Python and Data Science from Scratch With RealLife Exercises

Welcome to my "Python and Data Science from Scratch With Real Life Exercises" course.

Python Data Science with Python programming, NumPy, Pandas, Matplotlib and dive into Data Science with Python Projects

Numpy, Pandas, Data science, data science from scratch, python, pandas, python data science, NumPy, python programming, python and data science from scratch with real life exercises, python for data science, data science python, matplotlib

OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis,
Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.
Python instructors on OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.

Do you know data science needs will create 11.5 million job openings by 2026?
Do you know the average salary is $100.000 for data science careers!

DATA SCIENCE CAREERS ARE SHAPING THE FUTURE

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

• If you want to learn one of the employer’s most request skills?

• If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

• If you are an experienced developer and looking for a landing in Data Science!

No prior knowledge is needed!

Python doesn't need any prior knowledge to learn it and the Python code is easy to understand for beginners.

What you will learn?

In this course, we will start from the very beginning and go all the way to programming with hands-on examples. We will first learn how to set up a lab and install the needed software on your machine. Then during the course, you will learn the fundamentals of Python development like

• Variables, Data types, Numbers, Strings

• Conditionals and Loops

• Functions and modules

• Lists, Dictionaries, and Tuples

• File operations

• Object-Oriented Programming

• How to use Anaconda and Jupyter notebook,

• Datatypes in Python,

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

• Conditional concept, if statements

• The 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

• The logic of Data Munging

• How to deal effectively with missing data effectively

• Combining the Data Frames

• How to work with Dataset files

• And also you will learn fundamental things about the Matplotlib library such as

• Pyplot, Pylab and Matplotlb concepts

• What Figure, Subplot, and Axes are

• How to do figure and plot customization

• Python

• Python Data science

• Numpy

• Numpy python

• Pandas

• Python pandas

With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.

Do not forget! Python has the second largest number of job postings relative to all other languages. So it will earn you a lot of money and will bring a great change in your resume.

Why would you want to take this course?

Our answer is simple: The quality of teaching.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise.

Video and Audio Production Quality

All our videos are created/produced as high-quality video and 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

We offer full support, answering any questions.

If you are ready to learn Python and Data Science from Scratch With Real Life Exercises course
Dive in now!
See you in the course!

Reed courses certificate of completion

Digital certificate - Included

• Course Intro 01:18

  • 1: Python and Data Science from Scratch With RealLife Exercises 01:18

• Data Science: Python is Easy to Learn with Python, Python Data Science, Pandas 09:57

  • 2: Python Is The New King and Pandas Are So Cute 02:57
  • 3: FAQ regarding Data Science, Numpy, Pandas 03:00
  • 4: FAQ regarding Python, Numpy, Pandas 03:00
  • 5: Project Files and Course Documents for Python Data Science Course 01:00

• Data Science: Setting Up Python for Mac and Windows 31:46

  • 6: Anaconda for Windows 10:35
  • 7: Anaconda for Mac 06:17
  • 8: PyCharm for Mac 05:46
  • 9: PyCharm for Windows 04:11
  • 10: Installing Jupyter Notebook For MAC 02:31
  • 11: Installing Jupyter Notebook For Windows 02:26

• If there are variables there is Python 09:36

  • 12: What is a variable 08:36
  • 13: quiz 01:00

• Math is not so confusing with Python 3 now 11:20

  • 14: Numbers and Math Operators with example 10:20
  • 15: quiz 01:00

• Strings in Python Programming 13:29

  • 16: String Operations and Useful String Methods 05:38
  • 17: Data Type Conversion 03:54
  • 18: Exercise Company Email Generator 02:57
  • 19: quiz 01:00

• Conditionals in Python 39:57

  • 20: Conditionals 01:41
  • 21: bool() Function 02:08
  • 22: Comparison and Logical Operators 09:05
  • 23: If Statements 10:00
  • 24: Exercise Calculator 11:06
  • 25: Exercise User Login 04:57
  • 26: quiz 01:00

• Loops in Python 27:23

  • 27: Loops 01:32
  • 28: While Loops 04:16
  • 29: For Loops 04:29
  • 30: Range Function 03:24
  • 31: Control Statements 05:03
  • 32: Exercise Perfect Numbers 02:33
  • 33: Exercise User Login with Loops 05:06
  • 34: Python Data Science Quiz 01:00

• Functions in Python Bootcamp 19:02

  • 35: Functions 02:12
  • 36: Create A New Function and Function Calls 03:53
  • 37: Return Statement 04:36
  • 38: Lambda Functions 02:59
  • 39: Exercise Finding Prime Number 04:22
  • 40: quiz 01:00

• Modules in Python 3 15:42

  • 41: Logic of Using Modules 02:20
  • 42: How It is Work 03:17
  • 43: Create A New Module 03:07
  • 44: Exercise Number Game 05:58
  • 45: quiz 01:00

• Lists in Data Science Python 19:09

  • 46: Lists and List Operations 04:56
  • 47: List Methods 05:35
  • 48: List Comprehensions 02:25
  • 49: Exercise Fibonacci Numbers 02:43
  • 50: Exercise Merging Name and Surname 02:30
  • 51: quiz 01:00

• Tuples in Python Programming 07:43

  • 52: Tuples 06:43
  • 53: quiz 01:00

• Data Science Python: Dictionaries 18:59

  • 54: Dictionaries 10:16
  • 55: Dictionary Comprehensions 02:20
  • 56: Exercise Letter Counter 02:33
  • 57: Exercise Word Counter 02:50
  • 58: quiz 01:00

• Exceptions in Python Programming 18:46

  • 59: What is Exception 03:30
  • 60: Exception Handling 12:53
  • 61: Exercise if Number 02:23

• Python Programming: Files 26:24

  • 62: Files 03:01
  • 63: File Operations 11:28
  • 64: Exercise Team Building 05:44
  • 65: Exercise Overlap 05:11
  • 66: quiz 01:00

• Python 3: Sets 18:08

  • 67: Sets and Set Operations and Methods 11:09
  • 68: Set Comprehensions 05:59
  • 69: quiz 01:00

• Object Oriented Programming (OOP) 34:31

  • 70: Logic of OOP 04:59
  • 71: Constructor 06:34
  • 72: Methods 04:42
  • 73: Inheritance 06:42
  • 74: Overriding and Overloading 10:34
  • 75: quiz 01:00

• Data Science Python: Project Project 21:36

  • 76: Project Remote Controller Application 21:36

• Python For Data Science: In Foreign Lands: Data Science 09:49

  • 77: What Is Data Science 05:40
  • 78: Data Literacy 03:09
  • 79: Python Data Science Quiz 01:00

• Using Numpy for Data Manipulation 51:09

  • 80: What is Numpy 06:49
  • 81: Array and Features 12:08
  • 82: Array Operators 04:53
  • 83: Indexing and Slicing 10:15
  • 84: Numpy Exercises 16:04
  • 85: quiz 01:00

• Pandas: Using Pandas for Data Manipulation 6:47:21

  • 86: Introduction to Pandas Library 06:38
  • 87: Creating a Pandas Series with a List 10:21
  • 88: Creating a Pandas Series with a Dictionary 04:53
  • 89: Creating Pandas Series with NumPy Array 03:10
  • 90: Object Types in Series 05:14
  • 91: Examining the Primary Features of the Pandas Series 04:55
  • 92: Most Applied Methods on Pandas Series 12:53
  • 93: Indexing and Slicing Pandas Series 07:13
  • 94: Creating Pandas DataFrame with List 05:33
  • 95: Creating Pandas DataFrame with NumPy Array 03:03
  • 96: Creating Pandas DataFrame with Dictionary 04:01
  • 97: Examining the Properties of Pandas DataFrames 06:32
  • 98: Element Selection Operations in Pandas DataFrames Lesson 1 07:41
  • 99: Element Selection Operations in Pandas Data Frames Lesson 2 06:04
  • 100: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 08:42
  • 101: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 07:33
  • 102: Top Level Element Selection in Pandas DataFrames Structure of loc and iloc Le 05:35
  • 103: Element Selection with Conditional Operations in Pandas Data Frames 11:23
  • 104: Adding Columns to Pandas Data Frames 08:16
  • 105: Removing Rows and Columns from Pandas Data frames 04:00
  • 106: Null Values in Pandas Dataframes 14:42
  • 107: Dropping Null Values Dropna() Function 07:14
  • 108: Filling Null Values0 Fillna() Function 11:36
  • 109: Setting Index in Pandas DataFrames 07:03
  • 110: Multi-Index and Index Hierarchy in Pandas DataFrames 09:17
  • 111: Element Selection in Multi-Indexed DataFrames 05:12
  • 112: Selecting Elements Using the xs() Function in Multi-Indexed DataFrames 07:03
  • 113: 28 Concatenating Pandas Dataframes Concat() Function 12:40
  • 114: Merge Pandas Dataframes Merge() Function Lesson 1 10:45
  • 115: Merge Pandas Dataframes Merge() Function Lesson 2 05:37
  • 116: Merge Pandas Dataframes Merge() Function Lesson 3 09:44
  • 117: Merge Pandas Dataframes Merge() Function Lesson 4 07:34
  • 118: Joining Pandas Dataframes Join() Function 11:41
  • 119: Loading a Dataset from the Seaborn Library 06:41
  • 120: Examining the Data Set 07:29
  • 121: Aggregation Functions in Pandas DataFrames 21:45
  • 122: Examining the Dataset 10:38
  • 123: Coordinated Use of Grouping and Aggregation Functions in Pandas Dataframes 18:14
  • 124: Advanced Aggregation Functions Aggregate() Function 07:40
  • 125: Advanced Aggregation Functions Filter() Function 06:30
  • 126: Advanced Aggregation Functions The Transform() Function 11:38
  • 127: Advanced Aggregation Functions The Apply() Function 10:06
  • 128: Examining the Dataset 08:14
  • 129: Pivot Tables in Pandas Library 10:35
  • 130: Accessing and Making Files Available 05:11
  • 131: Data Entry with Csv and Txt Files 13:35
  • 132: Data Entry with Excel Files 04:25
  • 133: Output of File with CSV Extension 07:09
  • 134: Outputting as an Excel File 03:43

• (Optional) Recap, Exercises, and Bonus Info from the Pandas Library 3:15:51

  • 135: What is Pandas 05:48
  • 136: Series and Features 20:06
  • 137: Data Frame attributes and Methods Part – I 18:14
  • 138: Data Frame attributes and Methods Part – II 13:05
  • 139: Data Frame attributes and Methods Part – III 11:39
  • 140: Multi Index 12:00
  • 141: Groupby Operations 13:31
  • 142: Missing Data and Data Munging Part I 21:08
  • 143: Missing Data and Data Munging Part II 10:37
  • 144: How We Deal with Missing Data 17:19
  • 145: Combining Data Frames Part – I 20:25
  • 146: Combining Data Frames Part – II 19:29
  • 147: Work with Dataset Files 11:30
  • 148: quiz 01:00

• Data Visualization 57:53

  • 149: What is Matplotlib 03:03
  • 150: Using Matplotlib 07:30
  • 151: Pyplot – Pylab - Matplotlib 07:19
  • 152: Figure, Subplot and Axes 17:29
  • 153: Figure Customization 14:47
  • 154: Plot Customization 06:45
  • 155: quiz 01:00

• Python For Data Science: Hands-On Projects 2:03:14

  • 156: Analyse Data With Different Data Sets Titanic Project 03:43
  • 157: Titanic Project Answers 19:54
  • 158: Project II Bike Sharing 04:24
  • 159: Bike Sharing Project Answers 27:45
  • 160: Project III Housing and Property Sales 03:19
  • 161: Answer for Housing and Property Sales Project 30:06
  • 162: Project IV English Premier League 04:22
  • 163: Answers for English Premier League Project 29:41

Welcome to my "Python and Data Science from Scratch With Real Life Exercises" course.

Python Data Science with Python programming, NumPy, Pandas, Matplotlib and dive into Data Science with Python Projects

Numpy, Pandas, Data science, data science from scratch, python, pandas, python data science, NumPy, python programming, python and data science from scratch with real life exercises, python for data science, data science python, matplotlib

OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you’re interested in machine learning, data mining, or data analysis
Data science is everywhere. Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets. Essentially, data science is the key to getting ahead in a competitive global climate.
Python instructors on OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.
Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.

Do you know data science needs will create 11.5 million job openings by 2026?
Do you know the average salary is $100.000 for data science careers!

DATA SCIENCE CAREERS ARE SHAPING THE FUTURE

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand.

• If you want to learn one of the employer’s most request skills?

• If you are curious about Data Science and looking to start your self-learning journey into the world of data with Python?

• If you are an experienced developer and looking for a landing in Data Science!

In all cases, you are at the right place!

We've designed for you "Python and Data Science from Scratch With Real Life Exercises!” a straight-forward course for the Python programming language.

In the course, you will have a down-to-earth way explanations with hands-on projects. With this course, you will learn Python Programming step-by-step. I made Python 3 programming simple and easy with exercises, challenges, and lots of real-life examples.

We will open the door of the Data Science world and will move deeper. You will learn the fundamentals of Python and its beautiful libraries such as Numpy, Pandas, and Matplotlib step by step.

Throughout the course, we will teach you how to use the Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms and we will also do a variety of exercises to reinforce what we have learned in this Python for Data Science course.

This Python and Data Science course is for everyone!

My "Python and Data Science from Scratch With Real Life Exercises!" is for everyone! If you don’t have any previous experience, not a problem! This course is expertly designed to teach everyone from complete beginners, right through to professionals ( as a refresher).

No prior knowledge is needed!

Python doesn't need any prior knowledge to learn it and the Python code is easy to understand for beginners.

What you will learn?

In this course, we will start from the very beginning and go all the way to programming with hands-on examples. We will first learn how to set up a lab and install the needed software on your machine. Then during the course, you will learn the fundamentals of Python development like

• Variables, Data types, Numbers, Strings

• Conditionals and Loops

• Functions and modules

• Lists, Dictionaries, and Tuples

• File operations

• Object-Oriented Programming

• How to use Anaconda and Jupyter notebook,

• Datatypes in Python,

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

• Conditional concept, if statements

• The 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

• The logic of Data Munging

• How to deal effectively with missing data effectively

• Combining the Data Frames

• How to work with Dataset files

• And also you will learn fundamental things about the Matplotlib library such as

• Pyplot, Pylab and Matplotlb concepts

• What Figure, Subplot, and Axes are

• How to do figure and plot customization

• Python

• Python Data science

• Numpy

• Numpy python

• Pandas

• Python pandas

With my up-to-date course, you will have a chance to keep yourself up-to-date and equip yourself with a range of Python programming skills. I am also happy to tell you that I will be constantly available to support your learning and answer questions.

Do not forget! Python has the second largest number of job postings relative to all other languages. So it will earn you a lot of money and will bring a great change in your resume.

Why would you want to take this course?

Our answer is simple: The quality of teaching.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise.

Video and Audio Production Quality

All our videos are created/produced as high-quality video and 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

We offer full support, answering any questions.

If you are ready to learn Python and Data Science from Scratch With Real Life Exercises course
Dive in now!
See you in the course!

• Anyone who wants to start learning Python and Data Science,
• Anyone who plans a career as a Python developer
• Anyone who needs a complete guide on how to start and continue their career with Python
• Software developer who want to learn python data science
• Anyone eager to learn Data Science python with no coding background
• Anyone who plans a career in data scientist, python data science, numpy python
• Anyone who wants to learn Pandas, numpy
• Anyone who wants to learn Numpy
• Anyone who wants to learn Matplotlib
• Anyone who wants to work on real data science project
• Anyone who wants to learn data visualization projects.
• People who want to learn numpy pandas matplotlib, python programming for data science

• No prior data science, python, pandas, numpy knowledge is required
• Free software and tools used during the python data science course
• Basic computer knowledge for python, python data science, python pandas, numpy pandas
• Desire to learn data science
• Motivation to learn the second largest number of job postings relative python program language among all others
• Curiosity for python programming
• Desire to work on data science Project
• Desire to learn python data science, data science from scratch
• Desire to learn python, pandas, numpy, numpy python
• LIFETIME ACCESS, course updates, new content, anytime, anywhere, on any device

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