2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

With Exercises : Learn to use NumPy, Pandas, Seaborn , Matplotlib for Data Manipulation and Exploration with Python

Language: english

Note: 4.3/5 (230 notes) 38,103 students

Instructor(s): Taher Assaf

Last update: 2021-10-19

What you’ll 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.



  • It is advantageous to have basic python knowledge, but it is not required to understand the material in this course. However, people with no previous basic knowledge of python need to focus first on module 2, 3, 4, and 5, that would be enough to comprehend the rest material in this course.



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!


Who this course is for

  • 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.


Course content

  • Introduction
    • Course Introduction
    • How to Download Course Notebooks
    • Overview of Course Curriculum
  • Module 2: Setting Python Environment
    • Decide Which Python Environment to Use
    • Local environment: Installing Anaconda
    • Cloud Environment: Google Colab Jupyter Notebooks
  • Module 3: Working with Jupyter Notebooks
    • Running Jupyter Notebook
    • Tour In Basics of Jupyter Notebooks
    • Cell Types in Jupyter Notebook
    • Getting Help in Jupyter Notebook
    • Magic Commands
  • Module 4: Data Structures And Sequences In Python
    • Tuple
    • List
    • Dictionary
    • Set
    • Short Quiz
  • Module 5: Functions in Python
    • Creating and Calling Functions
    • Returning Multiple Values
    • Lambda Functions
    • Short Quiz
  • Module 6: NumPy Arrays
    • What Is NumPy Arrays (Ndarrays)
    • Creating Ndarrays
    • Data Types for Ndarrays
    • Arithmetic with NumPy Arrays
    • Indexing and Slicing-Part One
    • Indexing and Slicing-Part two
    • Boolean Indexing
    • Fancy Indexing
    • Transposing Arrays
    • Mathematical and Statistical Methods
    • Sorting Arrays
    • File Input and Output with Arrays
    • Short Quiz
  • Module 7: Pandas Dataframe
    • Series in Pandas
    • Dataframe in Pandas
    • Index Objects
    • Reindexing in Series and DataFrames
    • Deleting Rows and Columns
    • Indexing, Slicing and Filtering
    • Arithmetic with Dataframe
    • Sorting Series and Dataframe
    • Descriptive Statistics with Dataframe
    • Correlation and Covariance
    • Short Quiz
  • Module 8: Data Loading, Storage with Pandas
    • Reading Data in Text Format-Part1
    • Reading Data in Text Format-Part2
    • Writing Data in Text Format
    • Reading Microsoft Excel Files
    • Short Quiz
  • Module 9: Data Cleaning and Preprocessing
    • Handling Missing Data
    • Filtering out Missing Data
    • Filling in Missing Data
    • Removing Duplicate Entries
    • Replacing Values
    • Renaming columns and Index Labels
    • Filtering Outliers
    • Shuffling and Random Sampling
    • Dummy Variables
    • String Object Methods
    • Short Quiz
  • Module 10: Data Wrangling1: Hierarchical Indexing
    • Hierarchical Indexing
    • Reordering and Sorting Index Levels
    • Summary Statistics by Level
    • Indexing with Columns in Dataframe
    • Short Quiz
  • Module 11: Data Wrangling2: Combining and Merging Datasets
    • Merging Datasets on Keys (common columns)
    • Merging Datasets on Index
    • Concatenating Along an Axis
    • Short Quiz
  • Module 12: Data Wrangling3: Reshaping and Pivoting
    • Reshaping by Stacking and Unstacking
    • Reshaping by Melting (Wide to Long )
    • Reshaping by Pivoting (Long to Wide)
    • Short Quiz
  • Module 13: Data Visualization with Matplotlib and Seaborn
    • Introducing Matplotlib Library
    • Creating Figures and Subplots
    • Changing Colors, Markers and Linestyle
    • Customizing Ticks and Labels
    • Adding Legends
    • Adding Texts and Arrows on a Plot
    • Adding Annotations and Drawings on a Plot
    • Saving Plots to a File
    • Line Plots with Dataframe
    • Bar Plots with Dataframes
    • Bar Plots with Seaborn
    • Histograms and Density Plots
    • Scatter Plots and Pair Plots
    • Factor Plots for Categorical Data
    • Short Quiz
  • Module 14 : Time Series
    • Date and time Data types
    • Converting Between String and Datetime
    • Basics of Time Series
    • Generating Date Ranges
    • Shifting Data Through Time (Lagging and Leading)
    • Handling Time Zone
    • Resampling and Frequency Conversion
    • Rolling and Moving Windows
    • Short Quiz
  • Module 15: Real World Data Analysis Example
    • Housing Dataset Analysis -Part One
    • Housing Dataset Analysis -Part Two
    • Housing Dataset Analysis -Part Three
    • Housing Dataset Analysis -Part Four
    • Housing Dataset Analysis -Part Five


2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

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