Data Scientist Skills:

Aspiring Data Scientists may wonder how to find an entry-level position in data science. While earning a certificate in data science or another type of data science degree looks good on your resume, hiring managers care more about a candidate’s ability to do the job. Highlighting your skills will prove the difference between landing the career you want and losing out to other candidates. Candidates must demonstrate the necessary hard and soft skills required to fulfill the responsibilities of a Data Scientist. Hard skills include knowledge of programming languages, an understanding of mathematics including calculus, statistics, and linear algebra, familiarity with machine learning, deep learning, and predictive modeling, data preparation, and data visualization. Soft skills include curiosity, communication skills, problem-solving skills, critical thinking, collaboration, and business knowledge. Read on to learn more about how to gain and showcase these skills in the following sections.

What is a Data Scientist?

Data Scientists extract meaning from raw data to detect patterns and propose solutions that meet an organization’s needs, especially the needs to compete and grow. A Data Scientist’s responsibilities include finding valuable data from data sources, developing machine learning algorithms, improving data collection procedures, cleansing and validating data integrity to ensure accuracy, and detecting patterns and solutions based on data. Data Scientists build models based on data, create data visualizations that communicate patterns and findings to stakeholders, and automate collection processes. Because data plays a critical role in the success of any organization, Data Scientists can build careers in business, technology, finance, nonprofits, and many other industries. 

Those who wish to become a Data Scientist should develop the analytical, statistical, and programming skills needed to manage and interpret raw data. These skills include understanding statistics, machine learning, and reporting tools. Aspiring Data Scientists also benefit from understanding the programming languages R, SQL, Python, Java, and C++. 

Read more about what a Data Scientist does.

Hard Skills

Programming Languages

Data Scientists need to have a strong foundational understanding of programming languages such as R programming, Python, and Java. The most effective way to learn and retain the knowledge of programming languages is to gain hands-on experience. In-person and live online courses connect students with an expert instructor who guides them through hands-on activities in real-time. Such classes also allow students to receive answers to questions right away, and allow students to collaborate with one another on real-world projects. To help decide what type of programming class may best suit your needs, you can explore different training methods and resources through Noble Desktop’s Python Learn Hub.

Machine Learning

Machine learning is a branch of artificial intelligence (AI) devoted to understanding and creating methods that automate certain tasks. This is achieved by levering data to improve performance through repeated use. As machine learning and AI play an increasingly important role in helping organizations automate processes, collect information, and improve performance, Data Scientists will need to keep up with the latest knowledge and machine learning technology. There are courses that focus specifically on machine learning and programs that include machine learning as part of a broader data science curriculum. Explore and compare machine learning classes to find the one that meets your needs.

Data Analysis

Although being a Data Scientist differs from being a Data Analyst, understanding how to analyze data is still a crucial part of a Data Scientist’s responsibilities. Data Scientists focus on gathering data, but also on extracting meaning from it. To do this, you must understand how to analyze the data you examine. This allows Data Scientists to detect patterns, identify opportunities for improvement, and predict future outcomes. Data analysis shows organizations their historical effectiveness, current effectiveness, weak points, opportunities for growth, and possible future outcomes so that organizations can create strategies and make informed decisions. Explore different data analytics learning resources to create the learning path that works for you.

Data Visualization

to communicate your findings and recommendations in a way stakeholders will understand, you need to master data visualization tools that can create charts, graphs, and more. Learning Tableau and other data visualization tools can help you achieve this and give you hands-on experience creating data visualizations. You can then add these projects to your professional portfolio.

Soft Skills

Communication

Communication is a key skill for Data Scientists. A Data Scientist presents findings and recommendations to stakeholders within an organization. This information helps organizations improve processes, grow, and increase revenue. Effective communication skills for Data Scientists include presenting to these stakeholders in a way they understand, answering questions, and making adjustments according to feedback. One way to become a more effective communicator is to practice public speaking. Many areas have local meetups dedicated to helping professionals improve their communication skills. Data science classes may also offer the chance to create presentations for class, allowing you to sharpen this skill. 

Data Scientists should also understand how to communicate their information in other ways, such as through data visualization and visual presentations. 

Collaboration

Data Scientists work closely with other members of the data science team such as Data Analysts and team leaders. They also communicate with internal stakeholders. Knowing how to collaborate effectively with professionals of different backgrounds goes a long way toward advancing your career as a Data Scientist. Data science classes that include small class sizes and hands-on activities provide the opportunity to collaborate with classmates. Learning to build trust and teamwork in the classroom will help you develop the collaboration skills Data Scientists use in the workplace.

What Other Jobs Require These Skills?

When learning about a career in data science, you may come across similar or related jobs including jobs with similar titles. For example, the job titles of “Data Scientist” and “Data Analyst” sound very similar. That is because both jobs work with and interpret data, but in different ways. A Data Analyst typically identifies patterns and trends in data, then creates visualizations of those patterns. A Data Scientist may also do this but typically work on predictive models, algorithms, and data processes, as well.

Other careers related to that of a Data Scientist include job titles such as: 

• Business Analyst
• Business Intelligence Analyst
• Data Journalist
• Financial Analyst
• Product Analyst
• Database Administrator

Most of the above careers have similar pay and some overlap in responsibilities with that of Data Scientists. A Data Scientist might upskill into one of these positions by learning a specialization, learning about web development, or taking financial courses. 

Read more about other career paths related to Data Scientist careers.

Learn the Skills to Become a Data Scientist at Noble Desktop

If you are looking to start a new career in data science, you might think the only way for you to become a Data Scientist is by enrolling in a four-year university or pursuing other costly and lengthy educational options. However, there are many alternative methods available to help you transition into a data science career, including data science bootcamps and certificate courses designed to help working professionals gain the skills needed to obtain an entry-level job as a Data Scientist. Exploring in-person and live online data science bootcamps and certificate programs can help you find the class that meets your career goals, budget, and schedule. The first step to finding the class that fits your needs is to understand the differences between in-person classes and live online classes. 

In-person data science classes meet in a traditional classroom setting at a physical location. In-person classes have the advantage of providing all necessary equipment, such as computers and software, and allowing students to network with local professionals such as your classmates and instructor. You also have the advantage of learning from an expert instructor face-to-face. The primary drawback to in-person courses is the extra time and money required to commute to the physical learning location. Live online data science classes offer many of the same benefits as in-person classes, including the ability to learn in real-time from an expert instructor. You can also collaborate with classmates, and you have the advantage of learning remotely. 

Noble Desktop offers several different in-person and live online data science classes that can help you start a career as a Data Scientist. The Python for Data Science Bootcamp teaches students foundational programming concepts and how to handle different data types, use conditional statements to control the flow of a program, use Scikit-Learn, Matplotlib, Numpy, Pandas, and other Python libraries and tools. Noble’s Data Science Certificate program and Data Analytics Certificate program provide a deep dive into the topics and skills essential to launching a career in data science or data analytics and offer one-on-one mentorship and job search assistance. All Noble Desktop classes provide students with hands-on experience, flexible financing options, setup assistance, a free retake, small class sizes, and real-time guidance from an expert instructor.

Learn more about Noble Desktop’s in-person and live online data science classes.

You can also learn more about data science careers and data science learning options with Noble’s free Data Science Learning Hub.

Key Takeaways

• Data Scientists use hard and soft skills to effectively perform tasks and handle their responsibilities. 
• Hard skills required to become a Data Scientist include familiarity with programming languages such as R and Python, expertise in machine learning, the ability to process and analyze data, and the ability to sort data into visualizations that communicate findings, patterns, and other information to stakeholders. 
• Soft skills include the ability to effectively communicate with team members and with internal stakeholders, as well as the ability to collaborate. Classes that include presentations and hands-on activities provide the opportunity to develop these soft skills.
• You can receive comprehensive training to become a Data Scientist through an in-person or live online course with Noble Desktop.