Python Programming II
Design and Solve Data-Related Problem
Python programming is one of the most frequently utilized programming tools in data processing, data modelling and data analytics. This subject will provide the necessary knowledge that is required to use Python when handling data. This course will take novice developers in Python to the next level toward data driven usages.
- Comprehend the principles, techniques, and practices relevant to Python programming using libraries applicable to data processing
- Design and solve a data-related problem by writing a workable program
Who should attend:
Python Programming I
Enterprise Data Scientist (EDS)
Python Programming II is one of the modules under our Enterprise Data Scientist (EDS) programme. EDS is a 42- day training program that provides participants with the tools to be key leaders and contributors of a data science team and be able to analyze data to drive informed business decisions.
Details of Subject
- Numpy – Numpy is the numeric library in Python. This library is used to handle multi-dimensional arrays. It is the funding library for Pandas and for scikit-learn. Build in C, this library is very powerful to handle arrays from Python
- Data Wrangling: Pandas –
Pandas, the Python Data Analysis Library is the library to handle dataframes. A dataframe is a bit like a spreadsheet.Pandas is convenient for manipulating numerical tables and time series.Pandas also allows to manipulate data rows like in SQL databases. This will be covered in the class
- Pandas dataframes and series • Creating,viewing saving data (from/to CSV)
- Selection, mask, preparing data
- Handling missing values
- Analyzing data, groupby, pivot, multiIndex
- Regular Expressions – Texts are made of words that are made of characters. Regular expressions, regexp, are a powerful tool to select and extract specific parts of the text. To do so there is a general syntax for Regular expressions. In this part of the class, we will cover regular expressions in general and also how to use regular expressions in Python
- Web Scrapping –
A lot of data is available on the Web. This data is to be displayed by Internet browsers. Extracting the data from webpages is possible but not easy. This class will focus on how to do it
- Structure of html page. The content of thewebpage is in HTML. Understanding the structure of an HTML document is the first step to extract the data it contains
- Scraping with beautifulsoup library. BeautifulSoup is the Python library to handle HTML files. BeautifulSoup makes it possible to parse HTML documents even when they are not properly formed (which happens often on the Web).
EDS CADS Certified Enterprise Data Scientist
Certification information for this module & track will be made available soon.
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Python Programming II