Expert advice on how to build a successful career in data science, including tips on education, skills, and job searching.

The first thing one can do when one wants to pursue a career in Data Science and build a successful career would be they should first enroll in courses or apply for courses either in university or college while a lot of institutions offer what one is seeking.

One can choose to study Data Science and also go for Computer Science with Statistics and Mathematics.

Research on Google that might help are road maps for you to track your progress and see how far you are with your goals.

Another way would be to also take part in free courses such as the IBM Data Science course through Coursera and many more but these will not guarantee that upon completion you will be job-ready or now a qualified scientist.

There are also paid platforms that focus on skills such as Data Science that normally run for a year others 18 months or just 12 that help you become a data scientist who is job-ready and confident such as ALX being one of the best in Africa is now in collaboration with Explore AI Academy and also Data Camp that has various courses they offer.

Most of the mentioned are indeed not only about learning but they mix fun learning and hard learning and support to students especially if you are a complete newbie in the tech space.

Having certificates on your resume will help and set you apart but for me, a resume is just a way to say this is who I am and this is what I have achieved through the paper which now leads me to the main point.

Having a portfolio is what tells the employer that the skills you mentioned in your CV you are able to put them in practice and have proof for your work, basically you are documenting your work to show that what you say you can do and have achieved you have something to show for it.

This is because when people or companies are going to hire they will be paying you and trusting you that you know your work and will not put their company in jeopardy with your insights.

Remember as an aspiring Data Scientist you will be working closely with stakeholders and delivering on your ideas about your company so delivering insights that are inaccurate will deeply affect the decisions of the company itself putting your job, your career, and someone else's company at risk of collapse.

Joining Bootcamps has also become a norm as of late but joining BootCamp should not limit you to doing more studying and research.

Skills

Technical Skills:

Learn skills such as Python, R, SQL, Pandas, NumPy, Matplotlib, Seaborn, and Tableau.
Statistical Skills:

Machine learning, Algorithms, and techniques, such as classification, regression, clustering.
Soft Skills:

Problem-solving and critical thinking skills, communication skills such as having the ability to present technical data to non-technical stakeholders at work.
Job Search

Having a portfolio that has projects that target or relate to the company you are trying to apply for and showcase real-world solutions that relate to the entity at hand.

Networking and allowing yourself to be seen in socials such as LinkedIn and in-person attending hackathons and tech-related events.

Having an ATS-compatible CV that stands out and shows you off as a brand, not just a future employee.

For interviews, ensure you polish your skills because tech interviews are conclusive of technicality meaning you will write and code and do challenges.

Explain what is on your portfolio in detail like your projects in detail to show your deep understanding of the skills.
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