Scientific Python: Data Science Visualization Bundle 18 Hrs!
Scientific Python: Data Science Visualization Bundle 18 Hrs!, available at $39.99, has an average rating of 3.7, with 88 lectures, based on 83 reviews, and has 15530 subscribers.
You will learn about Python – Bootcamp SciPy – Scientific Python Software Stack NumPy – Numerical Array Processing Matplotlib – 2D Plotting and Visualization Pandas – Data Frames & CSV Files Scikit Learn – Python Machine Learning Seaborn – Statistical Plotting REGEX – Python RE (Regula Expressions) PyTorch – Python Tensor Flow Python – Data Mining Pipeline This course is ideal for individuals who are Anyone with any background that interested in Data Science and Machine Learning or Who wants to perform computational computing with Python or Students who want to learn Scientific Python to improve their career prospects It is particularly useful for Anyone with any background that interested in Data Science and Machine Learning or Who wants to perform computational computing with Python or Students who want to learn Scientific Python to improve their career prospects.
Enroll now: Scientific Python: Data Science Visualization Bundle 18 Hrs!
Summary
Title: Scientific Python: Data Science Visualization Bundle 18 Hrs!
Price: $39.99
Average Rating: 3.7
Number of Lectures: 88
Number of Published Lectures: 87
Number of Curriculum Items: 88
Number of Published Curriculum Objects: 87
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Python – Bootcamp
- SciPy – Scientific Python Software Stack
- NumPy – Numerical Array Processing
- Matplotlib – 2D Plotting and Visualization
- Pandas – Data Frames & CSV Files
- Scikit Learn – Python Machine Learning
- Seaborn – Statistical Plotting
- REGEX – Python RE (Regula Expressions)
- PyTorch – Python Tensor Flow
- Python – Data Mining Pipeline
Who Should Attend
- Anyone with any background that interested in Data Science and Machine Learning
- Who wants to perform computational computing with Python
- Students who want to learn Scientific Python to improve their career prospects
Target Audiences
- Anyone with any background that interested in Data Science and Machine Learning
- Who wants to perform computational computing with Python
- Students who want to learn Scientific Python to improve their career prospects
18 HRS OF AWESOME FIVE STARS ⭐⭐⭐⭐⭐ VIDEOS!
This is the Best and Most Complete Scientific Python Course on the Udemy platform that will walk you through the required skills for Data Sciences and useful Machine Learning (ML) libraries such as NumPy, Pandas, Scikit-Learn, Seaborn, Python RE (REGEX), PyTorch and Matplotlib. Furthermore, you learn how to work with different real datasets and use them for developing your models. All the Python code templates that we write during the course together are available, and you can download them with the resource button of each section.
WHAT YOU WILL GET & LEARN?
In this awesome 18 hourslong course we will cover:
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SciPy is a free and open-source Python library used for scientific computing and technical computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers.
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NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
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Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Most of the Matplotlib utilities lies under the pyplot submodule, and are usually imported under the plt alias.
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Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.
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Scikit-learn: Simple and efficient tools for predictive data analysis · Accessible to everybody, and reusable in various contexts · Built on NumPy, SciPy, and matplotlib.
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Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
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Python REGEX Regular expressions (called REs, or regexes, or regex patterns) are essentially a tiny, highly specialized programming language embedded inside Python and made available through the re module.
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All data sets included!
Python is a great tool for the development of programs which perform data analysis and prediction. It has tons of classes and features which perform the complex mathematical analysis and give solutions in simple one or two lines of code so that we don’t have to be a statistic genius or mathematical Nerd to learn data science and machine learning. Python really makes things easy.
Students purchasing this course will receive free access to the interactive version (with Scientific code playgrounds) of this course from the Scientific Programming School (SCIENTIFIC PROGRAMMING IO). Based on your earlier feedback, we are introducing a Zoom live class lecture series on this course through which we will explain different aspects of Linux command line Python for Data analytics. Live classes will be delivered through the Scientific Programming School, which is an interactive and advanced e-learning platform for learning scientific coding.
MONEY BACK GUARANTEE IF NOT 100% SATISFIED!
When you enroll you will get lifetime access to all of the course contents and any updates and when you complete the course 100% you will also get a Certificate of completion that you can add to your resumé/CV to show off to the world your new-found Python & Scientific Computing Mastery!Don’t forget to join our Q&A live community where you can get free help anytime from other students and the instructor. This awesome course is a component of the Learn Scientific Computing master course.
So What are you Waiting For?Click that shiny enrollbutton and we’ll See you inside 😉
Course Curriculum
Chapter 1: Scientific Python
Lecture 1: Welcome!
Lecture 2: Why Enrol this Course?
Lecture 3: Instructor
Lecture 4: Free Insteractive Shell for Practice Python
Lecture 5: Introduction
Lecture 6: Python Tools
Lecture 7: Scientific Python Install – Windows
Lecture 8: Scientific Python Install – Linux
Lecture 9: Scientific Python Install – MacOS
Lecture 10: Jupyter Notebook Install – Windows & Linux
Lecture 11: Load in Jupyter Notebook
Lecture 12: Datasets (CSV) – Download First!
Chapter 2: Python – Bootcamp
Lecture 1: Python – Bootcamp
Chapter 3: NumPy – Array Processing
Lecture 1: NumPy
Lecture 2: NumPy arrays
Lecture 3: NumPy – Create
Lecture 4: NumPy – Reshape
Lecture 5: NumPy – Index
Lecture 6: NumPy – Operations
Lecture 7: NumPy – Sort
Lecture 8: Numpy – Stack and Split
Lecture 9: NumPy – Broadcast
Lecture 10: NumPy – Date & Time
Lecture 11: NumPy – Linear Algebra
Lecture 12: NumPy – Load & Save
Lecture 13: NumPy – Notebook Download
Chapter 4: Pandas – Data Frames and CSV Data Processing
Lecture 1: Pandas
Lecture 2: Pandas – Read data
Lecture 3: Pandas – Create Dataframe
Lecture 4: Pandas – Columns
Lecture 5: Pandas – Functions
Lecture 6: Pandas – Plotting
Lecture 7: Pandas – Count Value
Lecture 8: Pandas – Indexes
Lecture 9: Pandas – Filter & Queries
Lecture 10: Pandas – Delete Rows and Cols
Lecture 11: Pandas – Grouping
Chapter 5: Matplotlib – Plotting & Visualisation
Lecture 1: Matplotlib
Lecture 2: Matplotlib – Line plots
Lecture 3: Matplotlib – Bar charts
Lecture 4: Matplotlib – Histograms
Lecture 5: Matplotlib – Scratter plot
Lecture 6: Matplotlib – Pie chart
Lecture 7: Matplotlib – Curve plot
Lecture 8: Matplotlib – Subplots
Lecture 9: Matplotlib – Notebook Download
Chapter 6: Python RE – Regular Expressions (REGEX)
Lecture 1: REGEX
Lecture 2: Python – Import RE
Lecture 3: Python RE – Example
Lecture 4: Python RE – Characters
Lecture 5: Python RE – Alteration
Lecture 6: Python RE – Quantifiers
Lecture 7: Python RE – Greedy and Non-Greedy
Lecture 8: Python RE – Boundary Matches
Lecture 9: Python RE- Split
Lecture 10: Python RE – Substitution
Lecture 11: Python RE- Compilation Flags
Lecture 12: Python RE – Grouping
Lecture 13: Python RE – Backreferencing
Lecture 14: Python RE – Named and Non-capturing Groups
Lecture 15: Python RE – Lookarounds
Lecture 16: Python RE – Notebook Download
Chapter 7: Scikit-Learn – Machine Learning
Lecture 1: Scitkit-Learn
Lecture 2: Scikit-Learn – Training
Lecture 3: Scikit-Learn – Compare Models
Lecture 4: Scikit-Learn – Cross Validation
Lecture 5: Scikit-Learn – Selection
Lecture 6: Scikit-Learn – Evaluate Classifier
Lecture 7: Scikit-Learn – Linear Regression
Lecture 8: Scikit-Learn – Real Examples!
Chapter 8: Seaborn – Statistical Plots
Lecture 1: Seaborn
Lecture 2: Seaborn – Bar Plot
Lecture 3: Seaborn – Box Plot
Lecture 4: Seaborn – Strip Plot
Lecture 5: Seaborn – PairGrids
Lecture 6: Seaborn – Violin Plots
Lecture 7: Seaborn – Clustermaps
Lecture 8: Seaborn – Heatmaps
Lecture 9: Seaborn – Facet Grids
Lecture 10: Seaborn – KDE Plot
Lecture 11: Seaborn – Joint Plot
Lecture 12: Seaborn – Regression Plot
Lecture 13: Seaborn – Pair Plot
Chapter 9: PyTorch – Tensor Flow Project
Lecture 1: PyTorch – Initializing Tensors, Math, Indexing, Reshaping
Chapter 10: Addtional Contents
Lecture 1: How to Get the Interactive Playgrounds for this Course?
Lecture 2: Interactive Playground – FREE!
Lecture 3: Interactive Shell
Instructors
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Scientific Programmer™ Team
ScientificProgrammer.me | Instructor Team -
Scientific Programming School
Interactive Learning Platform
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 4 votes
- 3 stars: 8 votes
- 4 stars: 26 votes
- 5 stars: 42 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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