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Created byAnkit Mistry : 200,000+ Students, Data Science & Machine Learning Academy

Last updated 12/2023

English

What you'll learn

Update your resume with one of the in demand skill : Data analysis Pandas
Setting up Python in anaconda environment
Refresh Python basics with crash course
Learn Most demanded python data analysis library : Pandas
Three important data structure of pandas : Series, Data Frame, Panel
Learn how to analyse one, two and three dimensional data
How to group Data for analysis
How to deal with Text Data with Pandas Functions
Analyse data having multiple level index.
Array and Matrix manipulation Library NumPy
Master pandas with quizzes.
Data Visualization Matplotlib and Seaborn Library
Importing data from various different kinds of sources
Complete Machine Learning work flow implementation with Scikit-learn

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

31 sections • 150 lectures • 16h 50m total length

What is Data analysis5:20
In this video, we will see what is data analysis and why data analysis required. What are option available for analyzing data like R/Python.
Claim your Free Gift0:35
Course FAQ0:23
Join Online Classroom0:14
Introduction to Pandas4:22
In this video we will learn brief idea about data analysis library pandas. Why pandas kind of library has been created for data analysis. What are features of pandas library.
Course FAQ5:02
How to get Certificate0:03
Pandas

Different ways of installation2:59
In this video we will see 2 different ways to install pandas and python in your local machine.
Download and Install anaconda + Pandas6:09
In this video we will see from where to download anaconda, selection appropriate version, and step by step installation of anaconda distribution on Windows based machine.
Troubleshooting : 'conda' is not recognized as an internal or external command0:10
Anaconda + Conda Command7:25
In this video we will see how to start anaconda prompt, and various conda utility related command for performing various task like
update package,
install package,
remove package.
Conda Cheatsheet
anaconda, conda & pandas Update0:05
Getting started with Jupyter Lab12:43
In this video will learn how to start jupyter notebook and jupyter lab.
Walk through of jupyter UI, how to create a cell how to execute a cell and so on
Jupyter Notebook cheatsheet0:01
Import Library8:21
In this video we will learn how to import required library like numpy, pandas and Matplotlib into python environment.
Pandas Version
Installation

Introduction1:55
Learn what are the different Python related topic we will address in this Python crash course section.
Python Basics - I14:54
Learn about Python basic data type like numbers float Boolean string. What are the operation we can perform with all those basic data type.
Data types, Numbers, String
Data types
Correct error in string variable
Python Basics - II7:35
In this video will learn how to make your life easy by putting all those repetitive task inside looping mechanism available in Python wait for and while loop.
Another thing we will learn how to apply decision making based on mathematical and logical condition in Python.
Loops & Decision making
Lists and tuples15:26
Learn how to create and use to collection type available in a python list and tuples. And what are the different attributes and methods we can apply on a list object.
Lists and tuples
Dictionary and set10:40
Learn another important collection type available in Python dictionary and sets. Methods and attribute associated with dictionary object. In which scenario we need to use the dictionary object.
Dictionary and set
Functions7:57
In this video, we will learn concept of function in a python, how to create user defined function with parametric argument and without argument. How to use the function as a black box system.
Python - 1
Python - 2

Creating NumPy array16:10
In this video learn how to create numpy array with varieties of different ways like array method, arange, linspace, random, eye, ones and zeros. Learn about what are the attributes and method you can apply on this numpy array.
Numpy indexing and selection, Functions16:11
In this video learn about what is indexing in numpy array and how to select single element or a set of element with slicing plus what are the operations you can perform on top of numpy array.
Some more Numpy Functions12:29
Linear algebra with NumPy10:10
List vs NumPy Array11:16
Views vs Copy - Numpy Array8:20
Insert, Append and Delete NumPy array10:34
Split, Concatenate, Tile and Repeat array12:39
NumPy

Series0:06
Introduction to Series2:54
Learn basic introduction about series data type available in Pandas library.
Create Series from Python Object11:44
Learn how to create series Data type with String, float, numbers, list, Boolean, Dictionary data object.
Create Series from CSV file8:58
Learn how to create series data type from the comma separated file, .CSV file. read_ csv()
Create Series Object
Series attributes & methods12:39
Values, index, dtype
Series attributes & methods
Label indexing4:36
In this video we will learn about, instead of simple number base indexing how to use custom label based indexing in a series data type of Pandas.
Label indexing
inplace parameter, sort_values & sort_index7:50
Learn one of the very important parameter 'inplace' while doing any kind of modifications to series object and how to sort values and index of series object.
inplace parameter, sort_values & sort_index
Apply Python built in function on Series3:00
Learn how to get meta information associated to series object with Python built in functions max, min, sort, list, dict etc...
Extract Value from Series6:24
Learn how to extract single value, multiple value or range of value based on bracket notation and slicing notation.
Extract Value from Series
.value_counts() Method1:48
Learn how to count frequency of each value with value_count method on series data type of pandas.
.apply() and .map() method8:53
Learn how to invoke function on every single value of series with apply function and map value with map function.
.apply() and .map() method
Series

Introduction to Data Frame4:10
Learn one of the very important core Data structure available in a Pandas library which represent two dimensional tabular data which is the common way of representing records based data withdata frame object.
Create Data Frame - random data + from File10:31
In this video we will learn how to create a data frame object from varieties of different ways like random generation of data or reading comma separated file and put all those data into data frame object.
Data frame attributes and methods11:34
Learn different attributes and methods we can apply on data frame object to get some meta information about object.
Adding new column4:09
Learn how to add new column to existing data frame object.
Select one or more than one column
Learn how to select one column or more than one column from the data frame object with bracket notation.
Broadcasting operation4:16
Drop missing row or column8:10
Learn how to delete particular rows and column of data frame object based on index number and column name respectively.
Filtering Data with one condition10:26
Filtering Data with multiple condition6:44
Filtering Data with .isin() method5:41
Learn .isin() method to filter the data from Data frame object.
Filtering Data with .between() method3:49
Learn how to use
.between() Method to filter the data from Data frame object.
unique() & nunique() method4:43
Learn how to get unique sets of values and total number of unique values in a particular column of data frame object.
sorting values9:22
Learn how to use sort_values method to sort the column.
sort index and inplace parameter4:50
Learn how to sort index of data frame object and inplace parameter to do permanent modification in original data frame object.
.loc() and .iloc() method14:08
Learn how to extract rows of values based on label indexing and number based indexing.
.ix() method4:07
Learn how to extract rows with .ix() method.
.astype() method - optimize memory requirement7:57
Learn how to change the data type of individual column of data frame object to optimally represent data frame in a memory and better way to do further analysis on the data.
set_index() : change index column4:18
Learn how to make different column as a index column of data frame object.
.apply() method on single column5:42
Learn how to apply custom function veet apply method on a one single column of data frame object.
.apply() method on multiple column8:09
Learn how to apply custom function on than one column of data frame object.
Fetch random sample4:54
Learn how to fetch random record from whole data sets.

Requirements

Windows/Linux/MAC machine
Basic idea about Programming concepts

Description

JOIN OTHER 40,000 SUCCESSFUL STUDENTS WHO HAVE ALREADY ENROLLED & MASTERED PYTHON & PANDAS SKILLS (DATA ANALYSIS LIBRARY) WITH ONE OF MY BEST SELLING, TOP RATED COURSE.

Student Testimonial :

Great going, ankit is good at explanation of data processing stuff. i bought many of his course related to python and machine learning. - Jay

Every concept is clearly explained and the tutor of this course replies to every question asked in Q&A section. - Mukka Akshay

It was very good session. The instructor has enough knowledge and able to make me understand clearly. Thank you Ankit! - Bibek Baniya

This is an amazing course if you want to understand the extent of the power of Pandas. - Venkat Raj

It's one of the best course !!! Most of the topics has been covered and explained up to the expectation - Ankur SIngh

it is a good match with what i was looking for, the instructor is quite knowledgeable. - Shivi Dhir

This class is not too fast or too slow, the way he teaches is perfect. - Frankie Y

It is excellent - Rakhshee Misbah

good experience - Weiting

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Update : New section on Data visualization library Matplotlib and Seaborn added.

Update : New section on Numpy Library get added.

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If you want to master most in-demand data analysis library pandas, carry on reading.

Hi, I am Ankit, one of the Best Selling author on Udemy, taught various courses on Data Science, Python, Pandas, PySpark, Model Deployment.

By the end of this course, you will able to apply all majority of Data analysis function on various different datasets with built in function available in pandas. Analysis techniques like exploratory data analysis, data transformation, data wrangling, time series data analysis, analysis through visualization and many more. Carry on reading to know more about course.

The era of Microsoft Excel is going to be over, so would you like to learn the next generation one of the most powerful data processing tool and in demand skill required for data analyst, data scientist and data engineer.

Then this course is for you, welcome to the course on data analysis with python's most powerful data processing library Pandas.

Why this course?

Data scientist is one of the hottest skill of 21st century and many organisation are switching their project from Excel to Pandas the advanced Data analysis tool .

This course is basically design to get you started with Pandas library at beginner level, covering majority of important concepts of data processing data analysis and a Pandas library and make you feel confident about data processing task with Pandas at advanced level.

What is this course?

This course covers

Basics of Pandas library
Python crash course for any of you want refresh basic concept of python
Python anaconda and Pandas installation
Detail understanding about two important data structure available in a Pandas library
Series data type
Data frame data type
How you can group the data for better analysis
How to use Pandas for text processing
How to visualize the data with Pandas inbuilt visualization tool
Multilevel index in Pandas.
Time series analysis
Numerical Python : NumPy Library
Matplotlib and Seaborn for Data visualization
Machine Learning Theoretical background
Complete end to end Machine Learning Model implementation with Scikit-learn API
- (from Importing Data to Splitting data, Fitting data and Evaluating Data) & How to Improve Machine Learning Model
Importing Data from various different kind of file

You will get following after enrolling in this course.

150+ HD quality video lecture
16+ hours of content
Discussion forum to resolve your query.
quizzes to to test your understanding

This course is still in a draft mode. I am still adding more and more content, quiz, projects related to data processing with different functionalities of Pandas. So stay tuned and enroll now.

Regards

Ankit Mistry

Who this course is for:

Beginner Python developer who is curious about Data Science, Not for experienced Data Scientist
Anyone who want to make career in Data Science, Data analytics
Anyone wants to learn data analysis with python language
Excel user who wants to enhance data analysis skills.

What you'll learn

Explore related topics

Coding Exercises

Course content

Introduction7 lectures • 16min

Installation and IDE9 lectures • 38min

Code Download1 lecture • 1min

Python Crash Course [Optional]6 lectures • 58min

Python Exercises2 lectures • 26min

Numpy8 lectures • 1hr 38min

Series : Pandas11 lectures • 1hr 9min

Data Frame : Pandas21 lectures • 2hr 18min

Pandas Exercise3 lectures • 35min

Panel : Pandas1 lecture • 4min

Requirements

Description

Who this course is for: