What Does a Data Scientist Do?

Data is everywhere. It is generated by humans, machines, sensors, and devices every second of every day. It is stored in databases, cloud platforms, and distributed systems. It is processed, analyzed, and transformed into information, knowledge, and insights. It is used to make decisions, predictions, and recommendations. It is the fuel that drives innovation, growth, and development in various domains and industries.

But how can we harness the full potential of data? How can we extract meaningful and actionable insights from massive and complex data sets? How can we create intelligent systems that can learn from data and perform tasks that require human-like reasoning and understanding? This is where data scientists come in.

What is a Data Scientist?

A data scientist is a data expert that extrapolates insights from large data sets to help organizations solve complex problems. To do so, data scientists combine computer science, mathematics, statistics, and modeling with a strong understanding of their business and industry to unlock new opportunities and strategies.

Data scientists are often expected to form their own questions about the data, while data analysts might support teams that already have set goals in mind. A data scientist might also spend more time developing models, using machine learning, or incorporating advanced programming to find and analyze data. Many data scientists can begin their careers as data analysts or statisticians.

Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share meaningful insights with peers.

Data scientists use various tools and techniques, such as programming languages, databases, frameworks, libraries, and algorithms, to develop and deploy their data solutions. They also use data visualization tools, such as charts, graphs, and dashboards, to communicate their findings and recommendations to the audience.

Data scientists also need to have skills in communication, collaboration, and storytelling, as they often work in cross-functional teams and need to explain complex concepts and results to non-technical stakeholders. Data scientists also need to have a curious and creative mind, a problem-solving attitude, and a passion for learning and innovation.

 What Does a Data Scientist Do on a Daily Basis?

A data scientist's daily tasks can vary depending on the project, the domain, and the organization they work for. However, some common tasks that a data scientist might do on a daily basis are:

- Find patterns and trends in data sets to uncover insights

- Create algorithms and data models to forecast outcomes

- Use machine learning techniques to improve the quality of data or product offerings

- Communicate recommendations to other teams and senior staff

- Deploy data tools such as Python, R, SAS, or SQL in data analysis

- Stay on top of innovations in the data science field

Here are some examples of how data scientists

apply their skills and knowledge to different domains and industries:

- Healthcare: Data scientists can help diagnose diseases, recommend treatments, monitor patients, discover new drugs, and personalize medicine. For example, IBM Watson is an AI system that can analyze medical data and provide evidence-based recommendations to doctors and patients¹.

- Education: Data scientists can help personalize learning, assess students, provide feedback, tutor students, and create adaptive and interactive learning environments. For example, Knewton is an adaptive learning platform that uses data and AI to customize the learning experience for each student.

- Business: Data scientists can help optimize operations, enhance customer service, increase sales, reduce costs, and generate insights. For example, Amazon uses data and AI to recommend products, deliver goods, and run its cloud services.

- Finance: Data scientists can help detect fraud, manage risk, automate trading, provide financial advice, and improve financial inclusion. For example, PayPal uses data and AI to prevent fraud and protect its customers.

- Entertainment: Data scientists can help create content, generate music, produce movies, and recommend media. For example, Netflix uses data and AI to recommend movies and shows to its users based on their preferences and behavior.

- Social Good: Data scientists can help address global challenges, such as poverty, hunger, climate change, health, and education. For example, the AI for Good Foundation is a non-profit organization that uses data and AI to support social good initiatives around the world.

How to Become a Data Scientist?

Becoming a data scientist generally requires some formal training and education. Here are some steps to consider:

- Earn a data science degree. Employers generally like to see some academic credentials to ensure you have the know-how to tackle a data science job, though it’s not always required. That said, a related bachelor’s degree can certainly help—try studying data science, statistics, or computer science to get a leg up in the field. You can also pursue a master's or a doctoral degree in data science or a related field to gain more advanced skills and knowledge.

- Learn data science skills. Data science is a broad and interdisciplinary field that requires a combination of technical and soft skills. Some of the most important skills for data scientists are:

  - Programming: Data scientists need to be proficient in at least one programming language, such as Python, R, Java, or C++, to write code, manipulate data, and implement algorithms. They also need to be familiar with data structures, algorithms, and object-oriented programming concepts.

  - Statistics: Data scientists need to have a solid foundation in statistics, such as descriptive statistics, inferential statistics, hypothesis testing, and probability theory, to analyze data and draw conclusions. They also need to know how to use statistical tools and software, such as Excel, SPSS, or SAS, to perform calculations and tests.

  - Machine Learning: Data scientists need to have a good understanding of machine learning, which is a subset of AI that enables systems to learn from data and improve their performance. They need to know how to use machine learning techniques, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning, to build predictive models and discover patterns from data. They also need to know how to use machine learning frameworks and libraries, such as TensorFlow, PyTorch, Scikit-learn, or Keras, to implement machine learning algorithms.

  - Data Visualization: Data scientists need to have a good sense of data visualization, which is the art and science of presenting data in a clear and engaging way. They need to know how to use data visualization tools and software, such as Tableau, Power BI, or Matplotlib, to create charts, graphs, and dashboards that communicate their findings and recommendations to the audience.

  - Communication: Data scientists need to have strong communication skills, both written and verbal, to explain complex concepts and results to non-technical stakeholders. They need to know how to use storytelling techniques, such as narratives, metaphors, and analogies, to make their data stories more compelling and persuasive. They also need to know how to use presentation tools and software, such as PowerPoint, Keynote, or Prezi, to deliver effective and engaging presentations.

  - Collaboration: Data scientists need to have good collaboration skills, as they often work in cross-functional teams and need to coordinate with other data professionals, such as data engineers, data analysts, and data architects, as well as business stakeholders, such as managers, clients, and users. They need to know how to use collaboration tools and software, such as Slack, Zoom, or GitHub, to communicate, share, and manage their work.

- Build a data science portfolio. One of the best ways to showcase your data science skills and knowledge is to create a data science portfolio that demonstrates your projects and achievements. A data science portfolio can include:

  - Data science projects: Data science projects are the core of your portfolio, as they show your ability to apply data science skills and techniques to real-world problems and data sets. You can use various sources, such as Kaggle, UCI, or Google Dataset Search, to find interesting and relevant data sets to work on. You can also use various platforms, such as Jupyter Notebook, Google Colab, or AWS, to develop and deploy your data science solutions. You can also use various formats, such as blog posts, articles, or videos, to document and present your data science projects.

  - Data science certifications: Data science certifications are another way to validate your data science skills and knowledge, as they show that you have completed a course or a program that covers a specific topic or area of data science. You can use various platforms, such as Coursera, edX, Udemy, or Udacity, to find and enroll in data science courses and certifications that suit your interests and goals. You can also use various badges, logos, or links, to display your data science certifications on your portfolio.

  - Data science resume: Data science resume is a summary of your data science education, experience, skills, and achievements, that highlights your value and fit for a data science job. You can use various templates, formats, and tips, to create a professional and effective data science resume that showcases your data science portfolio.

- Apply for data science jobs. Once you have a data science degree, skills, and portfolio, you are ready to apply for data science jobs that match your qualifications and interests. You can use various sources, such as LinkedIn, Indeed, or Glassdoor, to find and apply for data science jobs that suit your preferences and goals. You can also use various strategies, such as networking, referrals, or cold emails, to increase your chances of getting noticed and hired by data science employers.

Conclusion

Data science is a fascinating and rewarding field that can help us unlock the power of data and create intelligent and impactful solutions for various domains.