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Sorry for my ignorance. What's the usage you give to those tools? What's your domain? And finally, how to get started with them? I'm highly interested on Data S

Sorry for my ignorance. What's the usage you give to those tools? What's your domain? And finally, how to get started with them? I'm highly interested on Data Science but it's hard to start, there's so much stuff going on.

e19293001 10y ago
You might want to try installing anaconda[0]. After you have installed, follow the instructions here[1]. There are also lots of videos in youtube if you have not yet done a little bit of googling. By the way, this all requires that you know how to code in python else you can get your hands wet here[2]. Now if you already know how to code in python and know how to use jupyter, you're ready to learn about neural networks. This book[3] did a really good job in explaining the subject. And, this is what jupyter notebook can do[4]. [0] - https://www.continuum.io/downloads https://www.continuum.io/downloads [1] - http://jupyter.readthedocs.io/en/latest/install.html http://jupyter.readthedocs.io/en/latest/install.html [2] - https://www.codecademy.com/learn/python https://www.codecademy.com/learn/python [3] - https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp... [4] - https://try.jupyter.org/ https://try.jupyter.org/
analog31 10y ago
You didn't ask me, but I use the same tools in my work. If a scientist tells you they're using "Python," there's a good chance they're referring to this stack, possibly without knowing what's under the hood. I'm a scientist working at a private company that makes measurement equipment, and I use Python for practically everything now: Modeling, data analysis, and even talking to hardware. Our engineers have been kind enough to provide Python friendly interfaces to our hardware, so I can write things like test scripts that are used in product development and sometimes in manufacturing. I write similar scripts when I'm prototyping hardware myself. I write some stand-alone Python programs, when it becomes convenient to provide a GUI (via Tkinter). I also use Jupyter quite a lot for data analysis and exploratory work. Often, I use a stand-alone Python program to generate data, and a Jupyter notebook (with inline Matplotlib graphs) to analyze it. At another level of abstraction, I now use Jupyter as my lab notebook. Just last week, I completed a fairly extensive study that included development of a prototype, automated testing, and data analysis. When I was satisfied with my results, rather than writing a separate report, I simply added explanatory text to the Jupyter notebook, with some cell phone pictures for documentation. And then I scheduled a meeting to present my results. My presentation was a slideshow that was served up from the same Jupyter notebook using the nbconvert utility. Even though my work will never be revealed to the public, I have embraced the idea that "real science is open and reproducible," and am pushing towards an ideal state where a single file folder contains everything needed to reproduce my work, including hardware design, source code, experimental data, and so forth. I'm also learning to use git, so that this stuff can all be revision controlled. If someone asks me for help getting started with Python, I suggest that they should get Anaconda, or an alternative that I prefer called WinPython.
- e19293001 10y ago
  What resources or books you would like to recommend for data analysis?
  - analog31 10y ago
    Unfortunately, I might be a bad person to answer that question. I'm 52, and have been programming since high school, not as a professional developer, but just using programming as a tool. So I learned data analysis long before learning Python, just through my general science education and work. The result is that I don't really know what a good modern tutorial is. The old timers would just hand you a copy of Numerical Recipes and wish you good luck. ;-)
  - LolWolf 10y ago
    It depends on what kind of analysis you're doing. If you're doing pure exploratory stuff on large datasets, you'll mostly be using NumPy as a tool to help with Pandas. See McKinney's Python for Data Analysis (I believe it's from O'Reilly books) if you'd like a fairly good intro. If you're more like me on using numerical methods, then I'd recommend first going through the math (I can be more specific, depending on what kind of work you'd like to do/are interested in) using Julia/MATLAB/Octave and then jump into NumPy stuff. In my experience, I've found that using NumPy/SciPy numerical libraries effectively requires some knowledge of the underlying numerical algorithms since the default arguments sometimes require tweaking.
petters 10y ago
These days, I mostly use those tools for plotting and simple exploratory data analysis. I also do some modeling for integer programming.

e19293001 10y ago

You might want to try installing anaconda[0]. After you have installed, follow the instructions here[1]. There are also lots of videos in youtube if you have not yet done a little bit of googling. By the way, this all requires that you know how to code in python else you can get your hands wet here[2]. Now if you already know how to code in python and know how to use jupyter, you're ready to learn about neural networks. This book[3] did a really good job in explaining the subject. And, this is what jupyter notebook can do[4]. [0] - https://www.continuum.io/downloads https://www.continuum.io/downloads [1] - http://jupyter.readthedocs.io/en/latest/install.html http://jupyter.readthedocs.io/en/latest/install.html [2] - https://www.codecademy.com/learn/python https://www.codecademy.com/learn/python [3] - https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G https://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp... [4] - https://try.jupyter.org/ https://try.jupyter.org/

analog31 10y ago

You didn't ask me, but I use the same tools in my work. If a scientist tells you they're using "Python," there's a good chance they're referring to this stack, possibly without knowing what's under the hood. I'm a scientist working at a private company that makes measurement equipment, and I use Python for practically everything now: Modeling, data analysis, and even talking to hardware. Our engineers have been kind enough to provide Python friendly interfaces to our hardware, so I can write things like test scripts that are used in product development and sometimes in manufacturing. I write similar scripts when I'm prototyping hardware myself. I write some stand-alone Python programs, when it becomes convenient to provide a GUI (via Tkinter). I also use Jupyter quite a lot for data analysis and exploratory work. Often, I use a stand-alone Python program to generate data, and a Jupyter notebook (with inline Matplotlib graphs) to analyze it. At another level of abstraction, I now use Jupyter as my lab notebook. Just last week, I completed a fairly extensive study that included development of a prototype, automated testing, and data analysis. When I was satisfied with my results, rather than writing a separate report, I simply added explanatory text to the Jupyter notebook, with some cell phone pictures for documentation. And then I scheduled a meeting to present my results. My presentation was a slideshow that was served up from the same Jupyter notebook using the nbconvert utility. Even though my work will never be revealed to the public, I have embraced the idea that "real science is open and reproducible," and am pushing towards an ideal state where a single file folder contains everything needed to reproduce my work, including hardware design, source code, experimental data, and so forth. I'm also learning to use git, so that this stuff can all be revision controlled. If someone asks me for help getting started with Python, I suggest that they should get Anaconda, or an alternative that I prefer called WinPython.

e19293001 10y ago
What resources or books you would like to recommend for data analysis?
- analog31 10y ago
  Unfortunately, I might be a bad person to answer that question. I'm 52, and have been programming since high school, not as a professional developer, but just using programming as a tool. So I learned data analysis long before learning Python, just through my general science education and work. The result is that I don't really know what a good modern tutorial is. The old timers would just hand you a copy of Numerical Recipes and wish you good luck. ;-)
- LolWolf 10y ago
  It depends on what kind of analysis you're doing. If you're doing pure exploratory stuff on large datasets, you'll mostly be using NumPy as a tool to help with Pandas. See McKinney's Python for Data Analysis (I believe it's from O'Reilly books) if you'd like a fairly good intro. If you're more like me on using numerical methods, then I'd recommend first going through the math (I can be more specific, depending on what kind of work you'd like to do/are interested in) using Julia/MATLAB/Octave and then jump into NumPy stuff. In my experience, I've found that using NumPy/SciPy numerical libraries effectively requires some knowledge of the underlying numerical algorithms since the default arguments sometimes require tweaking.

petters 10y ago

These days, I mostly use those tools for plotting and simple exploratory data analysis. I also do some modeling for integer programming.