11 Essential Data Scientist Skills You Need for a Thriving Career
With the rise of AI technologies, having the right combination of hard and soft data scientist skills is essential.
The impact of artificial intelligence (AI) technologies on business is profound. With the ongoing trends in AI and big data, and the shift to more data-driven decision making from organizations, the demand for data scientists has grown dramatically year on year, as evidenced by a spike in search terms like “data scientist skills.” To thrive in this field, it is essential that you are equipped with the relevant skills for data scientists. By using Alpine Skills API, we can extract all the required skills for data scientists to help point you in the right direction.
Source: Google Trends
Core Technical Hard Skills for Data Scientists
Hard skills for data scientists range from analytics and statistical abilities to technical competencies like programming. Here are the top technical skills required for a role in data science.
1. Statistical Analysis and Mathematics
Statistical analysis and mathematics are foundational in data science, enabling the extraction of insights, pattern discovery, and prediction from a huge amount of datasets. Proficiency in statistical concepts like probability, hypothesis testing, and regression empowers data scientists to validate findings and draw meaningful conclusions from unstructured data. Additionally, mathematical skills facilitate data manipulation, advanced calculations, and building accurate data models.
2. Programming and Software Engineering
Data scientists must have a proficient level of knowledge in programming languages to process and visualize data. For example, Python is widely used for its versatility and extensive libraries. R, on the other hand, known for statistical computing and graphics, is valuable for any data science task. Other languages like SQL enable efficient retrieval of data from relational databases. Building foundational knowledge of software engineering principles ensures scalable data solutions through clean, modular code, version control, testing, and software design patterns.
3. Data Manipulation and Visualization
Data scientists require strong data manipulation skills to handle large and complex datasets through cleaning, preprocessing, and transformation. Proficiency in techniques like data wrangling and dealing with missing values ensures accurate results. Effective data visualization also helps in pattern recognition and generating meaningful insights.
4. Machine Learning & Artificial Intelligence (AI)
Machine learning, a subset of Artificial Intelligence (AI), uses algorithms to uncover patterns in data, enabling computers to learn from data and make predictions without explicit programming. Machine learning skills are essential for data scientists, allowing them to extract insights, make accurate predictions, and solve complex problems. You will need machine learning skills to analyze large datasets and identify variables.
5. Deep Learning
Deep learning is a subset of machine learning that focuses on training models with multiple layers to learn hierarchical representations of data. Understanding deep learning allows you to tackle sophisticated problems in areas like image and speech recognition, and natural language processing.
6. Natural Language Processing (NLP)
Natural Language Processing (NLP) skills are essential for data scientists as they enable processing and analysis of unstructured text data. By understanding human language, data scientists can derive valuable insights, perform sentiment analysis, extract entities, and automate tasks like language translation. NLP plays a vital role in various data science applications, making it a crucial skill in the field.
7. Big Data
Big data skills encompass the knowledge, techniques, and tools required to handle and extract insights from large and complex datasets. Big data has become a significant skill of the data science field. It allows data scientists to effectively manage and process massive datasets that exceed the capacity of traditional data processing tools. Big data skills are also applied in real-time processing to generate timely insights.
8. Cloud computing
Cloud computing skills refer to the knowledge required to leverage cloud platforms and services for data storage, processing, and analysis. The scalability empowered by cloud computing enables data scientists to handle large volumes of datasets and perform complex computations without extensive infrastructure setup.
Critical Soft Skills for Data Scientists
In addition to technical abilities, data scientists also need strong soft skills that help them effectively collaborate across departments, communicate their work and impact, and tie their efforts and investment to business outcomes.
9. Communications
Effective communication skills refer to the ability to convey information and insights clearly and accurately. In the data science field, effective communication skills are crucial because it allows data scientists to explain complex data-driven narratives and create compelling visualizations in a way that is accessible and impactful to the non-scientific audience.
10. Business Acumen
Business acumen refers to the understanding of how businesses operate and generate value. Understanding business acumen allows data scientists to identify and prioritize business problems that can be addressed through data analysis, and form contextual understanding that enhances the relevance and applicability of the insights for making informed business decisions.
11. Problem-Solving
Problem-solving skills refer to the ability to analyze complex problems, identify potential solutions, and implement effective strategies to resolve them. Having strong problem-solving skills help data scientists to identify and resolve issues and troubleshoot problems in data pipelines or analysis workflows. Data scientists use various tools to interpret data and to uncover relevant insights that can guide decision-making processes.
Extracting Data Science Skills using the ESCO Classification
Unlike typical skills databases, skills ontology like ESCO Classification facilitates a flexible and ever-evolving repository of skills, establishing meaningful connections among skills, even in disparate domains. Using Visier Skills Intelligence Engine APIs, skills related to data scientists can be extracted with the most current information.
Skills | Explanation |
Analytical skills | Ability to analyze and research a problem or a topic, to decompose it in smaller pieces and develop an in-depth understanding about it. |
Query languages | The field of standardized computer languages for retrieval of information from a database and of documents containing the needed information. |
Statistics | The study of statistical theory, methods and practices such as collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments in order to forecast and plan work-related activities. |
Communicate with a non-scientific audience | Communicate about scientific findings to a non-scientific audience, including the general public. Tailor the communication of scientific concepts, debates, findings to the audience, using a variety of methods for different target groups, including visual presentations. |
Decision-making and judgment | Considering the relative costs and benefits of potential actions in order to choose the most appropriate one. |
Source: Part of skills extracted via Skills Intelligence Engine APIs on Alpine Developer Platform
Becoming a data scientist requires a combination of core technical skills and critical soft skills. Skill intelligence data provides insights to take more informed actions to proactively close skills gaps and increase the transparency for skills capabilities for any role. With Visier Skills Intelligence APIs, accessed via Alpine developer platform, skills intelligence and capabilities data become more accessible so they can be used in the most impactful areas.
