Data Analysis with Python – Full Course for Beginners (Numpy, Pandas, Matplotlib, Seaborn)
Learn Data Analysis with Python on this complete tutorial for newcomers, with workouts included!
NOTE: Check description for up to date Notebook hyperlinks.
Data Analysis has been round for a very long time, however up till a number of years in the past, it was practiced utilizing closed, costly and restricted instruments like Excel or Tableau. Python, SQL and different open libraries have modified Data Analysis eternally.
In this tutorial you may study the entire technique of Data Analysis: studying information from a number of sources (CSVs, SQL, Excel, and so forth), processing them utilizing NumPy and Pandas, visualize them utilizing Matplotlib and Seaborn and clear and course of it to create reviews.
Additionally, we have included an intensive Jupyter Notebook tutorial, and a fast Python reference to refresh your programming expertise.
💻 Course created by Santiago Basulto from RMOTR
🔗 Check out all Data Science programs from RMOTR: https://rmotr.com
⚠️ Note: Instead of loading the notebooks on notebooks.ai, it’s best to use Google Colab as a substitute. Here are directions on loading a pocket book immediately from GitHub into Google Colab: https://colab.analysis.google.com/github/googlecolab/colabtools/blob/grasp/notebooks/colab-github-demo.ipynb#scrollTo=Okay-NVg7RjyeTk
⭐️ Course Contents ⭐️
⌨️ Part 1: Introduction
What is Data Analysis, why Python?, what different choices are there? what is the cycle of a Data Analysis undertaking? What’s the distinction between Data Analysis and Data Science?
🔗 Slides for this part: https://docs.google.com/presentation/d/1fDpjlyMiOMJyuc7_jMekcYLPP2XlSl1eWw9F7yE7byk/edit?usp=sharing
⌨️ Part 2: Real Life Example of a Python/Pandas Data Analysis undertaking (00:11:11)
An indication of an actual life information evaluation undertaking utilizing Python, Pandas, SQL and Seaborn. Don’t fear, we’ll dig deeper within the following sections
🔗 Notebooks: https://github.com/ine-rmotr-curriculum/FreeCodeCamp-Pandas-Real-Life-Example
⌨️ Part 3: Jupyter Notebooks Tutorial (00:30:50)
A step-by-step tutorial to learn to use Juptyer Notebooks
🔗 Twitter Cheat Sheet: https://twitter.com/rmotr_com/standing/1122176794696847361
🔗 Notebooks: https://github.com/ine-rmotr-curriculum/ds-content material-interactive-jupyterlab-tutorial
⌨️ Part 4: Intro to NumPy (01:04:58)
Learn why NumPy was such an necessary library for the information-processing world in Python. Learn about low stage particulars of computations and reminiscence storage, and why instruments like Excel will all the time be restricted when processing giant volumes of knowledge.
🔗 Notebooks: https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-numpy
⌨️ Part 5: Intro to Pandas (01:57:08)
Pandas is arguably crucial library for Data Processing within the Python world. Learn the way it works and the way its principal information construction, the Data Frame, compares to different instruments like spreadsheets or DFs used for Big Data
🔗 Notebooks: https://github.com/ine-rmotr-curriculum/freecodecamp-intro-to-pandas
⌨️ Part 6: Data Cleaning (02:47:18)
Learn the several types of points that we’ll face with our information: null values, invalid values, statistical outliers, and so forth, and tips on how to clear them.
🔗 Notebooks: https://github.com/ine-rmotr-curriculum/information-cleansing-rmotr-freecodecamp
⌨️ Part 7: Reading Data from different sources (03:25:15)
🔗 Notebooks: https://github.com/ine-rmotr-curriculum/RDP-Reading-Data-with-Python-and-Pandas
⌨️ Part 8: Python Recap (03:55:19)
If your Python or coding expertise are rusty, try this part for a fast recap of Python principal options and management move constructions.
🔗 Notebooks: https://github.com/ine-rmotr-curriculum/ds-content-python-under-10-minutes
Learn to code for free and get a developer job: https://www.freecodecamp.org
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