Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn
- Description
- Curriculum
- FAQ
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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!
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1
Course IntroductionCourse Introduction and how to get help in my course.
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2
How to Download Course Notebooks
In this lecture I am going explain to you how to download and open the course notebooks.
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3
Overview of Course Curriculum
In this lecture I will give a brief overview of the course curriculum.
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4
Decide Which Python Environment to Use
In this lecture, I will discuss in some details different python environments that you can use to go with the examples in this course.
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5
Local environment: Installing Anaconda
In this lecture you will learn how to install anaconda locally on your computer to be able to use its integrated Jupyter notebooks.
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6
Cloud Environment: Google Colab Jupyter Notebooks
In this lecture I will explain how to use the online python environment like Google Colab.
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7
Running Jupyter Notebook
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.
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8
Tour In Basics of Jupyter Notebooks
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.
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9
Cell Types in Jupyter Notebook
In this lecture I will cover an important aspect of Jupyter notebooks which is cell types.
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10
Getting Help in Jupyter Notebook
In this lecture I will show you how to get help inside Jupyter notebooks, regarding any expression in python including packages, methods or functions.
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11
Magic Commands
In this lecture I will cover briefly the common use of the so called magic commands in Jupyter notebooks.
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12
Tuple
In this lecture I will introduce tuples. I will explain Tuples unpacking, As well as some important tuples methods.
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13
List
In this lecture I will introduce another data structure, which is list.
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14
Dictionary
In this lecture, we will talk about a very important data structure which is dictionary.
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15
Set
How to create a set in python as well as the common functions and operations applied to sets in python
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16
Short QuizShort Quiz
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17
Creating and Calling Functions
In this lecture, you will learn the structure of functions and how to create functions in python.
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18
Returning Multiple Values
In this lecture I will explain the case where we need to return multiple values from the function.
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19
Lambda Functions
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.
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20
Short Quiz
Short Quiz
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21
What Is NumPy Arrays (Ndarrays)
In this lecture, I will introduce numpy arrays, which is known as ndarray or multi-dimensional array.
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22
Creating Ndarrays
In this lecture you will learn how to create numpy arrays.
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23
Data Types for Ndarrays
In this lecture I will cover the basics of numpy data types or dtypes.
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24
Arithmetic with NumPy Arrays
In this lecture I will cover how to use arithmetic operations with numpy array.
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25
Indexing and Slicing-Part One
In this lecture. indexing and slicing of numpy arrays will be explained.
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26
Indexing and Slicing-Part two
In this lecture we will continue with indexing and slicing but this time for multi-dimensional arrays.
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27
Boolean Indexing
In this lecture you will learn a very important slicing method which is based on Boolean expressions.
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28
Fancy Indexing
In this lecture, another type will be introduced which is fancy indexing.
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29
Transposing Arrays
In this lecture you will learn what we mean by transposing arrays and how you can do transposing.
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30
Mathematical and Statistical Methods
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.
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31
Sorting Arrays
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.
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32
File Input and Output with Arrays
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.
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33
Short Quiz
Short Quiz
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34
Series in Pandas
You will learn how to create series in pandas.
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35
Dataframe in Pandas
In this lecture we will go through the second data structure in pandas which is the dataframe.
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36
Index Objects
You will learn about index objects and their characteristics.
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37
Reindexing in Series and DataFrames
In this lecture you will learn about a method called reindex and how it can be used in pandas.
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38
Deleting Rows and Columns
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.
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39
Indexing, Slicing and Filtering
In this lecture you will learn about very important skills which are indexing, slicing and filtering dataframes.
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40
Arithmetic with Dataframe
In this lecture you will learn how to perform arithmetic operations to dataframes. This is a common task for data analysis and data science.
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41
Sorting Series and Dataframe
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.
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42
Descriptive Statistics with Dataframe
In this lecture you will learn how to calculate descriptive statistics for dataframes.
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43
Correlation and Covariance
In this lecture, you will learn how to calculate correlation and covariance among features or columns in dataframe.
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44
Short Quiz
Short Quiz
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45
Reading Data in Text Format-Part1
In this lecture I will focus on reading data in text formats and how it can be converted into dataframes.
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46
Reading Data in Text Format-Part2
In this lecture we will continue with the topic of reading data in text formats.
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47
Writing Data in Text Format
In this lecture you will learn how write and store a dataframe in a text format on your local disk.
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48
Reading Microsoft Excel Files
In this lecture, you will learn how to read Microsoft excel files into pandas dataframes.
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49
Short Quiz
Short quiz
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50
Handling Missing Data
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.
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51
Filtering out Missing Data
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.
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52
Filling in Missing Data
In this lecture you will learn methods for filling in missing values, instead of deleting them.
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53
Removing Duplicate Entries
In this lecture, you will learn how to remove duplicate entries from pandas series or dataframe.
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54
Replacing Values
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().
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55
Renaming columns and Index Labels
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.
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Filtering Outliers
In this lecture you will learn how to detect and filter outliers.
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Shuffling and Random Sampling
In this lecture you will learn how to shuffle a dataframe and also how to select a random sample from datasets.
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58
Dummy Variables
In this lecture you will learn how to create dummy variables.
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String Object Methods
In this lecture, you will learn various methods to manipulate string objects.
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60
Short Quiz
Short Quiz
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61
Hierarchical Indexing
You will learn about hierarchical indexing in pandas.
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62
Reordering and Sorting Index Levels
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.
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Summary Statistics by Level
In this lecture you will learn how to apply descriptive statistics by level in multi-index dataframes.
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64
Indexing with Columns in Dataframe
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.
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65
Short Quiz
Short Quiz
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66
Merging Datasets on Keys (common columns)
In this lecture you will learn a common method used to merge two dataframes based on a common column, which is called a ‘key column’ in merging terms.
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67
Merging Datasets on Index
In this lecture, you have learn how to merge dataframes based on dataframe index.
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Concatenating Along an Axis
In this lecture, you will learn how to concatenate or join dataframes along an axis, whether it is a row axis or a column axis. This is a very important method, and it is widely and commonly used in data science and data analysis.
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69
Short Quiz
Short Quiz
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70
Reshaping by Stacking and Unstacking
In this lecture we will focus on the stack and unstack methods
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Reshaping by Melting (Wide to Long )
In this lecture, you will learn another method of reshaping, which is converting the dataframe from wide to long format using the method ‘melt()’.
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Reshaping by Pivoting (Long to Wide)
In this lecture, we will discuss another very famous and useful reshaping method which is the method pivot(). Pivoting is the reverse of melting, which means that you transform a one column into multiple columns.
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73
Short Quiz
Short Qiuz
