# Python for Scientific Computing
###### tags: `Python for SciComp`, `python`
## What do you wish to learn?

## Answers from the question "what do you wish to learn?"
:::spoiler
* More tricks for beautiful python figures
* (1) Create python scripts/functions that can be submitted as batch jobs. (2) Become more familiar with using dataframes and other data structures. (3) Become more familiar with adjusting python figures/plots. (4) Become familiar with Jupyter notebook
* Python to brake the shackles of MATLAB
* I would like to learn more to use matplotlib
* Improve skills in python scripting - I'd like to work on automating a lot of my current work for my thesis.
* I want to feel comfortable with python so I can integrate it in my work flow for my upcoming master thesis. I want foremost a better overall understanding of plotting data and also how to compute data.
* Best way to utilise Python and Jupyter for computing. If possible I would like to see examples from Machine learning and Image processing.
* I would like to improve my hability with Matplotlib aswell as with numpy and pandas. I would like to start doing things with biopython library, but I see is not in the course schedule.
* How to plot graphs for research papers, what are recomendations, common requirements for scientific papers, how to organize code to be easily readable by others
* Jupyter, NumPy, Data visualization with Matplotlib, Pandas
* Python packages and its use in biological research
* This course will enrich my knowledge about the use of python for scientific computing.
* Strengthening my basic python data analysis skills, and learning to apply it my science of interest.
* I'd mostly like to get familiar with some standard python libraries for calculations and plots. I have used them before but lack understanding of what for instance matplotlib's plot and axes instances actually are and subsequently have trouble working with them. I would also really like some guiding in some popular modules such as pandas, numpy and bokeh as I believe they have very powerful prebuild modules and functions but I lack a lot of basic knowledge to use them efficiently.
* Working efficiently with 2D and 3D arrays, writing and reading txt files with for loops, indexing lists
* python modules and libraries, maybe make it more effective the search for commands and stuff
* how to process data frame, scientific computation, perform statistic test, make plot, write script, etc.
* I want to learn to use Python scripting and libraries for data analysis. I work mainly with data in text files or csv files, and often require to plot and visualise them for analysis. I am looking to learn a basic template for opening a data file, performing basic operations on it, and visualising it in various plots. Some information on making publication quality images ni matplotlib would also be great.
* Scientific coding in an effective and efficient way
* I am currently working on deep learning applications for image formation. I aim to refresh my knowledge in Python and learn concepts that I am not previously experienced with.
* Interested in scripts, multithreading, libraries, perhaps intro to more advanced stuff (as a springboard for us to learn on our own)
* process with data frame, run common statistic test and scientific computation, make plot, write script.
* I'd like to learn how to use the modules you've mentioned in the course description. I'd like to use Python much more than I currently do in handling NGS data and large files.
* Enough Python to consider using it occasionally
* For my project I will use different bioinformatics genomics tools that will generate large amounts of data. Therefore, I would like to improve my skills in the use of big data but with python.
* learn how to code in python
* data visualisation and eventually data management using python
* python programming for climate research, statistical methods (basic statistics and machine learning), packages: xarray, pandas, matplotlib. How to optimize the programming ( by using dask)
* I want to get a good basic knowledge in programming with Python to develop my scripts for atmospheric science. I know the basics but I feel need a better basic understanding to be able to keep learning on my own and faster.
* Data mining
* I want to enhance my knowledge on Numpy, Matplotlib, Pandas.
* Effective programming approaches to handle and operate with very large datasets.
* efficient loops, handling netcdf files
* python libs
* Introduction to numpy and plotting in python
* I want to improve my current knowledge of python.
* many nice things
* I would like to use Python for postprocessing HDF5 files.
* Everything
* Parallel Computing
* To learn about scientific python technics
* Advanced tools for numerical computations in Python, design aspects of scientific programmes, maybe things like numba, parallel programming. Basically I have general knowledge of the standard numerical libraries and would like to take it one level up.
* Machine Learning(classification, regression)
* Get a good introduction to the broader Python for science ecosystem and be able to use the right tools for my work and keep my code in good shape.
* I am using python for research. So, this is perfect match.
* Creating efficient pipelines and modular code. I can write scripts and use python to solve problems but I'd like to learn to build more efficient, elegant and purposeful code.
* How to use python in scalable environment, in terms of memory and processing power
* Tricks and tips in python
* how to code in a better way
* Ibreally don’t have a preference. I just want to learn Python. I studied R for a while, but being interested also in programming, Python seemed a better language.
* I am using the Python for data processing and graphing and am familiar with an array of basic commands and ways to develop my scipts. I have been using also Machine Learning scripts in Matlab. What I want to learn is how to use the Machine learning scripts via python, where to start, what needs to be installed, etc. My goal would be to create a script which preprocesses the data and creates a database, followed by the machine learning aided division of the data. I had some basic course in Machine learning for doctoral students and material scientists in Aalto. My application would be using few steps. the first step uses spectroscopic data used as a differentor of composition. However, from raw spectra there is an ability to divide raw materials into classes, but still between products there is an overlap in response. I add a second measurement at elevated temperatures and obtain a different set of spectra. Based on that a division between products could happen. At this point I am still thinking how to structure my database, could use some hints. I know how to divide the samples on the graph, but I still do not know how to then take the PCA result into some conclusion, e.g. this is product A and that is product B. Mostly I have been strugling because I am better in python scripting than in matlab. This is why I would like to do the machine learning in python as well to combine and make my life easier. So far I have close to 500 spectras from raw products and then I had been adding the spectra from heating for those that were overlapping for proof of concept. From every heating experiment I have about 32 spectras, and there is about 45 heating experiments conducted, but not every of those experiments was perfect, so there is some data I want to exclude.
* Some best practices (I am self-taught)
* My field is computational fluid dynamics: I'm experienced in scientific computing/data processing with Matlab, and would like to broaden my skillset to include Python/Numpy
* Jupyter, get comfortable to use python more
* Transitioning from MATLAB to Python for Scientific Computing
* An overview of all the different tools that are available when using Python for research purposes
* Basics of python
* Need to get a better exposure to using Python beyond the running simple scripts for analyzing simulation data
* I looking forward to learn data analysis through python, basics of python and its applications for scientific data handling and visualization.
* Refresh my knowledge of Numpy/Scipy/Pandas and learn more about making codes reproducible (environments, dependencies)
* Improving my Python skills
* nothing specific
* I wish to learn how I can automate post-processing and visualizing my data (including spectral data from various methods, e.g. DSC, X-ray, Raman) for various purposes, e.g. publications and presentations.
* Use Python for scientific computing
* Using Scipy for numerical optimization (Scipy.optimize) and Pandas for machine learning
* doing some projects
* Matplolib Library, Numpy advance feature, Pandas library etc.
* I am a newbie to python and would like to get started. I want to start using python for RF measurement equipment control and want to see what other things I can achieve by learning python.
* Design of scientific figures with Python
* I wish to learn to use the datasheet to build some model about materials.
* Learn to use data and coding of algorithms in python
* The python applied in scientific research
* "- Matrix calculations/Linear Algebra using Python
* - Data visualisation basics
* - Scientific Computational Libraries
* - GUI development using Python"
* Convert my Matlab skills into Python
* develop my skills further
* I am looking forward to learn basics of python (e.g., matrix manipulation, loops, soring, plotting, data analysis etc.) as I am trying implement some ML algorithms in my research work.
* ML, Data Engineering, Data Visualization
* SciPy and Pandas libraries
* The use of Python for Machine Learning programming
* more about the use of different python packages for scicomp
* How to use Python tools such as NumPy, SciPy, Matplotlib, and Pandas
* I would like to learn more about tools that can be sued for Python for scientific computing
* Visualization in matplotlib for scientic purposes, processing of scientific data, creation of graphical interfaces for handling scientific tools
* different libraries, plotting, mathematical operations, etc.
* brush up my current python knowledge
* Great opportunity to learn python coding from the best.
* Primary information of Python
* Scientific computing
* Python Tricks
* Learn Phyton
* Looking to transition to Python from a MATLAB background.
* aalto email
* Basics of python
* "Python has been a very emerging tool when it comes to open source computing even in simple processes where MATLAB used to be a leader. However, various license requirements and expensive toolboxes deter advancement of research and development when MATLAB needs to be used.
*
* As part of my PhD, I want to learn and use python so that I can be technology agnostic and not rely on paid tools for my work. This not only helps me realize the actual substance in what I learn (for example implementing a partial differential equation solution rather than using some MATLAB black box set) but also help the open source community in development of many people tools that remove road blocks in more conservative fields of engineering like motor design. These fields highly depend on industry standard software and I always disliked that as being a very open source oriented person.
*
* I wish to learn computing in terms of scientific scripting, iterative solution calculation, implementing things like data analysis outcomes in form of graphs and visualization in pursuit of bigger engineering community and knowledge building goals."
* As I am doctoral student to enhance my programming skills
* Nothing particular, just to enhance my python skills.
* Optimization tools in Phython
* I want to know the basic on Python and how to use it deal with my lab work data
* I would like to refresh my Python skills.
* Computational programming for python
* Python with Data Science orientation
* numpy. Mainly I'm curious.
* Tips on using python for fmri data analysis
* More effective python coding; environments; packaging
* basic scripts, plotting graphs, statistics
* Use of python for scientific computing
* I develop models on Matlab for electric machines design and I would like to convert it to Python
* Machine learning algorithms, OOP
* Leaning more Python
* Matrix operations, data filtering, visualization...etc I am looking for a replacement of Matlab in a intermediate level.
* matrix operation, data analysis
* To get a medium level understanding of Python.
* I just started with Python, so everything is helpfull
* Imporve my python skills.
* I learnt Python by myself and used that for some research work. It would be interesting for me to know that in a more structured way + find insight about potential tools as you mentioned in the course page.
* It would be nice to learn a bit about general use of version control/git and other relevant good practices. Maybe a quick introduction to various available differential equation solvers.
* get more knowledge on the language
* Using python in scientific research.
* I wish to deepen my understanding of coding with Python.
* "I wish to learn mainly:
* 1. Defining and installing new python package,
* 2. Parallel processing,
* 3. UI designing,
* 4. Machine learning in python."
* Scientific computing to help with data analysis
* applying python for machine learning algorithms
* The preview seemed comprehensive enough
* Numpy
:::

## SE Answers from the question "what do you wish to learn?"
:::spoiler
* I have some python basics, I want to learn some useful tools and libraries, such as Pandas. Besides, I also have some environment conflicts problems when I run python tools in PyCharm, so I want to know more about the environmental settings.
* Become more proficient at python
* Learn how to manipulate and plot data with NumPy, SciPy, Matplotlib, and Pandas and also work with Binder. Incorporate these tools into programming habits.
* use of python for data analysis
* Parallel programming and Python libraries
* Parallel programing, binder, fill in my gaps in pandas
* My PhD project has a lot of computational analysis, specifically single cell RNA sequencing analysis as well as RNA velocity analysis, which require advanced python skills, in terms of plotting the data and manipulating large data. So I think this course will definitely help me with some of weaknesses, that is Scipy, which I recently started using as well as familiarize more with pandas and Matplotlib and I will be very happy to take it.
* I wish to start using libraries. I can read python scripts and am able to write basic scripts, but would like to start using Python in a much more advanced manner to analyse my sequencing data.
* scripts, plots, analysis of numerical simulation outputs..
* Get fluent with Python libs
* I wish to learn basic data processing and analysis using Python libraries. I work with large datasets which are generated as text or csv files. I would like to know how to open them in Python and perform analysis using Numpy, Scipy, Pandas, and visualise using Matplotlib.
* To understand how one things using python, and important coding strategics
* NumPy and Pandas libraries. More specifically, I would like to learn how to extract or add information in columns from the inputs or outputs that I get from my calculations.
* comprehensive introduction of different python libs
* I like to learn about Classes and how to use them. Why is it good to use Classes? Why not just using modules or functions?
* I have difficulties to understand the difference between array, tuples and ... compared to MATLAB."
* I'd like to learn python to be able to implement tools for use in nucleic acid analysis (e.g., PySam).
* I want to participate in the course to learn the best practices from libraries that I use in my own research (numpy, scipy, pandas), as well as to understand how to organize my Python environments and to use Binder (which is completely new to me). I also saw on the materials some small bits about parallelization in Python, which I am extremely interested in!
* To gain required knowledge of Python for Machine Learning Application and Post Processing
* Refresh Python knowledge and kick-start transformation of computer assignments from Matlab to Python
* Pros and cons of NumPy, SciPy, Matplotlib, and Pandas
* General overview of helpful tools for biostatistics.
* statistical analysis using python
* Brush up on the basics and learn beyond basics
* I want to learn more about the differents packages, since I have used some of them but not really learned fully how to use them. My learning and experience has been mostly in programming without packages, but now I would like to increase my knowledge in that other side so I can more easily use the available tools.
* Deepen and widen my knowledge of Python to increase my understanding of the systems used at my workplace.
* Python fundamentals for scientific computing
* Since that python is the most common language for machine learning. So I would appreciate it if the course can be taught in a way that can be more useful in machine learning.
* Scientific data analysis and plotting
* Pandas, super, classes, Decorators
* I would like to learn how to manage the libraries to do statistical calculations and analysis and graph them in the context of greenhouse gases.
* I would like to learn how to solve eigenvalue problems and generalized eigenvalue problems using numpy and scipy, making multiindex tables with pandas, making contour plots and 3D plots with matplotlib.
* Daily programming with Python for solving ODE/PDE with numerical methods.
* Understand what is a python library, its structure and purpose. Know good python dependecies for scientific computing and refine scripting skills with Pandas, Numpy, and other packages.
* General skills improvement in Matplotlib and Numpy
* get to know and use python libraries
* data analysis, data visualization, scripting
* the way of designing a large project based on Python
* Learning how to work with NumPy and Matplotlib.
* Improve my Python coding skills. I already use Python in some of my research but would need to
* I would like to get an updated handle on pandas, scipy, numpy for scientific use.
* More advanced knowledge of libraries of Python for scientific research
* I fit the researcher A2 description on the landing page. I use some pandas and visualization but do not know the basis and best practices in many of them.
* I want to develop a deeper understanding of Python. I'm using it for image analysis and I would like to build a better foundation of Python knowledge.
* I was a Matlab user, I wanna switch to Python
* NumPy and Pandas
* I use Pyhton a little for ASE calculations, but I would love to get more experience to start creating workflows in AiiDA, etc.
* I would like to increase my knowledge in Python and apply theme in my research projects.
* power me in scientific comuting
* I'd like to learn how to use Python for processing and analysis of single cell RNA sequencing data. I also take the course to get a general introduction to Python which can be a foundation for subsequent self-learning.
* usefulneed of python in computational chemistry and molecular modeling field
* all
* basic about mpi programming
* Pandas
* More detailed knowledge on the functions of the lenguage and its main properties, besides learn how to look for infoamation for myself in the future
* Python for Scientific Computing
* I like to get a better overview about the capabilities and use cases of pandas, numpy and scientific computing packages of python. This is because all my knowledge is auto-didactic and I never had a read introduction into all of this packages. Moreover, I like to gain new knowledge regrading code optimization and parallel programming to use the full potential of my current hardware.
* I would like to extend my knowledge of using important scientific Python libraries such as NumPy, SciPy, Matplotlib, and Pandas. I would like to learn how to work with dataframes using Pandas, numerical data arrays with NumPy, statistical and machine learning methods with SciPy and data representation with Matplotlib.
* How to write code in Python
* Python for research. I want to be able use python to create models of PV cells and wind power technologies. My goal is to aid on the development of open source solutions that can aid the implementation of these technologies.
* I wish to learn valuable tools in python as our lab plans to transition over to it, The specific ones I am not sure. I wish to learn effective and clean data management and hopefully some valuable tools suitable to our future research. We plan on using python for analysis of both brain imaging and eye-tracking data. Among other things we plan on using MVPA, source reconstruction and neutral nets for our analyses.
* How to look for information and practicalities of python
* I have several years of experience in Python, however, I have not learnt so much about scientific computing and I feel the need more and more in my work. I'd like to specifically learn more about numpy, scipy, pandas but also how to package scripts and present them as a module for other scientists to use.
* I wish to get a more comprehensive introduction to Python and learn ways my programming can get more efficient. Atm I know the basics but my programming strategy is pretty much based on recycling and editing some existing scripts to fit the application that I need, complemented with some Googeling.
* I have only a very limited understanding of Python and wish to expand my knowledge of the programming language to help me implement pre-scripted code for neural signal processing. I want to furthermore learn how to use Python for the implementation of statistics.
* Can't think of anything particular from the top of my head, but I'm open to deepen my knowledge of some useful python libs and maybe get some useful general tips on how to effectively make use of Python
* Learn about the inner mechanisms of Numpy arrays and other frameworks
* How to improve my programming skills for my scientific research
* overview of python
* I want to learn enough Python to start considering using it, what I know so far through a previous basic course isn't enough to be worth the time investment next to other languages
* how to process data frame, conduct statistic test, make plot, write script, and usage of most popular packages
* learn how to code in python
* Looking forward to learning (by following along with the videos and hackMD) what I can do with Python to apply it to my research project!
* Python for Scientific Computing
* I want to learn data handling, plotting etc. in python. I want to use this in regards to chemistry.
* I would like to enhance my knowledge in using some python libraries for example, numpy, pandas.
:::

## Norway Answers from the question "what do you wish to learn?"
:::spoiler

* Parallelization, Python packages relevant for solving partial differential equations and deep learning packages.
* Transfer my R skills to Python
* how to efficiently analyze data
* Graph Plotting
* Use python for fast prototyping of code (e.g. testing new ideas, structures, algorithms) before proceeding to a HPC implementation in another language (such as Fortran or C++)
* Numerical simulations using Boltzman Lattice method.
* How to use jupter
* Use cases of python libraries for Machine learning
* Machine learning and statistical tools
* I wish to obtain a more formal overview of python for scientific computing. When I was a PhD/postdoc/researcher I did use many of the tools listed in the course with the only scope of progressing in my research, i.e. without focussing much on the overall quality of the code. I wish now to code more professionally to better support my group in providing services.
* Get an overview and some practice niche functionality offered by libraries
* My python knowledge thus far is mostly self-taught. I want to broaden my knowledge and fill any gaps I might have. Particularly in script writing and jupyter.
* I am hoping to learn more about the libraries mentioned and how can I use them for my workflow.
* My main goal is to become more efficient when it comes to process our scientific data. I wish to practice pandas and learn about parallel programming as well as packaging and interfacing libraries written in different languages with Python.
* I am working on the different medical data sets and this workshop will be very helpful for me.
* I wish to learn more about scientific computation and data processing using Python, plotting packages, arrays, mapping and projections
* I am interested in learning how to use python efficiently for analysis of large data sets and for plotting. With respect to the latter, contour plotting on maps with different projections is of particular interest.
* I am currently confused by all the different types of data structures in python, and would love to get a better grasp on what to use when.
* Transcriptomic data analysis is a part of my project. I have learned to use some packages and libraries on R and python. But I still hope to systematically learn to use these tools more efficiently.
* I would like to be able to use python for machine learning.
* Python for everything as a Complex sollution
* I wish to learn methods to perform advance computation and prediction using machine learning. The packages uses Pandas and Numpy etc. Hence I think this course will really give me the edge i need.
* postprocess datasets (csv./.txt-files)