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2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

Name: 2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn
Rating: 4.2 (288 reviews)

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

Created byTaher Assaf

Last updated 8/2022

English

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.

Course content

15 sections • 91 lectures • 6h 42m total length

Course Introduction3:20
Course Introduction and how to get help in my course.
How to Download Course Notebooks4:53
In this lecture I am going explain to you how to download and open the course notebooks.
Overview of Course Curriculum4:42
In this lecture I will give a brief overview of the course curriculum.

Decide Which Python Environment to Use3:26
In this lecture, I will discuss in some details different python environments that you can use to go with the examples in this course.
Local environment: Installing Anaconda3:41
In this lecture you will learn how to install anaconda locally on your computer to be able to use its integrated Jupyter notebooks.
Cloud Environment: Google Colab Jupyter Notebooks4:28
In this lecture I will explain how to use the online python environment like Google Colab.

Running Jupyter Notebook5:28
In this lecture you will learn how to run Jupyter notebook, how to create a new notebook, also how to open a saved one or a downloaded notebook.
Tour In Basics of Jupyter Notebooks6:16
In this lecture, I will introduce you to some common and basic concepts related to using Jupyter notebook for coding in python in general and for data science in particular.
Cell Types in Jupyter Notebook4:04
In this lecture I will cover an important aspect of Jupyter notebooks which is cell types.
Getting Help in Jupyter Notebook3:47
In this lecture I will show you how to get help inside Jupyter notebooks, regarding any expression in python including packages, methods or functions.
Magic Commands3:36
In this lecture I will cover briefly the common use of the so called magic commands in Jupyter notebooks.

Tuple4:25
In this lecture I will introduce tuples. I will explain Tuples unpacking, As well as some important tuples methods.
List7:54
In this lecture I will introduce another data structure, which is list.
Dictionary2:55
In this lecture, we will talk about a very important data structure which is dictionary.
Set2:19
How to create a set in python as well as the common functions and operations applied to sets in python
Short Quiz

Creating and Calling Functions5:28
In this lecture, you will learn the structure of functions and how to create functions in python.
Returning Multiple Values2:11
In this lecture I will explain the case where we need to return multiple values from the function.
Lambda Functions3:38
In this lecture you will learn how to create lambda functions which is a concise way of writing functions with a single line of code.
Short Quiz

What Is NumPy Arrays (Ndarrays)2:41
In this lecture, I will introduce numpy arrays, which is known as ndarray or multi-dimensional array.
Creating Ndarrays6:46
In this lecture you will learn how to create numpy arrays.
Data Types for Ndarrays4:24
In this lecture I will cover the basics of numpy data types or dtypes.
Arithmetic with NumPy Arrays2:51
In this lecture I will cover how to use arithmetic operations with numpy array.
Indexing and Slicing-Part One3:54
In this lecture. indexing and slicing of numpy arrays will be explained.
Indexing and Slicing-Part two4:14
In this lecture we will continue with indexing and slicing but this time for multi-dimensional arrays.
Boolean Indexing5:32
In this lecture you will learn a very important slicing method which is based on Boolean expressions.
Fancy Indexing3:57
In this lecture, another type will be introduced which is fancy indexing.
Transposing Arrays1:57
In this lecture you will learn what we mean by transposing arrays and how you can do transposing.
Mathematical and Statistical Methods6:02
In this lecture I will go through mathematical and statistical methods that can be applied on numpy arrays, as a whole or on specific axis.
Sorting Arrays3:34
In this lecture you will learn how to sort numpy arrays. In many cases, you will find yourself in a situation, where you need to sort the values of an array or a subset of an array.
File Input and Output with Arrays3:12
In this lecture I will show you how to save and load numpy arrays, to and from your local disk. However, as data scientist, you will find yourself using pandas most of the time, for loading and saving datasets. But in particular cases, you might need to use numpy to save and load arrays.
Short Quiz

Series in Pandas5:32
You will learn how to create series in pandas.
Dataframe in Pandas7:00
In this lecture we will go through the second data structure in pandas which is the dataframe.
Index Objects3:40
You will learn about index objects and their characteristics.
Reindexing in Series and DataFrames2:52
In this lecture you will learn about a method called reindex and how it can be used in pandas.
Deleting Rows and Columns3:02
In this lecture you will learn another important method in pandas which is how to delete rows or columns from pandas data structures whether it is a series or a dataframe.
Indexing, Slicing and Filtering5:37
In this lecture you will learn about very important skills which are indexing, slicing and filtering dataframes.
Arithmetic with Dataframe4:39
In this lecture you will learn how to perform arithmetic operations to dataframes. This is a common task for data analysis and data science.
Sorting Series and Dataframe3:56
The topic of this lecture is sorting. Sorting is one of the most common used functions in pandas for data processing. Sorting can be applied on series as a well as on dataframes.
Descriptive Statistics with Dataframe4:04
In this lecture you will learn how to calculate descriptive statistics for dataframes.
Correlation and Covariance3:44
In this lecture, you will learn how to calculate correlation and covariance among features or columns in dataframe.
Short Quiz

Reading Data in Text Format-Part15:05
In this lecture I will focus on reading data in text formats and how it can be converted into dataframes.
Reading Data in Text Format-Part23:52
In this lecture we will continue with the topic of reading data in text formats.
Writing Data in Text Format3:38
In this lecture you will learn how write and store a dataframe in a text format on your local disk.
Reading Microsoft Excel Files2:09
In this lecture, you will learn how to read Microsoft excel files into pandas dataframes.
Short Quiz

Handling Missing Data3:07
In this lecture, you will learn how to handle missing data. In real world, most datasets have some sort of missing or invalid data. So you will need to manage missing data, to minimize its side effects on your data analysis or data modeling. You will also learn how to use pandas functionality to deal with missing data.
Filtering out Missing Data3:54
In this lecture you are going to learn how to filter out missing data in a dataframe using pandas. You have several options to filter out missing data.
Filling in Missing Data4:00
In this lecture you will learn methods for filling in missing values, instead of deleting them.
Removing Duplicate Entries2:20
In this lecture, you will learn how to remove duplicate entries from pandas series or dataframe.
Replacing Values3:03
In this lecture you are going to learn how to replace values in pandas series and dataframes. To replace a value in pandas, we use a function called replace().
Renaming columns and Index Labels2:23
In this lecture, you will learn how to rename labels for columns and for the row index as well. And you can do this using a function called rename.
Filtering Outliers4:14
In this lecture you will learn how to detect and filter outliers.
Shuffling and Random Sampling3:45
In this lecture you will learn how to shuffle a dataframe and also how to select a random sample from datasets.
Dummy Variables3:33
In this lecture you will learn how to create dummy variables.
String Object Methods4:24
In this lecture, you will learn various methods to manipulate string objects.
Short Quiz

Hierarchical Indexing5:50
You will learn about hierarchical indexing in pandas.
Reordering and Sorting Index Levels2:39
In this lecture we will continue working with the multi-index topic, and we will explore sorting and reordering the levels in the multi-indexed data.
Summary Statistics by Level3:28
In this lecture you will learn how to apply descriptive statistics by level in multi-index dataframes.
Indexing with Columns in Dataframe4:25
In this lecture a very simple but an important skill will be explored, which is how to use a column in a dataframe as its index.
Short Quiz

Requirements

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.

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.

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.

2022 Python Bootcamp for Data Science Numpy Pandas & Seaborn

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 13min

Module 2: Setting Python Environment3 lectures • 12min

Module 3: Working with Jupyter Notebooks5 lectures • 23min

Module 4: Data Structures And Sequences In Python4 lectures • 18min

Module 5: Functions in Python3 lectures • 11min

Module 6: NumPy Arrays12 lectures • 49min

Module 7: Pandas Dataframe10 lectures • 44min

Module 8: Data Loading, Storage with Pandas4 lectures • 15min

Module 9: Data Cleaning and Preprocessing10 lectures • 35min

Module 10: Data Wrangling1: Hierarchical Indexing4 lectures • 16min

Requirements

Description

Who this course is for: