Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Composable Analytics
IBM ILOG CPLEX Optimization Studio
RapidMiner Studio
Pyramid Analytics
Amadea
Jupyter
Solvuu
PurpleCube
Pandas Composable AnalyticsPandas 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.
Based on our record, Pandas seems to be a lot more popular than Composable Analytics. While we know about 219 links to Pandas, we've tracked only 1 mention of Composable Analytics. 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.
Libraries for data science and deep learning that are always changing. - Source: dev.to / 30 days 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 / about 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 / about 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 / 9 months ago
- Front-End UI Developers passionate about creating well-architected user interfaces and fluent in current best practices for responsive and accessible design. - Junior and Senior level Software Engineers that have the ability to work across all layers of the application, from back-end databases to the UI. - Data engineers and data scientists knowledgeable in developing and training data models and building... - Source: Hacker News / almost 4 years ago
NumPy - NumPy is the fundamental package for scientific computing with Python
IBM ILOG CPLEX Optimization Studio - IBM ILOG CPLEX Optimization Studio is an easy-to-use, affordable data analytics solution for businesses of all sizes who want to optimize their operations.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
RapidMiner Studio - Visual workflow designer for predictive analytics that brings data science and machine learning to everyone on the analytics team
OpenCV - OpenCV is the world's biggest computer vision library
Pyramid Analytics - Pyramid brings data prep, business analytics, and data science together into one frictionless business and decision intelligence platform that helps you deliver timely and effective decision-making.