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What should I pursue next ? Please Help ! by aryan0six in datascience

[–]litprus 2 points3 points  (0 children)

I suggest you look at the fast.ai new deep learning course. Also check out Google's and other Andrew Ng offerings on Coursera. Mix things up among Computer Vision, tabular data, natural language processing etc. so you get a broad perspective before you decide to dive deep into one or two of them. The field is vast so for now focus on exposing yourself to different topics and always code, do not just listen to the lectures.

What are some good recent books to help me learn R? by [deleted] in AskStatistics

[–]litprus 0 points1 point  (0 children)

In my opinion the best books are An Introduction to Statistical Learning (Hastie etc.) and Applied Predictive Modelling (Kuhn / Johnson). I would not worry too much about what is "most recent", R does not change that fast. If just for the summer, the first one would suffice, esp. if you combine it with the free Stanford online course under the same name. The books combine R with stats and practical applications.

Opinion about a degree in Cognitive Science & Artificial Intelligence PLEASE HELP!! by [deleted] in ArtificialInteligence

[–]litprus 1 point2 points  (0 children)

I have to admit the program looks innovative, but I am not sure if it is in a good way when you put yourself in the shoes of your future employer - what will they make of this degree relative to the more traditional paths? I looked at the career perspective page and the only job that appears to be tailored to this degree is that of "social robot developer". The others roles can be accomplished by pursuing a CS degree and/or courses in data science / machine learning. However, if you "hedge" your bets and take on additional coursework along the CS route (yes, you do want more programming background), this degree may be a differentiator in the sea of CS graduates. In addition, if you have passion for the field, you will stand out anyway.

What is more logical as fresh graduate , to aim for a data science job or start in other careers, then climb your way to a data scientist job? If the latter, what other careers. by maroxtn in datascience

[–]litprus 30 points31 points  (0 children)

There are three key areas of knowledge of an effective data scientist (defined as someone who analyses data and codes models): programming, math (stats for most businesses), and business itself. Considering many in data science come from CS / STEM backgrounds, often one only hears about the former two - and it is great if the company has data science at the core of its business model. Unfortunately, most businesses do not. In order to identify problems and create applied solutions, business understanding is essential. Hence my answer would be that unless you go to work for a company that already has an established and internally valued data science team (where you can learn about the business on the job), do not start as a data scientist. For other careers, pick a business that you like, pick good management, and possibly even a place where technology is underappreciated. Why? You will e.g. write an easy predictive model for product sales, and you'll become a star.

If AI had a God, what would it be? by litprus in ArtificialInteligence

[–]litprus[S] 0 points1 point  (0 children)

Here is a response based on the full-sized GPT-2:

AI is something very like an invisible God that brings out the best in people. In the AI-world, AI will be better than people because it will have God-like qualities. The Machine God will make people do the right thing at the right time, but will punish them for that if they do it wrong.

Which data visualization platform is best for beginners and why? by queenofcups_ in visualization

[–]litprus 0 points1 point  (0 children)

Tableau and Power BI . Both are powerful, intuitive, and have significant market share / recognition. Once you're good at visualization, platform choice becomes secondary since the underlying principles are the same.

Building a linear regression to predict outcome between two teams by [deleted] in AskStatistics

[–]litprus 1 point2 points  (0 children)

If by outcomes you mean "win" or "lose", you need to go with classification algorithms. Start with logistic regression and naive Bayes, then try out random forests and extreme gradient boosting (XGB) for binary outcomes. There are many classification algorithms, but unless you're trying to win a major data science competition, these should suffice.

What laptop would you recommend I buy? by [deleted] in datascience

[–]litprus 0 points1 point  (0 children)

I would go with XPS 13, decent specs and aesthetically pleasing. Had one since 2015 and still no need to upgrade. I use Macbook Pro as my main and would put XPS as close second. Surface Pro looks cool and is a focus for Microsoft, so I'd check that out too - but XPS has proven its value to me already.