Adobe Analytics
Google Analytics
Mixpanel
Matomo
Plausible.io
Heap
Fathom Analytics
Simple Analytics
Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Adobe Analytics 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.
Based on our record, Pandas seems to be a lot more popular than Adobe Analytics. While we know about 219 links to Pandas, we've tracked only 2 mentions of Adobe 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.
Google Analytics was launched in 2005 as a tool for reporting web traffic. It is one of many web analytics tools. Adobe Analytics and Hubspot Analytics are example competitors to Google Analytics. - Source: dev.to / over 3 years ago
What it is: Adobe Analytics provides a set of tools that lets you collect, measure, and explore data you can use to predict traffic and gain insights. It has an interactive analytics workspace that helps you easily drag and drop data tables, visualizations, and components. - Source: dev.to / over 3 years ago
Libraries for data science and deep learning that are always changing. - Source: dev.to / 29 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 1 month 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
Google Analytics - Improve your website to increase conversions, improve the user experience, and make more money using Google Analytics. Measure, understand and quantify engagement on your site with customized and in-depth reports.
NumPy - NumPy is the fundamental package for scientific computing with Python
Mixpanel - Mixpanel is the most advanced analytics platform in the world for mobile & web.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Matomo - Matomo is an open-source web analytics platform
OpenCV - OpenCV is the world's biggest computer vision library