Python Numpy Data Analysis for Data Scientist - AI - ML - DL

Python Numpy Data Analysis for Data Scientist | AI | ML | DL, available at $27.99, has an average rating of 4.39, with 76 lectures, 1 quizzes, based on 140 reviews, and has 19789 subscribers.

You will learn about 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. This course is ideal for individuals who are Data Scientists who need to analyze large data sets and want to use Pythons powerful tools for this purpose. or AI and Machine Learning engineers who want to work with numerical data using Python and Numpy. or Deep Learning enthusiasts who want to understand the fundamentals of Numpy arrays and use it to manipulate and process image and audio data. or Researchers who want to use Python and Numpy for scientific computing and numerical analysis. or Programmers who want to learn a powerful and widely-used library for numerical computing with Python. or 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. It is particularly useful for Data Scientists who need to analyze large data sets and want to use Pythons powerful tools for this purpose. or AI and Machine Learning engineers who want to work with numerical data using Python and Numpy. or Deep Learning enthusiasts who want to understand the fundamentals of Numpy arrays and use it to manipulate and process image and audio data. or Researchers who want to use Python and Numpy for scientific computing and numerical analysis. or Programmers who want to learn a powerful and widely-used library for numerical computing with Python. or 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.

Enroll now: Python Numpy Data Analysis for Data Scientist | AI | ML | DL

Summary

Title: Python Numpy Data Analysis for Data Scientist | AI | ML | DL

Price: $27.99

Average Rating: 4.39

Number of Lectures: 76

Number of Quizzes: 1

Number of Published Lectures: 76

Number of Published Quizzes: 1

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Number of Practice Tests: 1

Number of Published Practice Tests: 1

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

Data Scientists who need to analyze large data sets and want to use Pythons 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.

Target Audiences

Data Scientists who need to analyze large data sets and want to use Pythons 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.

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

1. Data analysis: Pandas is often used in data analysis to perform tasks such as data cleaning, manipulation, and exploration.

2. Data visualization: Pandas can be used with visualization libraries such as Matplotlib and Seaborn to create visualizations from data.

3. Machine learning: Pandas is often used in machine learning workflows to preprocess data before training models.

4. Financial analysis: Pandas is used in finance to analyze and manipulate financial data.

5. Social media analysis: Pandas can be used to analyze and manipulate social media data.

6. Scientific computing: Pandas is used in scientific computing to manipulate and analyze large amounts of data.

7. Business intelligence: Pandas can be used in business intelligence to analyze and manipulate data for decision-making.

8. Web scraping: Pandas can be used in web scraping to extract data from web pages and analyze it.

Instructors Experiences and Education:

Faisal Zamiris 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

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

Course Curriculum

Chapter 1: Python Numpy Chapter 01

Lecture 1: 01 Numpy Chapter 01 Introduction

Lecture 2: 02 Introduction to Numpy

Lecture 3: 03 Numpy Environment Setup

Lecture 4: 04 Numpy Programming Example

Chapter 2: Python Numpy Chapter 02

Lecture 1: 05 Numpy Chapter 02 Introduction

Lecture 2: 06 Creating Array in Numpy

Lecture 3: 07 Indexing and Slicing with Array

Lecture 4: 08 ndarray Object in Numpy

Lecture 5: 09 Data Types in Numpy Part 01

Lecture 6: 10 Data Types in Numpy Part02

Lecture 7: 11 Data Types Conversion in Numpy

Chapter 3: Python Numpy Chapter 03

Lecture 1: 12 Numpy Chapter 03 Introduction

Lecture 2: 13 Array Attributes

Lecture 3: 14 Array vs ndarray Attributes

Lecture 4: 15 Array Methods

Lecture 5: 16 Empty Array Creation

Lecture 6: 17 Zeros Array Creation

Lecture 7: 18 Ones Creation Array

Lecture 8: 19 Asarray Method in Numpy

Chapter 4: Python Numpy Chapter 04

Lecture 1: 00 Numpy Chapter 04 Introduction

Lecture 2: 01 Broadcasting and its Rule 01

Lecture 3: 02 Broadcasting and its 02 and 03 Rules

Lecture 4: 03 Frombuffer method in Array

Lecture 5: 04 Fromiter method in Numpy

Lecture 6: 05 Arange method in Numpy

Lecture 7: 06 Linespace and logspace in Numpy

Lecture 8: 07 For Loop Interations with Array

Lecture 9: 08 nditer in Numpy

Lecture 10: 09 ndenumerate in Numpy

Lecture 11: 10 Fill Method for Updating Array

Lecture 12: 11 Indexing and Slicing method to update

Lecture 13: 12 Put Method to Update

Lecture 14: 13 Boolean Indexing Method to Update

Chapter 5: Python Numpy Chapter 05

Lecture 1: 01 Numpy Chapter 05 Introduction

Lecture 2: 02 Reshape in Array Manipulation

Lecture 3: 03 Flat in Array Manipulation

Lecture 4: 04 Flatten in Array Manipulation

Lecture 5: 05 ravel Method in Array Manipulation

Lecture 6: 06 concatenate in Array Manipulation

Lecture 7: 07 Transpose Operation in Numpy

Lecture 8: 08 Split in Numpy Array

Lecture 9: 09 More Operations on Numpy Array

Chapter 6: Python Numpy Chapter 06

Lecture 1: 01 Numpy Chapter 06 Outlines

Lecture 2: 02 Bitwise AND Operator working

Lecture 3: 03 Bitwise OR Operator working

Lecture 4: 04 Bitwise NOT Operator working.wmv

Lecture 5: 05 Bitwise Left and Right Shift

Chapter 7: Python Numpy Chapter 07

Lecture 1: 01 Outline Numpy Chapter 07

Lecture 2: 02 add and title in Array of String

Lecture 3: 03 Lower and upper in Array of String

Lecture 4: 04 Strip Split and Join in Array of String

Lecture 5: 05 replace method in Array of String

Lecture 6: 06 Trignometric in Array in Numpy

Lecture 7: 07 Math in Array in Numpy

Chapter 8: Python Numpy Chapter 08

Lecture 1: 01 Outline Numpy Chapter 08

Lecture 2: 02 All Arithmetic Operations in Numpy

Lecture 3: 03 Statistical Function in Numpy

Lecture 4: 04 Counting function in Numpy

Chapter 9: Python Numpy Chapter 09

Lecture 1: 01 Numpy Chapter 09

Lecture 2: 02 Sort method in Numpy

Lecture 3: 03 Argsort in Numpy

Lecture 4: 04 Searchsorted in Numpy

Lecture 5: 05 Partition in Numpy

Lecture 6: 06 where and argwhere in Numpy

Lecture 7: 07 searchsorted in Numpy

Lecture 8: 08 nonzero in Numpy

Lecture 9: 09 extract in Numpy

Lecture 10: 10 Boolean Indexing in Numpy

Lecture 11: 11 where filteration in Numpy

Lecture 12: 12 extract filteration in Numpy

Lecture 13: 13 compress in Numpy

Chapter 10: Python Numpy Chapter 10

Lecture 1: 01 Numpy Chapter 10

Lecture 2: 02 View in Numpy

Lecture 3: 03 Methods to Create view in Numpy

Lecture 4: 04 Copy in Numpy

Lecture 5: 05 Create Copy in Numpy

Chapter 11: Updated Section

Chapter 12: Practice Test

Instructors

Faisal Zamir
Programmer

Jafri Code
Programming and Web Instructor

Pro Python Support

Rating Distribution

1 stars: 1 votes

2 stars: 3 votes

3 stars: 21 votes

4 stars: 60 votes

5 stars: 55 votes

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