Last updated 10/2021Course Language EnglishCourse Caption English [Auto]Course Length 06:42:12 to be exact 24132 seconds!Number of Lectures 91
This course includes:
6.5 hours hours of on-demand video
Full lifetime access
Access on mobile and TV
Certificate of completion
2 additional resources
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 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 EnvironmentHow to Work with Jupyter NotebooksLearning Data Structures and Sequences for Data Science In PythonHow to Create Functions in PythonMastering NumPy ArraysMastering Pandas Dataframe and SeriesLearning Data Cleaning and PreprocessingMastering Data WranglingLearning Hierarchical IndexingLearning Combining and Merging DatasetsLearning Reshaping and Pivoting DataFramesMastering Data Visualizations with Matplotlib, Pandas and SeabornManipulating Time SeriesPracticing with Real World Data Analysis ExampleEnroll 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:
Sections are minimized for better readability, click the section title to view the course content
3 Lectures | 12:55
Course Introduction
03:20
Course Introduction and how to get help in my course.
How to Download Course Notebooks
04:53
In this lecture I am going explain to you how to download and open the course notebooks.
Overview of Course Curriculum
04:42
In this lecture I will give a brief overview of the course curriculum.
3 Lectures | 11:35
Decide Which Python Environment to Use
03:26
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 Anaconda
03:41
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 Notebooks
04:28
In this lecture I will explain how to use the online python environment like Google Colab.
5 Lectures | 23:11
Running Jupyter Notebook
05:28
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 Notebooks
06:16
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 Notebook
04:04
In this lecture I will cover an important aspect of Jupyter notebooks which is cell types.
Getting Help in Jupyter Notebook
03:47
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 Commands
03:36
In this lecture I will cover briefly the common use of the so called magic commands in Jupyter notebooks.
4 Lectures | 17:33
Tuple
04:25
In this lecture I will introduce tuples. I will explain Tuples unpacking, As well as some important tuples methods.
List
07:54
In this lecture I will introduce another data structure, which is list.
Dictionary
02:55
In this lecture, we will talk about a very important data structure which is dictionary.
Set
02:19
How to create a set in python as well as the common functions and operations applied to sets in python
Short Quiz
3 questions
Short Quiz
3 Lectures | 11:17
Creating and Calling Functions
05:28
In this lecture, you will learn the structure of functions and how to create functions in python.
Returning Multiple Values
02:11
In this lecture I will explain the case where we need to return multiple values from the function.
Lambda Functions
03:38
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
3 questions
Short Quiz
12 Lectures | 49:04
What Is NumPy Arrays (Ndarrays)
02:41
In this lecture, I will introduce numpy arrays, which is known as ndarray or multi-dimensional array.
Creating Ndarrays
06:46
In this lecture you will learn how to create numpy arrays.
Data Types for Ndarrays
04:24
In this lecture I will cover the basics of numpy data types or dtypes.
Arithmetic with NumPy Arrays
02:51
In this lecture I will cover how to use arithmetic operations with numpy array.
Indexing and Slicing-Part One
03:54
In this lecture. indexing and slicing of numpy arrays will be explained.
Indexing and Slicing-Part two
04:14
In this lecture we will continue with indexing and slicing but this time for multi-dimensional arrays.
Boolean Indexing
05:32
In this lecture you will learn a very important slicing method which is based on Boolean expressions.
Fancy Indexing
03:57
In this lecture, another type will be introduced which is fancy indexing.
Transposing Arrays
01:57
In this lecture you will learn what we mean by transposing arrays and how you can do transposing.
Mathematical and Statistical Methods
06:02
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 Arrays
03:34
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 Arrays
03:12
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
3 questions
Short Quiz
10 Lectures | 44:06
Series in Pandas
05:32
You will learn how to create series in pandas.
Dataframe in Pandas
07:00
In this lecture we will go through the second data structure in pandas which is the dataframe.
Index Objects
03:40
You will learn about index objects and their characteristics.
Reindexing in Series and DataFrames
02:52
In this lecture you will learn about a method called reindex and how it can be used in pandas.
Deleting Rows and Columns
03:02
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 Filtering
05:37
In this lecture you will learn about very important skills which are indexing, slicing and filtering dataframes.
Arithmetic with Dataframe
04:39
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 Dataframe
03:56
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 Dataframe
04:04
In this lecture you will learn how to calculate descriptive statistics for dataframes.
Correlation and Covariance
03:44
In this lecture, you will learn how to calculate correlation and covariance among features or columns in dataframe.
Short Quiz
4 questions
Short Quiz
4 Lectures | 14:44
Reading Data in Text Format-Part1
05:05
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-Part2
03:52
In this lecture we will continue with the topic of reading data in text formats.
Writing Data in Text Format
03:38
In this lecture you will learn how write and store a dataframe in a text format on your local disk.
Reading Microsoft Excel Files
02:09
In this lecture, you will learn how to read Microsoft excel files into pandas dataframes.
Short Quiz
2 questions
Short quiz
10 Lectures | 34:43
Handling Missing Data
03:07
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 Data
03:54
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 Data
04:00
In this lecture you will learn methods for filling in missing values, instead of deleting them.
Removing Duplicate Entries
02:20
In this lecture, you will learn how to remove duplicate entries from pandas series or dataframe.
Replacing Values
03:03
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 Labels
02:23
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 Outliers
04:14
In this lecture you will learn how to detect and filter outliers.
Shuffling and Random Sampling
03:45
In this lecture you will learn how to shuffle a dataframe and also how to select a random sample from datasets.
Dummy Variables
03:33
In this lecture you will learn how to create dummy variables.
String Object Methods
04:24
In this lecture, you will learn various methods to manipulate string objects.
Short Quiz
4 questions
Short Quiz
4 Lectures | 16:22
Hierarchical Indexing
05:50
You will learn about hierarchical indexing in pandas.
Reordering and Sorting Index Levels
02:39
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 Level
03:28
In this lecture you will learn how to apply descriptive statistics by level in multi-index dataframes.
Indexing with Columns in Dataframe
04:25
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
3 questions
Short Quiz
3 Lectures | 16:32
Merging Datasets on Keys (common columns)
07:12
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.
Merging Datasets on Index
02:49
In this lecture, you have learn how to merge dataframes based on dataframe index.
Concatenating Along an Axis
06:31
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.
Short Quiz
2 questions
Short Quiz
3 Lectures | 15:17
Reshaping by Stacking and Unstacking
04:57
In this lecture we will focus on the stack and unstack methods
Reshaping by Melting (Wide to Long )
04:58
In this lecture, you will learn another method of reshaping, which is converting the dataframe from wide to long format using the method ‘melt()’.
Reshaping by Pivoting (Long to Wide)
05:22
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.
Short Quiz
4 questions
Short Qiuz
14 Lectures | 01:07:32
Introducing Matplotlib Library
02:51
In this lecture I will introduce matplotlib library and its use for data plotting.
Creating Figures and Subplots
06:02
In this lecture you will learn how to create and display multiple subplots using matplotlib.
Changing Colors, Markers and Linestyle
04:02
In this lecture, you will learn how to change other features of plots like colors, markers and the Linestyle.
Customizing Ticks and Labels
04:48
In this lecture you will learn how to customize ticks and labels to decorate you plots.
Adding Legends
04:10
In this lecture, you will learn how to add legends to you figure.
Adding Texts and Arrows on a Plot
04:44
In this lecture, we continue with plots decoration methods. In specific, we will explore adding texts and arrows on plot.
Adding Annotations and Drawings on a Plot
06:37
In this lecture you will learn how to add annotation and shapes on a subplot. Annotations in figures are useful to draw attention to special features or important points on the graph.
Saving Plots to a File
03:37
In this lecture, you will learn how to save your figure to a file using different image formats.
Line Plots with Dataframe
04:34
In this lecture we will explore how we can use pandas for plotting series and dataframes.
Bar Plots with Dataframes
04:17
In this lecture we will continue with another chart type which is bar plots.
Bar Plots with Seaborn
05:24
In this lecture, you will learn how to create bar plots using seaborn library.
Histograms and Density Plots
05:05
In this lecture, we will explore histograms and density plots.
Scatter Plots and Pair Plots
05:16
In this lecture, you will learn how to create scatter and pair plots. These plots are very common in data science and statistical modeling.
Factor Plots for Categorical Data
06:05
In this lecture you will learn how to create factor plots for categorical data.
Short Quiz
4 questions
Short Quiz
8 Lectures | 43:39
Date and time Data types
04:27
In this lecture, we will focus on a very important skill regarding time series, which is handling date and time data types. Date and time data are used as the index for all time series, therefore, leaning to deal with these data types is vital for time series analysis.
Converting Between String and Datetime
05:08
In this lecture you will learn how to convert between string and datetime.
Basics of Time Series
07:05
In this lecture we will begin learning the basics of time series operations that are needed for real world analysis.
Generating Date Ranges
04:31
In this lecture, you will learn how to generate date ranges for time series using pandas.
Shifting Data Through Time (Lagging and Leading)
06:06
In this lecture, you will learn how to shift data in time series.
Handling Time Zone
05:19
In this lecture, an important but often neglected topic will be discussed which is handling time zone in time series.
Resampling and Frequency Conversion
04:53
In this lecture you will learn an important method related to time series which is called resampling or frequency conversion.
Rolling and Moving Windows
06:10
In this lecture a common time series task will be discussed which is called rolling or moving window. This method is important especially for statistical analysis of time series.
Short Quiz
4 questions
Short Quiz
5 Lectures | 23:42
Housing Dataset Analysis -Part One
03:40
You will learn how to use the techniques presented in this course to explore raw data and to prepare this raw data for the advanced statistical analysis.
Housing Dataset Analysis -Part Two
03:45
In this lecture we will continue with our example of exploring the housing dataset.
Housing Dataset Analysis -Part Three
03:50
In this lecture we will continue with our example of exploring the housing dataset.
Housing Dataset Analysis -Part Four
05:19
In this lecture we will continue with our example of exploring the housing dataset.
Housing Dataset Analysis -Part Five
07:08
You will learn how to check and fix if needed the normality assumption.
4.51
(242 course ratings)
1
1/242
2
5/242
3
23/242
4
87/242
5
126/242
JOIN OUR WHATSAPP GROUP TO GET LATEST COUPON AS SOON AS UPDATED
If you like to get inspired by great web projects, you should check out Made with Javascript. If you have a project that you wish to share with the world, feel free to submit your project on Made with Javascript Club website.
Free Online Tools And Converters for your use
URL Encoder
Input a string of text or a URL and encode the entered string
FAQ: Udemy Free course Most frequent questions and answers
Does Udemy offer Free Udemy coupons?
Yes, Udemy is the largest online education platform, with the broadest selection of video-on-demand courses and qualified instructors available to meet your needs. At theprogrammingbuddy.club we curate the latest udemy coupons, their expiry, and the number of uses left of these udemy coupons.
How to get free Udemy courses?
There are two ways to get free Udemy courses:
Go to udemy.com and search for your desired course category. Then select free from the filter options.
You can also get paid courses for free if you have a coupon. You can head to theprogrammingbuddy.club, where you can get a daily udemy paid course for free.
How to get Udemy Certificates for free?
Udemy offers certification on completion of each course. In order to receive a certificate of completion from Udemy, you need to complete your course 100%. There is a simple hack, you can open a video and jump on the timeline to complete a lecture.
To download the certificate from Udemy, you need to head over to your account on a desktop browser. Udemy certificates can't be accessed on the mobile app.
Do Udemy courses expire?
No, once you enroll, you will have lifetime access to the course. You can complete the course on your schedule.
Why are the Udemy instructors giving away free Udemy Coupons?
Every instructor has worked for hours on each of their courses. As new courses get launched, the instructors have no way to get their course in front of an audience to get some feedback. So, instructors share free coupons for their courses to get feedback from the students. We attheprogrammingbuddy.club work with these instructors to get their courses available to our buddies.
Is Udemy safe to use?
Yes, payments on Udemy are safe. It is no different than paying for other services on an application or website and inputting your payment information before receiving your goods. Just be sure to keep your account secure, do not share your udemy accounts.
Can Udemy courses get you a job?
Earning a skill is more valuable than earning a job these days. Skills are your most valuable asset. They can help you qualify for jobs you want and get promoted to more advanced positions within your organization. Unfortunately, it is difficult for many people to balance taking courses with work and family obligations. We have had many students, who have taken just Udemy courses, started a job as well as started freelancing with the skills they have learned.