Python Projects: Python & Data Science with Python Projects

Hello dear friends,

Welcome to Python Projects: Python & Data Science with Python Projects course.

Python Marathon & Data Science with NumPy, Pandas, Matplotlib, Machine Learning, Deep Learning, and Python Project

In this course, We will open the door of the Data Science world and try to move deeper. We will step by step to learn the fundamentals of Python and its beautiful libraries such as Numpy, Pandas, and Matplotlib step by step. Throughout the course, we will do a variety of exercises to reinforce what we have learned. Data science, data science from scratch, pandas, python data science, numpy, programming, python and data science from scratch, python for data science, data science python, matplotlib, python pandas, python exercises, data science Project, pandas exercises, python pandas numpy, data literacy, numpy pandas, pandas python, python programming for data science

In this course you will learn;

How to use Anaconda, PyCharm, Jupyter notebook and Google Colab,

Fundamentals of Python such as

• Datatypes in Python,

• Lots of datatype operators, methods and how to use them,

• Conditional concept, if and elif statements

• Logic of Loops and control statements

• Functions and how to use them

• How to use modules and create your own modules

Data science and Data literacy concepts

Fundamentals of Numpy for Data manipulation such as

• Numpy arrays and their features

• How to do indexing and slicing on Arrays

Lots of stuff about Pandas for data manipulation such as

• Pandas series and their features

• Dataframes and their features

• Hierarchical indexing concept and theory

• Groupby operations

• Logic of data munging

• How to deal effectively with missing data effectively

• Combining the data frames

• How to work with Dataset files

In the ad also you will learn fundamental things about the Matplotlib library such as

• Pyplot, pylab and matplotlb concept

• What Figure, Subplot, and Axes are

• How to do figure and plot customization

Finally, we run a marathon. We got lots of examples to improve your Python skills with different difficulty levels.

Video and Audio Production Quality

All our content are created/produced as high-quality video/audio to provide you the best learning experience.

You will be,

• Seeing clearly

• Hearing clearly

• Moving through the course without distractions

  You'll also get:

• Lifetime Access to The Course

• Fast & Friendly Support in the Q&A section

Dive in now!

Python Projects: Python & Data Science with Python Projects

We offer full support, answering any questions.

See you in the course!

Reed courses certificate of completion

Digital certificate - Included

• Introduction to Python Projects with Data science, numpy, pandas 08:24

  • 1: Intro Data Science Projects. Python Projects course? 04:24
  • 2: Python Projects Files and Course Documents 01:00
  • 3: FAQ about Python Project, Data Science Project, Python 03:00

• Python Setup 47:13

  • 4: Installing Anaconda Distribution for Linux 14:43
  • 5: Installing Anaconda Distribution for Windows 10:35
  • 6: Installing Anaconda Distribution for MacOs 06:17
  • 7: Installing PyCharm IDE for Windows 04:20
  • 8: Installing PyCharm IDE for Mac 05:46
  • 9: Overview of Jupyter Notebook and Google Colab 05:32

• Fundamentals of Python 2:34:30

  • 10: Data Types in Python 12:43
  • 11: Operators in Python 10:32
  • 12: Conditionals in Python 09:50
  • 13: Loops in Python 13:07
  • 14: Lists-Tuples-Dictionaries-Sets in Python 17:54
  • 15: Data Type Operators and Methods 11:21
  • 16: Modules in Python 05:15
  • 17: Functions in Python 08:06
  • 18: Files 03:01
  • 19: File Operations in Python 11:28
  • 20: Exceptions - I in Python 03:30
  • 21: Exceptions - II in Python 12:53
  • 22: OOP: Logic of OOP 04:59
  • 23: OOP: Constructor 06:53
  • 24: OOP: Methods 04:42
  • 25: OOP: Inheritance 06:42
  • 26: OOP: Overriding and Overloading 10:34
  • 27: quiz 01:00

• Fundamentals of Data Science 3:36:33

  • 28: What is Data Science? 05:40
  • 29: Data Literacy 03:09
  • 30: What is Numpy? 06:49
  • 31: Why Numpy? 04:23
  • 32: Array and features in Numpy Python 12:08
  • 33: Array’s Operators in Numpy Python 04:53
  • 34: Numpy Functions in Numpy Python 18:25
  • 35: Indexing and Slicing in Numpy Python 10:15
  • 36: Numpy Exercises in Numpy Python 16:04
  • 37: What is Pandas? 05:48
  • 38: Series and Features in Pandas 20:06
  • 39: Data Frame attributes and Methods in Pandas 18:14
  • 40: Data Frame attributes and Methods in Pandas - Part II 13:05
  • 41: Data Frame attributes and Methods in Pandas - Part III 11:39
  • 42: Groupby Operations in Pandas 13:31
  • 43: Combining DataFrames I in Pandas 20:25
  • 44: Combining DataFrames II in Pandas 19:29
  • 45: Work with Dataset Files 11:30
  • 46: quiz 01:00

• Fundamentals of Matplotlib 59:53

  • 47: What is Matplotlib 03:03
  • 48: Using Pyplot 07:30
  • 49: Pyplot – Pylab - Matplotlib 07:19
  • 50: Figure, Subplot, Multiplot, Axes in Matplotlib 17:29
  • 51: Figure Customization in Matplotlib 14:47
  • 52: Plot Customization in Matplotlib 06:45
  • 53: quiz 01:00
  • 54: quiz 01:00
  • 55: quiz 01:00

• Python Marathon 4:35:14

  • 56: Example : E-mail Generator 04:02
  • 57: Example : BMI Calculator 02:11
  • 58: Example : Tip Calculator 03:58
  • 59: Example : Bottle Deposits 03:42
  • 60: Example : Name The Shape 04:00
  • 61: Example : Admission Price 05:26
  • 62: Example : Note to Frequency 04:54
  • 63: Example : Frequency to Note 05:12
  • 64: Example : Parity Bits 04:56
  • 65: Example : Reduce a Fraction to Lowest Terms 06:26
  • 66: Example : Two Dice Simulation 06:50
  • 67: Example : String Edit Distance 08:09
  • 68: Example : Run-Length Encoding 05:54
  • 69: Example : Caesar Cipher 06:12
  • 70: Example : Number Guessing Game 06:54
  • 71: Example : Login Controller 05:30
  • 72: Example : Password Generator 06:33
  • 73: Example: Sorted Order 03:40
  • 74: Example : Fibonacci 02:46
  • 75: Example : Team Builder 06:13
  • 76: Example : Finding Prime Number 04:44
  • 77: Example : Word Counter 03:21
  • 78: Example : Overlap 05:43
  • 79: Example : Perfect Number Finder 04:09
  • 80: Example : Playing Card 07:17
  • 81: Example : The Sieve of Eratosthenes 04:11
  • 82: Example : Anagrams 04:32
  • 83: Example : Roulette Game 07:24
  • 84: Example : Bingo Card 07:29
  • 85: Example : Rock Paper Scissors 11:58
  • 86: Example : Remote Controller 21:12
  • 87: Example : Titanic Disaster Questions Part 03:42
  • 88: Example : Titanic Disaster Answer Part 19:55
  • 89: Example : Bike Shares in London Questions Part 04:25
  • 90: Example : Bike Shares in London Answer Part 27:45
  • 91: Example : EPL Team Stats Questions Part 04:23
  • 92: Example : EPL Team Stats Answer Part 29:36

Hello dear friends,

Welcome to Python Projects: Python & Data Science with Python Projects course.

Python Marathon & Data Science with NumPy, Pandas, Matplotlib, Machine Learning, Deep Learning, and Python Project

In this course, We will open the door of the Data Science world and try to move deeper. We will step by step to learn the fundamentals of Python and its beautiful libraries such as Numpy, Pandas, and Matplotlib step by step. Throughout the course, we will do a variety of exercises to reinforce what we have learned. Data science, data science from scratch, pandas, python data science, numpy, programming, python and data science from scratch, python for data science, data science python, matplotlib, python pandas, python exercises, data science Project, pandas exercises, python pandas numpy, data literacy, numpy pandas, pandas python, python programming for data science

In this course you will learn;

How to use Anaconda, PyCharm, Jupyter notebook and Google Colab,

Fundamentals of Python such as

• Datatypes in Python,

• Lots of datatype operators, methods and how to use them,

• Conditional concept, if and elif statements

• Logic of Loops and control statements

• Functions and how to use them

• How to use modules and create your own modules

Data science and Data literacy concepts

Fundamentals of Numpy for Data manipulation such as

• Numpy arrays and their features

• How to do indexing and slicing on Arrays

Lots of stuff about Pandas for data manipulation such as

• Pandas series and their features

• Dataframes and their features

• Hierarchical indexing concept and theory

• Groupby operations

• Logic of data munging

• How to deal effectively with missing data effectively

• Combining the data frames

• How to work with Dataset files

In the ad also you will learn fundamental things about the Matplotlib library such as

• Pyplot, pylab and matplotlb concept

• What Figure, Subplot, and Axes are

• How to do figure and plot customization

Finally, we run a marathon. We got lots of examples to improve your Python skills with different difficulty levels.

Video and Audio Production Quality

All our content are created/produced as high-quality video/audio to provide you the best learning experience.

You will be,

• Seeing clearly

• Hearing clearly

• Moving through the course without distractions

  You'll also get:

• Lifetime Access to The Course

• Fast & Friendly Support in the Q&A section

Dive in now!

Python Projects: Python & Data Science with Python Projects

We offer full support, answering any questions.

See you in the course!

• Anyone who has programming experience and wants to enter the python world. In this world your journey never ends.
• You can develop yourself at data science or Machine learning and even developing an application.
• Statisticians and mathematicians who want to learn python for data science.
• Tech geeks who curious data science.
• Data analysts who want to data science and data visualization.
• If you are one of these, you are in the right place. But please don't forget. You must know a little bit of coding and scripting.
• Software developer who wants to learn "Machine Learning"
• Students Interested in Beginning Data Science Applications in Python Environment

• 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

Currently there are no Q&As for this course. Be the first to ask a question.