The Presentation explores actionable strategies and tips to help the data enthusiasts build highly impressive data science portfolios, start/advance their career trajectories in the data world, explore the emerging trends in the field and strongly grounding the attendees with a sense of purpose and direction to innovate, take up leads and shape up the data sphere.

1. Formulating Your Career Path in Data
Science:
Building a Solid Strong Data Science Portfolio that Stands
Out

2. Kennedy Kamande Wangari
Data Scientist Intern at NCBA Bank
PLC, Active Kaggler and Junior
Machine Learning Engineer at
Omdena
@kennedykwangari
Profile !

3. The question is how do you get experience if you need experience to get your first job?

4. Take up training programs: tutorials, books, courses, materials, articles, bootcamps: read and implement ML
Research papers and contribute to open source projects on Github and improve your programming skills in
Python/ R: Set a 100 days of ML/ DS Challenge. (Hacker rank, Datacamp, Dataquest, Udacity, Coursera, Udemy,
Edx: Springboard Data Science, NYC Data Academy, Data Science Dojo, and the Data Incubator)
Read, follow and contribute to online data science and analytical blogs (KD Nuggets, The Data Incubator Blog,
Machine Learning Mastery, Medium, Analytics Vidhya, Towards Data Science, Reddit and Revolution Analytics
(R Stack))
Working actively in data science competitions, hackathons and coding challenges: build an impressive profile: this
improves your technical experience and augments your portfolio (Analytics Vidhya, Crowd.ai, Data World,
Kaggle, Zindi)

5. Attend volunteer/ Make presentations in local meetups/ academic conferences:
(Tensorflow Nairobi, Nairobi AI, Nairobi Women in Machine Learning and Data Science, Teens in AI
Africa, Data Science Africa, Deep Learning Indaba)
Visibility and Branding: Have impressive (LinkedIn, Github, Twitter, Kaggle, Medium, Stack Overflow,
Tableau Public, Quora and You Tube) Profiles: Document and consolidate blogs, push projects to Github,
create a personal website, Follow Influential Data Professionals on LinkedIn, Twitter. Follow Machine
Learning / Data Science Topics/ Questions on Quora/ Reddit.
Work on public data, help organizations with their data: Join Omdena/ Google AI for Social Good Challenge
to solve Social, Humanitarian and Environmental Human Grand Challenges.
Build strong networks and connections with data professionals: Get mentors to amplify, edify and shape your
data science trajectory. Sign up on Meetup and Eventbrite for updates on local DL/ML gatherings

6. Build your soft skills: Communication, project management and influence and negotiation skills: Learn to
build partnerships, working in teams and community building: Help democratize ML/ AI Knowledge and
wanna be data scientists.
As you curve your Learning Path , Do Ensure:
Gain a good business domain and use cases understanding to enable you to solve business problems with the
right analytical approach and reasonable data intuition
Gain experiment design and A/B Testing knowledge: familiarity in designing and analyzing experiments and
AB Test for business use cases to guide product development in scientific ways.
Gain statistical modelling/ Machine Learning Competencies: ability to formalize business problems into
feasible machine learning projects, selecting the proper modelling algorithms, build and deploy your models.

7. Resources and Further Reading
The Ultimate Learning Path to becoming a Data Scientist and Mastering ML
https://www.analyticsvidhya.com/blog/2019/01/learning-path-data-scientist-machine-learning-2019/
A Comprehensive Learning Path for Deep Learning in 2019
https://www.analyticsvidhya.com/blog/2019/01/comprehensive-learning-path-deep-learning-2019/
IBM Data Science Professional Certificate (Coursera, Online)
https://www.coursera.org/professional-certificates/ibm-data-science

8. Connect and Engage:
@kennedykwangari
kennedykwangari@gmail.com
+254792816053/ +254743635496