Python Projects: Python & Data Science with Python Projects By Oak Academy
https://DevCourseWeb.com
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + srt | Duration: 85 lectures (12h 44m) | Size: 2.73 GB Python Marathon and Data Science with NumPy, Pandas, Matplotlib, Machine Learning, Deep Learning, and Python Project What you'll learn: If you are new to Python, data science or have no idea about what data scientist does no problem, you will learn anything you need to start to Python data scien If you are a software developer or familiar to other programming language and you want to start a new world, you are also in the right place. You will encounter many businesses that use Python and its libraries for data science. In this course you need no previous Knowledge about Python, Pandas or data science. In this course you need no previous Knowledge about Python, Pandas or data science. Almost all companies working on machine learning and data science use Python’s Pandas library Thanks to the large libraries provided, the number of companies and enterprises using python is increasing day by day Python is the most popular programming language for data science process in recent yeThe world we are in is experiencing the age of informatics. In order to take part in this world and create your own opportunities, Python and its Pandas library will be the right choice for you. Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. What is data science? We have more data than ever before. But data alone cannot tell us much about the world around us. What does a data scientist do? Data Scientists use machine learning to discover hidden patterns in large amounts of raw data to shed light on real problems. What are the most popular coding languages for data science? Python is the most popular programming language for data science. How long does it take to become a data scientist? This answer, of course, varies. The more time you devote to learning new skills, the faster you will learn. How can I learn data science on my own? It is possible to learn data science on your own, as long as you stay focused and motivated. Does data science require coding? The jury is still out on this one. Some people believe that it is possible to become a data scientist without knowing how to c What skills should a data scientist know? A data scientist requires many skills. They need a strong understanding of statistical analysis and mathematics, which Is data science a good career? The demand for data scientists is growing. We do not just have data scientists; we have data engineers, data administrators Most programmers will choose to learn the object oriented programming paradigm in a specific language. That’s why Udemy features a host of top-rated OOP courses tailored for specific languages, like Java, C#, and Python. What does it mean that Python is object-oriented? Python is a multi-paradigm language, which means that it supports many programming approaches. Data science python with numpy, pandas, machine learning, deep learning, reinforcement learning
Requirements You'll need a desktop computer (Windows, Mac) capable of running Anaconda 3 or newer. We will show you how to install the necessary free software. A little bit of coding experience. At least high school level math skills will be required. Desire to learn machine learning python with numpy, data science, python, pandas Desire to master on python, machine learning a-z, deep learning a-z Learn to create Machine Learning and Deep Algorithms in Python Code templates included. Desire to learn data science with python Desire to learn python data science, numpy pandas
Description Hello dear friends,
Welcome to Python Projects: Python & Data Science with Python Projects course. |
[ DevCourseWeb.com ] Udemy - Python Projects - Python and Data Science with Python Projects By Oak Academy
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1. Introduction to Python Projects with Data science, numpy, pandas
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1. Introduction and what will you learn in this Python and data science course.mp4 (13.7 MB)
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1. Introduction and what will you learn in this Python and data science course.srt (5.4 KB)
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2. FAQ about Python, Data Science, Python Projects.html (24.1 KB)
2. Python Setup
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1. Installing Anaconda Distribution for Linux.mp4 (119.8 MB)
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1. Installing Anaconda Distribution for Linux.srt (16.9 KB)
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2. Installing Anaconda Distribution for Windows.mp4 (122.7 MB)
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2. Installing Anaconda Distribution for Windows.srt (12.4 KB)
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3. Installing Anaconda Distribution for MacOs.mp4 (11.3 MB)
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3. Installing Anaconda Distribution for MacOs.srt (7.3 KB)
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4. Installing PyCharm IDE for Windows.mp4 (34.6 MB)
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4. Installing PyCharm IDE for Windows.srt (4.2 KB)
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5. Installing PyCharm IDE for Mac.mp4 (69.4 MB)
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5. Installing PyCharm IDE for Mac.srt (4.4 KB)
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6. Overview of Jupyter Notebook and Google Colab.mp4 (25.6 MB)
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6. Overview of Jupyter Notebook and Google Colab.srt (5.8 KB)
3. Fundamentals of Python
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1. Data Types in Python.mp4 (41.1 MB)
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1. Data Types in Python.srt (14.0 KB)
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10. File Operations in Python.mp4 (61.7 MB)
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10. File Operations in Python.srt (10.7 KB)
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11. Exceptions - I in Python.mp4 (13.3 MB)
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11. Exceptions - I in Python.srt (3.9 KB)
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12. Exceptions - II in Python.mp4 (58.0 MB)
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12. Exceptions - II in Python.srt (11.8 KB)
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13. OOP Logic of OOP.mp4 (16.4 MB)
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13. OOP Logic of OOP.srt (5.3 KB)
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14. OOP Constructor.mp4 (33.9 MB)
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14. OOP Constructor.srt (7.2 KB)
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15. OOP Methods.mp4 (23.6 MB)
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15. OOP Methods.srt (4.2 KB)
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16. OOP Inheritance.mp4 (32.6 MB)
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16. OOP Inheritance.srt (6.9 KB)
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17. OOP Overriding and Overloading.mp4 (58.8 MB)
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17. OOP Overriding and Overloading.srt (9.8 KB)
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2. Operators in Python.mp4 (29.6 MB)
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2. Operators in Python.srt (11.2 KB)
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3. Conditionals in Python.mp4 (34.6 MB)
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3. Conditionals in Python.srt (9.7 KB)
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4. Loops in Python.mp4 (49.1 MB)
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4. Loops in Python.srt (12.6 KB)
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5. Lists-Tuples-Dictionaries-Sets in Python.mp4 (66.3 MB)
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5. Lists-Tuples-Dictionaries-Sets in Python.srt (18.1 KB)
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6. Operators and Methods in Python.mp4 (40.5 MB)
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6. Operators and Methods in Python.srt (9.4 KB)
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7. Modules in Python.mp4 (21.0 MB)
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7. Modules in Python.srt (5.5 KB)
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8. Functions in Python.mp4 (26.0 MB)
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8. Functions in Python.srt (9.3 KB)
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9. Files.mp4 (9.3 MB)
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9. Files.srt (3.9 KB)
4. OOP Overriding and Overloading
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1. What is Data Science.mp4 (20.2 MB)
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1. What is Data Science.srt (6.8 KB)
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10. What is Pandas.mp4 (8.7 MB)
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10. What is Pandas.srt (6.6 KB)
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11. Series and Features in Pandas.mp4 (74.2 MB)
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11. Series and Features in Pandas.srt (19.5 KB)
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12. Data Frame attributes and Methods in Pandas.mp4 (79.8 MB)
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12. Data Frame attributes and Methods in Pandas.srt (16.1 KB)
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13. Data Frame attributes and Methods in Pandas 1.mp4 (57.0 MB)
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13. Data Frame attributes and Methods in Pandas 1.srt (11.8 KB)
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14. Data Frame attributes and Methods in Pandas - Part III.mp4 (48.0 MB)
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14. Data Frame attributes and Methods in Pandas - Part III.srt (9.6 KB)
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15. Groupby Operations in Pandas.mp4 (52.6 MB)
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15. Groupby Operations in Pandas.srt (12.5 KB)
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16. Combining DataFrames I in Pandas.mp4 (103.5 MB)
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16. Combining DataFrames I in Pandas.srt (17.7 KB)
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17. Combining DataFrames II in Pandas.mp4 (84.2 MB)
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17. Combining DataFrames II in Pandas.srt (17.5 KB)
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18. Work with Dataset Files.mp4 (70.7 MB)
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18. Work with Dataset Files.srt (12.0 KB)
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2. Data Literacy.mp4 (9.7 MB)
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2. Data Literacy.srt (3.4 KB)
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3. What is Numpy.mp4 (26.7 MB)
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3. What is Numpy.srt (7.6 KB)
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4. Why Numpy.mp4 (13.6 MB)
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4. Why Numpy.srt (5.0 KB)
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5. Array and features in Numpy Python.mp4 (47.9 MB)
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5. Array and features in Numpy Python.srt (11.7 KB)
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6. Array’s Operators in Numpy Python.mp4 (17.6 MB)
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6. Array’s Operators in Numpy Python.srt (4.3 KB)
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7. Numpy Functions in Numpy Python.mp4 (78.5 MB)
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7. Numpy Functions in Numpy Python.srt (19.3 KB)
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8. Indexing and Slicing in Numpy Python.mp4 (40.4 MB)
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8. Indexing and Slicing in Numpy Python.srt (9.1 KB)
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9. Numpy Exercises in Numpy Python.mp4 (74.2 MB)
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9. Numpy Exercises in Numpy Python.srt (15.3 KB)
5. Fundamentals of Matplotlib
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1. What is Matplotlib.mp4 (18.3 MB)
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1. What is Matplotlib.srt (3.8 KB)
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2. Using Pyplot.mp4 (26.5 MB)
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2. Using Pyplot.srt (7.6 KB)
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3. Pyplot – Pylab – Matplotlib.mp4 (28.2 MB)
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3. Pyplot – Pylab – Matplotlib.srt (7.6 KB)
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4. Figure, Subplot, Multiplot, Axes in Matplotlib.mp4 (65.6 MB)
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4. Figure, Subplot, Multiplot, Axes in Matplotlib.srt (18.1 KB)
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5. Figure Customization in Matplotlib.mp4 (61.5 MB)
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5. Figure Customization in Matplotlib.srt (14.7 KB)
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6. Plot Customization in Matplotlib.mp4 (25.7 MB)
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6. Plot Customization in Matplotlib.srt (6.8 KB)
6. Python Marathon
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1. Example E-mail Generator.mp4 (11.6 MB)
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1. Example E-mail Generator.srt (4.0 KB)
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10. Example Reduce a Fraction to Lowest Terms.mp4 (18.0 MB)
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10. Example Reduce a Fraction to Lowest Terms.srt (6.5 KB)
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11. Example Two Dice Simulation.mp4 (23.9 MB)
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11. Example Two Dice Simulation.srt (7.8 KB)
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12. Example String
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