Data Analysis with Python and Pandas

$99.00 $49.99

Create data frames using the Pandas add-on!  By the end of this Data Analysis with Python and Pandas  course, you’ll have not only grasped the fundamental concepts of data analysis, but through using Python to analyze 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 uses for just about any business in the world.

Length: 6 hrs

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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 an expert data analyzer

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

This Data Analysis with Python and Pandas course has been 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, visualizing, and so on. Next, you’ll get into the more intricate operations run in conjunction with Pandas including data manipulation, logical categorizing, 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 grasped the fundamental concepts of data analysis, but through using Python to analyze 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 uses 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.

Sample clip

 

CHAPTER 1: INTRODUCTION TO THE COURSE
Course Introduction
Getting Pandas and Fundamentals
Section Conclusion
 
CHAPTER 2: 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
 
CHAPTER 3: 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
 
CHAPTER 4: 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 Outro
 
CHAPTER 5: HANDLING FOR MISSING DATA / OUTLIERS
Section Intro
drop na
Filling Forward And Backward Na
detecting outliers
Section Conclusion
 
CHAPTER 6: COMBINING DATAFRAMES
Section Introduction
Concatenation
Appending data frames
Merging dataframes
Joining dataframes
Section Conclusion
 
CHAPTER 7: 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
 
CHAPTER 8: 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

Harrison Kinsley is a husband, runner, friend of all dogs, programmer, teacher, and entrepreneur.

Harrison utilized his love for learning and building with technology to start multiple businesses, all of which leverage the Python programming language. Python and programming is a major part of his life and work. He believes programming is a super power, and the social impact of making this education easily accessible to anyone is one of the most important things he can do with his life.

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