JASP
jamovi
Statista
BlueSky Statistics
SOFA Statistics
Jupyter
IBM ILOG CPLEX Optimization Studio
datarobot
Pandas
NumPy
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OpenCV
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Exploratory
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JASP PandasPandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.
JASP works very similarly to jamovi. That's not a coincidence, as some JASP developers split off to create jamovi. You can open a single dataset and use the most popular statistics and machine learning methods. But if you have multiple datasets to merge, you must do that in another tool. Also, the dataset must maintain a single structure throughout your analyses. Restructuring or transposing is not allowed. It is commonly said that data scientists spend 80% of their time wrangling data like that, so that's a significant limitation for general use. However, those simplifications make JASP a good choice for teaching. Another advantage for teaching is that the menus are very sparse, but you can add to them easily by downloading additional modules. That's the opposite of similar software such as BlueSky Statistics, SPSS, or Minitab, which install all features at once. If you're looking for free and open-source software, JASP and jamovi are best for teaching while BlueSky Statistics is best for general-purpose analysis.
Based on our record, Pandas seems to be a lot more popular than JASP. While we know about 219 links to Pandas, we've tracked only 15 mentions of JASP. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
For anyone looking for a quick and hands-on dive into the world of Bayesian modelling and inference, I can't recommend JASP enough, made freely available by the University of Amsterdam[0]. I've recommended it before, and it's just a breeze to work with, seeing frequentist and Bayesian analyses side-by-side. [0]: https://jasp-stats.org/. - Source: Hacker News / 4 months ago
Anyone looking to apply and compare frequentist and bayesian methods within a unified GUI (which is essentially an elegant wrapper to R and selected/custom statistical packages), should check out JASP developed by the University of Amsterdam [0]. It's free to use, and the graphs + captions generated on each step are of publication quality out of the box. Using it truly feels like a 'fresh way' to do... - Source: Hacker News / over 1 year ago
Https://jasp-stats.org fully free. Its advisible to learn python, R or matlab for graduate school. Source: almost 2 years ago
Also for alternative software that are much easier to use take a look at JASP or jamovi (both are very similar); and as a bonus, neither of these two will require you to manually add product variables to your dataset. Source: almost 2 years ago
If you have no access to SPSS (or SAS, or JMP), then look into JASP (https://jasp-stats.org/). I've only just touched that. One thing I believe is that JASP (as well as JMP) will allow/block off tests and analyses depending on the nature of each column. This means that, for example, if you have groups A, ..., Z, the software will treat those as non-numbers, which can only be used as inputs for variables which... Source: about 2 years ago
Libraries for data science and deep learning that are always changing. - Source: dev.to / about 1 month ago
# Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / 2 months ago
As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 2 months ago
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 10 months ago
jamovi - jamovi is a free and open statistical platform which is intuitive to use, and can provide the...
NumPy - NumPy is the fundamental package for scientific computing with Python
Statista - The Statistics Portal for Market Data, Market Research and Market Studies
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
BlueSky Statistics - BlueSky Statistics is a fully featured statistics application and development framework built on...
OpenCV - OpenCV is the world's biggest computer vision library