SQL-for-Data-Analytics VS python-tutorials

Compare SQL-for-Data-Analytics vs python-tutorials and see what are their differences.

python-tutorials

JupyterLab based Python Tutorials. These are detailed markdown tutorials covering installation, markdown, text datatypes, numeric data types, collections, programming constructs, the collections, itertools, math, random, datetime, statistics, sys, operating system, abc, csv and pickle modules, the numpy, pandas and matplotlib libraries. (by PhilipYip1988)
InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
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SQL-for-Data-Analytics python-tutorials
1 6
252 16
0.0% -
0.0 7.3
about 1 year ago about 2 months ago
Jupyter Notebook Jupyter Notebook
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

SQL-for-Data-Analytics

Posts with mentions or reviews of SQL-for-Data-Analytics. We have used some of these posts to build our list of alternatives and similar projects.

python-tutorials

Posts with mentions or reviews of python-tutorials. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-04.

What are some alternatives?

When comparing SQL-for-Data-Analytics and python-tutorials you can also consider the following projects:

NBA-attendance-prediction - Attendance prediction tool for NBA games using machine learning. Full pipeline implemented in Python from data ingestion to prediction. Attained mean absolute error of around 800 people (about 5% capacity) on test set.