Python for Data Science- Complete Masterclass

Python for Data Science: Complete Masterclass, available at $54.99, with 94 lectures, and has 9 subscribers.

You will learn about Uses and Importance of Python Python Vs Java Vs C++ Python Installation Operators in Python Sets in Python Data Collection Variables in Python Data Types in Python Conditional Statements Loops in Python Classes and Objects File Handling in Python Functions in Python Numeric Data Types Strings in Python Lists and Tuples NumPy: Data Science Pandas: Data Science This course is ideal for individuals who are For python learners or For data science beginners It is particularly useful for For python learners or For data science beginners.

Enroll now: Python for Data Science: Complete Masterclass

Summary

Title: Python for Data Science: Complete Masterclass

Price: $54.99

Number of Lectures: 94

Number of Published Lectures: 94

Number of Curriculum Items: 94

Number of Published Curriculum Objects: 94

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Uses and Importance of Python

Python Vs Java Vs C++

Python Installation

Operators in Python

Sets in Python

Data Collection

Variables in Python

Data Types in Python

Conditional Statements

Loops in Python

Classes and Objects

File Handling in Python

Functions in Python

Numeric Data Types

Strings in Python

Lists and Tuples

NumPy: Data Science

Pandas: Data Science

Who Should Attend

For python learners

For data science beginners

Target Audiences

For python learners

For data science beginners

“Python for Data Science: Complete Masterclass” is a comprehensive online course designed to provide you with a deep understanding of Python and its applications in data science. This course is suitable for beginners as well as advanced learners who want to enhance their knowledge and skills in Python programming for data science.

Throughout the course, you will learn about the fundamental concepts of Python programming language, such as variables, data types, loops, functions, and modules. You will also learn how to use libraries and frameworks, such as NumPy, Pandas, matplotlib, and Scikit-Learn to work with data.

The course covers a range of topics related to data science, including data manipulation, data analysis, data visualization, and machine learning. You will learn how to clean, preprocess, and manipulate data using Python libraries like Pandas, and how to analyze and visualize data using tools like Matplotlib and Seaborn. You will also learn how to build machine learning models using Scikit-Learn, including regression, classification, clustering, and dimensionality reduction.

By the end of the course, you will have a strong understanding of Python programming language and its applications in data science. You will have gained hands-on experience working with real-world datasets, and you will be able to use Python for data analysis, visualization, and machine-learning tasks.

In addition to the topics mentioned above, the “Python for Data Science: Complete Masterclass” course also covers other important data science concepts, such as data preprocessing, exploratory data analysis, hypothesis testing, and data modeling.

You will learn how to preprocess data, including handling missing values, encoding categorical variables, and scaling numerical data. You will also learn how to perform exploratory data analysis to gain insights into the data and identify patterns and trends.

Furthermore, the course covers hypothesis testing and statistical inference, including t-tests, ANOVA, and chi-squared tests. You will learn how to use these methods to test hypotheses and make inferences about the data.

Finally, the course covers data modeling, including linear regression, logistic regression, decision trees, and random forests. You will learn how to use these models to make predictions and classify data.

The “Python for Data Science: Complete Masterclass” course also includes several hands-on projects and exercises to help you apply what you have learned. You will work with real-world datasets, analyze and visualize the data, and build machine-learning models to make predictions.

Whether you are a beginner or an advanced learner, the “Python for Data Science: Complete Masterclass” course is designed to provide you with a comprehensive understanding of Python and its applications in data science. By the end of the course, you will have the skills and knowledge needed to work with data using Python and be ready to tackle real-world data science problems.

The “Python for Data Science: Complete Masterclass” course is designed to be accessible and engaging, with a focus on hands-on learning and practical applications.

The course is structured in a modular format, with each module focusing on a specific topic. The modules are taught through a combination of video lectures, interactive exercises, and real-world projects, allowing you to learn at your own pace and apply what you have learned immediately.

The course also includes a range of resources to support your learning, including downloadable code examples, reference materials, and quizzes to test your understanding of key concepts.

In addition, the “Python for Data Science: Complete Masterclass” course is taught by experienced instructors who are experts in the field of data science and Python programming. They provide clear explanations of complex concepts and offer practical advice and guidance throughout the course.

Finally, the course is designed to be flexible and customizable, allowing you to tailor your learning experience to your specific needs and interests. Whether you are interested in data analysis, data visualization, or machine learning, the course provides the tools and knowledge you need to succeed.

Overall, the “Python for Data Science: Complete Masterclass” course is a comprehensive and engaging resource for anyone who wants to learn Python for data science. With its hands-on approach, practical focus, and expert instructors, the course provides the skills and knowledge you need to succeed in the field of data science.

AD Chauhdry

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction of Course

Lecture 2: Introduction to Python

Lecture 3: Uses and Importance of Python

Lecture 4: Python Vs Java Vs C++

Lecture 5: Jupyter Notebook

Chapter 2: Python Installation

Lecture 1: Python Installation

Lecture 2: Installation of Visual Studio

Lecture 3: Python IDLE

Chapter 3: Operators in Python

Lecture 1: Operators in Python

Lecture 2: Assignment Operators in Python

Lecture 3: Boolean Operators in Python

Chapter 4: Sets and Data Collection in Python

Lecture 1: Python Data Collection

Lecture 2: Putting and Inserting Specific Value in a List

Lecture 3: Sets in Python

Chapter 5: Variables in Python

Lecture 1: Variables in Python

Lecture 2: Writing Variables

Lecture 3: Types of Variables

Chapter 6: Data Types in Python

Lecture 1: Data Types of Python

Lecture 2: Dictionary in Python

Lecture 3: Printing Specific Words From Strings

Lecture 4: Tuple Data Collection

Chapter 7: Conditional Statements in Python

Lecture 1: Conditional Statements in Python

Lecture 2: Practical Python Example

Lecture 3: If Statement

Lecture 4: Nested IF Statement

Lecture 5: Example 2

Lecture 6: if elif statement in Python

Lecture 7: elif Statement 2

Lecture 8: elif Statement 3

Chapter 8: Loops in Python

Lecture 1: Loops in Python

Lecture 2: else Statement in Python

Lecture 3: While Loop

Lecture 4: While and else Loop

Lecture 5: for Loop

Lecture 6: for Loop and Variable

Lecture 7: for i in range

Chapter 9: Classes and Objects in Python

Lecture 1: Classes and Object in Python

Lecture 2: Objects in Python

Lecture 3: Objects in Python 2

Lecture 4: Attributes and Class Variables

Lecture 5: Class Variables

Lecture 6: Example of Class Variables

Chapter 10: File Handling in Python

Lecture 1: File Handling in Python

Lecture 2: Text File

Lecture 3: f dot read

Lecture 4: Open and Close a File

Chapter 11: Functions in Python

Lecture 1: Functions in Python

Lecture 2: Addition Function

Lecture 3: Types of Arguments

Lecture 4: Default Arguments

Lecture 5: Arguments and Key Words Arguments

Lecture 6: Key Words Arguments

Chapter 12: Numeric Data Types

Lecture 1: Numeric Data Types in Python

Lecture 2: Addition of Variables

Lecture 3: Multiplying Floating Value with Complex

Lecture 4: Integer into Float

Lecture 5: Math Library

Chapter 13: Strings in Python

Lecture 1: Hello World

Lecture 2: Printing Variable Value

Lecture 3: Strings in Python Coding

Lecture 4: Floating Value in Python

Lecture 5: Slice in Strings

Lecture 6: Multiple strings in Python

Lecture 7: Upper Case String

Lecture 8: Replace and Splitting of Strings

Lecture 9: Concatenation of Strings

Lecture 10: Strings and Integers

Chapter 14: Lists and Tuples

Lecture 1: Tuples in Python

Lecture 2: Loops in Python

Lecture 3: Lists in Python

Lecture 4: Termination of Loop

Lecture 5: Printing Multiple Tuples

Lecture 6: Continuation of a Loop

Chapter 15: NumPy: Data Science

Lecture 1: Library in Python

Lecture 2: Array in NumPy

Lecture 3: Tuples into NumPy Array

Lecture 4: Multi-dimensional Array

Lecture 5: Operations in NumPy

Lecture 6: Values in an Array

Lecture 7: Array Sorting

Lecture 8: List into Array

Chapter 16: Pandas: Data Science

Lecture 1: What is Pandas?

Lecture 2: Series in Pandas

Lecture 3: Assigning Labels in Pandas

Instructors

AD Chauhdry
Researcher, Mathematician, and Data Scientist

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!