2015
Udemy
Stone River eLearning
05:56:04
English

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.

Section 1: Introduction to the Course
Lecture 1
Course Introduction
04:11
Lecture 2
Getting pandas and fundamentals
09:06
Section 2: Introduction to Pandas
Lecture 3
Section intro
00:48
Lecture 4
Creating and Navigating a Dataframe
08:34
Lecture 5
Slices, head and tail
07:59
Lecture 6
Indexing
07:27
Lecture 7
Visualizing The Data
09:19
Lecture 8
Converting To Python List Or Pandas Series
04:15
Lecture 9
Section Outro
01:38
Section 3: IO Tools
Lecture 10
Section intro
02:12
Lecture 11
Read Csv And To Csv
09:26
Lecture 12
io operations
05:23
Lecture 13
Read_hdf and to_hdf
08:25
Lecture 14
Read Json And To Json
09:54
Lecture 15
Read Pickle And To Pickle
11:39
Lecture 16
Section Outro
03:52
Section 4: Pandas Operations
Lecture 17
Section intro
02:04
Lecture 18
Column Manipulation (Operatings on columns, creating new ones)
07:27
Lecture 19
Column and Dataframe logical categorization
07:12
Lecture 20
Statistical Functions Against Data
07:34
Lecture 21
Moving and rolling statistics
10:00
Lecture 22
Rolling apply
08:54
Lecture 23
Section Outro
03:17
Section 5: Handling for Missing Data / Outliers
Lecture 24
Section Intro
03:13
Lecture 25
drop na
06:48
Lecture 26
Filling Forward And Backward Na
11:09
Lecture 27
detecting outliers
12:36
Lecture 28
Section Outro
05:17
Section 6: Combining Dataframes
Lecture 29
Section Intro
03:53
Lecture 30
Concatenation
09:15
Lecture 31
Appending data frames
07:06
Lecture 32
Merging dataframes
09:41
Lecture 33
Joining dataframes
09:40
Lecture 34
Section Outro
04:29
Section 7: Advanced Operations
Lecture 35
Section Intro
02:48
Lecture 36
Basic Sorting
08:56
Lecture 37
Sorting by multiple rules
08:32
Lecture 38
Resampling basics time and how (mean, sum etc)
10:03
Lecture 39
Resampling to ohlc
07:12
Lecture 40
Correlation and Covariance Part 1
10:03
Lecture 41
Correlation and Covariance Part 2
00:11
Lecture 42
Mapping custom functions
09:21
Lecture 43
Graphing percent change of income groups
07:23
Lecture 44
Buffering basics
10:12
Lecture 45
Buffering into and out of hdf5
10:01
Lecture 46
Section Outro
03:00
Section 8: Working with Databases
Lecture 47
Section Intro
01:00
Lecture 48
Writing to reading from database into a data frame
10:22
Lecture 49
Resampling data and preparing graph
07:54
Lecture 50
Finishing Manipulation And Graph
09:32
Lecture 51
Section and course outro
05:27

https://www.udemy.com/data-analysis-with-python-and-pandas/

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