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
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Can I take my courses with me wherever I go?
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