•1 min read•from Machine Learning
[D] Could really use some guidance . I'm a 2nd year Data Science UG Student
Our take
As a second-year Data Science undergraduate, you're at an exciting crossroads in your educational journey. With a solid grasp of foundational concepts like linear regression and decision trees, you're well-positioned to deepen your expertise. However, navigating the wealth of learning resources can be daunting. Prioritize building practical skills through projects and hands-on experience, while exploring structured courses like Andrew Ng’s or fast.ai. Engaging in Kaggle competitions will also provide valuable insights. Focus on what resonates with you, and let your curiosity guide your next steps.
I'm currently finishing up my second year of a three year Bachelor of Data Science degree. I've got the basics down quite well, linear regression, logistic regression, decision trees, (not knowledgable about neural networks/nlp though) I'm comfortable with Python, pandas, sklearn, and I plan to start learning PyTorch/Keras(whichever might be better). I also know SQL at a decent level.
But I feel a bit lost on what to do next. There's so much material out there and deciding a source to learn from gets confusing. I've seen people mention fast.ai, Andrew Ng's courses, Kaggle competitions, building projects, and I genuinely don't know what order makes sense or what's actually worth my time. Any help is GREATLY appreciated
[link] [comments]
Read on the original site
Open the publisher's page for the full experience
Related Articles
- Could really use some guidance . I'm a 2nd year Bachelor of Data Science StudentHey everyone, hoping to get some direction here. I'm finishing up my second year of a three year Bachelor of Data Science degree. I'm fairly comfortable with Python, SQL, pandas, and the core stats side of things, distributions, hypothesis testing, probability, that kind of stuff. I've done some exploratory analysis and basic visualization + ML modelling as well. But I genuinely don't know what to focus on next. The field feels massive and I'm not sure what to learn next, should i start learning tools? should I learn more theory? totally confused in this regard submitted by /u/Crystalagent47 [link] [comments]
- Feeling Really Lost, just wrapped up my 2nd year in my DS Bachelor Degree,I do have 2-3 projects on my cv + knowledgable in Python,SQL,and all the imp libraries.What should I learn from this point onwards?Should i do AI?Cloud?go deeper in DS? ML? Would love for someone with industry exp. to help meThe entry level job market being in shambles is another factor contributing to my confusion on what to learn now, also ML? AI? Further into DS? My main goal is employability. What are hiring managers looking for in fresh grads? submitted by /u/Crystalagent47 [link] [comments]
- [D] USQL Joins Were Cool, But Now I Want to Join the GenAI PartyHi Experts, I have 1.5 years of experience in Data Engineering, and now I want to start learning AI, ML, and Generative AI. I already have some knowledge of AI and ML from my college days as a CSE (AI) student. I’ve also worked on a few image classification projects and explored the application of AI in real-life problems. Currently, I want to dive deeper into Generative AI. However, before that, I’d like to strengthen my understanding of the core concepts behind it—such as neural networks and NLP—so that I can later focus on real-world applications. If you have a roadmap or guidance that data scientists or other professionals usually follow, it would be very helpful for me as I want to switch from a Data Engineering role to a Data Scientist role. submitted by /u/Far-Mixture-2254 [link] [comments]
- Best way to get real experience over the summer?I'm starting my master's program in data science in a highly regarded Ivy League University this coming fall. While I'm very excited, I was also hoping to get the opportunity to gain real world experience doing data science and get a head start on my incoming debt with an internship. Unfortunately true data science internships seem few and far between. I apply to every new data science adjacent internship posting I see per day, but have only gotten an interview for a MLE related role in which they went with another candidate. My question is: Besides internships, is there any way to gain real world experience to put on a resume? As a disclaimer, I have already done personal projects, am on kaggle, and am aware of datakind. Any advice is much appreciated submitted by /u/PM_ME_CALC_HW [link] [comments]
Tagged with
#real-time data collaboration#big data management in spreadsheets#generative AI for data analysis#conversational data analysis#rows.com#Excel alternatives for data analysis#intelligent data visualization#data visualization tools#enterprise data management#big data performance#data analysis tools#data cleaning solutions#machine learning in spreadsheet applications#enterprise-level spreadsheet solutions#financial modeling with spreadsheets#real-time collaboration#Data Science#linear regression#logistic regression#decision trees