10 Data Science Skills Every Tech Professional Needs to Master

The demand for data science expertise continues to soar as organizations across industry sectors increasingly adopt data-driven methods. Employment for data scientists is projected to grow by 36% from 2023 to 2033, significantly outpacing the average for all occupations.¹ Mastering data science skills not only enhances your ability to drive data-informed decisions but also opens doors to lucrative and diverse career opportunities.² This blog explores ten essential skills every tech professional needs to thrive in this evolving field.

The 10 Most In-Demand Data Science Skills

The interdisciplinary field of data science uses statistics, computer science, programming, and domain knowledge to collect, process, and analyze data for acquiring knowledge or solving a problem. As data processes and management become more sophisticated, new skills may gain importance. However, the following list provides a solid foundation of the essential data science skills you should master to thrive in the data science field. Sample job titles for data scientists with these particular skills are also included to help you translate the skills to possible career paths.

1. Mastering Programming Languages

Proficiency in coding is one of the essential data scientist skills. Mastering programming languages like Python and R is crucial, and learning other languages, including SQL, Java, and C++, is also important. These languages enable data cleaning, statistical analysis, and machine learning model development.

Programming skills support web scraping, statistical analysis, machine learning model development and data visualization, forming the backbone of data-driven decision-making. If you are a skilled programmer, you might find roles including data scientist, machine learning engineer, and data analyst to be a good fit.^{3, 4}

2. Data Manipulation, Cleaning, and Database Management

Making data analyzable involves data cleaning, preparation, and manipulation, along with infrastructure management. This work is central for some data science roles and heavily dependent on proficiency with SQL.³ Essential tasks in this area include querying and manipulating relational databases, data extraction and transformation (ETL and ELT processes), and data warehousing.

Data analysts, data engineers, database administrators, and data architects use their technical skills to optimize database performance and manage database systems.^{3, 4}

3. Understanding Statistical Analysis and Probability

Statistical analysis is a core part of data science, and statistical concepts like probability are at the heart of predictive analytics. Key statistical tasks include exploratory data analysis (EDA), hypothesis testing, and statistical modeling. Other analytics models used widely in business and financial forecasting include regression and time series analysis. Skill in supporting strategic decision-making with statistical analysis is vital for roles like data analyst, business analyst, statistician, and quantitative analyst.^{3, 4}

4. Data Visualization Techniques and Translating Data Insights for Stakeholders

Data visualization techniques help transform complex data into clear, actionable insights. In addition to helping present data and communicate findings to non-technical stakeholders, effective visualization techniques can help data scientists perform exploratory data analysis to better understand a business problem. Tools such as Tableau, Power BI, and Looker help data scientists to visualize data. Well-developed data storytelling skills facilitate the creation of effective communications for diverse audiences. Proficiency in these techniques is central to roles such as data analyst, business intelligence analyst, and data storyteller.^{3, 4}

5. Competence in Machine Learning and Deep Learning Algorithms

Machine learning and deep learning are subsets of artificial intelligence, with deep learning being an advanced evolution of machine learning. Critical machine learning skills you need as a data scientist differ slightly from those required for managing deep learning projects.

Machine learning models rely on human intervention for data preparation and feature engineering tasks. Deep learning models automate these processes through neural networks, reducing manual involvement.⁵ Natural language processors are a type of deep learning model. Machine learning algorithms use datasets with thousands of data points and structured problems. They're often used for tasks such as credit scoring and disease diagnosis. Deep learning handles datasets with millions of data points and complex, unstructured data and is well suited for complex tasks like image recognition and natural language processing.⁶

Data scientists working with either type of AI model need a firm grasp of data science fundamentals, including data cleaning, statistical modeling and feature engineering. Those data scientists working with deep learning need an understanding of neural network architectures and optimization techniques as well. Relevant roles for professionals with expertise in these areas include machine learning engineer, AI researcher, and research scientist.^{3, 4}

6. Data Engineering

Data engineering involves designing and constructing data pipelines, integrating and transforming data (ETL processes), and ensuring data quality and governance. Data engineers develop infrastructure that enables data scientists to access and analyze clean, well-organized data. Job titles for people who specialize in ensuring seamless data flow across systems include data engineer, data architect, and ETL developer.^{3, 4}

7. Big Data Technologies: Hadoop and Spark

Big data technologies like Hadoop and Spark support the efficient processing and analysis of massive datasets. These distributed computing frameworks enable computing across multiple processors, real-time data processing, and creating scalable data pipelines. Data professionals can leverage these platforms to seamlessly manage and analyze big data sets. Job titles for data science professionals who ensure that data systems are capable of handling complex data tasks include big data engineer, data architect, and cloud engineer.^{3, 4}

8. Cloud Computing for Data Science

Cloud computing refers to hosted services delivered over the internet, encompassing servers, databases, software, networks, analytics, and more. Applications range from software-as-a-service (SaaS) and platform-as-a-service (PaaS) to big data analytics, data governance, and cybersecurity. Cloud computing is vital in modern business, enabling scalable data storage, processing, and project deployment. AWS, Azure, and GCP offer serverless computing and cost-effective data solutions, creating flexible data environments. Job titles for data scientists who ensure robust cloud-based data solutions include cloud engineer, DevOps engineer, and data engineer.^{3, 4}

9. Machine Learning Operations (MLOps)

Machine learning operations (MLOps) bridges the gap between data science and IT operations, focusing on deploying, monitoring, and maintaining machine learning models in production. MLOps facilitates continuous machine learning integration and delivery (CI/CD) to keep models reliably updated and scaled. MLOps practices support model governance and compliance, enabling organizations to harness AI effectively. In addition to MLOps Engineer, job titles for professionals specializing in MLOps include machine learning engineer, and DevOps engineer.^{3, 4}

10. Data Ethics and Governance

Data ethics and governance are distinct yet interconnected frameworks that should guide data scientists in all areas of the field. Data ethics focuses on handling data responsibly — complying with regulations like GDPR and CCPA and prioritizing customer trust — which helps businesses protect their reputation and gain a competitive edge.⁷ Meanwhile, data governance involves internal policies to manage and secure enterprise data, ensuring data consistency and quality, improving decision-making, and maintaining compliance.⁸ Together, these practices improve organizational outcomes by fostering trust, compliance, and efficient data handling. Practicing sound data governance and ethics positions you as someone who can manage responsibilities, positioning you for increased opportunities for advancement in the field.

How to Improve Your Skills as a Data Scientist

To enhance your skills as a data scientist, consider pursuing online courses, certifications, or a master’s program to deepen your technical expertise. Engaging in data science projects and networking through courses or conferences is equally important for practical experience. Data scientists with well-developed soft skills—behaviors, personality traits, and work habits that add value to an organization—are more likely to advance to management positions. Key soft skills to help advance your successful data science career include communication, teamwork, dependability, and problem-solving.

Dominate the Field With an Online Master’s in Data Science

The New York Institute of Technology's Online Data Science, M.S. will help you develop both the soft skills and technical expertise to drive innovation and influence decision-making in your chosen role. Data science skills are in demand across industries, and New York Tech can help you unlock new career opportunities in this fast-growing field. Visit the admissions page to learn more, or schedule an appointment with an admissions outreach advisor who can answer all your questions about the program.