Python Programming II

Track: Enterprise Data Scientist (EDS)

Get into the next level toward data driven usages with Python Programming II. Design and solve a data- related problem by writing a workable program. Topics covered in this subject are Numpy, Data Wrangling, Regular Expression and Webscrapping.

Duration: 5 days
Fee: Request
Start Date: Request

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.

Learning outcome:

Upon completion, participants should be able to demonstrate each of the following;

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:

Professionals that work with data

5 days of in depth learning

Face to face with experienced Data Scientist.

Course Methodology

This course will utilize a combination of Presentations and Workshops.

CADS Certification

Earn certification upon completion.

Pre-requisite:
Python Programming I

Minimum Qualification:
Undergraduate Degree

Training Track

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).

Lead Instructor

Jan Sauer

A Data Scientist at The Center of Applied Data Science (CADS), Jan Sauer was a biostatistician in the field of deep learning and image/pattern recognition. He has a master’s degree in physics and have extensive experience as a software developer. Throughout his career, he has been involved in different areas of data science, ranging from automated data collection and data analysis, data pipeline and database design, and advanced machine learning where he uses Tensorflow extensively in image processing.

CADS Certification

EDS CADS Certified Enterprise Data Scientist

Certification information for this module & track will be made available soon.

Hear from Our Alumni

The training has benefited me a lot with the skills required to be a data analyst and has upskilled me in data analysis. What I like about the program is where real life problems in the office is used as the materials during the trainings which provides more understanding of the topics. Eventually, it has broaden up my market in the career development.

The program started by setting an expectation on Day 1, that it is not a Read-And-Pass syllabus. The beauty of this program is that it requires the trainees to get their hands dirty and do the actual work throughout the course. It is indeed very different from a lot of programs in the market which just produces a certificate in the end without the trainee actually able to do some real stuff. The course is definitely benefiting anyone who really want to do Machine Learning and Data Science, by creating talents who are able to connect human thought with the machine algorithm, being the bridge within the corporate, into the digital worlds.

The training was beneficial as it trains myself to think from another perspective - one that may not be new, but wasn't practiced as often as I'd like. It was fun and interactive thanks to the facilitator, Jay, too. He was very helpful to answer any questions the attendees have. Believe that this training gives the attendees an extra edge by giving them the knowledge and perspective on how to better utilize data to what they want/need to do - for the organization, the business, and even for personal use, of which I believe will bring myself further in my career.

The training has been an eye opener for me. As someone who is not of programming background, it introduces me to the possibilities of what data can do. It has also helped me bridge the difference in thinking between the Business Team and the Data Science Team. Had a great time learning!

This training enhance on my skill of analyzing of using another tools of programming instead of excel and how to model and visualized our data in a better way instead of our traditional way. Especially when involve with a large scale of data. If using excel, there will a lot of time consuming there instead of R. For me, in market nowadays of big data, this course will brings more value and create another point of view on how to visualize our data especially in making any decision. And of course will enhance our career development.

Register Interest

Python Programming II

Python Programming II

Design and Solve Data-Related Problem

Learning outcome: