My choices are #Jupyter and #VSCode. Because interesting tools for data science.

Learn Array with Numpy especially vectors in Numpy, ndarray, mathematical functions with examples

Prerequisite : Python basic concepts, matplotlib to plot the examples

What is Numpy?

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

Why is NumPy Fast?

Vectorization describes the absence of any explicit looping, indexing, etc., in the code - these things are taking place, of course, just “behind the scenes” in optimized, pre-compiled C code.

Link: https://numpy.org/devdocs/user/whatisnumpy.html
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Quick start link: https://numpy.org/devdocs/user/quickstart.html

#DataScience #DataMuging #WebScrapping
Data Munging is important to get and process messy and complicated data into structured and tabular format.

We will learn more about.

Change color of the DataFrame Cell

Learning Predictive Analytics with Python
#book
#Machine_learning
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Types of Errors in Python

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Thanks for watching!

https://youtu.be/xZBPHQLJ4Ag

#DataScience #ML
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#KeyNote #SQL #Database #DataAnalyzes #RDMS #Python
Benefits of Python for Database Programming
- Python is a popular scripting language to connect to the database and analyzes the data.
- Python ecosystem: - NumPy, pandas, matplotlib, SciPy
- Ease of use
- Python supports relational database systems
- Python database API's to connect to the database
- Detailed documentation: The python is easily available
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PYTHON FOR DATA SCIENCE: The Ultimate Beginners’ Guide to Learning Python Data Science Step by Step (2019)

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#Database #SQL #Datascience #python #DP_API
A Python code to connect to the database using #DB-API

#Datascience #database #sql #python
SQL is one of the most common computer languages in use for working with data today. It is a standardized language for accessing and manipulating relational databases. While it is relatively limited compared to a general programming language such as Python, it is highly optimized for efficient retrieval and aggregation of data from database tables. Its broad support and use virtually guarantees that any professional data scientist or analyst will encounter SQL eventually. Furthermore, SQL is often the paradigm used to discuss the relational data model, which has implications that apply beyond SQL compliant databases.

Relational data model

The relational data model for the most part corresponds with our intuitive notion of a table. Each row is a relation, usually representing some object, event, or idea. Each column corresponds with an attribute which characterizes the relation. In order to reduce redundancy in a database, when creating at able we typically include the minimum amount of attributes required to fully define a relation. This (admittedly vague) guideline is formalized in the idea of database normalization.

Operators in Python
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https://youtu.be/HhTdMVRNO6E

QUESTION OF THE DAY


ARE DATA NORMALIZATION AND DATA STANDARDIZATION THE SAME?
EXPLAIN? WITH EXAMPLE?

#DataScience #datatrnasformation #datacleansing #datapreprocessing

SEND YOUR ANSWER TO @PYDISCUSSION

Introducing MySQL Shell: Administration Made Easy with Python
Charles Bell (2019)

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#KeyNote #DataScience #datanalytics #modeltrain #futureprediction

Data Analytics, we often use Model Development to help us predict future observations from the data we have.

A Model will help us understand the exact relationship between different variables and how these variables are used to predict the result.

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This is for absolute beginners. Expressions in Python.
In this lab you will get to know about:-
What are the expressions in Python?
How to construct expressions in Python?
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https://youtu.be/KOA3j2tbr4M