You want to learn data science, but where do you start?

To start learning data science, begin with fundamental concepts in statistics and programming. Master tools like Python and libraries like NumPy and Pandas. Explore machine learning basics, and work on real-world projects to apply your knowledge. Online courses and communities can provide valuable resources for continuous learning.

Always start with data quality. Nothing good comes from bad data. You can always train up on lots of data analytics skill sets, but data quality is the absolute foundation.

The foundations of data science are rooted at the intersection of mathematical principals, statistical applications, and programming. As a mathematician, I strongly recommend familiarizing yourself with concepts from linear algebra and numerical methods. Statistics plays a big part in experimentation and descriptive analytics. Programming (Python and R are popular) is the customer-facing skill used to paint a picture of your narrative and automate analytic tasks at scale.

Learn stats, recommend: An Introduction To Statistical Learning (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani) Learn Python with practical appplications: Automate the Boring Stuff with Python Learn Deep learning : Dive into Deep Learning (d2i) and Deep Learning for Coders with Fastai and PyTorch by Jeremy Howard, Sylvain Gugger Practice and learn from Kaggle and other competition platforms

Most companies don't need LLM experts or LLM products. Most business cases can be handled with a simple regression model. I don't think focusing on a method is a good route, but rather focus on a problem space. There are product data scientists that optimize KPIs through experiments. Operations research helps companies maximize their ROI. Machine learning data scientists focus more on building models but also overlap in both Product and Operations. There are a variety of problems in all spaces that will use NLP, Computer Vision, or Personalization as tools to solve problems.

There are so many datasets out there. Explore datasets that you have personal interest in (fantasy sports, politics, finances). Build hypothesis around those topics and test them. That's the data science lifecycle. Bonus points if you curate the dataset yourself (web scraping, survey, etc).

Learning DS sometimes feels like I'm trying to navigate a vast ocean solo. But here's the thing, there's an entire community out there who've been in my shoes! This isn't just about knowledge, it's about support and inspiration. That's why connecting with the community is essential. Online forums and blogs? That's where I'll find those who are also deep in tutorials, or celebrating wins, or commiserating over bugs. Social media lets me follow the big names, but also peers on a similar learning path. Meetups are my jam - nothing like being in that room full of passionate minds, sparking ideas, or even collaborating on projects.

?? Begin with the Basics: Focus on foundational topics like statistics, programming (Python/R), and data analysis. ?? Follow Online Courses: Utilize platforms like Coursera, edX, and DataCamp to access structured learning paths. ?? Engage in Projects: Start small with personal projects or participate in competitions on Kaggle to apply what you learn. ?? Stay Updated: Follow data science blogs, podcasts, and news to keep up with the latest trends and tools. ?? Join Communities: Engage with online forums, local meetups, and workshops to learn from others and get feedback.

Methods are always improving. These are things you should keep up to date with. Whether you're working on a very niche NLP problem, new sampling techniques for experiments, or developing predictive models, there's always work going on that is pushing the boundaries. Stay up to date and understand how it affects your work. As far as personal improvement, you can always work on your communication skills. A data scientists most valuable skill is aligning cross-functional stakeholders on the strategy of solving complex problems in a data-driven manner.

I've had experience as a Product Data Scientist, Operations Data Scientist, and ML Data Scientist. Each role had their own problems to solve with a lot of the same techniques (descriptive analysis, hypothesis testing, model development). Focus more on what you find interesting and can see yourself doing for a career and let that guide you.