who pursue it.

As businesses rely heavily on data for decision-making, the demand for skilled professionals will only grow. Whether you're just starting your career or looking to pivot into this exciting field, mastering these top skills will put you on the path to success in the dynamic world of data.

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