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How do you create a data science portfolio that speaks to different audiences?

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A data science portfolio is a showcase of your skills, projects, and achievements in the field of data science. It can help you demonstrate your value, communicate your insights, and land your dream job. But how do you create a data science portfolio that speaks to different audiences, such as recruiters, hiring managers, clients, or peers? Here are some tips to help you craft a compelling and versatile portfolio that showcases your data science talent.

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1 Know your audience

Before you start building your portfolio, you need to identify who you are creating it for and what they are looking for. Different audiences have different expectations, goals, and preferences when it comes to data science portfolios. For example, recruiters may want to see a variety of projects that demonstrate your technical skills and problem-solving abilities, while hiring managers may want to see more depth and detail on how you approach specific challenges and deliver business value. Clients may want to see how you can communicate your findings and recommendations in a clear and engaging way, while peers may want to see how you use advanced techniques and tools to tackle complex problems. Knowing your audience will help you tailor your portfolio to their needs and interests, and highlight your strengths and achievements.

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Adam Davis

Data Scientist | Programmer | Problem Solver
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-It is important to think about added business value as well. What can actually be accomplished through data science? How will hiring you impact the business positively?

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Somnath Ojha

Artesian of Insights, Navigator of Data Realms, Delivery Manager: Data Science & AI@MathCo | Ex Lead Data Science Consultant at Accenture | Ex CIMB | Ex Mindtree | Ex ITC Infotech | Data Science | Machine Learning
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To create a data science portfolio for different audiences: 1. Include technical projects with detailed code. 2. Showcase projects with business impact. 3. Tell a compelling story using visualizations. 4. Highlight diverse skills like classification and NLP. 5. Contribute to open source or create tools. 6. Develop projects in specific domains. 7. Maintain a blog for insights and tutorials. 8. Tailor projects for the role you're pursuing.

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Gulaid Ahmed

Senior Data Analyst @ Unilever | GenAI Enthusiast
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One of my biggest tips for this is to do research on the companies products & services. You can find this on the company's website. From this you'll understand what sort of tools or technology they utilise to make their work possible. This should give you an idea of what tools/tech they'd like you use and now what's left for you is to demonstrate your skills with these and how you can solve their business problems with it.

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Joyce Mwangi

[email protected] | Process Optimization | PLM | |AI
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Engaging with your community really helps. Immersing oneself in the day-to-day operations goes a long way in knowing how to tailor your portfolio to any audience.

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2 Choose your projects wisely

Your portfolio should showcase your best and most relevant projects that reflect your data science skills and experience. You don't need to include every project you have ever done, but rather select a few that demonstrate your range of abilities, your domain knowledge, and your creativity. Ideally, you should choose projects that align with your career goals, your target industry, and your audience's expectations. For example, if you are applying for a data science role in the healthcare sector, you may want to include projects that involve working with medical data, applying machine learning models, and providing actionable insights. You should also choose projects that showcase your passion, curiosity, and learning process, and that highlight the impact and value of your work.

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Gulaid Ahmed

Senior Data Analyst @ Unilever | GenAI Enthusiast
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In addition to this, I'd classify projects into two categories: (1) passion projects and (2) projects with a purpose. In an ideal world, having a project that align with a company's work are what you should do, but in my experience, having unique projects that make you stand-out are the "icing on the cake". This could help spark conversation and alternative ways of thinking.

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Kurt-Mario Bonello

Model Validation / Risk
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When showcasing your work, avoid using your employer's data or at least seek permission. Sometimes data it not owned by the company you work/ed for, but owned by 3rd parties. Not doing so could get you into trouble and discourage future employers from even considering you. Another step is to anonimise data so that no one can be identified. Laws vary according to country or continent. GDPR laws in the EU carry fines "up to 20 million euros, or in the case of an undertaking, up to 4 % of their total global turnover of the preceding fiscal year, whichever is higher".?

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3 Showcase your process and results

Your portfolio should not only show what you have done, but also how and why you have done it. You should explain your data science process, from defining the problem and gathering the data, to exploring, analyzing, modeling, and evaluating the data, to presenting and communicating the results. You should also provide context and background information for each project, such as the motivation, the objective, the data sources, the tools and techniques used, the challenges faced, and the solutions implemented. You should also highlight your results and outcomes, such as the key findings, the insights, the recommendations, the metrics, and the feedback. You should use visualizations, charts, graphs, and tables to illustrate your data and results, and make them easy to understand and interpret.

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Vedant Pople

Software Engineer @ Aikitech Inc
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- Visualizations and charts should be explainable to laymen. The convenience to understand them is key. For example, by presenting the correct parameters and appropriate visualizations.

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4 Use a suitable platform and format

Your portfolio should be accessible, attractive, and user-friendly. You should use a platform and format that suits your audience, your projects, and your style. There are many options available, such as websites, blogs, GitHub repositories, Jupyter notebooks, slides, videos, or podcasts. You should choose a platform and format that allows you to showcase your code, your data, your visualizations, and your narratives in a clear and organized way. You should also use a consistent and professional design, layout, and color scheme, and make sure your portfolio is easy to navigate, read, and share.

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Gulaid Ahmed

Senior Data Analyst @ Unilever | GenAI Enthusiast
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Deploying your web app allows others to easily access it but alternatively you can discuss your projects in blogs too. With a little of editing and effort, you can even create a YouTube channel, if you'd like to do demos, step-by-step guides, walkthroughs, etcs.

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5 Update and improve your portfolio

Your portfolio is not a static document, but a dynamic and evolving reflection of your data science journey. You should update and improve your portfolio regularly, as you learn new skills, complete new projects, and achieve new goals. You should also seek feedback and suggestions from your audience, your peers, your mentors, and your network, and use them to enhance your portfolio. You should also review and revise your portfolio periodically, and remove or replace any outdated, irrelevant, or low-quality projects. Updating and improving your portfolio will help you showcase your growth, your potential, and your value as a data scientist.

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Gulaid Ahmed

Senior Data Analyst @ Unilever | GenAI Enthusiast
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Try and be commercially aware of what the latest new areas area and try and do a quick analysis of whether it's worth your time to work on/upskill in. For example, for a relatively new area like generative AI, is a widely discussed area and you can start off by understanding what is currently out there, what are the gen AI builders working on and identify any things you can replicate/clone or find any gaps build yourself. All depends on your confidence levels, the resource is out there!

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Gulaid Ahmed

Senior Data Analyst @ Unilever | GenAI Enthusiast
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Projects can take a lot of personal time to complete so be sure to choose the ones that you'll enjoy. There'll be highs and lows but try and keep yourself accountable to ensure completion. P.S. Start of with shorter projects that can be quick wins for you. Then you'll start a snowball effect!

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How do you create a data science portfolio that speaks to different audiences?

1

2

3

4

5

6

1 Know your audience

2 Choose your projects wisely

3 Showcase your process and results

4 Use a suitable platform and format

5 Update and improve your portfolio

6 Here’s what else to consider

Data Science

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How do you create a data science portfolio that speaks to different audiences?

1

2

3

4

5

6

1 Know your audience

2 Choose your projects wisely

3 Showcase your process and results

4 Use a suitable platform and format

5 Update and improve your portfolio

6 Here’s what else to consider

Data Science

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