Imagine the modern world as a giant jigsaw puzzle. In this ever-evolving, data-driven landscape, data scientists are skilled puzzle solvers who can piece together valuable insights hidden within massive data sets.
These professionals are in high demand and play a vital role in shaping the future. If you're an aspiring data scientist or looking to advance in the field, it's essential to master certain skills to excel in this competitive environment.
As Clive Humby, Mathematician and Marketeer, astutely observed:
“Data is the new oil."
In this comprehensive guide, we'll explore the ten must-have skills every data scientist needs, combining both technical and non-technical aspects.
1. The Art of Storytelling with Data
As a data scientist, your job is not only to analyze data but also to communicate your findings effectively. Transforming complex data into compelling stories is an invaluable skill that helps you connect with your audience, whether they are fellow data professionals or business stakeholders.
By harnessing the power of storytelling, you'll bring data to life, making it more engaging, relatable, and actionable.
As the legendary detective Sherlock Holmes once cautioned, "It is a capital mistake to theorize before one has data."
To develop your storytelling skills:
● Practice transforming data into visualizations that effectively convey your message
● Explore different chart types (lbar graphs, line charts, pie charts, etc.) to understand which best suits your data.
● Use tools like Tableau, Power BI, or Python libraries such as Matplotlib and Seaborn.
● Participate in workshops or online courses that focus on storytelling with data
● Seek feedback from colleagues or mentors to refine your approach.
2. The Eagle-Eyed Critical Thinker
Data scientists need a keen eye for detail and the ability to objectively analyze questions, hypotheses, and results. Charles Babbage, English Mathematician, wisely pointed out that:
"Errors using inadequate data are much less than those using no data at all."
Critical thinking enables you to look at problems from different perspectives, understand the significance of data sources, and remain curious in your pursuit of insights.
To sharpen your critical thinking skills:
● Engage in activities that challenge your analytical abilities
● Solve puzzles, participate in debates.
● Join a discussion group to expose yourself to different viewpoints.
● Read articles or books on critical thinking, (such as "Thinking, Fast and Slow" by Daniel Kahneman).
● Practice applying those concepts to real-world scenarios.
● Evaluate your own thought processes and be open to feedback to help you grow as a critical thinker.
3. The Proactive Problem Solver
Problem-solving is at the core of data science. A proactive problem solver can identify hidden issues, question existing assumptions, and determine the most effective methods to find answers.
British Statistician George E. P. Box famously quipped:
"All models are wrong, but some are useful."
As a data scientist, you'll be expected to not only solve problems but also identify opportunities for improvement and drive innovative solutions.
Develop your problem-solving skills by working on diverse data science projects, particularly those that address unique business challenges:
● Participate in hackathons or online competitions like Kaggle to tackle real-world problems with your skills.
● Learn from your peers and mentors, and explore different problem-solving techniques like the 5 Whys, root cause analysis, or brainstorming.
● Stay up to date with industry trends to identify emerging problems and explore potential solutions.
4. The Curious Explorer
Curiosity may have killed the cat, but it certainly makes for a successful data scientist. A healthy dose of intellectual curiosity fuels your drive to dig deeper, ask questions, and uncover the hidden treasures within data sets. Embrace your inner explorer and never settle for surface-level insights. The more questions you ask, the more value you'll bring to your organization.
To nurture your curiosity:
● Develop a habit of asking questions and seeking answers.
● Read about diverse subjects, attend workshops, and network with professionals from various backgrounds.
● Engage in online communities like Reddit or Stack Overflow, where you can ask questions, share knowledge, and learn from others.
● Make time for personal exploration and reflection, and don't be afraid to dive deep into areas that pique your interest.
5. The Business-Savvy Data Maestro
Data science isn't just about crunching numbers; it's about understanding the business context and addressing specific challenges. A data scientist with a strong business sense can identify organizational problems, translate data into actionable insights, and drive success by aligning their work with the company's goals.
As Jim L. Barksdale, American Executive, put it with a touch of humor:
"If we have data, let’s look at data. If all we have are opinions, let’s go with mine."
To hone your business acumen:
● Take business courses or pursue an MBA.
● Network with professionals from various industries and attend conferences or seminars to gain exposure to different business models and challenges.
● Learn about business strategy, finance, marketing, and operations to understand the various factors that drive organizational success.
● Collaborate with colleagues from diverse departments to better understand their perspectives and objectives.
6. The Data Preparation Wizard
A key part of a data scientist's role is preparing data for analysis. Mastering the art of data preparation, including sourcing, processing, and modeling data, ensures that your analyses are accurate and reliable. Familiarize yourself with data prep tools and best practices to become a true data preparation wizard.
As W. Edwards Deming, American Statistician, once said with conviction:
"In God we trust; all others must bring data."
To master data preparation:
● Familiarize yourself with data cleaning and preprocessing techniques.
● Learn to use tools like Excel, Python libraries (Pandas, NumPy), or R for data manipulation.
● Take online courses or attend workshops to learn about data wrangling, data cleaning, and feature engineering.
● Practice with real-world data sets, and work on projects that require you to prepare data from diverse sources and formats.
7. The Self-Service Analytics Whiz
Being able to leverage self-service analytics platforms allows you to effectively communicate your findings and empower others to explore data independently. Develop your skills in using data visualization tools and creating interactive dashboards, making it easier for stakeholders to understand and act upon your insights.
As Hans Rosling, Swedish Statistician, passionately explained:
"Having the data is not enough. I have to show it in ways people both enjoy and understand."
To excel in self-service analytics:
● Learn to use data visualization and dashboarding tools like Tableau, Power BI, or QlikView.
● Enroll in online courses or attend workshops to improve your skills in creating visually appealing and informative dashboards.
● Share your work with colleagues or mentors and solicit feedback to continuously improve.
● Experiment with different visualization techniques to discover the most effective ways to present your findings.
8. The Code Whisperer
A successful data scientist needs to be proficient in writing efficient and maintainable code. Familiarize yourself with programming languages such as Python, R, or SQL, which are commonly used in the data science field.
By mastering the art of coding, you'll be able to create algorithms, parse data, and interact with APIs, enhancing your overall effectiveness in data analysis and visualization.
In the words of British Computer Scientist Tim Berners-Lee:
"Data is a precious thing and will last longer than the systems themselves."
To become proficient in coding:
● Choose a programming language like Python, R, or SQL, and take online courses or attend coding boot camps to learn the fundamentals.
● Practice regularly by working on personal projects or contributing to open-source initiatives. Engage in coding communities like GitHub, Stack Overflow, or LeetCode, where you can learn from others, share your work, and receive feedback.
● Study best practices for code organization, documentation, and version control.
9. The Math and Statistics Guru
A strong foundation in math and statistics is essential for any data scientist. These skills enable you to perform exploratory data analysis, identify patterns and relationships, and apply rigorous statistical thinking to extract meaningful insights from data. Understanding the strengths and limitations of various statistical models and tests is critical in designing novel solutions and selecting the right tools for the job.
British Economist Ronald Coase once cleverly remarked:
"If you torture the data long enough, it will confess."
To strengthen your math and statistics knowledge:
● Enroll in college courses or online classes focused on calculus, linear algebra, probability, and statistical modeling.
● Read foundational books on statistics, such as The Art of Statistics by David Spiegelhalter or An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
● Practice applying these concepts to real-world data sets by working on data science projects that involve statistical analysis.
● Participate in online forums, like Cross Validated, to discuss and learn from the experiences of other statisticians and data scientists.
10. The Machine Learning and AI Enthusiast
Machine learning and artificial intelligence (AI) are powerful tools that can enhance your work as a data scientist. By understanding when and how to use these technologies, you can train and deploy models that deliver productive AI solutions. Additionally, being able to explain these models and predictions in business-relevant terms will help you maximize their impact and drive organizational success.
As Andrew Ng, AI and Machine Learning Expert, stated with a visionary mindset:
"Artificial intelligence is the new electricity."
To delve into machine learning and AI:
● Take online courses or attending workshops that cover the basics of these fields. Some popular resources include Coursera's "Machine Learning" course by Andrew Ng, and "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
● Familiarize yourself with machine learning libraries like TensorFlow, Keras, or PyTorch for building and training models.
● Work on diverse projects that involve supervised and unsupervised learning, reinforcement learning, or natural language processing.
● Stay updated on the latest advancements in AI by following research papers, attending conferences, or joining AI-focused communities.
As a data scientist, you have the power to unlock valuable insights hidden within mountains of data. By mastering these ten essential skills, you'll be well-equipped to tackle the challenges and opportunities that the data science world has to offer.
A data science bootcamp at Thinkful can prepare you for a variety of data science jobs, like data scientist, data analytics manager, machine learning engineer, data architect and database engineer.