#pythondata…

[codeonedigest]

(via YouTube Short - Quick Cheat Sheet to Python Data types for Beginners | Learn Python Datatypes in 1 minute)

Hi, a short #video on #python #datatype is published on #codeonedigest #youtube channel. Learn the python #datatypes in 1 minute.

#pythondatatypes #pythondatatypes #pythondatatypestring #pythondatatypedeclaration #pythondatatypeprogram #pythondatatyperange #pythondatatypeofvariable #pythondatatypescheatsheet #pythondatatypeinteger #pythondatatypenumeric #pythondatatypefloat #pythondatatypecomplex #pythondatatypestring #pythondatatypelist #pythondatatypetuple #pythondatatypedictionary #pythondatatypeBoolean #pythondatatypeset #pythondatatype #pythondatatyperange #integer #numeric #string #range #tuple #list #dictionary #Boolean #complex #long

[onlinetrainingcourse]

Free online data science certifications break down financial barriers, allowing individuals from diverse backgrounds to access quality education. Whether you are a student, a working professional looking to upskill, or someone passionate about data science, these Free online data science certifications democratize learning and make it accessible to everyone.

[datas-poof]

detect outliers in Python

https://www.dataspoof.info/post/how-to-detect-and-remove-outliers-in-python/

Detecting outliers in Python can be done using various statistical methods and libraries. One common approach is the use of the Z-score or the IQR (Interquartile Range) method. Here's how you can do it using the scipy library:

Install the required library (if you haven't already):

bashData type:                                    RDF-Turtle                  JSON-LD                  JSON                  CSV                  RDF/XML                  Markdown            Copy codepip install scipy

Detect outliers using Z-score:

pythonData type:                                    RDF-Turtle                  JSON-LD                  JSON                  CSV                  RDF/XML                  Markdown            Copy codeimport numpy as np from scipy import stats def detect_outliers_zscore(data, threshold=3):    z_scores = np.abs(stats.zscore(data))    outliers = np.where(z_scores > threshold)    return outliers[0] # Example usage: data = [1, 2, 3, 5, 100, 6, 7, 8] outliers = detect_outliers_zscore(data) print("Outliers indices:", outliers)

Detect outliers using IQR:

pythonData type:                                    RDF-Turtle                  JSON-LD                  JSON                  CSV                  RDF/XML                  Markdown            Copy codedef detect_outliers_iqr(data, k=1.5):    quartile_1, quartile_3 = np.percentile(data, [25, 75])    iqr = quartile_3 - quartile_1    lower_bound = quartile_1 - (k * iqr)    upper_bound = quartile_3 + (k * iqr)    outliers = np.where((data < lower_bound) | (data > upper_bound))    return outliers[0] # Example usage: data = [1, 2, 3, 5, 100, 6, 7, 8] outliers = detect_outliers_iqr(data) print("Outliers indices:", outliers)

Both methods will return the indices of the outliers present in the data array. The Z-score method is based on standard deviations from the mean, while the IQR method is based on the range between the 25th and 75th percentiles. The choice of method depends on the characteristics of your data and the assumptions you want to make about the distribution of the data.

[cloudpunjabi]

What are data types in Python? What are their uses? What is their subdivision?

Every language has its own predefined data types. The data types in python are the explicit type of values that can be stored in the variable of the program.

[writecode4]

Coercion / python

During an operation with two arguments of the same type, the implicit conversion of one type's instance to another. For example, int(4.28) converts a floating point number to an integer 4.

   in[]    x = int(4.28)             print(x)  out[]    4

But in 4+2.8, each argument is of a different type (one int, one float), and both must be converted to the same type before they can be added, or a TypeError is raised. Without coercion, the programmer would have to normalize all arguments, even those of compatible types, to the same value, e.g., float(4)+4.5 instead of just 4+2.8.

   in[]    sum = (4 + 4.28)             print(sum) out[]    8.28

[datacodewithsharad]

#datacodewithsharad #datascience #pythonData Science - Statistics Correlation Matrix || Data Science Tutorial in Hindi 13

[ebouks]

Python Fundamentals: A practical guide for learning Python, complete with real-world projects for you to explore

Python Fundamentals: A practical guide for learning Python, complete with real-world projects for you to explore

Table of contents : Table of ContentsIntroducing PythonData TypesControl StatementsFunctionsLists and TuplesDictionaries and SetsObject-Oriented ProgrammingModules, Packages, and File OperationsError Handling

[freeudemycoursesfree]

Learn Python with Google Colab - A Step to Machine Learning

Learn Python with Google Colab – A Step to Machine Learning

Hands-on course in python basics with Google Colab, a step toward Machine Learning What you’ll learn in Free Udemy Course Learn Python with Google Colab – Basics of PythonData typesPrinting outputIf-Else conditionsLooping using it for, a whileArithmetic operationsWorking with functionsWorking with List and ArraysPerforming an action on the ListTuple, Set, and DictionaryWorking with…

[super-mounika-posts]

HYSE DIGITAL ACADEMY IS PROVIDING 2 Months Training +1 Month Internship + Working On Live Projects + Job Assistance On Following #Top 10 #IT Domains:Mean StackFull StackPhpDigital Marketing PythonData ScienceAndroidBlock chainJAVA.NETManual and automation testingLEARN & EARN :Also we are Providing Work from home Projects To Our students Where they can earn 7000Rs to 15000Rs. In This Three Months Training Period…Course Fees: Rs.4999/- Hurry Up, Limited Seats Only#Googleform: https://forms.gle/mRUAF6J4F982DAjn9For Registration Contact: 9014316209 / 9121259821/ 9666856432WhatsApp:- 9121259821

[onlinetrainingcourse]

At the heart of the best online Python Data Science course lies a meticulously designed curriculum, carefully woven to seamlessly integrate Python mastery with advanced data science principles. The Best Online Python Data Science Course The journey commences with a solid foundation in Python programming, ensuring learners are not just acquainted with syntax but also equipped to leverage Python’s capabilities for data manipulation.

[jatin1503-blog]

Data Analysis & Modelling nearest available beds in hospitals for Corona virus Patients using python - The Battle Of Covid19

/*! * * Twitter Bootstrap * */ /*! * Bootstrap v3.3.7 (http://getbootstrap.com) * Copyright 2011-2016 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) */ /*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ html { font-family: sans-serif; -ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%; width:100%; } body { margin: 0; width:100% } article, aside, details, figcaption, figure, footer, header, hgroup, main, menu, nav, section, summary { display: block; } audio, canvas, progress, video { display: inline-block; vertical-align: baseline; } audio:not() { display: none; height: 0; } , template { display: none; } a { background-color: transparent; } a:active, a:hover { outline: 0; } abbr { border-bottom: 1px dotted; } b, strong { font-weight: bold; } dfn { font-style: italic; } h1 { font-size: 2em; margin: 0.67em 0; } mark { background: #ff0; color: #000; } small { font-size: 80%; } sub, sup { font-size: 75%; line-height: 0; position: relative; vertical-align: baseline; } sup { top: -0.5em; } sub { bottom: -0.25em; } img #aiintelligence #analysis #artificialintelligencee #artificialintelligenceincomputerscience #artificialintelligenceis #battle #bestlanguagefordatascience #bigdataandanalytics #covid19 #css #Dataanalysis #datascienceforbeginners #datasciencelanguages #datascienceusingpython #explainartificialintelligence #html #learndatasciencewithpython #learningmachinelearning #Of #python #pythonanddatascience #pythondata #pythonr #section #useofbigdata #using Read the full article

[yuliasdream-blog]

An 100% free Data Science learning

Here is the content I’ll be using to master data science.

I got the major part of the list with Giles, but I also added some resources by my own as I found them. Check ‘em out:

PYTHON BASICS

Introduction to Python, The Scientific Libraries, Advanced Python Programming and the Pandas Section of Data and Empirics https://lectures.quantecon.org/py/

https://github.com/jakevdp/PythonData... (Chapters 1 - 4)

https://pandas.pydata.org/pandas-docs… (Pandas tutorial)

https://github.com/wesm/pydata-book (pandas code examples)

https://scipython.com/book/ (Work in examples in Chapters 6 and 7)

Projects

https://github.com/tuvtran/project-ba...

https://projecteuler.net/

Data science + py

https://github.com/jakevdp/PythonData... (chap 5)

https://scikit-learn.org/stable/tutor...

Python Pandas Learning Resources

 https://pandas.pydata.org/

https://tomaugspurger.github.io/moder...

https://github.com/jakevdp/PythonData...

https://www.kaggle.com/mlg-ulb/credit...

  Data structure and algorithms in python

https://eu.udacity.com/course/data-st...

http://interactivepython.org/runeston...

MATHS

LINEAR ALGEBRA

https://www.youtube.com/watch?v=fNk_z... (Essence of Linear Algebra )

https://www.khanacademy.org/math/line...

https://betterexplained.com/articles/...

http://physics.bgu.ac.il/~gedalin/Tea... (Introduction to Methods of Applied Mathematics)

http://www.physics.miami.edu/~nearing... (Mathematical Tools for Physics)

https://www.math.ubc.ca/~carrell/NB.pdf (Linear Algebra Reference)

https://math.byu.edu/~klkuttle/Essent... (Reference)

CALCULUS

https://www.youtube.com/watch?v=WUvTy... (Essence of Calculus)

https://www.khanacademy.org/math/calc...

https://www.khanacademy.org/math/calc...

https://www.khanacademy.org/math/mult...

STATISTICAL LEARNING

An Introduction to Statistical Learning

https://www-bcf.usc.edu/~gareth/ISL/i... (Essential)

https://work.caltech.edu/telecourse.html Elements of Statistical Learning (Extremely useful)

https://web.stanford.edu/~hastie/Elem...

PROBABILITY AND STATISTICS

https://www.khanacademy.org/math/stat...

http://greenteapress.com/thinkstats/t…

https://bookboon.com/en/applied-stati...

http://www.wzchen.com/probability-che...

DATA EXPLORATION

https://github.com/StephenElston/Expl...

https://www.kaggle.com/c/titanic#desc...

data bases for training

https://www.kaggle.com/datasets

https://www.springboard.com/blog/free...

https://www.dataquest.io/blog/free-da…

TENSORFLOW

https://developers.google.com/machine...

SQL

https://www.khanacademy.org/computing...

https://www.devmedia.com.br/sql/

GIT AND VERSION CONTROL

https://git-scm.com/book/en/v2

Computer Science class

https://cs109.github.io/2015/index.html

R

https://www.r-bloggers.com/how-to-lea...

SUPPLEMENTARY MATERIAL

https://docs.python.org/3/tutorial/in...

https://www.reddit.com/r/Python/ https://www.reddit.com/r/datascience/

https://stackoverflow.com/questions/t...

https://datascience.stackexchange.com/ https://jupyter.org/

How to think like a computer scientist http://www.openbookproject.net/thinkc...

[onlinetrainingcourse]

Best online Python data science course

In the dynamic realm of data science, Python stands as an undisputed champion, wielding its versatility and simplicity to unlock the mysteries hidden within vast datasets. As a programming language, Python seamlessly integrates into the data science workflow, offering an extensive range of libraries and tools that streamline the entire analytical process.

Python's Prowess in Data Manipulation and Analysis

At the heart of Python's prowess lies its ability to handle data with finesse. The Pandas library, a cornerstone of data manipulation, empowers data scientists to effortlessly clean, transform, And  data sets. Its intuitive Data science course online with placement structure facilitates efficient handling of tabular data, making tasks such as filtering, grouping, and aggregating a breeze.

Whether dealing with CSV files or SQL databases, Pandas simplifies the complexities of data wrangling, laying a solid foundation for subsequent analysis. Complementing Pandas is NumPy, a fundamental library for numerical operations in Python. NumPy's array-based computations accelerate mathematical operations, providing the computational muscle necessary for scientific computing. From basic arithmetic to Advance linear algebra, NumPy enhances the efficiency of data manipulation, laying the groundwork for statistical analyses and machine learning algorithms.

Visualizing Insights with Matplotlib and Seaborn

Data, when transformed into visual narratives, becomes more accessible and interpretable. Matplotlib, a comprehensive 2D plotting library, enables the creation of a myriad of static, animated, and interactive visualizations. Customizable and feature-rich, Matplotlib caters to diverse needs, from line plots and scatter plots to bar charts and heatmaps.

seaborn, built on top of Matplotlib, adds an aesthetic layer to data visualization. With its high-level interface, Seaborn simplifies the creation of attractive statistical graphics. Heatmaps, violin plots, and pair plots come to life with just a few lines of code, enhancing the communication of complex patterns and relationships within the data.

Machine Learning Mastery with Scikit-Learn

Python's prowess extends seamlessly into the realm of machine learning, and the Scikit-Learn library serves as the vanguard. Offering a cohesive and user-friendly interface, Scikit-Learn transforms complex machine learning algorithms into accessible tools for data scientists. Classification, regression, clustering, and dimensionality reduction are within reach, thanks to a comprehensive suite of algorithms. scikit-Learn also provides utilities for model selection, hyperparameter tuning, and performance evaluation.

With an extensive array of metrics and tools, it empowers data scientists to fine-tune models and assess their predictive capabilities. Harnessing the power of Scikit-Learn, Python becomes a playground for experimenting with machine learning models and translating data into actionable insights.

The Notebooks Revolution

 Notebooks, an interactive computing environment, have revolutionized the way data scientists work with Python. Combining code, visualizations, and narrative text in a single document, Notebooks facilitate collaborative and reproducible data science. From exploratory data analysis to building machine learning models, Notebooks provide an interactive canvas where ideas come to life.

The notebooks support a plethora of programming languages, but their seamless integration with Python has made them a staple in the data science toolkit. With the ability to share notebooks via platforms like GitHub and Hub collaboration among data scientists transcends geographical boundaries. This interactive and collaborative nature accelerates the pace of discovery and innovation within the data science community.

Deep Learning: Python's Gateway to Artificial Intelligence

In the era of artificial intelligence, Python emerges as a key player, with libraries like TensorFlow and Python leading the charge in deep learning. These frameworks enable the creation and training of neural networks for tasks ranging from image recognition to natural language processing. Python's simplicity and community support make it an ideal environment for delving into the complexities of deep learning.

TensorFlow, developed by Google, provides a comprehensive platform for building and deploying machine learning models, especially those involving neural networks. Its flexibility and scalability make it suitable for projects of varying sizes and complexities. Python, with its dynamic computation graph, appeals to researchers and practitioners alike, offering an intuitive and flexible approach to building deep learning models.

The Python Community: A Driving Force in Innovation

Beyond its technical capabilities, Python's strength lies in its vibrant and expansive community. From forums like Stack Overflow to dedicated data science communities, Python enthusiasts actively engage in knowledge-sharing and problem-solving. This collaborative spirit ensures that challenges encountered in data science projects are met with a wealth of collective wisdom and experience.

Moreover, the open-source nature of Python and its libraries fosters continuous development and innovation. The community-driven evolution of tools and packages ensures that data scientists always have access to cutting-edge technologies and methodologies. This dynamic ecosystem reinforces Python's position as the language of choice for those venturing into the ever-evolving landscape of data science.

Conclusion: Python's Enduring Legacy in Data Science

In the realm of data science, Python stands as a reliable and innovative companion, seamlessly guiding Best data science course with placement through the intricate maze of data manipulation, visualization, and machine learning. From the simplicity of Pandas to the sophistication of deep learning with TensorFlow and Python offers a comprehensive toolkit that adapts to the diverse needs of data science Projects As technology advances and data science continues to evolve, Python's enduring legacy lies not just in its technical capabilities but also in the collaborative spirit of its community. With Python as the driving force, data scientists navigate the complexities of their work with confidence, transforming raw data into meaningful insights and driving innovation in the ever-expanding field of data science.

[super-mounika-posts]

HYSE DIGITAL ACADEMY IS PROVIDING 2 Months Training +1 Month Internship + Working On Live Projects + Job Assistance On Following #Top 10 #IT Domains:Mean StackFull StackPhpDigital Marketing PythonData ScienceAndroidBlock chainJAVA.NETManual and automation testingLEARN & EARN :Also we are Providing Work from home Projects To Our students Where they can earn 7000Rs to 15000Rs. In This Three Months Training Period…Course Fees: Rs.4999/- Hurry Up, Limited Seats Only#Googleform: https://forms.gle/mRUAF6J4F982DAjn9For Registration Contact: 9014316209 / 9121259821/ 9666856432WhatsApp:- 9121259821

[jatin1503-blog]

Home

Web Development Data Science About Me Jatin ChawdaUI Developer and Data analyst I am a Technology Student For Life Who is also a Computer Science Engineer by Education. (Not an IITAN though 🙂 ).I own my mistakes and learn from them.I take keen interest in finding solutions thus Problem Solving is one of my assets. #&ampnbsphtml #&amptimeshtml #ai #aiintelligence #aitechnology #Analytics #artificialintelligence #artificialintelligencee #artificialintelligenceincomputerscience #artificialintelligenceis #bestdigitalmarketingcompany #bestinternetmarketingcompany #bestlanguagefordatascience #bigdataanalytics #bigdataandanalytics #css #contentmarketingagency #contentmarketingservices #css #datascience #datascienceforbeginners #datasciencelanguages #datascienceusingpython #digitalagency #digitalmarketingagency #digitalmarketingagencynearme #digitalmarketingcompany #digitalmarketingcompanynearme #digitalmarketingservice #digitalmarketingserviceprovider #explainartificialintelligence #html #iknowmachinelearning #internetmarketing #internetmarketingcompany #javascript #javascriptand #javascriptcode #javascripton #javascriptonline #javascriptprogram #javascriptprogramminglanguage #js #learndatasciencewithpython #learningjavascript #learningmachinelearning #machinelearning #onlinemarketing #onlinemarketingcompanies #python #pythonand #pythonanddatascience #pythoncode #pythondata #pythonfor #pythonfordataanalysis #pythonfordatascience #pythonlanguage #pythonprogramminglanguage #pythonr #pythonwith #rfordatascience #rpogramming #scriptjs #searchoptimization #seo #seois #seomarketing #seooptimisation #seooptimization #seosearch #seosearchengineoptimization #seosite #seoweb #seowebmarketing #seowebsite #seowebsiteoptimization #stylecss #ui #useofbigdata #webdevelopment #webdevelopmentwebsite #websitedevelopment #websitedevelopmentcompany #websiteoptimization #websiteui Read the full article