Hi Everyone, Some of you have done Masters in Data Science; I am wondering if these areas of study are enough to cover the field? To a certain extent, I am tired of cybersecurity and I want to move to something like data science, AI, ML, and Blockchain development. I do not want to spend money on another degree or certificate, but rather grasp the knowledge and skills. Are these IBM's Professional Certificates through Coursera are enough? I have access to the certificates for FREE under the Veteran program. Coursera IBM Data Warehouse Engineer Coursera IBM Data Engineering Coursera IBM Data Analyst Coursera IBM Data Analytics with Excel and R Coursera IBM DevOps and Software Engineering Coursera IBM Full Stack Cloud Developer Coursera IBM Data Science Coursera IBM Applied Artificial Intelligence Coursera IBM Artificial Intelligence Engineering Coursera IBM Machine Learning Coursera IBM Cybersecurity Analyst I had an interview for a Senior Vice President position at my firm for Senior Risk Officer focusing on Enterprise Data Architecture. I felt the interview went well except for one question regarding the data warehouse, which I think I might not get the second interview. I want to steer some skills that I might lead to independent employment. Thanks in advance!
I would suggest that you go for Data Engineering. It is more demanded than Data Scientist, at least in the SEA. If you can do both engineering and analyst (full stack), that's even better. Also, consider studying the cloud because a lot of data science and machine learning workflow now running on the cloud (AWS, GCP, Alibaba, etc). I completed some IBM Coursera courses. The resources they provide (pdf, notebooks) are very good and detailed. I often refer back to them. Even though they are good, I think they lack hands-on exercises. For hands-on, I can recommend DataCamp. They have multiple career tracks that you can choose and they have certifications that you can obtain. After you complete the certification and some workshops, you will receive 1-on-1 consultation with a career coach. Alternatively, you can study by enrolling in free courses and Bootcamp: https://dphi.tech/bootcamps (data science BootCamp will start soon at 12 March 2022) https://www.kaggle.com/learn https://www.freecodecamp.org/learn/data-analysis-with-python/ https://www.freecodecamp.org/learn/machine-learning-with-python/
I think people can self-teach Data Science or Data Engineering as well as people self-teach any other area of tech, which is to say well - but with a wide variation in quality. There's nothing you get from the Master's that you can't get somewhere else. As asianphd notes, Data Engineering is much more in demand because data engineers prepare data to be worked on by data scientists and most organization's data is poorly structured or unstructured, badly labeled, with many missing fields - and what's there doesn't actually capture what they need to capture in order to do the kind of analytics they're asking the data scientists. Data engineers help mitigate what they can and push a more useful architecture. I completed DataCamp's Data Analyst with Python, and felt it was okay but it provided so much "scaffolding" or sample code that in the later parts of the curriculum, when I should have been able to open a blank Jupyter Notebook and start typing, I found myself stuck because DataCamp prompted me so much. I've heard DataQuest is better but never tried it myself to know. Looking at the curriculum of the Coursera IBM Machine Learning for example, it covers exploratory data analysis, regression, classification and unsupervised learning techniques like K-means clustering. That specialization (the 4 courses) appear equivalent to a university course, except without the assignments. So you probably could, with practice, use those courses to learn but you'll need to build a portfolio to demonstrate that you know what you're doing.
I was briefly a data analyst until I accepted a higher paying job. Since you already have graduate degrees, you won't have this issue, but the graduates of these certificate programs are discovering that there are not many job openings for them. Most of the data science and data analyst jobs openings they were seeing required a bachelor's degree, and some employers had preferences for advanced degrees. Having advanced knowledge of quantitative research methods takes years to obtain. All someone with a PhD in a STEM field needs to do is learn R, SQL, and python, and they'll be competitive. I think a lot of graduates of these bootcamp programs are going to be in for a rude awakening. They'll be lucky to land a data analyst position, which pays less than being a data scientist or data engineer. I think data engineering is more conducive to self-teaching. Some companies might expect their data scientists to have data engineering capabilities. Since I'm not a veteran, I'm using my free access to LinkedIn Learning to pick up additional skills. I landed my previous job because of my research experience and statistics knowledge.
I think Dustin is right in saying people can learn things on their own -and, he notes, with varying degrees of success. And I believe sanantone is right. It's one thing to learn skills - and another to get a first job in that field - especially if you learned on your own - and/or in a hurry, by bootcamp or similar. In that case, a dynamite portfolio might be your best shot. Once you (hopefully) get in - then you work your way up. And yes - if you have advanced degrees and skills and want to migrate into a new area? That's easier. You don't have to start at the bottom - nor should you have to. Lateral move - or better.
