Python for Data Science: From Zero to Data Analysis
Python for Data Science: From Zero to Data Analysis, available at $54.99, with 202 lectures, and has 8 subscribers.
You will learn about Foundational Python Programming: Acquire a strong grasp of Python basics, including data types, control structures, functions, and object-oriented programming. Data Analysis and Manipulation: Master the use of Python libraries like NumPy and pandas to clean, manipulate, and analyze datasets. Advanced Data Visualization: Learn to create visualizations using Matplotlib and Plotly to effectively communicate data-driven insights and trends. Gain hands-on experience with PyTorch to build and evaluate machine learning models, including classification and regression tasks. Develop robust and reliable code using error handling techniques and performing unit testing with pytest, ensuring your data analysis scripts run smoothly As a bonus, explore Python fundamentals while having fun with turtle graphics, making the course accessible for both parents and children learning together This course is ideal for individuals who are Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science. or Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python. or Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge. or Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making. or Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies. It is particularly useful for Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science. or Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python. or Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge. or Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making. or Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.
Enroll now: Python for Data Science: From Zero to Data Analysis
Summary
Title: Python for Data Science: From Zero to Data Analysis
Price: $54.99
Number of Lectures: 202
Number of Published Lectures: 199
Number of Curriculum Items: 202
Number of Published Curriculum Objects: 199
Original Price: $99.99
Quality Status: approved
Status: Live
What You Will Learn
- Foundational Python Programming: Acquire a strong grasp of Python basics, including data types, control structures, functions, and object-oriented programming.
- Data Analysis and Manipulation: Master the use of Python libraries like NumPy and pandas to clean, manipulate, and analyze datasets.
- Advanced Data Visualization: Learn to create visualizations using Matplotlib and Plotly to effectively communicate data-driven insights and trends.
- Gain hands-on experience with PyTorch to build and evaluate machine learning models, including classification and regression tasks.
- Develop robust and reliable code using error handling techniques and performing unit testing with pytest, ensuring your data analysis scripts run smoothly
- As a bonus, explore Python fundamentals while having fun with turtle graphics, making the course accessible for both parents and children learning together
Who Should Attend
- Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.
- Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.
- Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.
- Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.
- Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.
Target Audiences
- Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.
- Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.
- Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.
- Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.
- Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.
Welcome to “Python Foundations for Data Science“!
This course is your gateway to mastering Python for data analysis, whether you’re just getting started or looking to expand your skills. We begin with the basics, ensuring you build a solid foundation, then gradually move into data science applications.
I’d like to stress that we do not assume a programming background and no background in Python is required.
What You’ll Learn:
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Python Foundations: Grasp the essentials of Python, including data types, strings, slicing, f-strings, and more, laying a solid base for data manipulation.
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Control and Conditional Statements: Master decision-making in Python using if-else statements and logical operators.
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Loops: Automate repetitive tasks with for and while loops, enhancing your coding efficiency.
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Capstone Project – Turtle Graphics: Apply your foundational knowledge in a fun, creative project using Python’s turtle graphics.
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Functions: Build reusable code with functions, understanding arguments, return values, and scope.
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Lists: Manage and manipulate collections of data with Python lists, including list comprehension.
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Equality vs. Identity: Dive deep into how Python handles data with topics like shallow vs. deep copy, and understanding type vs. isinstance.
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Error-Handling: Write robust code by mastering exception handling and error management.
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Recursive Programming: Solve complex problems elegantly with recursion and understand how it contrasts with iteration.
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Searching and Sorting Algorithms: Learn fundamental algorithms to optimize data processing.
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Advanced Data Structures: Explore data structures beyond lists, such as dictionaries, sets, and tuples, crucial for efficient data management.
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Object-Oriented Programming: Build scalable and maintainable code with classes, inheritance, polymorphism, and more, including an in-depth look at dunder methods.
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Unit Testing with pytest: Ensure your code’s reliability with automated tests using pytest, a critical skill for any developer.
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Files and Modules: Handle file input/output and organize your code effectively with modules.
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NumPy: Dive into numerical computing with NumPy, the backbone of data science in Python.
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Pandas: Master data manipulation and analysis with pandas, a must-know tool for data science.
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Matplotlib – Graphing and Statistics: Visualize data and perform statistical analysis using Matplotlib.
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Matplotlib – Image Processing: Explore basic image processing techniques using Matplotlib.
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Seaborn: Enhance your data visualization skills with Seaborn, creating more informative and attractive statistical graphics.
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Plotly: Learn interactive data visualization with Plotly, producing interactive plots that engage users.
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PyTorch Fundamentals: Get started with deep learning using PyTorch, understanding tensors and neural networks.
Why Enroll?
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Expert Guidance: Benefit from step-by-step tutorials and clear explanations.
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Responsive Support: Get prompt, helpful feedback from the instructor, with questions quickly addressed in the course Q&A.
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Flexible Learning: Study at your own pace with lifetime access to regularly updated course materials.
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Positive Learning Environment: Join a supportive and encouraging space where students and instructors collaboratively discuss and solve problems.
Who This Course is For:
-
Python Beginners: Ideal for those new to programming who want to start their Python journey with a focus on data science.
-
Data Analysis Newcomers: Perfect for individuals with little to no experience in data analysis who want to build a strong foundation in Python.
-
Aspiring Data Scientists: Designed for those looking to transition into data science, equipping you with essential skills and knowledge.
-
Professionals Enhancing Their Skills: Suitable for professionals across various industries aiming to leverage Python for data-driven decision-making.
-
Students and Academics: Valuable for students and researchers who need to analyze data for academic projects, research, or studies.
Enroll now and start your journey to mastering Python for data science and data analysis!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Foundations
Lecture 1: Introduction to Python Basics
Lecture 2: First steps in Python and the Python Programing Language Structure
Lecture 3: Python Program Structure – Input and Output
Lecture 4: Indentation and Code Blocks
Lecture 5: Using the Python Interpreter
Lecture 6: More Details on the Print function
Lecture 7: Basic Data Types in Python
Lecture 8: Numerical Operations
Lecture 9: Assignment and Incremental Assignment
Lecture 10: Multiple Assignments
Lecture 11: Variable Names, Snake Case, Camel Case
Lecture 12: Keywords and our first Import Statement
Lecture 13: Escape Sequences
Lecture 14: Data Type Conversions
Lecture 15: Substrings and Slicing
Lecture 16: Multiline Strings and Docstrings
Lecture 17: Installing and Introducing PyCharm
Chapter 3: Control Flow and Conditional Statements
Lecture 1: Introduction to Control Flow and Conditionals
Lecture 2: If Statement and Logical Operators
Lecture 3: Complex Conditions
Lecture 4: Nested If Statements
Chapter 4: Loops
Lecture 1: For Loops using Range
Lecture 2: General For Loops using Range
Lecture 3: Looping over Lists and Tuples
Lecture 4: Prime Numbers and Breaking out of Loops
Lecture 5: Looping over a List of Strings using Split
Lecture 6: While Loops
Lecture 7: The While Loop and Validating Input
Lecture 8: Factorial using the While Loop. Example of an Infinite While Loop
Lecture 9: Factorial using the While Loop and Incremental Assignment
Lecture 10: Nested Loops
Chapter 5: Capstone Project using Turtle Graphics
Lecture 1: Introducing Turtle Graphics
Lecture 2: Avoiding Magic Numbers
Lecture 3: Generalizing Example and using Parameters
Lecture 4: Completing Turtle Graphics Background
Lecture 5: Turtle Graphics Capstone Project
Chapter 6: Functions
Lecture 1: Introduction to Functions
Lecture 2: Simple Functions
Lecture 3: More Examples of Functions
Lecture 4: Functions with Default Parameters
Lecture 5: Breaking down Problems using Functions
Lecture 6: Function Scope, Local and Global Variables
Lecture 7: Accessing a global variable from within a function
Lecture 8: Call by Order vs Call by Name/Keyword Arguments
Lecture 9: Variable Number of Arguments in a Function call
Lecture 10: Sum Example with Type-Checking
Lecture 11: String Methods
Lecture 12: Type Annotations and Functions
Lecture 13: Type Annotations with Lists
Chapter 7: Lists
Lecture 1: Introduction to Lists
Lecture 2: List Methods
Lecture 3: Nested Lists
Lecture 4: List Slicing
Lecture 5: List Comprehensions
Lecture 6: List Comprehensions and Filtering
Lecture 7: For Loop Appending vs List Comprehension
Chapter 8: Equality vs Identity
Lecture 1: Aliasing
Lecture 2: Beware of the 'is' Operator
Lecture 3: Shallow Copy
Lecture 4: Deep Copy
Lecture 5: type vs isinstance
Lecture 6: Comparison and Inequalities
Lecture 7: Inequalities and Sorting
Lecture 8: Reverse Sorting
Lecture 9: General Sorting by a Key Function
Chapter 9: Exception and Error Handling
Lecture 1: Syntax vs Run-Time Errors
Lecture 2: TypeError in Average Function
Lecture 3: Catch all Errors
Lecture 4: Catch Multiple Exceptions
Lecture 5: Handling Exceptions Separately
Lecture 6: Using else and finally
Lecture 7: Safe Division Example
Lecture 8: Raising a Built-in Exception
Lecture 9: Example of Raising an Exception
Lecture 10: Raising a Custom Exception
Chapter 10: Recursive Programming
Lecture 1: Factorial Recursive vs Non-Recursive Implementation
Lecture 2: Implementing the Exponential Function using Recursion
Lecture 3: Simple Recursive Fibonacci.
Lecture 4: Counting number of calls in Simple Recursive Fibonacci
Lecture 5: Assignment Expressions and Efficient Fibonacci
Lecture 6: Comparing the Run-Time of Fibonacci Implementations
Chapter 11: Searching and Sorting Algorithms
Lecture 1: Linear Search Boolean
Lecture 2: Linear Search Return Index
Lecture 3: Searching a Sorted List – Birds-eye View of Binary Search
Lecture 4: Searching a Sorted List – Implementing Binary Search
Lecture 5: Worst-Case Run-time Complexity Linear vs Binary Search
Lecture 6: MaxSort
Lecture 7: BubbleSort
Instructors
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Dr. Ron Erez
Computer programmer, Educator and Mathematician
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