Data Manipulation in Python: Master Python, Numpy & Pandas

Data Manipulation in Python: Master Python, Numpy & Pandas, available at $64.99, has an average rating of 4.34, with 108 lectures, 22 quizzes, based on 2354 reviews, and has 168766 subscribers.

You will learn about Learn to use Pandas for Data Analysis Learn to work with numerical data in Python Learn statistics and math with Python Learn how to code in Jupyter Notebook Learn how to install packages in Python This course is ideal for individuals who are No previous skills or expertise required. Only a drive to succeed! It is particularly useful for No previous skills or expertise required. Only a drive to succeed!.

Enroll now: Data Manipulation in Python: Master Python, Numpy & Pandas

Summary

Title: Data Manipulation in Python: Master Python, Numpy & Pandas

Price: $64.99

Average Rating: 4.34

Number of Lectures: 108

Number of Quizzes: 22

Number of Published Lectures: 108

Number of Published Quizzes: 22

Number of Curriculum Items: 130

Number of Published Curriculum Objects: 130

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to use Pandas for Data Analysis
Learn to work with numerical data in Python
Learn statistics and math with Python
Learn how to code in Jupyter Notebook
Learn how to install packages in Python

Who Should Attend

No previous skills or expertise required. Only a drive to succeed!

Target Audiences

No previous skills or expertise required. Only a drive to succeed!

When it comes to being attractive, data scientists are already there. In a highly competitive job market, it is tough to keep them after they have been hired. People with a unique mix of scientific training, computer expertise, and analytical abilities are hard to find.

Like the Wall Street “quants” of the 1980s and 1990s, modern-day data scientists are expected to have a similar skill set. People with a background in physics and mathematics flocked to investment banks and hedge funds in those days because they could come up with novel algorithms and data methods.

That being said, data science is becoming one of the most well-suited occupations for success in the twenty-first century. It is computerized, programming-driven, and analytical in nature. Consequently, it comes as no surprise that the need for data scientists has been increasing in the employment market over the last several years.

The supply, on the other hand, has been quite restricted. It is challenging to get the knowledge and abilities required to be recruited as a data scientist.

Lots of resources for learning Python are available online. Because of this, students frequently get overwhelmed by Python’s high learning curve.

It’s a whole new ball game in here! Step-by-step instruction is the hallmark of this course. Throughout each subsequent lesson, we continue to build on what we’ve previously learned. Our goal is to equip you with all the tools and skills you need to master Python, Numpy & Pandas.

You’ll walk away from each video with a fresh idea that you can put to use right away!

All skill levels are welcome in this course, and even if you have no prior programming or statistical experience, you will be able to succeed!

Course Curriculum

Chapter 1: Python Quick Refresher (Optional)

Lecture 1: Welcome to the course!

Lecture 2: Introduction to Python

Lecture 3: Course Materials

Lecture 4: Setting up Python

Lecture 5: What is Jupyter?

Lecture 6: Anaconda Installation: Windows, Mac & Ubuntu

Lecture 7: How to implement Python in Jupyter?

Lecture 8: Managing Directories in Jupyter Notebook

Lecture 9: Input/Output

Lecture 10: Working with different datatypes

Lecture 11: Variables

Lecture 12: Arithmetic Operators

Lecture 13: Comparison Operators

Lecture 14: Logical Operators

Lecture 15: Conditional statements

Lecture 16: Loops

Lecture 17: Sequences: Lists

Lecture 18: Sequences: Dictionaries

Lecture 19: Sequences: Tuples

Lecture 20: Functions: Built-in Functions

Lecture 21: Functions: User-defined Functions

Chapter 2: Essential Python Libraries for Data Science

Lecture 1: Installing Libraries

Lecture 2: Importing Libraries

Lecture 3: Pandas Library for Data Science

Lecture 4: NumPy Library for Data Science

Lecture 5: Pandas vs NumPy

Lecture 6: Matplotlib Library for Data Science

Lecture 7: Seaborn Library for Data Science

Chapter 3: Fundamental NumPy Properties

Lecture 1: Introduction to NumPy arrays

Lecture 2: Creating NumPy arrays

Lecture 3: Indexing NumPy arrays

Lecture 4: Array shape

Lecture 5: Iterating Over NumPy Arrays

Chapter 4: Mathematics for Data Science

Lecture 1: Basic NumPy arrays: zeros()

Lecture 2: Basic NumPy arrays: ones()

Lecture 3: Basic NumPy arrays: full()

Lecture 4: Adding a scalar

Lecture 5: Subtracting a scalar

Lecture 6: Multiplying by a scalar

Lecture 7: Dividing by a scalar

Lecture 8: Raise to a power

Lecture 9: Transpose

Lecture 10: Element wise addition

Lecture 11: Element wise subtraction

Lecture 12: Element wise multiplication

Lecture 13: Element wise division

Lecture 14: Matrix multiplication

Lecture 15: Statistics

Chapter 5: Python Pandas DataFrames & Series

Lecture 1: What is a Python Pandas DataFrame?

Lecture 2: What is a Python Pandas Series?

Lecture 3: DataFrame vs Series

Lecture 4: Creating a DataFrame using lists

Lecture 5: Creating a DataFrame using a dictionary

Lecture 6: Loading CSV data into python

Lecture 7: Changing the Index Column

Lecture 8: Inplace

Lecture 9: Examining the DataFrame: Head & Tail

Lecture 10: Statistical summary of the DataFrame

Lecture 11: Slicing rows using bracket operators

Lecture 12: Indexing columns using bracket operators

Lecture 13: Boolean list

Lecture 14: Filtering Rows

Lecture 15: Filtering rows using & and | operators

Lecture 16: Filtering data using loc()

Lecture 17: Filtering data using iloc()

Lecture 18: Adding and deleting rows and columns

Lecture 19: Sorting Values

Lecture 20: Exporting and saving pandas DataFrames

Lecture 21: Concatenating DataFrames

Lecture 22: groupby()

Chapter 6: Data Cleaning

Lecture 1: Introduction to Data Cleaning

Lecture 2: Quality of Data

Instructors

Meta Brains
Let's code & build the metaverse together!

Rating Distribution

1 stars: 34 votes
2 stars: 56 votes
3 stars: 360 votes
4 stars: 839 votes
5 stars: 1065 votes

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!

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