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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
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.
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.
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|Section 1: Introduction to the Course|
Getting pandas and fundamentalsPreview
|Section 2: Introduction to Pandas|
Creating and Navigating a Dataframe
Slices, head and tail
Visualizing The Data
Converting To Python List Or Pandas Series
|Section 3: IO Tools|
Read Csv And To Csv
Read_hdf and to_hdf
Read Json And To Json
Read Pickle And To Pickle
|Section 4: Pandas Operations|
Column Manipulation (Operatings on columns, creating new ones)
Column and Dataframe logical categorization
Statistical Functions Against Data
Moving and rolling statistics
|Section 5: Handling for Missing Data / Outliers|
Filling Forward And Backward Na
|Section 6: Combining Dataframes|
Appending data frames
|Section 7: Advanced Operations|
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 into and out of hdf5
|Section 8: Working with Databases|
Writing to reading from database into a data frame
Resampling data and preparing graph
Finishing Manipulation And Graph
Section and course outro
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