Data Analysis with Python and Pandas

data analysis with Python,Visualize datasets
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  • Lectures 52
  • Length 6 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 8/2015 English

Course 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.

What are the requirements?

  • Students should have Python installed
  • Students should be familiar with the Python programming language, specifically Python 3+

What am I going to get from this course?

  • 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

What is the target audience?

  • Those interested in data analysis with Python
  • People looking for methods to normalize the handling of multiple data types and databases
  • Those interested in efficient data manipulation
  • Those brand new to programming or Python should not take this course

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to the Course
Course Introduction
Preview
04:11
Getting pandas and fundamentals
Preview
09:06
Section 2: Introduction to Pandas
Section intro
00:48
Creating and Navigating a Dataframe
08:34
Slices, head and tail
07:59
Indexing
07:27
Visualizing The Data
09:19
Converting To Python List Or Pandas Series
04:15
Section Outro
01:38
Section 3: IO Tools
Section intro
02:12
Read Csv And To Csv
09:26
io operations
05:23
Read_hdf and to_hdf
08:25
Read Json And To Json
09:54
Read Pickle And To Pickle
11:39
Section Outro
03:52
Section 4: Pandas Operations
Section intro
02:04
Column Manipulation (Operatings on columns, creating new ones)
07:27
Column and Dataframe logical categorization
07:12
Statistical Functions Against Data
07:34
Moving and rolling statistics
10:00
Rolling apply
08:54
Section Outro
03:17
Section 5: Handling for Missing Data / Outliers
Section Intro
03:13
drop na
06:48
Filling Forward And Backward Na
11:09
detecting outliers
12:36
Section Outro
05:17
Section 6: Combining Dataframes
Section Intro
03:53
Concatenation
09:15
Appending data frames
07:06
Merging dataframes
09:41
Joining dataframes
09:40
Section Outro
04:29
Section 7: Advanced Operations
Section Intro
02:48
Basic Sorting
08:56
Sorting by multiple rules
08:32
Resampling basics time and how (mean, sum etc)
10:03
Resampling to ohlc
07:12
Correlation and Covariance Part 1
10:03
Correlation and Covariance Part 2
11:56
Mapping custom functions
09:21
Graphing percent change of income groups
07:23
Buffering basics
10:12
Buffering into and out of hdf5
10:01
Section Outro
03:00
Section 8: Working with Databases
Section Intro
01:00
Writing to reading from database into a data frame
10:22
Resampling data and preparing graph
07:54
Finishing Manipulation And Graph
09:30
Section and course outro
05:27
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