Python Numpy Data Analysis for Data Scientist | AI | ML | DL
What you'll learn
- Understand the basics of Numpy and how to set up the Numpy environment.
- Create and access arrays, use indexing and slicing, and work with arrays of different dimensions.
- Understand the ndarray object, data types, and conversion between data types.
- Work with array attributes and different ways of creating arrays from existing data or ranges functions.
- Apply broadcasting, iteration, and updating array values.
- Perform array manipulation, joining, transposing, and splitting operations.
- Apply string, mathematical, and trigonometric functions.
- Perform arithmetic operations, including add, subtract, multiply, divide, floor_divide, power, mod, remainder, reciprocal, negative, and abs.
- Apply statistical functions and counting functions.
- Sort arrays using different methods, including sort(), argsort(), lexsort(), searchsorted(), partition(), and argpartition().
- Understand the different types of array copies, including view, copy, "no copy", shallow copy, and deep copy.
Requirements
- You should have beginner level experince with Python programming for Numpy
- You did not need to buy extra software or course for this Numpy course
- If you have basic knowledge of Matrix, it is good for you
Description
Introduction to Python Numpy Data Analysis for Data Scientist | AI | ML | DL
The Python Numpy Data Analysis for Data Scientist course is designed to equip learners with the necessary skills for data analysis in the fields of artificial intelligence, machine learning, and deep learning.
This course covers an array of topics such as creating/accessing arrays, indexing, and slicing array dimensions, and ndarray object. Learners will also be taught data types, conversion, and array attributes.
The course further delves into broadcasting, array manipulation, joining, splitting, and transposing operations.
Learners will gain insight into Numpy binary operators, bitwise operations, left and right shifts, string functions, mathematical functions, and trigonometric functions.
Additionally, the course covers arithmetic operations, statistical functions, and counting functions. Sorting, view, copy, and the differences among all copy methods are also covered.
By the end of the course, learners will be proficient in using Python Numpy for data analysis, making them ready to take on the challenges of the data science industry.
What you can do with Pandas Python
Data analysis: Pandas is often used in data analysis to perform tasks such as data cleaning, manipulation, and exploration.
Data visualization: Pandas can be used with visualization libraries such as Matplotlib and Seaborn to create visualizations from data.
Machine learning: Pandas is often used in machine learning workflows to preprocess data before training models.
Financial analysis: Pandas is used in finance to analyze and manipulate financial data.
Social media analysis: Pandas can be used to analyze and manipulate social media data.
Scientific computing: Pandas is used in scientific computing to manipulate and analyze large amounts of data.
Business intelligence: Pandas can be used in business intelligence to analyze and manipulate data for decision-making.
Web scraping: Pandas can be used in web scraping to extract data from web pages and analyze it.
********** Instructors Experiences and Education: **********
Faisal Zamir is an experienced programmer and an expert in the field of computer science. He holds a Master's degree in Computer Science and has over 7 years of experience working in schools, colleges, and university. Faisal is a highly skilled instructor who is passionate about teaching and mentoring students in the field of computer science.
As a programmer, Faisal has worked on various projects and has experience in multiple programming languages, including PHP, Java, and Python. He has also worked on projects involving web development, software engineering, and database management. This broad range of experience has allowed Faisal to develop a deep understanding of the fundamentals of programming and the ability to teach complex concepts in an easy-to-understand manner.
As an instructor, Faisal has a proven track record of success. He has taught students of all levels, from beginners to advanced, and has a passion for helping students achieve their goals. Faisal has a unique teaching style that combines theory with practical examples, which allows students to apply what they have learned in real-world scenarios.
Overall, Faisal Zamir is a skilled programmer and a talented instructor who is dedicated to helping students achieve their goals in the field of computer science. With his extensive experience and proven track record of success, students can trust that they are learning from an expert in the field.
What you will learn in this course Python Numpy Data Analysis for Data Scientist
These are the outlines, you can read that will be covered in the course:
Chapter 01
Introduction to Numpy
Numpy Environnent Setup
Chapter 02
Creating /Accessing Array
Indexing & Slicing
Array dimensions (1, 2, 3, ..N)
ndarray Object
Data types
Data type Conversion
Chapter 03
Array attributes
Array ndarray object attributes
Array creation in different ways
Array from existed data
Array from ranges function
Chapter 04
Broadcasting
Array iteration
Update Array values
Broadcasting iteration
Chapter 05
Array Manipulation Operations
Array Joining Operations
Array Transpose Operations
Array Splitting Operations
Array More Operations
Chapter 06
Numpy binary operators – Binary Operations
bitwise_and
bitwise_or
numpy.invert()
left_shift
right_shift
Chapter 07
String Functions
Mathematical Functions
Trigonometric Functions
Chapter 08
Arithmetic operations
Add
Subtract
Multiply
Divide
floor_divide
Power
Mod
Remainder
Reciprocal
Negative
abs
Statistical functions
Counting functions
Chapter 09
Sorting
sort()
argsort()
lexsort()
searchsorted()
partition()
argpartition()
Chapter 10
View
Copy
“No Copy”
Shallow Copy
Deep Copy
The difference among all copies method
30-day money-back guarantee for Python Numpy Data Analysis for Data Scientists
Great! It's always reassuring to have a money-back guarantee when making a purchase, especially for an online course. With the "Python Numpy Data Analysis for Data Scientist | AI | ML | DL" course, you can have peace of mind knowing that you have a 30-day money-back guarantee.
This means that if you are not satisfied with the course within the first 30 days of purchase, you can request a full refund.
This shows the confidence of the course provider in the quality of their content, and it gives you the opportunity to try out the course risk-free.
So if you're looking to improve your skills in Python data analysis for data science, AI, ML, or DL, this course is definitely worth considering.
Thank you
Faisal Zamir
Who this course is for:
- Data Scientists who need to analyze large data sets and want to use Python's powerful tools for this purpose.
- AI and Machine Learning engineers who want to work with numerical data using Python and Numpy.
- Deep Learning enthusiasts who want to understand the fundamentals of Numpy arrays and use it to manipulate and process image and audio data.
- Researchers who want to use Python and Numpy for scientific computing and numerical analysis.
- Programmers who want to learn a powerful and widely-used library for numerical computing with Python.
- Students who are interested in pursuing a career in Data Science or related fields and want to learn the basics of Numpy for data analysis.
Instructor
- 4.3 Instructor Rating
- 2,885 Reviews
- 176,145 Students
- 20 Courses
Hello, my name is Faisal Zamir and I am a Master of Computer Science with over 7 years of experience in teaching. I have a passion for teaching and have dedicated my career to educating students on various topics related to web designing, web development, and programming languages such as Python, PHP, JavaScript, C++, C#, and more.
As an instructor, I believe in providing students with a comprehensive learning experience that goes beyond just theory. That's why I always provide programming examples, discussion (Q&A), quizzes, assignments, problems with solutions, exercises, and more to ensure that my students have a hands-on approach to learning.
My courses are designed to provide students with lifetime access to all course materials and resources, as well as ongoing support. I am committed to helping my students achieve their goals, whether it's to improve their skills for personal or professional reasons.
In addition to teaching, I have also worked on various projects in the field of computer science and have acquired practical experience in implementing programming concepts in real-world scenarios. I bring this experience into my teaching to provide my students with a practical and relevant education.
Thank you for considering my courses and I look forward to helping you achieve your goals in the world of computer science.