Udemy

Platform

English

Language

Programming Languages

Category

Data Analysis with Python and Pandas

data analysis with Python,Visualize datasets

4.35 (111 reviews)

Students

6 hours

Content

Feb 2017

Last Update
Regular Price


What you will learn

Input and output data from a variety of data types

Manipulate data sets quickly and efficiently

Visualize datasets

Apply logic to data sets

Combine datasets

Handle for missing and erroneous data


Description

Python programmers are some of the most sought-after employees in the tech world, and Python itself is fast becoming one of the most popular programming languages. One of the best applications of Python however is data analysis; which also happens to be something that employers can't get enough of. Gaining skills in one or the other is a guaranteed way to boost your employability – but put the two together and you'll be unstoppable!

Become and expert data analyser

  • Learn efficient python data analysis
  • Manipulate data sets quickly and easily
  • Master python data mining
  • Gain a skillset in Python that can be used for various other applications

Python data analytics made Simple

This course contains 51 lectures and 6 hours of content, specially created for those with an interest in data analysis, programming, or the Python programming language. Once you have Python installed and are familiar with the language, you'll be all set to go.

The course begins with covering the fundamentals of Pandas (the library of data structures you'll be using) before delving into the most important functions you'll need for data analysis; creating and navigating data frames, indexing, visualising, and so on. Next, you'll get into the more intricate operations run in conjunction with Pandas including data manipulation, logical categorising, statistical functions and applications, and more. Missing data, combining data, working with databases, and advanced operations like resampling, correlation, mapping and buffering will also be covered.

By the end of this course, you'll have not only have grasped the fundamental concepts of data analysis, but through using Python to analyse and manipulate your data, you'll have gained a highly specific and much in demand skill set that you can put to a variety of practical used for just about any business in the world.

Tools Used

Python: Python is a general purpose programming language with a focus on readability and concise code, making it a great language for new coders to learn. Learning Python gives a solid foundation for learning more advanced coding languages, and allows for a wide variety of applications.

Pandas: Pandas is a free, open source library that provides high-performance, easy to use data structures and data analysis tools for Python; specifically, numerical tables and time series. If your project involves lots of numerical data, Pandas is for you.

NumPy: Like Pandas, NumPy is another library of high level mathematical functions. The difference with NumPy however is that was specifically created as an extension to the Python programming language, intended to support large multi-dimensional arrays and matrices.


Screenshots

Data Analysis with Python and Pandas
Data Analysis with Python and Pandas
Data Analysis with Python and Pandas
Data Analysis with Python and Pandas

Content

Introduction to the Course

Course Introduction

Getting pandas and fundamentals

Introduction to Pandas

Section Introduction

Creating and Navigating a Dataframe

Slices, head and tail

Indexing

Visualizing The Data

Converting To Python List Or Pandas Series

Section Conclusion

IO Tools

Section Introduction

Read Csv And To Csv

io operations

Read_hdf and to_hdf

Read Json And To Json

Read Pickle And To Pickle

Section Conclusion

Pandas Operations

Section Introduction

Column Manipulation (Operatings on columns, creating new ones)

Column and Dataframe logical categorization

Statistical Functions Against Data

Moving and rolling statistics

Rolling apply

Section Conclusion

Handling for Missing Data / Outliers

Section Introduction

drop na

Filling Forward And Backward Na

detecting outliers

Section Conclusion

Combining Dataframes

Section Introduction

Concatenation

Appending data frames

Merging dataframes

Joining dataframes

Section Conclusion

Advanced Operations

Section Introduction

Basic Sorting

Sorting by multiple rules

Resampling basics time and how (mean, sum etc)

Resampling to ohlc

Correlation and Covariance Part 1

Correlation and Covariance Part 2

Mapping custom functions

Graphing percent change of income groups

Buffering basics

Buffering into and out of hdf5

Section Conclusion

Working with Databases

Section Introduction

Writing to reading from database into a data frame

Resampling data and preparing graph

Finishing Manipulation And Graph

Section and Course Conclusion

Request a Course

Bonus Material

Bonus Lecture: Course Discounts


Reviews

K
Keith11 February 2018

Lots of knowledge that is applicable, but some of the videos need to be updated as the options presented have been deprecated.

M
Max10 January 2018

It is a very old version from 2015 and there were a lot of changes to the python libs. So it isnt quiet up to date. Also the switching between windows drives me crazy. With Jupiter Notebook this would be a good course but please update it a little bit.

J
Julie19 March 2017

There's a lot of info in this course, but no resources to use to practice. Quite a bit of the video time is also consumed by creating small tables/dataframes/dictionaries

M
Mihai11 January 2017

An interesting course with many useful information and examples. I would like that some topics to be more detailed (for instance the candlestick charts) or some reading / writing to excel files. It is a good course and the instructor is knowledgeable and pleasant.

M
Marty3 December 2016

Covers a great deal of good topics but often rushes through. Doesn't seem to appreciate that what is routine or familiar to the instructor is new to the student. Often doesn't so much explain as quickly type/copy/run things. Rarely explains the parameters or arguments that are used in method calls. Also uses out of date calls that don't work when used (e.g. ImportError: The pandas.io.data module is moved to a separate package). Instructor is clearly quite knowledgeable but I am not and not getting much smarter even though I am trying.

P
Pe27 April 2016

Good overview. Interesting to know from where and how one can download statistical data. A little bit to much of talking for my taste.

V
Vincent13 April 2016

I'd already taken an intro to Python course and an intro to Numpy and Pandas course, so I already knew most of the material in the course. I still learned some interesting tips and tricks from this course. I like that the instructor coded all of the material during the videos instead of reading from slides or copying and pasting the code into the editor. The instructor was also very clear and explained the logic behind what he typed. The video length of the videos and amount of content in each video are just right. Overall, I think that this is a good intro to Pandas, but I think that the other course gave a better foundation for both Pandas and Numpy. The advanced material in this course is very good.

R
Rafael11 April 2016

This course could easily be 5 star course! Great instructor, very knowledgeable, real life examples! Unfortunately the Instructor is not available to update the materials or to ask him questions. Also the course needs major updates in editing and updating the Pandas commands for 2016. This course is worth the money and time for me, it was a bit of challenge (I went through it twice to understand it better...) and to work through some of the outdated and deprecated Pandas commands . My hope is that the producers of this course would try to re-enlist the Instructor and create an updated version of this very powerful Pandas tutorial. Rafael G.

N
Nuhiya6 April 2016

I have just started with this course, I have a very basic understanding of Pandas and Numpy, I have seen them being used as an addition to my academic course. To really get started and understand how to do all this myself, I have taken this course. The instructor explains very well and the content seems good so far. But what I do not like is that I cannot see any text flowing alongside the lecture, like copy-able subtitles/notes. In case I would want to see or note down a word that I couldn't catch while hearing the instructor. I have seen video lectures on Lynda.com, they have this feature where you can copy from this text field and paste in your own personal notes. The note taking feature is another feature I thought this course would have but I do not see any such option. I would really like to keep points that I feel are important or relevant in a notebook with the course material for my reference.

L
Luis12 March 2016

This course gives very good insights about the utilities and functionalists of pandas. A good place to fill your computational skills in data analysis if you are a beginer. This course will be much more useful with a smart exercise and real example guide which allows student to practice each of the taught techniques . Question posted usually are not answered.

J
Jess8 November 2015

Why? he answered many questions I had on my mind, but in general 1) Hands on learning 2) explanations as you go along 3) not verbose 4) absolutely no slides (that's great! I can read that stuff online, without paying someone to read it for me) 5) great pace, good chunks of info (not too big not too small) 6) useful knowledge for work 7) useful build of knowledge, rather than dumping knowledge and leaving us to our own devises. I have already used some of these concepts to explore other data. I've also ventured into other functionality, such as SQL Server connections, and found it quite easy. I used Spyder Python 3.4, sql server 2014. Will buy again.


579706

Udemy ID

8/11/2015

Course created date

10/29/2020

Course Indexed date
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