Data Science Portfolios
Week 11 | Lesson 5.1
After this lesson, you will be able to:
- Communicate your personal data science brand
- Leverage opportunities for networking and industry exposure
- Create a personalized professional development plan
Before this lesson, you should already be able to:
- Complete GA's Data Science curriculum
- Generate a list of desired employers & target roles
- Create long-term professional development goals
Before this lesson, instructors will need to:
- Generate a brief slide deck
- Ask guiding questions and review previously covered topics
- Prepare and tailor specific materials to your student's needs
|5 min||Opening||Outcomes Review|
|10-15 min||Branding||Build Your Data Science Brand|
|10-15 min||Portfolio||Assemble Your Portfolio|
|10-15 min||Networking||Importance of Networking|
|10-15 min||Industry||Get to Know Your Industry|
|10-15 min||Skills||Grow Your Skills|
|5 min||Conclusion||Takeaways & Advice|
Opening (5 mins)
Instructors: This is a modular course session. When planning, make sure to coordinate with your local outcomes team and determine what topic(s) have already been covered. Ask your students about their goals and what questions they have, and use this to shape your lesson.
Congratulations! You're a data scientist! Now you need to tell your story and share it with others. But how to do that, exactly?
Check: Let's briefly review some of what we've learned from our local outcomes team. What topics have you already covered so far?
Note: List tropics previously covered and compare against materials provided. Feel free to modify or skip any sections of this lesson that have already been covered or don't apply.
Check: Next, take 5 minutes and create a short list of 5-10 companies you'd like to work for, and identify what positions and skills those employers are targeting by doing some quick research.
Once you're finished, form a group with the other members of your table. Write down the required skills you found on the whiteboard (or your desks) and based on your other members, denote the frequency with which these skills were described. How often did each one come up? Which ones were most important? Least?
Finally, share this skill log with the rest of the class. Again, tally the most and least listed skills out of the desired jobs sought by the class. What common trends do you see?
Note: Reference the most common examples when discussing career planning and incorporate them into your discussions with the class.
Next we'll discuss more specific topics for data science, including:
- Data Science Branding
- Building a Portfolio
- Networking & mentoring
- Connecting with the industry
- Growing Your Skills
1. Build Your Data Science Brand
Note: Use this section to discuss/review the importance of personal branding, as needed.
After this class, you'll officially be data science alumni. Now what? First, you'll want to make sure your personal brand is updated with your new career trajectory.
If you haven't already, craft a 30 second elevator pitch about yourself to use when networking or talking to recruiters.
This summary statement should highlight relevant features and include something specific to you. Your goal is to be interesting and memorable!
Remember, facts tell, but stories sell! Human brains are wired to remember emotional impact. So don't just focus on skills, include relevant information from your background, interests, or accomplishments.
Here are some guiding questions that may help. Consider:
- Who are you? How would others describe you?
- What makes you awesome? What value do you bring to teams or projects?
- Where are you going? Where would you like to make an impact? How?
Check: Take a 5-10 minutes to craft a 30-second elevator pitch, then share it with others at your table. Ask for their reactions and feedback; what elements stood out to them? Iterate as needed.
When networking and applying for jobs, make sure your online information and supporting materials are ready to share.
- 73% of recruiters have hired a candidate through social media.
- 93% of hiring managers will review a candidate's social media presence before making a hiring decision.
- 55% of hiring managers have reconsidered candidates based on what they found on social media (for better or worse)!
So check those privacy settings! Or better yet, just delete anything you really don't want someone to find.
Check: Take a few minutes and try googling yourself on a partner's computer (or in a private browser tab). What results show up first? What impression do your public social media profiles make? Make sure these results are up-to-date and consistent with your new data science brand!
Note: To cover additional points, stop and host a discussion on the topic of personal branding in data science. Use your own examples, if possible.
Next, we'll discuss how to showcase your brand in a data science portfolio.
2. Assemble Your Data Science Portfolio
Note: Use this section to discuss/review common portfolio myths and tips, as needed.
- Consider the elements you want your audience to focus on; what are they? Why?
- Don't just post work and assume your audience will understand it. Spell it out for them! What would a non-technical audience need to know, at a minimum? What would you want a technical audience to know?
- Use visuals and tell a story! String visual elements together into a consistent narrative.
Check: Assume you are communicating your capstone project in your portfolio. What visuals do you need to communicate your project effectively in an online portfolio?
Check: Look online and identify 4-6 images that could help represent your goals and your work. Share these with a partner without explaining your intentions; see if they can describe your project based on visuals alone.
Take note of any gaps or confusion; you can use this data to guide your iteration. Make sure to update visuals and supplement them with specific help text to help address any issues!
Communicating your Projects
Here are some additional reminders to guide you when building your portfolios:
- When crafting a case study or describing a project, always clearly identify your role and your influence on the results. E.g. was this an individual or group project? What part(s) were you responsible for?
- Contextualize your projects for your target audience; for example, relate your work to business goals and impact.
- When explaining your processes and results, also be sure to highlight areas that demonstrate your strengths or that offer interesting takeaways.
- Make sure to define assumptions, constraints, and limitations of your findings.
- Synthesize your takeaways for others; don't put the burden of interpretation on your audience.
The following are some common questions and concerns students have about building their portfolios:
- Q: Should I only show my finished work?
A: Not at all! Remember that the point of a portfolio is to showcase your skills. The best way to demonstrate skills is to highlight your process, emphasizing your ability to think logically and critically.
Q: No one wants to see errors or progress, they want results, right?
A: As long as you explain your thoughts / rationale and demarcate elements in a sequence (say, a series of posts during the course of a project, or parts of a model), then you should show it!
Q: Doesn't this need to be perfect before I release it into the world?
- A: Remember, done is better than perfect.
Note: To cover additional points, stop and host a discussion on the topic of portfolio development in data science. Use your own examples, if possible.
Ok, so you've perfected your personal brand and created a sample portfolio. Now what?
3. Get Out There & Network!
Note: Use this section to discuss/review the importance of mentoring and networking in job discovery and career growth, as needed.
After you've cultivated your brand and built up a sample portfolio, the next step is to network! Your goal should be to connect with people in the field for insight, advice, mentoring, and leads.
Find a mentor
There's an old saying that goes: "When you want advice, ask for money. When you want money, ask for advice."
The surest way to succeed in your job search is to surround yourself with other people in the field and ask for advice and direction. Finding mentors in the field will give you the guidance you need to increase your network, sharpen your skills, and contribute to the data science community.
When pitching potential mentors, you'll want to make sure you aren't just asking for help; look up a common interest and try to provide value in return.
It's typically best to begin small; you might ask for an informational interview - like a quick chat over coffee - and discuss their background, roles, or favorite projects. Then relate these to your own skills, interests, and goals in the field.
Note: Instructors - if you have any experience either being mentored or acting as a mentor, describe that here. Was this a conscious decision? How did you go about it and why? What benefits did mentorship have for you?
Check: Career counselors often categorize job hunting techniques as high value or low value activities. How would you categorize mentoring? Why? Would time spent finding a mentor be more or less effective than applying to online job boards?
Answer: Emphasize the fact that time spent networking with potential mentors is more high-value than spending the same amount of time applying to jobs where you don't have any prior connections.
Note: To cover additional points, stop and host a discussion on the topic of networking in data science. Use your own examples, if possible.
But where to find mentors? How to meet them? We'll address that next.
4. Get to Know Your Field
Luckily, the field of data science is growing rapidly and there are tons of opportunities to connect with likeminded practitioners all over the world. From meet-ups to conferences, online communities and open-source projects, there are many ways to get connected with your fellow data scientists.
Hanging out and networking at Meet-ups a great place to look for jobs and mentors, as well as pick up on trends and industry jargon.
The best way to enter a new field is to immersive yourself in a wide range of activities. This will help you better identify where your interests and skills can fit in.
Most meet-up events center around a talk or a panel of speakers, and afterward people mingle and network. Specialized meet-ups may also offer technical workshops.
Conferences are a great place to network, find mentors, learn about jobs, and generally get involved with the field. Conferences range from local/regional affairs to large international events - with a corresponding range of ticket prices.
Different events offer different benefits, from workshops and presentations to industry connections and cutting-edge projects. Here are a few you might check out:
- The Data Science Conference. A conference specifically for business analytics professionals in data science & predictive modeling, focused on networking and information sharing.
- Open Data Science Conference. A global event, ODSC hosts regional conferences to grow the open source community around a host of data science topics.
- Knowledge Discovery & Data Mining. KDD is a large conference whose members host lots of discussion on data science teachings and trends.
- Strata + Hadoop Conference. A mix of academia & private industry, Strata offers practical workshops, networking events, and cutting-edge talks.
Looking for more? Don't see one you like?
Note: Consider volunteering at an event (conference or meet-up) in order to better connect with a wide range of people who share your specific interests. Volunteering is a great way to gain access to events that may otherwise be out of your price range!
Check: What are some low-cost ways to participate in meet-ups and conferences?
Answer: Volunteering, duh.
Check: Take a few minutes and look up 3-5 local meet-ups you'd be interested in attending. Next, look up 2-3 regional conferences that cover areas of interest. Share these with your group then with the class, and tally any repeat items.
Create a plan to attend one of the top three local events (meetups) as a group by assembling a signup sheet and determining group transportation logistics. Nominate one person as the coordinator to follow-up with people individually as needed.
Note: To cover additional points, stop and host a discussion on the topic of industry events in data science. Use your own examples, if possible.
What if there aren't any upcoming or applicable events in your area? We'll cover that next.
5. Practice Makes Perfect
When in doubt, get involved!
Even if there aren't any local events in your area, there are an ever-growing assortment of online communities and resources you can take advantage of to expand your network and domain expertise.
Competitions & Challenges
Hiring experts disagree on the impact of participating in data science competitions. On the one hand, participating in public data related challenges can be a great way to sharpen your skills, grow your network, and add to your project portfolio.
On the other hand, some companies don't pay quite as much attention to them when hiring, as competitions are typically very clearly defined and organized - unlike most real world projects!
Regardless, we still think competitions are a great way to participate in the field! However, when applying for jobs, make sure to relate any competition projects you've worked on to actual real world objectives or personal skill development.
Data Science Competitions
- For additional public data science competitions, check out this list
- For more advice on competitions and hiring, see this Quora post
Immerse Yourself in Information
When you aren't building your portfolio, networking, or applying for jobs, consider reading up on some other resources for learning about the field. The more information you have at your disposal, the better you'll be able to target your skills and approach.
Check: What are some other resources you've come across during the class so far? Create a list of 5 of your top favorite data science resources, and a sentence or two on what you like about them.
As before, share these first with your group. Tally up items by type and topic. Share these with the class and do the same.
All this talk of personal development and networking is fine, but where do you go to look for actual work?? Keep in mind that one of the best ways to find a job is through word-of-mouth and personal referrals.
That said, it's also a good idea to know what types of jobs are out there so you can drill down to the common skills and requirements in order to hone your profile.
Want more? Here are a few additional resources:
- Create a list of your top 5 desired employers.
- Create a nested list for each employer denoting the top 5 relevant positions they currently list related to data science.
- Create another nested list for each position including the top 5 skills these posts mention as requirements.
- Go back through each post and tally the number of times each of those 5 skills are mentioned.
- Compare these skills across all roles within the company, then among all companies. Which skills appear the most?
- Make a list of 5 of your top strengths/skills based on your prior background and performance in this course. Rank each item on a 1-5 scale.
- How does your personal skill level compare with the desired requirements for your target companies?
- Make an action plan for filling in any gaps between your current top skills and the requirements of your target positions.
- Include a goal statement: To do X, I will Y
- Include a "to do by" date for completion of this goal
- Include a method of assessing your desired performance improvement; how will you know when your goal has been achieved?
- Include a link to a sample resource that will help you achieve this goal.
- Congratulations! You've now made a targeted Skill Development Plan that will help you reach your desired career goals. As soon as you complete the capstone, you should immediately start following and implementing this plan.
Note: To cover additional points, stop and host a discussion on the topic of continuous improvement in data science. Use your own examples, if possible.
Conclusion (# mins)
Landing your dream job in data science requires preparation and practice. Your goal is to build a career, which means you'll want to show progress over time. The best way you can do this is by building a portfolio that demonstrates your progression in the field.
Show your work - including progress, mistakes, and revisions - so that employers can see your thought process in action. Your growth as a data scientist will be a process of continuous improvement; so don't wait to get started!
Check: Create your own Portfolio Checklist. What items need to be included for your desired audience(s)? Share your checklist with peers; what do you have in common? What can you add or change? Review your final checklist with your instructors.
Beyond that, you'll need to get immersed in your field. Network as much as possible by talking with potential mentors, participating in community events and competitions, and keeping up with industry information online.
Check: Make a personal action plan for engaging with your field after the end of the course. Generate a list of at least 3 potential people to connect with, 3 events you'd like to attend, and 3 recurring sources of information to review.
Finally, you'll want to spend time reviewing relevant job boards and other resources, in order to target your search, reinforce your skills, and tailor your personal brand based on your desired goals.
Check: Review your skill development plan. What is a realistic timeline for closing the skill gap and meeting your desired career goals? What are some potential resources for improving those skills?
There are literally tons of "job hacking" advice sites out there. Here are a few you may find useful: