Data science mentoring is my passion. I love helping data professionals step out of their comfort zones and achieve career growth.
Recently, I had the opportunity to host a Gathers meetup called “Data Science Mentorship: A Win-Win Meetup.” At the meetup, I shared my thoughts on the benefits of data science mentoring and answered questions from the audience.
This blog post is a summary of the questions and answers from the meetup. I hope this information is helpful to you, whether you are a mentor or a mentee.
Benefits of data science mentoring
- Mentors: Mentoring can help you to develop your leadership skills, give back to the community, and learn new things from your mentees.
- Mentees: Mentoring can help you to learn new skills, advance your career, and build relationships with experienced professionals.
Tips for mentors:
- Be supportive and encouraging. Your mentee needs to know that you believe in them and that you are there to help them succeed.
- Provide guidance and feedback. Help your mentee to set goals, develop a plan, and identify resources.
- Be a role model. Share your experiences and insights with your mentee.
Tips for mentees:
- Be proactive. Don’t be afraid to ask for help and advice.
- Be open to feedback. Be willing to learn from your mistakes and grow.
- Be respectful of your mentor’s time and expertise.
Ready to jump right in and uncover answers to some of the burning questions in the world of data science mentorship?
1. What is Data Science?
Data science is a versatile field that equips data professionals with the tools to tackle complex problems and make informed decisions by applying mathematical and statistical concepts in a systematic and reproducible manner.
Another way of explaining this is how I explain it to my kids:
Data science is like playing a special game of hide and seek with your teddy bear. Imagine you really, really love your teddy bear, but you can’t remember where you left it in your room. You want to find it so you can hug it and feel happy again.
So, you ask someone to help you, like a magic friend. This magic friend uses their superpowers to figure out where your teddy bear might be hiding. They look around your room, and when they get closer to the teddy bear, they say, ‘You’re getting warmer!’ But if they go in the wrong direction, they say, ‘You’re getting colder!’
Data scientists are like those magic friends. They help grown-ups with important stuff, like making sure cars don’t break down unexpectedly, deciding who can borrow money from a bank, and figuring out who might stop using a favorite game. They use their special skills to solve big problems and make the world a better place, just like how you want to find your teddy bear to make yourself happy again.
For a more formal and concise definition of Data Science that you can use during an interview, consider the following:
Data Science is the systematic application of scientific methods, algorithms, and data processing systems to extract knowledge and insights from diverse forms of data, encompassing both structured and unstructured sources.
2 .Where to start?
Where to start in your Data Science journey depends on your current background. If you have experience in data-related fields like data analysis, software development, or software engineering, you already have a solid foundation. However, for beginners, the first steps often involve gaining a grasp of fundamental concepts in statistics and algebra.
Here are some resources to help you get started:
- MIT OpenCourseWare: Statistics for Applications
- MIT OpenCourseWare: A 2020 Vision of Linear Algebra
- Data Science Roadmap
3. Which field should I master in?
Data scientists who are versatile and adaptable are the most successful. This means being able to quickly understand any business and learn new technologies.
Here are some tips for becoming a versatile data scientist:
- Learn how to learn. Data science is a constantly evolving field, so it is important to be able to learn new things quickly. This includes learning new programming languages, new machine learning algorithms, and new data science tools and technologies.
- Start with Python. Python is a popular programming language for data science because it is easy to learn and has a wide range of libraries and tools available. However, be open to learning other programming languages as well, such as Java, R, and Scala.
- Learn programming languages for general purposes, not just for data science. This will make you more versatile and adaptable. For example, learning Java will make it easier for you to work with big data technologies, and learning R will make it easier for you to work with statistical analysis tools.
- Learn clean coding practices. Clean coding is important for all software development, but it is especially important for data science because data science code is often complex and needs to be easily understood and maintained by others. This is a good article to read on Clean Coding.
- Learn modularity and design patterns. Modularity and design patterns are important for writing maintainable and reusable code.
- Stay up-to-date with the latest trends and technologies. The field of data science is constantly evolving, so it is important to stay up-to-date with the latest trends and technologies. Read industry publications and blogs, attend conferences and workshops, and take online courses.
- Initially discover the business you’re trying to help with ML / AI. Take the time to understand the business and the problem you are trying to solve. This will help you to develop effective machine learning solutions.
- Spend time in the business understanding phase and interview your stakeholder to unlock insights about the problem you need to solve. This will help you to develop a better understanding of the business and the needs of the stakeholders.
By following these tips, you can become a versatile and adaptable data scientist who is well-prepared for success in a variety of roles.
Here are some additional tips for choosing a field to specialize in:
- Consider your interests and skills. What are you passionate about? What are you good at? Choose a field that aligns with your interests and skills.
- Consider the job market. What fields are in high demand? What fields are growing rapidly? Choose a field with a strong job market.
- Consider your career goals. What do you want to achieve in your career? Choose a field that will help you to achieve your career goals.
4. How much time does it take to become a Junior Data Scientist?
The journey to becoming a Junior Data Scientist varies from person to person and depends on several factors. These factors include your prior education and experience, your learning style, your level of motivation, the quality of your mentoring and support, and the time you can dedicate to your studies.
That said, many individuals can attain the role of Junior Data Scientist in 12 months or less with focused effort. To expedite your progress, consider these steps:
- Begin by mastering the fundamentals of statistics, programming, and data science concepts. Numerous online resources, both free and paid, are available for this purpose.
- Prioritize the acquisition of key skills, such as proficiency in Python, SQL, and machine learning techniques.
- Gain practical experience by working on real-world projects, participating in hackathons, and contributing to open source projects.
- Engage with the data science community, both online and offline, to learn from others, seek advice, and receive feedback on your work.
If you are committed to pursuing a career in data science, set clear goals and start your journey today. With dedication and persistence, you can achieve the status of a Junior Data Scientist within a year.
5. What skills to learn?
Data science encompasses a diverse set of skills, including:
- Programming: Proficiency in languages like Python or R.
- Mathematics and Statistics: A solid foundation in mathematical concepts and statistical analysis.
- Machine Learning: Understanding and practical application of machine learning algorithms.
- SQL and Databases: Proficiency in working with databases and writing SQL queries.
- Data Wrangling: The ability to clean and prepare data for analysis.
- Data Visualization: Creating compelling and informative visualizations.
- Cloud Computing: Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Big Data Technologies: Knowledge of big data tools like Hadoop and Spark.
- Domain Knowledge: Understanding the industry or field being analyzed.
- Communication and Collaboration: Effective communication and teamwork.
- Problem-Solving: Critical thinking and adaptability.
- Leadership: Motivation, decision-making, and leadership qualities.
6. How to choose projects?
Effectively selecting data science projects is a key aspect of your learning journey. Here’s a structured approach:
- Define a Problem: Start by defining a problem statement or question you want to address. This ensures that your project has a clear goal and purpose.
- Supervised Learning Emphasis: Given that many real-world data science tasks involve supervised learning, prioritize mastering various algorithms in this category, encompassing both regression and classification.
- Dataset Selection: When choosing a dataset, consider the following:
- Data Freshness: Opt for datasets that are regularly updated (at least monthly). This practice allows you to gain experience in data monitoring and feedback loops.
- Domain Alignment: If you have expertise or a keen interest in a particular industry or field, select datasets that align with that domain. This enables you to leverage your domain knowledge effectively.
- Self-Reliance: Acknowledge that during your learning phase, you may often work independently. Therefore, choose projects that match your current skill level and align with your learning objectives.
7. How advanced should portfolio projects be?
I didn’t build a personal Data Science portfolio because I gained my Data Science experience through real-world applications across different roles before formally specializing. This hands-on approach allowed me to learn with actual data and address genuine business challenges, albeit over several years. In my view, a Data Scientist’s portfolio doesn’t have to be excessively advanced. Instead, it should closely mirror real-world experiences. Start by tackling a genuine problem, work with unclean datasets, and apply supervised machine learning techniques to make predictions on new data. The ability to demonstrate this practical experience is more valuable than showcasing overly intricate projects.
Here are some examples of projects that you could include in your portfolio:
- Build a machine learning model to predict customer churn.
- Develop a data pipeline to process and analyze social media data.
- Create a dashboard to visualize and track key business metrics.
- Build a recommendation system to suggest products to customers.
- Develop a fraud detection system to identify fraudulent transactions.
You could also include projects that you have worked on at work, as long as you have permission from your employer to do so.
No matter what projects you choose to include in your portfolio, make sure that they are well-documented and that you can explain them clearly and concisely.
8. How should I organize my CV?
The best way to organize your CV will depend on your individual experience and the types of roles you are targeting. However, there are a few general tips that can help you to create a CV that is effective and easy to read:
- Start with a strong summary. Your summary should be a brief overview of your skills and experience, and it should highlight why you are a good fit for the role you are applying for. If you have little or no experience, you can use your summary to highlight your transferable skills and your eagerness to learn.
- List your skills and experience in reverse chronological order. This means starting with your most recent role and working your way back. Be sure to include the name of the company, your job title, and your dates of employment for each role.
- Use keywords throughout your CV. This will help your CV to be picked up by applicant tracking systems (ATS). When choosing keywords, think about the skills and experience that are required for the roles you are targeting.
- Tailor your CV to each job you apply for. This means highlighting the skills and experience that are most relevant to the role. You can also use your cover letter to explain why you are interested in the specific role and how your skills and experience make you a good fit.
Here is an example of a CV structure that you could use:
- Contact information
- Experience (listed in reverse chronological order)
- Awards and honors
- Publications (if applicable)
While hobbies are typically omitted from a CV, they can serve as conversation starters during interviews. It is important to keep your CV concise and to the point.
9. What skills should I acquire to be hired and to do the job?
While proficiency in mathematics, statistics, algorithms, and programming languages like Python or R is essential for a data scientist, here are some additional skills that can make you stand out in the field:
- Learning Agility: Cultivate the ability to continuously acquire new knowledge and adapt to emerging technologies and methodologies.
- Problem-Solving: Develop strong problem-solving skills to tackle diverse business challenges and derive data-driven solutions.
- Effective Communication: Master the art of communicating your findings, both to non-technical stakeholders and fellow data scientists, ensuring that your insights are easily understood and actionable.
- Technical Documentation: Learn to document your work thoroughly, facilitating collaboration and ensuring the reproducibility of your analyses.
- Version Control: Familiarize yourself with versioning practices for data, code, and models, which are crucial for maintaining data integrity and tracking changes.
- MLOps Proficiency: Understand MLOps principles, including the productionization of machine learning models, containerization, and CI/CD pipelines, to streamline the deployment of data-driven solutions. This is a a good article to read on MLOps.
- Value Creator Mindset: Develop the confidence to recognize opportunities for value creation and contribute effectively in various work settings.
- Collaboration skills: Data scientists often work on teams with other data scientists, engineers, and product managers. To be successful in this role, you need to be able to collaborate effectively with others and share your ideas.
By acquiring these skills, you not only increase your chances of getting hired but also enhance your ability to excel in data science roles.
Here are some additional tips for acquiring the skills you need to be a data scientist:
- Find a mentor. A mentor can provide you with guidance and support as you learn and develop your skills.
- Join a data science community. There are many online and offline data science communities where you can connect with other data scientists and learn from each other.
- Contribute to open source projects. Contributing to open source data science projects is a great way to gain experience and learn from other data scientists.
10. Recommendations for events and networking in general.
It can be overwhelming to choose from the many data science events and networking opportunities available. Here are a few tips:
- Consider your goals. What do you want to achieve by attending events and networking? Are you looking for a new job? Do you want to learn new skills? Do you want to connect with other data scientists in your industry? Once you know your goals, you can start to identify events that will help you achieve them.
- Choose events that are relevant to your interests and experience. If you’re interested in machine learning, for example, look for events that focus on machine learning topics. If you have experience in a particular industry, look for events that are focused on that industry.
- Look for events that offer opportunities to network. Some events have built-in networking opportunities, such as breakout sessions, social hours, and job fairs. Other events may not have formal networking activities, but you can still network by talking to other attendees during breaks and meals.
Here are some specific recommendations for data science events and networking opportunities:
- Company events: If you’re employed, attend events hosted by your company, such as hackathons, workshops, and social events. This is a great way to meet other data scientists at your company and learn more about their work.
- Industry conferences: There are many data science conferences held throughout the year. These conferences offer a variety of sessions and workshops on a variety of data science topics. They also provide opportunities to network with other data scientists from all over the world.
- Meetups: Meetups are smaller, more informal gatherings of data scientists. They are a great way to connect with other data scientists in your local area and learn about their work.
- Academic events: Many colleges and universities host events related to data science, such as guest lectures, workshops, and career fairs. These events are a great way to learn about the latest trends in data science and meet potential employers.
No matter what events you choose to attend, the most important thing is to be proactive and engage with other people. Don’t be afraid to introduce yourself and ask questions. The more people you meet, the more likely you are to find valuable connections.
Here are some additional tips for networking at events:
- Be prepared. Before you go to an event, take some time to think about what you want to say to people. Have a few talking points prepared and be able to articulate your skills and experience.
- Be genuine. People can tell when you’re being fake, so be yourself and be genuine in your interactions.
- Be respectful. Be respectful of people’s time and don’t monopolize conversations.
- Follow up. After you attend an event, follow up with the people you met. Send them a LinkedIn connection request or email them to introduce yourself again.
Networking takes time and effort, but it’s worth it in the long run. By attending events and networking with other data scientists, you can build relationships that can lead to new job opportunities, collaborations, and mentorships.
11 .How to gain visibility in the job market when Data Science jobs are scarce?
Data science jobs are not scarce, but the competition is high. As you can see from the Google Trends charts below, the search interest for “data scientist” and “data science jobs” has been steadily increasing over the past five years. This suggests that the demand for data scientists is growing, but there are also more people entering the field.
To gain visibility in the job market, you need to stand out from the competition. Here are a few tips:
- Have a strong online presence. Create a LinkedIn profile and website that showcases your skills and experience. Make sure to include your resume, portfolio, and blog posts (if you have any).
- Network with other data scientists. Attend industry events and meetups, and connect with people on LinkedIn. The more people you know, the more likely you are to hear about job openings.
- Contribute to open source projects. This is a great way to gain experience and demonstrate your skills to potential employers.
- Stay up-to-date on the latest trends and technologies. Read industry publications and blogs, and attend conferences and workshops. This will show potential employers that you are passionate about data science and that you are committed to your professional development.
In addition to the above tips, you should also tailor your resume and cover letter to each job you apply for. Be sure to highlight the skills and experience that are most relevant to the role.
Here are some additional tips for gaining visibility in the job market when data science jobs are scarce:
- Specialize in a particular industry or domain. This will make you more marketable to employers in that industry.
- Be willing to relocate. If the job market is tight in your area, be willing to relocate to another city or region where there are more data science jobs available.
- Be flexible with your salary expectations. If you are willing to start at a lower salary, you will be more attractive to employers.
12. How to approach people on LinkedIn for advice, referral, or networking if you don’t know them?
When approaching people on LinkedIn for advice, referral, or networking, it is important to be respectful of their time and to make your ask clear and concise. Here are a few tips:
- Personalize your message. Don’t send a generic message to everyone. Take the time to learn about the person you are contacting and what they do. Then, tailor your message to their interests and expertise.
- Be specific. What kind of advice or help are you looking for? Be as specific as possible in your request.
- Keep it short. Senior professionals are busy, so get to the point quickly.
- Offer something in return. What can you offer the person in return for their help? For example, you could offer to connect them with someone in your network, write a testimonial for their business, or promote their content on social media.
Here is an example of a message you could send:
I hope this message finds you well.
My name is [Your Name] and I am a [Your Job Title] at [Your Company]. I am reaching out to you today because I am very passionate about data science and I am eager to learn from your experience.
I have been following your work on LinkedIn for some time now and I am particularly interested in your expertise in [Area of Expertise]. I am currently working on a project related to [Project Topic] and I am having some difficulty with [Specific Difficulty].
I would be grateful if you could spare a few minutes to provide me with some advice. I am particularly interested in learning more about [Specific Topic].
Thank you for your time and consideration. I look forward to hearing from you soon.
- If you are asking for a referral, be sure to mention the specific job or company you are interested in. You should also highlight the skills and experience that make you a good fit for the role.
- If you are networking, be sure to introduce yourself and explain what you are looking for. You could also ask some questions about the person’s career and experience.
It is important to be patient and persistent when reaching out to people on LinkedIn. It may take some time to get a response, but it is worth it in the long run.
13. How to stand out from the competition?
In the competitive landscape of data science, setting yourself apart requires a holistic approach:
- Beyond Model Optimization: While model optimization is essential, aim to excel in the entire data science pipeline. Understand model deployment, prediction, and monitoring to ensure continued success.
- ML Lifecycle Mastery: Gain expertise in managing the complete machine learning lifecycle, including regular updates, retraining, and redevelopment. Prove your ability to keep models relevant over time.
- Effective Business Influence: Develop strong communication skills to convey the significance of data science, machine learning, and AI to business stakeholders. Show how these technologies can lead to immediate and tangible benefits.
- Passion for MLOps: Embrace the principles of MLOps enthusiastically. Promote the integration of data science into operational workflows, emphasizing its role in informed decision-making.
14. Why did you apply for the Data Science position in this business area/industry? Do you have related knowledge?
I found myself in this Data Science position in a unique way—it wasn’t a role I actively applied for. Instead, a recruiter reached out to me on LinkedIn, sparking my interest in the opportunity. Here’s why I was drawn to it:
- Scaling ML in Business: The role’s primary focus on scaling machine learning within a business context immediately caught my attention. It promised not only job satisfaction but also the chance to acquire new skills and knowledge, including aspects of software engineering and DevOps.
- Venturing into Manufacturing: Although I had no prior experience in manufacturing, I believed that Data Scientists, much like versatile problem solvers, could excel in any industry. This belief encouraged me to embrace the challenge and embark on a learning journey.
- Support and Validation: During the interview process, I candidly discussed my initial concerns about my lack of industry-specific knowledge. Fortunately, the conversation with the Product Owner reassured me. They emphasized the presence of a dedicated team of business analysts ready to provide essential support and bridge any knowledge gaps.
In the end, my decision to take on this Data Science role was driven by a blend of excitement for the role’s challenges and confidence in my adaptability.
15. How quickly can I start doing practical tasks/pet projects?
The timeline for embarking on practical tasks or pet projects in data science depends entirely on your commitment and enthusiasm. The good news is that you can initiate a project right from the outset.
- Day One Action: Don’t delay. On your very first day in data science, put your existing skills to use and launch a project that aligns with your interests. Even with limited knowledge, hands-on experience is invaluable.
- Progressive Learning: Recognize that learning in data science is often an incremental process. Embrace small achievements, learn from challenges, and continually build your skills. With dedication, your abilities will grow, and your projects will become more sophisticated.
- Community Resources: Tap into the supportive data science community, online tutorials, and mentors for guidance. These resources can offer valuable advice, ideas, and feedback to enhance your projects and accelerate your learning journey.
In essence, your journey in data science starts when you take the initiative to begin practical tasks and pet projects.
16. What should I prioritize to improve? Deep dive into theory, build a portfolio, or what else?
When it comes to enhancing your data science skills, the ideal approach is to prioritize both deep diving into theory and building a portfolio. These two aspects complement each other, forming a powerful combination that propels your growth in the field. Here’s a more detailed breakdown:
Deep Diving into Theory:
- Read Widely: Start by immersing yourself in books and articles related to data science and machine learning. There is a wealth of valuable resources available online and in libraries that can expand your understanding of the field.
- Online Learning: Take advantage of online courses and workshops dedicated to data science and machine learning. Many platforms offer both free and paid options, enabling you to explore a wide range of topics and gain practical knowledge.
- Coding Challenges: Engage in coding challenges and hackathons. These activities not only test your skills but also provide opportunities to learn from others and apply your theoretical knowledge in real-world scenarios.
- Open Source Contributions: Contribute to open-source data science projects. Collaborating with other data scientists on real-world projects allows you to learn from experienced peers, gain hands-on experience, and understand how theory translates into practical solutions.
Building a Portfolio:
- Start Small: Begin your portfolio journey with manageable, small-scale projects. Avoid the temptation to tackle complex endeavors right away. Starting small allows you to complete projects efficiently and build your confidence.
- Relevance Matters: Choose projects that align with your interests and career aspirations. Working on projects that genuinely captivate your interest not only keeps you motivated but also encourages you to explore new concepts and technologies.
- Documentation: Document your work meticulously. This involves writing clear code comments, creating informative README files, and sharing insights through project blogs or reports. Well-documented projects showcase your professionalism and make your work accessible to others.
- Sharing and Networking: Don’t keep your portfolio to yourself. Share your completed projects on platforms like GitHub, LinkedIn, and other relevant social media platforms. Additionally, consider attending meetups, conferences, or networking events to present your work to fellow data scientists and potential employers.
17. How can I get a job?
Data science jobs are in high demand, but they can be competitive, especially for people with little experience. Companies are looking for data scientists who can create immediate value, so they may be hesitant to invest in training someone new.
However, there are a number of things you can do to increase your chances of getting a data science job, even if you have limited experience:
- Get formal education or training. This could include a bachelor’s degree in data science, a master’s degree in data science, or a data science bootcamp.
- Build a portfolio of work. This could include personal projects, open source contributions, or work experience projects.
- Showcase your skills online. Create a LinkedIn profile and GitHub account, and use them to share your portfolio and other relevant information.
- Network with other data scientists. Attend industry events and meetups, and connect with other data scientists on LinkedIn.
- Tailor your resume and cover letter to each job you apply for. Highlight the skills and experience that are most relevant to the role.
I agree with your statement that you can “fake it till you make it,” but only to the extent that you are confident in your ability to learn and grow. Companies will be able to tell if you are genuinely passionate about data science and if you are willing to put in the work to succeed.
I hope this information is helpful. If you have any questions about data science mentoring, please leave a comment below.
This is a personal blog. My opinion on what I share with you is that “All models are wrong, but some are useful”. Improve the accuracy of any model I present and make it useful!
This aphorism is often used in data science to remind us that no model is perfect. There will always be some error in our predictions. However, even a model with some error can be useful if it can help us to make better decisions than we would otherwise.
This guide to data science mentorship is like a model. It is not perfect, but it can be useful if it helps you to secure a data science job. However, the performance of “my models” is strongly connected to your commitment.
If you are committed to learning and growing, and you are willing to put in the hard work, then this guide can help you to achieve your goals. However, if you are not committed, then this guide will not be helpful.