No more feeling lost and wasting time on irrelevant courses!
Data science is an interdisciplinary area. It requires learning about many different topics and applying your knowledge with many different tools. Learning everything takes motivation, dedication and time...
Thankfully there are many online resources out there for learning data science. Too many maybe? Just on Coursera, you would get 1,389 hits looking for a data science course.
How do you go about choosing courses? Would you choose the one with the latest update or with the best reviews? How can you know it is on the right level for you? What if you buy it and it turns out to be too advanced for you, or too easy? Money is refundable, but is your time?
Even though online course platforms advertise the abundance of courses, in this case having more courses is not necessarily a good thing. It is overwhelming at best to try to pick a course when there is too much yet to learn about what data science is and what skills are “actually” relevant to become a data scientist.
I went through this struggle many times while attempting to learn other subjects. That's why I would like to help you learn the relevant skills and take the courses that are right for you by using my knowledge from the other side. I am planning to do this by:
1. Giving you a clear definition of what data science is and how it is different from other careers related to data
You need to have a clear understanding of what to learn for the specific goal you have. Unfortunately, there is a crazy amount of misinformation on the internet about what it takes to be a data scientist. Many people who are just starting out, confuse it with a data analyst or even a data engineering position. It is important to understand how the area is structured and be able to differentiate disciplines before you start immersing yourself in courses.
2. Guiding you on the relevant skills
There is a big confusion on what you need to learn and how much to “call yourself” a data scientist. Data science is an over-arching term and it is hard to make a comprehensive list of skills focus on. You can find people claiming you need to know everything there is to know about linear algebra before you can even think about being a data scientist. I don’t agree with this line of thinking. I think you can become a data scientist if you can focus on the relevant skills and only the relevant skills for your specific goal and your background.
3. Sharing insights on the profession of data science from the industry
Many times, aspiring data scientists are not aware of what kind of projects are done in the industry or even what kind of deliverables are expected of a data scientist. If this is you, you should not worry and know that it is a common struggle. I will share with you example projects from different industries and types of working (freelance, consultancy, in-house data science). This will help you understand what will be expected of you once you get a job and thus, will help you communicate with a prospective employer more clearly. Knowing common struggles of day-to-day data science applications, on top of your technical training, will set you apart from the competition.
How am I going to do these?
I will periodically publish free resources you can use including but not limited to:
- A podcast that features data scientist and professionals from other data related jobs to give you insights about a career in data
- Articles on a data science career, relevant skills and efficient ways to approach the journey of learning data science
- Blog posts answering questions from you about learning data science
- Reviews of the latest courses and in-demand skills on job postings
Ready to take the leap?
Subscribe for my email list and let's keep in touch. By subscribing, you get free access to my Data Science Kick-starter course which I prepared to help you lay the ground work for learning data science. Learn more about the course here.
Read more about me below.