Statistics reveals that nearly 328.77 million terabytes of data are generated every day. This vast amount of data contains valuable insights that can help governments, businesses and organisations predict future trends, understand target audiences and make data-driven decisions.
So, they hire data gurus, AKA data scientists, to interpret complex data, identify valuable information, and share it in an understandable way with a lay audience.
Employers and recruiters look for a combination of technical and interpersonal skills in data science candidates to choose the one that best suits their needs. Whether you are an aspiring or an expert data scientist, you should have these skills and the ability to evolve with the changing industry demands.
This article reveals the top 15 data scientist skills sought after by hirers in potential candidates. It also explains how you can develop them to excel in your career and more.
Technical skills for data scientists
Programming languages
Data scientists require proficiency in various programming languages to manipulate and analyse data effectively. While thousands of programming languages exist, python, R, SQL, Scala, and Java are considered the most popular among data scientists.
You should have a strong command of these languages, so you can use them to extract meaningful insights from data and make impactful decisions.
Big data
The explosion of data demands the use of big data to gather, store, process, and analyse valuable insights from diverse datasets. Data scientists should have a mastery of big data analytics.
They should have an understanding of machine learning algorithms, data mining techniques, and statistical methods. It will allow them to uncover patterns, trends, and correlations within large, complex datasets.
Statistical analysis
Data science is about using algorithms and statistical methods to understand data better. Data scientists apply their understanding of statistics and probability to predict future data behaviours and advise their company’s leaders with this knowledge.
They are skilled in spotting trends, forecasting outcomes, identifying unusual data patterns, linking data points, and filling in gaps in datasets.
Machine learning (ML) and artificial intelligence (AI)
In today’s data science field, understanding AI and ML is highly important. ML, a subset of AI, involves crafting algorithms capable of learning tasks autonomously without direct coding.
Skilled data scientists employ various methodologies, such as supervised and unsupervised learning, alongside tools to analyse vast datasets and devise adaptive solutions.
Data collection, transformation and loading
Data collection, transformation, and loading are essential processes in data management. They involve gathering data from various sources, converting it into a format suitable for analysis, and then storing it in a database or data warehouse.
This skill is crucial for ensuring data integrity and accessibility, enabling analysts to gather insights and make informed decisions efficiently. Mastery of these processes is fundamental for effective data analysis.
Data structures and algorithms
For data scientists, proficiency in data structures and algorithms is essential for analysing and interpreting complex datasets.
Such knowledge enables them to design effective and scalable solutions, which is crucial for managing large volumes of data, optimising machine learning models, and solving intricate problems in data analysis and predictive modelling.
Data wrangling
Data wrangling involves the refinement of raw data by cleaning, transforming null values, and removing anomalies to prepare it for analysis across various platforms.
Mastery of data wrangling enables data scientists to integrate different data forms, facilitating the transfer of large datasets to analytical tools for trend detection.
Data visualisation
Data scientists need to translate complex data into an understandable format. It helps non-tech people like shareholders and marketers make decisions easily.
To do this, they use colourful and clear graphs, charts, maps, and other visualisation tools. They understand each tool’s purpose and know when to use one. Their proficiency extends to various tools, such as ggplot, Qlik Sense, Power BI, and Tableau, which create clear and impactful visual representations of data.
Database management
For data scientists, expertise in managing, preserving, and accessing large volumes of data is essential. They are familiar with fundamental database management principles that allow them to retrieval information from organisational databases.
As data scientists have to go through diverse databases across different companies, knowledge of various database tools and systems becomes crucial for effective data storage, modification, and analysis.
Website scraping
Website scraping is a valuable skill for data scientists. It enables the extraction of data from web pages. This involves accessing websites and collecting specific information, often for gathering large datasets from publicly available sources.
Proficiency in web scraping tools and techniques allows data scientists to automate data collection. This skill is crucial for compiling datasets not readily available in structured formats, facilitating comprehensive data analysis, trend identification, and informed decision-making.
Mathematics skills
Data scientists use advanced mathematics to construct machine-learning models and handle complex problems. Essential mathematics topics for a strong foundation in data science include linear algebra, calculus, probability, regression and vector models.
Mastery of these subjects is crucial for applying algorithms successfully in real-world scenarios.
Model building and deployment
This skill involves the development and implementation of predictive models that turn data into actionable insights.
It involves selecting the appropriate algorithms, training models on datasets, and evaluating their performance. Once optimised, these models are deployed into production environments where they can automate decision-making processes.
This skill ensures that data scientists can create reliable, scalable models that effectively address challenges and add value to their organisation.
Soft skills for data scientists
Business acumen
Understanding the business context is crucial for data scientists. This skill involves grasping the industry’s dynamics, goals, and challenges. It allows them to align their analyses and models with strategic business objectives.
It is about seeing the bigger picture and ensuring data-driven solutions are relevant and impactful.
Communication
The ability to communicate complex data insights in understandable terms to non-technical stakeholders is vital.
Good communication helps connect the world of data science with the planning side of business. This makes it easier for companies to make smart choices and helps everyone in the company get better at understanding and using data.
Critical thinking
Data scientists must evaluate assumptions, analyse data sources critically, and challenge conclusions to ensure accuracy and reliability. This skill helps them to uncover true insights and drive objective and evidence-based conclusions.
Problem solving
Data scientists face various challenges every day. They can tackle them efficiently if they have good problem-solving skills.
This involves breaking down problems into smaller, manageable parts and applying analytical approaches to solve them. It often requires creativity and a deep understanding of data and algorithms.
Intellectual curiosity
A keen interest in exploring new questions, learning new techniques, and understanding emerging trends is essential for data scientists.
This continuous pursuit of knowledge keeps them at the forefront of technological advances and analytical methods, enabling them to apply cutting-edge solutions to data challenges.
Teamwork
Collaborating effectively with other professionals, including engineers, business leaders, and product managers, is key. Data scientists must work within teams to integrate insights and models into operational processes.
How to develop data scientist skills?
1. Academic foundation
Begin with a solid foundation in mathematics, statistics, computer science, or a related field through formal education. Getting a bachelor’s or master’s degree can really help you understand the tough concepts of data science.
2. Technical proficiency
Gain proficiency in programming languages such as Python or R, which are essential for data manipulation, analysis, and machine learning. Familiarise yourself with databases and SQL for data retrieval, and dive into machine learning frameworks and libraries to build and deploy models.
3. Practical application
Apply what you have learned through projects. Start with simple data sets to practise cleaning, analysing, and visualising data. Gradually progress to more complex problems. Projects not only support learning but also build a portfolio that can showcase your skills to potential employers.
4. Continuous learning
The field of data science is rapidly evolving, so staying informed about the latest technologies, algorithms, and best practices is crucial.
Online courses, workshops, and webinars can provide essential education. Participate in data science competitions on platforms like Kaggle to challenge yourself and learn from the community.
5. Soft skills development
Focus on developing soft skills alongside technical abilities. Practice presenting your findings to both technical and non-technical audiences to enhance your communication skills.
Work on projects as part of a team to build collaboration skills. Cultivate intellectual curiosity by exploring new data sets and problems, and refine your critical thinking by questioning assumptions and testing hypotheses.
6. Networking and mentorship
Engage with the data science community through meetups, conferences, and online forums. Networking can provide insights into industry trends and job opportunities. Seek mentorship from experienced data scientists to gain guidance and feedback on your career path.
How to showcase your data science skills at the workplace?
Showcasing your data science skills at the workplace involves:
- Lead data-driven projects: Initiate projects that use your data science skills to address business objectives.
- Present insights effectively: Translate complex data findings into actionable insights for non-technical audiences.
- Contribute to team discussions: Offer data-backed solutions and fresh perspectives in team meetings.
- Stay updated: Continuously learn new tools and techniques to remain updated.
- Demonstrate technical abilities: Apply your expertise in data analysis, machine learning, and predictive modelling in your daily work.
How to highlight data scientist skills on your resume?
Highlighting data scientist skills on your resume involves showcasing both your technical and non-technical skills in a way that aligns with the job you are applying for.
Begin by listing key technical skills, such as proficiency in Python, R, SQL, machine learning, and data visualisation tools, under a dedicated ‘Skills’ section.
Incorporate specific projects and achievements that show your ability to apply these skills in real-world scenarios. Do not forget to include experiences that indicate your problem-solving capabilities, teamwork, and communication skills.
Tailor your resume to highlight the skills most relevant to the position you are applying for. You can do this by using keywords from the job description to ensure your resume resonates with hiring managers and passes through applicant tracking systems effectively.
Top data science tools you must learn
In the ever-evolving field of data science, learning basic tools is essential for any aspiring data scientist.
Python and R are the most popular programming languages, offering extensive libraries and frameworks for data analysis, visualisation, and machine learning, such as Pandas, NumPy, Scikit-learn, TensorFlow, and ggplot2.
SQL is necessary for data retrieval, manipulation, and management within databases. For big data processing, Apache Hadoop and Apache Spark provide powerful platforms for handling vast datasets efficiently.
Tableau and Power BI stand out for data visualisation, enabling the creation of insightful and interactive dashboards. GitHub serves as a vital tool for version control, collaboration, and showcasing projects.
Learning these tools makes data scientists able to extract insights from data, build predictive models, and effectively communicate results.
Data science ethics and responsible practice
- Transparent data handling: Maintain transparency in data collection, processing, and usage, ensuring stakeholders understand how their data is being utilised.
- Privacy protection: Safeguard individuals’ privacy by anonymising sensitive data and adhering to privacy regulations like GDPR and HIPAA.
- Fairness and bias mitigation: Implement measures to minimise bias in algorithms and models, ensuring fair treatment across various demographic groups.
- Accountability: Take responsibility for the consequences of data-driven decisions and try to correct any unintentional negative impacts.
- Continuous education: Stay informed about emerging ethical issues and best practices in data science ethics.
- Stakeholder engagement: Involve stakeholders in decision-making processes, seeking input and feedback to ensure ethical considerations are addressed.
- Ethics committees: Establish ethics committees within organisations to evaluate the ethical implications of data science projects and provide guidance on responsible practices.
Summing it up
The role of a data scientist requires a multifaceted skill set including technical expertise, soft skills, and ethical considerations.
From proficiency in programming languages and statistical methods to effective communication and critical thinking abilities, data scientists play a crucial role in extracting actionable insights from complex datasets.
With their diverse skill set, data scientists can drive innovation, solve complex problems, and make data-driven decisions that positively impact organisations and society as a whole.
