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Hello and welcome to the Data Science: Python Programming for Data Science 2023 Complete Bootcamp.
In this course, you will learn how to code in Python from the beginning, then you will master how to work with the most famous libraries and tools of the Python programming language related to data science, starting from data collection, acquiring and analysis to visualize data with advanced techniques, and based on that the necessary decisions are taken by companies.
What you'll learn
Code with Python Programming Language
Python Functional Programming
Structure Data using collection containers
Advanced Python Foundations
Handling Data with Python Libraries
Extracting and Analyzing data from different resources
Data Analysis with Pandas
Data Visualization using matplotlib
Advanced Visualization with Seaborn
Build Python solutions for data science
Get Instructor QA Support and help
# Reviews about this course
★★★★★ "This course is very helpful for basic to advance level of learning in python.
I also like this course."
★★★★★ "This was simple and easy to follow and an eye-opener for a beginner to get started with Data analysis and visualization."
★★★★★ "Very good course
The instructor spoke very clear and was very knowledgeable"
★★★★★ "Enrolled this course to sharpen my knowledge in python programming language. Very complete course to begin mastering python."
★★★★★ "easy to understand. well organized course"
★★★★★ "Clear, and easy explanation. Great course for beginners who want to learn python and data science."
★★★★★ "This course is very helpful for me I would like to start research on fault diagnosis"
★★★★★ "Nice course with great practical examples."
★★★★★ "It's totally amazing !! The explanation about the topics is clear and very understandable"
- 1: Welcome 01:40
- 2: Download and Install the tools 01:21
- 3: Jupyter Overview + Markdown in Jupyter tutorial 06:47
- 4: Using Jupyter Notebook to code with Python 05:41
- 5: Using Anaconda Prompt 03:15
- 6: Quiz 1 01:00
- 7: Variables and Types Tutorial 10:38
- 8: Describe what's inside the code 04:44
- 9: Define Blocks and Avoid IndentationError 04:19
- 10: String full tutorial 13:44
- 11: Numbers, Math and f-string tutorial 15:13
- 12: Handling inputs and outputs 04:29
- 13: Quiz 2 02:00
- 14: Structure Data using Lists 21:02
- 15: Structure data using Tuples 10:32
- 16: Structure Data using Dictionaries 08:05
- 17: Structure Data using Sets 07:21
- 18: Quiz 3 02:00
- 19: Comparing Values 08:36
- 20: Output from Logics 06:49
- 21: Conditional Statements 08:55
- 22: The while loop 02:56
- 23: The for loop in Python 07:18
- 24: Python Library Functions 11:21
- 25: The Lambda Power 08:51
- 26: User-Defined Functions 07:30
- 27: Break statement 04:05
- 28: Continue statement 04:35
- 29: For else statement 02:15
- 30: App to Put all together 01:43
- 31: Quiz 4 01:00
- 32: Core Python OOP 12:02
- 33: Exploring Inheritance 10:10
- 34: Quiz 5 01:00
- 35: Comprehensions 05:52
- 36: Constructed modules and random 06:37
- 37: Doing mathematics 04:47
- 38: Doing statistics 04:17
- 39: Errors Exploration 04:28
- 40: Exceptions Playground 06:45
- 41: IO data in memory 07:11
- 42: Interacting with operating system data 03:04
- 43: Moving data files between directories 04:42
- 44: Data will be in the trash bin 04:16
- 45: Zipping and Unzipping Data 06:49
- 46: NumPy Level 1 07:09
- 47: NumPy Level 2 05:03
- 48: NumPy Level 3 02:25
- 49: NumPy Level 4 03:44
- 50: NumPy Level 5 05:38
- 51: NumPy level 6 03:50
- 52: NumPy Level 7 03:46
- 53: NumPy Level 8 03:19
- 54: NumPy level 9 03:08
- 55: Pandas data analysis level 1 03:49
- 56: Pandas data analysis level 2 04:41
- 57: Pandas data analysis level 3 02:18
- 58: Pandas data analysis level 4 03:32
- 59: Pandas data analysis level 5 02:52
- 60: Pandas data analysis level 6 04:30
- 61: Matplotlib data visualization level 1 02:57
- 62: Matplotlib data visualization level 2 01:31
- 63: Matplotlib data visualization level 3 03:06
- 64: Matplotlib data visualization level 4 03:14
- 65: Matplotlib data visualization level 5 01:37
- 66: Matplotlib data visualization level 6 03:02
- 67: Matplotlib data visualization level 7 02:32
- 68: Seaborn statistical graphs level 1 03:41
- 69: Seaborn statistical graphs level 2 01:56
- 70: Seaborn statistical graphs level 3 01:17
- 71: Seaborn statistical graphs level 4 01:39
- 72: Seaborn statistical graphs level 5 02:31
- 73: Seaborn statistical graphs level 6 02:30
- 74: Seaborn statistical graphs level 7 01:49
- 75: Seaborn statistical graphs level 8 01:00
- 76: Python Programming 00:40
- 77: NumPy 00:29
- 78: Pandas 00:25
- 79: Matplotlib 00:35
- 80: Seaborn 00:28
Data science enables companies to measure, track, and record performance metrics for facilitating and enhancing decision making. Companies can analyze trends to make critical decisions to engage customers better, enhance company performance, and increase profitability.
And the employment of data science and its tools depends on the purpose you want from them.
For example, using data science in health care is very different from using data science in finance and accounting, and so on. And I’ll show you the core libraries for data handling, analysis and visualization which you can use in different areas.
One of the most powerful programming languages that are used for Data science is Python, which is an easy, simple and very powerful language with many libraries and packages that facilitate working on complex and different types of data.
By the end of this course, you'll learn:
Python tools for Data Analysis
Advanced Python Foundations
Data Handling with Python
Data Analysis with Pandas
Data Visualization with Matplotlib
Advanced Graphs with Seaborn
Instructor QA Support and Help
HD Video Training + Working Files + Resources + QA Support.
In this course, you will learn how to code in Python from the beginning and then you will master how to deal with the most famous libraries and tools of the Python language related to data science, starting from data collection, acquiring and analysis to visualize data with advanced techniques, and based on that, the necessary decisions are taken by companies.
I am Ahmed Ibrahim, a software engineer and Instructor and I have taught more than 200,000 engineers and developers around the world in topics related to programming languages and their applications, and in this course, we will dive deeply into the core Python fundamentals, Advanced Foundations, Data handling libraries, Numerical Python, Pandas, Matplotlib and finally Seaborn.
I hope that you will join us in this course to master the Python language for data analysis and Visualization like professionals in this field.
We have a lot to cover in this course.
Let’s get started!
Who is this course for?
- Python beginners and newbies
- Data Scientist who knows other language tools
- New Python Data Analysts
- Data Science Beginners
- New developers and Programmers
- Programmers and developers who know other programming language but are new to python
- Anyone who wants to use Python for data analysis and visualization in a short time!
- No Python prior experience is required to take this Training
- Computer and Internet access
- Python Data Scientist salaries currently range between $104,000 to $156,000
- Python Developer Avg. salary: $107,003 / Annual
- Software Engineer Avg. salary: $111,206 / Annual
- Software Developer Avg. salary: $100,000 / Annual
- Python Data Analyst Avg. salary: $115,156 per year
Questions and answers
No questions or answers found containing ''.
Can an Android phone be used in place of a Conputer fur this course?Answer:
To follow this course without any obstacles you need a computer or laptop with any operating system, and the jupyter notebook installed. Best regards Ahmed IbrahimThis was helpful. Thank you for your feedback.
After the course is completed is there a test/exam?Answer:
In this Bootcamp, you'll get video training, resources and quizzes to test your knowledge. Once you complete 100% of the course, you'll get certificate of completing to boost your profile and career.This was helpful. Thank you for your feedback.
Hello After purchasing the course, how long do I have to start/finish it? ThanksAnswer:
It's a self-paced course with lifetime access , means you can complete it in your own time and based on your own pace.This was helpful. Thank you for your feedback.
Reed courses certificate of completion
Digital certificate - Included
Will be downloadable when all lectures have been completed