2022 Python Bootcamp for Data Science Numpy Pandas Seaborn

2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn, available at $59.99, has an average rating of 4.3, with 91 lectures, 11 quizzes, based on 277 reviews, and has 39410 subscribers.

You will learn about Use Python for Data Science and Machine Learning Learn to use Pandas for Data Analysis Learn to use NumPy for Numerical Data Learn to use Seaborn for statistical plots Learn to use Matplotlib for Python Plotting You will learn how to use Jupyter Notebook for exploratory computations using python. You will learn basic and advanced features in NumPy (Numerical Python) You will learn various data analysis tools in Pandas library. You will learn the essential tools for load, clean, transform, merge, and reshape data. You will learn how to create informative visualizations with matplotlib, seaborn and Pandas You will learn how to analyze and manipulate time series data. You will learn how to handle real world data analysis, including data preparation and exploration. This course is ideal for individuals who are I designed this course to be valuable for people who are interested in data science and data analysis with python. or If you want to learn data science with python, this course will be a valuable starting point. or This course is for you if your intention is to learn how to use Python’s data science tools and libraries such as Jupyter notebook, NumPy, Pandas, Matplotlib, Seaborn, and related tools to effectively store, manipulate, and gain insight from data. It is particularly useful for I designed this course to be valuable for people who are interested in data science and data analysis with python. or If you want to learn data science with python, this course will be a valuable starting point. or This course is for you if your intention is to learn how to use Python’s data science tools and libraries such as Jupyter notebook, NumPy, Pandas, Matplotlib, Seaborn, and related tools to effectively store, manipulate, and gain insight from data.

Enroll now: 2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

Summary

Title: 2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

Price: $59.99

Average Rating: 4.3

Number of Lectures: 91

Number of Quizzes: 11

Number of Published Lectures: 91

Number of Published Quizzes: 11

Number of Curriculum Items: 102

Number of Published Curriculum Objects: 102

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Use Python for Data Science and Machine Learning

Learn to use Pandas for Data Analysis

Learn to use NumPy for Numerical Data

Learn to use Seaborn for statistical plots

Learn to use Matplotlib for Python Plotting

You will learn how to use Jupyter Notebook for exploratory computations using python.

You will learn basic and advanced features in NumPy (Numerical Python)

You will learn various data analysis tools in Pandas library.

You will learn the essential tools for load, clean, transform, merge, and reshape data.

You will learn how to create informative visualizations with matplotlib, seaborn and Pandas

You will learn how to analyze and manipulate time series data.

You will learn how to handle real world data analysis, including data preparation and exploration.

Who Should Attend

I designed this course to be valuable for people who are interested in data science and data analysis with python.

If you want to learn data science with python, this course will be a valuable starting point.

This course is for you if your intention is to learn how to use Python’s data science tools and libraries such as Jupyter notebook, NumPy, Pandas, Matplotlib, Seaborn, and related tools to effectively store, manipulate, and gain insight from data.

Target Audiences

I designed this course to be valuable for people who are interested in data science and data analysis with python.

If you want to learn data science with python, this course will be a valuable starting point.

This course is for you if your intention is to learn how to use Python’s data science tools and libraries such as Jupyter notebook, NumPy, Pandas, Matplotlib, Seaborn, and related tools to effectively store, manipulate, and gain insight from data.

This course is ideal for you, if you wish is to start your path to becoming a Data Scientist!

Data Scientist is one of the hottest jobs recently the United States and in Europe and it is a rewarding career with a high average salary.

The massive amount of data has revolutionized companies and those who have used these big data has an edge in competition. These companies need data scientist who are proficient at handling, managing, analyzing, and understanding trends in data.

This course is designed for both beginners with some programming experience or experienced developers looking to extend their knowledge in Data Science!

I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single skill in data manipulation using Python libraries for data science.

In this comprehensive course, I will guide you to learn how to use the power of Python to manipulate, explore, and analyze data, and to create beautiful visualizations.

My course is equivalent to Data Science bootcamps that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With over 90 HD video lectures, including all examples presented in this course which are provided in detailed code notebooks for every lecture.This course is one of the most comprehensive course for using Python for data science on Udemy!

I will teach you how to use Python to manipulate and to explore raw datasets, how to use python libraries for data science such as Pandas, NumPy, Matplotlib, and Seaborn, how to use the most common data structures for data science in python, how to create amazing data visualizations, and most importantly how to prepare your datasets for advanced data analysis and machine learning models.

Here a few of the topics that you will be learning in this comprehensive course:

How to Set Your Python Environment

How to Work with Jupyter Notebooks

Learning Data Structures and Sequences for Data Science In Python

How to Create Functions in Python

Mastering NumPy Arrays

Mastering Pandas Dataframe and Series

Learning Data Cleaning and Preprocessing

Mastering Data Wrangling

Learning Hierarchical Indexing

Learning Combining and Merging Datasets

Learning Reshaping and Pivoting DataFrames

Mastering Data Visualizations with Matplotlib, Pandas and Seaborn

Manipulating Time Series

Practicing with Real World Data Analysis Example

Enroll in the course and start your path to becoming a data scientist today!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Introduction

Lecture 2: How to Download Course Notebooks

Lecture 3: Overview of Course Curriculum

Chapter 2: Module 2: Setting Python Environment

Lecture 1: Decide Which Python Environment to Use

Lecture 2: Local environment: Installing Anaconda

Lecture 3: Cloud Environment: Google Colab Jupyter Notebooks

Chapter 3: Module 3: Working with Jupyter Notebooks

Lecture 1: Running Jupyter Notebook

Lecture 2: Tour In Basics of Jupyter Notebooks

Lecture 3: Cell Types in Jupyter Notebook

Lecture 4: Getting Help in Jupyter Notebook

Lecture 5: Magic Commands

Chapter 4: Module 4: Data Structures And Sequences In Python

Lecture 1: Tuple

Lecture 2: List

Lecture 3: Dictionary

Lecture 4: Set

Chapter 5: Module 5: Functions in Python

Lecture 1: Creating and Calling Functions

Lecture 2: Returning Multiple Values

Lecture 3: Lambda Functions

Chapter 6: Module 6: NumPy Arrays

Lecture 1: What Is NumPy Arrays (Ndarrays)

Lecture 2: Creating Ndarrays

Lecture 3: Data Types for Ndarrays

Lecture 4: Arithmetic with NumPy Arrays

Lecture 5: Indexing and Slicing-Part One

Lecture 6: Indexing and Slicing-Part two

Lecture 7: Boolean Indexing

Lecture 8: Fancy Indexing

Lecture 9: Transposing Arrays

Lecture 10: Mathematical and Statistical Methods

Lecture 11: Sorting Arrays

Lecture 12: File Input and Output with Arrays

Chapter 7: Module 7: Pandas Dataframe

Lecture 1: Series in Pandas

Lecture 2: Dataframe in Pandas

Lecture 3: Index Objects

Lecture 4: Reindexing in Series and DataFrames

Lecture 5: Deleting Rows and Columns

Lecture 6: Indexing, Slicing and Filtering

Lecture 7: Arithmetic with Dataframe

Lecture 8: Sorting Series and Dataframe

Lecture 9: Descriptive Statistics with Dataframe

Lecture 10: Correlation and Covariance

Chapter 8: Module 8: Data Loading, Storage with Pandas

Lecture 1: Reading Data in Text Format-Part1

Lecture 2: Reading Data in Text Format-Part2

Lecture 3: Writing Data in Text Format

Lecture 4: Reading Microsoft Excel Files

Chapter 9: Module 9: Data Cleaning and Preprocessing

Lecture 1: Handling Missing Data

Lecture 2: Filtering out Missing Data

Lecture 3: Filling in Missing Data

Lecture 4: Removing Duplicate Entries

Lecture 5: Replacing Values

Lecture 6: Renaming columns and Index Labels

Lecture 7: Filtering Outliers

Lecture 8: Shuffling and Random Sampling

Lecture 9: Dummy Variables

Lecture 10: String Object Methods

Chapter 10: Module 10: Data Wrangling1: Hierarchical Indexing

Lecture 1: Hierarchical Indexing

Lecture 2: Reordering and Sorting Index Levels

Lecture 3: Summary Statistics by Level

Lecture 4: Indexing with Columns in Dataframe

Chapter 11: Module 11: Data Wrangling2: Combining and Merging Datasets

Lecture 1: Merging Datasets on Keys (common columns)

Lecture 2: Merging Datasets on Index

Lecture 3: Concatenating Along an Axis

Chapter 12: Module 12: Data Wrangling3: Reshaping and Pivoting

Lecture 1: Reshaping by Stacking and Unstacking

Lecture 2: Reshaping by Melting (Wide to Long )

Lecture 3: Reshaping by Pivoting (Long to Wide)

Chapter 13: Module 13: Data Visualization with Matplotlib and Seaborn

Lecture 1: Introducing Matplotlib Library

Lecture 2: Creating Figures and Subplots

Lecture 3: Changing Colors, Markers and Linestyle

Lecture 4: Customizing Ticks and Labels

Lecture 5: Adding Legends

Lecture 6: Adding Texts and Arrows on a Plot

Lecture 7: Adding Annotations and Drawings on a Plot

Lecture 8: Saving Plots to a File

Lecture 9: Line Plots with Dataframe

Lecture 10: Bar Plots with Dataframes

Lecture 11: Bar Plots with Seaborn

Lecture 12: Histograms and Density Plots

Lecture 13: Scatter Plots and Pair Plots

Lecture 14: Factor Plots for Categorical Data

Instructors

Taher Assaf
Instructer

Rating Distribution

1 stars: 1 votes

2 stars: 6 votes

3 stars: 27 votes

4 stars: 103 votes

5 stars: 140 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!