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

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.

Description

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.


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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!

English
language

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

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