u/Kati1998

Would love some feedback on my revised resume (in comments). I’m hoping to attend two virtual career fair this fall and want my resume to be in the best shape.

My MS Data Science is more focused on the math/statistics side (taking time series analysis and machine learning under the math department this semester) but I’ve been seeing analytics roles increasingly ask for “analytics engineering” skills (dbt, ETL, Snowflake, etc) and it seems like it’s becoming baseline skills.

I’m wondering if I should focus on learning those tools next and use them in a project?

Long term goal is to be a data scientist but hoping to land an entry level data analyst role to get my foot in the door. Not having much luck with applications. Thanks in advance!

I’m a Data Science student (career changer, not in a data related role). My program is focused more on the applied statistics side, so most of my classes use R. I’m already familiar with Python since it was the main language used in my prerequisite courses, and I’ve completed projects using Python, so I’m comfortable with the syntax.

However, I’m really enjoying using and learning R in my classes and seeing what it can do. Many of the statistics textbooks I’m interested in use R as well. I’m starting to explore R more deeply on my own and plan to start using it for personal projects.

But I’m curious, is R still used in industry? I know it’s heavily used in academia. I also know that in the current AI/ML world, Python is used heavily, which is the main reason I use it for all of my personal projects at the moment.

I’d like to eventually be comfortable with both and take advantage of the strengths of each language. But, of course, there are also people who say learning R is a waste of time.

I’m a data science student, but most of my coursework is focused on applied statistics which heavily uses R.

I’m wondering if it makes sense to use R for the EDA, and statistical analysis portions of an ML project, then use Python for the AI/ML portion. Can I take advantage of the strengths of both languages within the same project, or would that be confusing to employers?

For some background, I’m already familiar with Python. It was the primary language used in the two introductory CS courses I took as prerequisites. Since I’m now using R heavily for my statistics coursework, I’d like to continue building my skills in both.

Resume in the comments.

Revised my resume and consolidated 3 roles into one for my most current role. I’m also planning to remove the intern experience once I add another project since it’s irrelevant. I’m targeting entry level data analyst roles.

I often read comments from hiring managers and interviewers saying they’re disappointed with recent data science graduates.

I’m curious, what do you think these graduates are lacking? If someone wants to become a data scientist, what skills should they focus on? Strong software engineering skills? Math and statistics? Something else?

A lot of the advice I see seems to be geared toward landing data analyst roles rather than data scientist roles.

So, what are employers actually looking for in entry-level data science candidates today? Especially as a career changer coming from another unrelated career.

I’m doing an MS in Data Science & Statistics. With how saturated the job market is and hundreds of applicants applying to data analyst roles, even local on-site ones, I’m kind of discouraged about this whole field and whether I’m actually ever going to land something here.

The industry experience that I have hasn’t helped me at all. My current job wants me to focus on my assigned tasks only, and getting access to data is not possible. Trust me, I’ve tried and requested it. I’m in a niche role that doesn’t really translate to anything outside the company, and our CEO’s plan is to downsize our department with AI.

With how popular data analytics is, it seems like local roles have hundreds of applicants. I’ve even considered taking a pay cut to get into a large company that has an analytics team and eventually pivot. That was my original plan with my current role, but I feel like that’ll just delay my start in the field even further.

So I’m curious what are people’s plans to get their foot in the door, given how saturated the market is? I feel like I’m kind of at a loss on what to do.

I'm pursuing an MS in Data Science with a focus on applied statistics. I currently work at a small fintech company in a niche operations role, and before that I worked at a credit repair company.

I've noticed that my personal interests keep gravitating toward healthcare. Many of the applied statistics methods I'm learning are used heavily in healthcare, and most of my professors either studied or worked as a biostatistician, or their research focused on some type of healthcare subdomain, so they're also passionate about it. I've even considered pursuing a graduate certificate in health informatics or public health because of my interest in the field and lack of domain knowledge, although I've completed a few personal projects using healthcare datasets.

However, I'm constantly reading here and on Linkedin that your current industry experience is a major advantage, and that it can take much longer to find a data-related role in a different industry. Because of that, I feel stuck. I worry that if my next role is in some area of financial services, l'll be pigeonholed into that industry. I don't hate it, but | don't want to be restricted to a single industry, and I know healthcare often prefers candidates with industry experience.

I'm just curious if anyone else has ever gravitated toward an industry they didn't have experience in. Were you able to successfully pivot into another industry for your first data analyst or data science role?
Thanks in advance!

I’m pursuing an MS in Data Science with a focus on applied statistics. I currently work at a small fintech company in a niche operations role, and before that I worked at a credit repair company.

I’ve noticed that my personal interests keep gravitating toward healthcare. Many of the applied statistics methods I’m learning are used heavily in healthcare, and most of my professors either studied or worked as a biostatistician, or their research focused on some type of healthcare subdomain, so they’re also passionate about it. I’ve even considered pursuing a graduate certificate in health informatics or public health because of my interest in the field and lack of domain knowledge, although I’ve completed a few personal projects using healthcare datasets.

However, I’m constantly reading here and on LinkedIn that your current industry experience is a major advantage, and that it can take much longer to find a data-related role in a different industry. Because of that, I feel stuck. I worry that if my next role is in some area of financial services, I’ll be pigeonholed into that industry. I don’t hate it, but I don’t want to be restricted to a single industry, and I know healthcare often prefers candidates with industry experience.

I’m just curious if anyone else has ever gravitated toward an industry they didn’t have experience in. Were you able to successfully pivot into another industry for your first data analyst or data science role?

Thanks in advance!

I’m interested in working in the financial crime space, but I’ve noticed it’s a niche area, so I’m not familiar with anyone who works in this field. I previously worked at a small credit repair company and currently work at a small fintech company as well, so I’m hoping my industry experience will help me transition into this area. I recently started an MS in Data Science with a focus on applied statistics, so I’m planning to take traditional statistics courses such as applied Bayesian analysis, nonparametric statistics, probability theory, network analysis, etc.

I’m curious, what personal projects and skills should I focus on to break into this space? I know that machine learning and statistics knowledge are important, but is there anything else that would make someone a strong candidate for this domain ?

Thanks in advance!

I'm doing a MS in Data Science with a focus on applied statistics and computational math. So I'm taking various courses in probability theory, nonparametric statistics, Bayesian data analysis, etc., and one course in machine learning under the math department.

They're applied courses so R is used (probability is all theory though) except for the machine learning course which uses Python. This isn't a concern for me since I'm familiar with Python and I can just do all of my personal projects using Python.

But it seems like for Al/ML roles, preference is for computer science students. I’ve also seen CS students easily become Data Scientists so I wonder if I should have gone the MS in Computer Science route instead.

I’m curious, is what I’m learning outdated for AI/ML roles? Are there any additional CS courses I should take to be more competitive for AI/ML? I did take Programming I/II in Java, and Discrete Math in order to be accepted into my degree program.