Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

This is a repository for the LinkedIn Learning course Python for Data Science and Machine Learning Essential Training

Python Data Science Essentials - Third Edition, Published by Packt

Code repository for Python Data Science Essentials Second Edition published by Packt

A collection of exercise from pandas, numpy and sklearn for data science beginners

Published by Packt Publishing

This repo is for LinkedIn Learning course: Python for Data Science and Machine Learning Essential Training Part 2

A fully curated, open-source Data Science curriculum focused on Python. Includes top-tier university courses (MIT, Stanford, Princeton) covering essential topics in computer science, data analysis, machine learning, and statistics — everything you need to build a solid foundation in Data Science, 100% free.

This project, conducted in collaboration with Global Core Tech, focuses on analyzing sentiment in Flipkart reviews. Using Python and essential data science libraries like Pandas, Matplotlib, NLTK, and Seaborn, we aim to extract valuable insights into customer sentiments from the reviews.

Essential Tools for Working with Data, it is meant to help Python users learn to use Python’s data science stack—libraries such as IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools—to effectively store, manipulate, and gain insight from data.

"Unlock the ultimate beginner's guide to data science! Our curated repository is packed with ML, DL, NLP, and Python essentials, offering clear pathways and valuable insights for your journey. Dive into data analysis and AI effortlessly with best-in-class tools and tutorials tailored for beginners."

This 4-week elective course offers undergraduate students a comprehensive introduction to Python programming, focusing on essential tools and techniques used in data science.

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

This project explores data analysis and visualization using NumPy, Pandas, Matplotlib, and Seaborn. It provides practical tutorials on data manipulation, statistical analysis, and creating insightful visualizations. Ideal for learners looking to master these essential Python libraries for data science and explore real-world datasets effectively.

Python for Data Science Essential Training: Basics

A curated collection of essential Data Science books, featuring foundational and advanced texts on analytical techniques, data visualization, and machine learning. This selection spans introductory to specialized guides, covering tools like Python, R, and more, suitable for both beginners and experts. For general information, see

Data Science Cheat Sheets is a compact reference repository covering essential topics like statistics, machine learning, deep learning, Python libraries, EDA, visualization, model evaluation, and MLOps. It’s designed to help learners and professionals quickly revise concepts, prepare for interviews, and streamline project work.

Data science Python notebooks: Machine Learning (Scikit-Learn), Deep learning (Keras, TensorFlow), big data (Spark), data analysis (Pandas, NumPy), data visualization (Matplotlib, Seaborn), SQL, web scraping, probability & statistics, kaggle tutorials & Python essentials.

This repository consist of Python tutorial concepts which are essential for Data Science and Machine Learning

Essential math for data science using python

Repository for 6+ months Bootcamp program aimed to cover all essential aspects of Data Science Methodology (DSM) , Data Wrangling, Python Statistics, Exploratory Data Analysis, Machine Learning (options of Supervised, Unsupervised and Deep Learning paths) using Python and SQL to equip a Data Scientist to code and implement in a real world

A practical repository containing documented and well-explained code exercises and examples from Python Data Science Handbook by Jake VanderPlas. Includes notebooks and scripts focused on essential Python tools for data science.

A small educational visualisation tool for most algorithms and data structures essential to learning computer science. This project was made in Python using the Pygame library for graphics

This project aims to perform a detailed data-driven analysis of the S&P 500 stock market using historical stock price data from the period 2014 to 2017. Leveraging Python and essential data science tools, the project uncovers key financial insights through statistical analysis, exploratory data analysis (EDA), and visualizations.

This repository is a comprehensive guide to learning NumPy, the foundational Python library for numerical computing and data science. Designed for beginners and aspiring data scientists, it covers all the essential concepts with real-world examples, making it easy to apply NumPy in data analysis, machine learning, and scientific computing.

This repository contains my learning path of python for data-science essential training(part-2). Here, I have included chapter-wise topics and my practice problems. Also, feel free to checkout for better understanding.

Python Essentials For Data Science Course

This repository provides a comprehensive guide on data cleaning using Python. Data cleaning is an essential process in any data science project as it helps to ensure that the data is accurate, consistent, and complete.

These examples provide an introduction to Data Science and classic Machine Learning using NumPy, pandas, Matplotlib, and scikit-learn. They are taken, with some changes, from the book "Python Data Science Handbook: Essential Tools for Working with Data", Second Edition, written by Jake VanderPlas and published by O'Reilly Media in 2023.

Guest Stars in The Office Updated 41 seconds ago In this project, i apply the skills i have learned in Introduction to Python and Intermediate Python to solve a real-world data science problem. You’ll press “watch next episode” to discover if Netflix’s movies are getting shorter over time and which guest stars appear in the most popular episode of "The Office", using everything from lists and loops to pandas and matplotlib. i gained experience in an essential data science skill — exploratory data analysis. This will allow you to perform critical tasks such as manipulating raw data and drawing conclusions from plots you create of the data. Press play to begin!