A summary of the Q&A from the webinar, "The Data Scientist Learning Journey: What I Wish I’d Known on My Way to Becoming a Data Scientist," is listed below.
You can watch the complete webinar On-Demand.
Q: Given the current situation with COVID-19, what do you suggest for new graduates to get their foot in the door in the data science career? Knowing that most of the jobs in the market require at least 2 years of experience or a PhD?
A: It's always difficult to get into a new job in a new industry, even more so during COVID-19. Attending this webinar is already helping you, and I would encourage you to continue to research, to learn (via articles, blogs and courses) so when you are interviewing, they can see that even if you don't have the direct experience, you have the mind and intuition required to be a data scientist.
Q: You stressed the importance of the understanding of business context. For a student who wants to be a data scientist, what would you recommend them to do to gain business acumen?
A: It's an interesting concept, learning new concepts is always great and it will benefit you. I think the key part is to understand the industry you're working in or with and understanding how data science fits into the business. Keep an open mind and continue to learn.
Q: Haidar spoke about specialism. Would you recommend a specific starting language for machine learning?
A: SAS is a great place to start since we've got plenty of material for all levels. I think all languages can be great, whether it’s Python, R or SAS. The main thing is to understand the key skills required that I spoke about during the webinar.
Q: Where is the best place to start as a data scientist?
A: We've got some great material to start learning about data science on SAS! Try this.
You can also access the SAS Academy for Data Science free for 30 days.
Q: Would you recommend learning Python or R for data science?
A: R and Python are great, as is SAS - they're all great languages.
Q: How do you go about translating technical insights to your business partners?
A: In general, the best way is to make it understandable to someone who doesn't understand statistics and show them the insight on how they can benefit from data science in a language they understand.
Q: For someone trying to pivot careers, what education or courses would you suggest I start with?
A: I would suggest looking somewhere like The Data Science Experience where there is plenty of material to get started.
If you would like to pivot into a data scientist role, I would suggest the SAS Academy for Data Science, which is free for 30 days.
Q: I am a non-IT student. Do I need to learn programming to become a data scientist?
A: It's never too late to learn. Although you won't always be writing code, it's going to be beneficial as a data scientist. Our free Programming 1 e-Learning is a great place to start for starting your programming journey. The course starts with the very basics and progresses towards writing your own programs for reporting on data.
Q: What are the first three things someone should focus on when starting a career in this field?
A: I'd focus on programming for data preparation, understanding the industry you're working with, and some of the fundamental algorithms such as logistic regression and gradient boosting.
Q: How did SAS Software help with your data scientist journey compared to other software (R Programming, other open source, etc.)?
A: SAS is more than just a software, there is an entire community and education packages, as well as technical support and teachers to answer your questions. Check out SAS education!
Q: When becoming a data scientist, I think about python, R, etc. What else should I think about it?
A: Some of the topics in this webinar focused on data science skills beyond the programming, hopefully they should help!
Q: How would you recommend we start our journey in SAS programming?
A: There are so many places to start, I would recommend checking The Data Science Experience.
Also, our free Programming 1 e-Learning is a great place to start for starting your programming journey. The course starts with the very basics and progresses towards writing your own programs for reporting on data.
Q: Does SAS no longer offer the data scientist certificate?
A: We do! Check it out here.
Q: Would you say that the four areas of specialization mentioned in the slide are the main areas? Can the list be elaborated a bit?
A: At SAS, we do consider those four (machine learning, text analytics, computer vision and forecasting and optimization) to be the key topics. But you're right, each can be expanded further. I did a webinar a while back on this called Overview of the AI Taxonomy where I spoke about more detail.
Q: I have a master’s in biotechnology and currently work in the pharmaceutical industry where I am exposed to drug discovery. Can I enter in this field?
A: Absolutely! The subject matter expertise you have in this would give you an advantage. Just continue to learn about data science and combine it with your business skills and it can be very successful.
Q: What are the requirements for entering a master’s degree program?
A: Each college or university will have different requirements, so I would suggest contacting the specific location directly for details.
Q: Do you have any advice on how to gain experience for getting an entry-level data scientist job when graduating from a master's degree?
A: Attending this webinar and continuing to learn is a brilliant start. I would suggest trying to understand and learn things from different places and in different methods to become more well-rounded in data science, to impress in your interviews!
Q: Can a data scientist decide to just focus on the data prep/cleaning and let other scientists build the models and present to management?
A: Absolutely. Data scientists can work anywhere across the analytics life cycle, but it's always great to be aware of all the other steps around it.
Q: What are some vital technical skills that employers will be looking for in data scientists in the next 5 to 10 years?
A: I think data scientists will need to be able to explain all their insights to key stakeholders, and to understand how to use AI ethically and with transparency. Ethical use of AI is incredibly important and will continue to be so.
Q: I'm interested in getting my master’s degree in data science. What do you think is the most important criteria in choosing a school or program? Should I know what area I want to specialize in before I go back to school?
A: You have two choices. You can either research to understand the areas in more detail (one way to do this would be to look at webinars, blogs and articles on the SAS website). Or you can do a 'generic' degree (just a general data science) to learn more within the course and decide on a specialization later. Either option is great because you'll always be able to change paths within your career!
Q: How many statistics classes do you need to have under your belt to embark in the data science world?
A: You'll probably get different answers from different people, but as a statistics graduate, I think it's very important! Especially since we're moving into an era where everything is becoming automated and a lot of the programming might become less important. An understanding of the statistics will be vital for a data scientist, in addition to having strong business knowledge.
Q: If you are looking for a career change to a data scientist and have acquired a master’s degree in analytics, what would be some tips for looking for an opportunity to land in the data science field? Most postings require years of experience in the field and if you've just graduated.
A: We understand the struggle, and I would say that if you're experienced within an industry sector, use that advantage and then learn data science skills on top of them, potentially entering related jobs within a known field.
Q: With predictive analytics, it is not rare to have surprising insights. When is a time that a result was not as you expected, and how did you go about handling that?
A: There's several techniques that allow you to explain how your algorithm came to a decision, the aim shouldn't be to 'shake the data hard enough' until it says what you want it to say, instead you need to use explainable and transparent algorithms to explain the insights.
Q: Do we have a data scientist community who uses SAS as a main tool for their work?
A: Absolutely, SAS is used by many organizations around the world for many different tasks, including data science. Check out the SAS Support Communities page to find different groups.
Q: If you could go back to the start of your career, what is one word of advice you would have given yourself?
A: I would possibly say never lose the appetite to learn. There's always more to learn, which will make you a better data scientist!
Q: am a Petroleum Engineer, and I am learning python, R, etc. What else should I learn to become a data scientist focused on oil and gas?
A: Apart from the actual data science skills, do some more research on how those techniques are being applied in your industry. It's great to learn about the details of algorithms but try to understand how it's used within your business. The Oil & Gas Analytics page on the SAS website should help.
Q: As a business analyst with a MS in computer science (2004), and many years of programming background, will a good certification from a well-ranked university help me get a foot in the door for data scientist roles?
A: Your experience is already brilliant! It's impossible to give you a direct answer because it does not exist. What I can do is provide advice to lead you towards a path. You're clearly talented and engaged. I often recommend further education for people who are new into the industry or want to make a complete career change, but you are not changing much. Your current expertise is great, and often an MSc course wouldn't teach you many relevant business skills. For that reason, I would say taking a certificate course(s) would be possibly a better step. However, it certainly depends on what you deem more suitable and your personal situation.
Q: What is the biggest challenge you are currently facing as a data scientist?
A: There are many challenges. The main one for me is to keep the focus on the business context, as opposed to the algorithms.
Q: What are the priority programming language(s) and software(s) for employability at entry level?
A: There are many great programming languages. I believe SAS, R or Python would be the best ones!
Q: Is the SAS Data Science certificate enough to get an entry-level data science job if you have 10 years of data analyst experience plus a master’s degree?
A: The SAS Data Science certificate will make you stand out by not only having the certificate, but also the knowledge you'll learn throughout to excel in interviews. Your experience is already brilliant, so it seems just a shift of focus could be sufficient for you.
Q: How do you help educate end users regarding data literacy so you can be more successful?
A: SAS offers several free e-Learning courses including Statistics 1 and Programming 1.
Q: Would you please include links for people who are JUST starting programming?
A: Our free Programming 1 e-Learning is a great place to start on your programming journey. The course starts with the very basics and progresses towards writing your own programs for reporting on data.
Q: If you were to recommend one book or article for beginning data scientist, which one would it be?
A: I love "Life 3.0." It's great! But regarding AI, I rarely read books, but I read lots of blogs and articles from SAS blogs or Data Science Central.
Q: What are the areas of specialization?
A: There's so many! You can specialize within anywhere of the analytics life cycle (data prep, deployment, modelling etc.), or you can specialize within an industry (e.g. AI in banking), and each of them can be sub-categorized too!
Q: Can one be a data scientist without pursuing programming and just focusing on modeling and data analysis?
A: I believe so, especially since everything is becoming more automated so the need for programming has decreased (but still very much exists). I would say it's important but not on a day-to-day basis. Understanding of the statistical insights is more important. SAS® Viya™ allows you to do an end-to-end analytical life cycle without coding at all - check it out!
Q: I have my SAS base programming certification. What is the jump to Python? It is a lot different and can it create confusion.
A: Python is a different language with different tools, so it won't be identical. However, after learning your first language, the next few after will be easier!
Q: What is the role of research methodology in data science?
A: Data science can be used widely within research where research methodology is of course essential, but I don't see it often used within the industry. However, what is important is to ensure algorithms are used ethically and that you are able to explain your insights.
Q: What degrees are most common for people going into data science? If not a degree in data science, is there another degree you'd recommend to a high school senior interested in the data science field?
A: There isn't just one single field. I've seen people focus on an industry (e.g. biology) and then get into data science! However, I'd say the key ones are math, statistics, computer science, engineering, and even physics.
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