Python Data Analytics Course: Pandas and Data Visualization

All these are brought to you by YiDu AI. Established in 2017, YiDu AI is one of the largest machine learning communities in Singapore. We have brought to you 30+ machine learning seminars and 10 industrial talks.AnNUS EnterpriseIncubatee, we will keep bringing you the best content, supported by the broader university network.Contact us:yiduai.sg@gmail.com

Yidu AI Pte Ltd

Due to popular demand,  Yidu AI, the machine learning community, will kickstart the 2nd part of the 4-week data science program on Sept 28th

AXA Tower

<h3>Why learn Pandas?
</h3>
The Pandas library is one of the most preferred tools for data scientists to do data manipulation and analysis, next to matplotlib for data visualization and NumPy, the fundamental library for scientific computing in Python on which Pandas was built.

The fast, flexible, and expressive Pandas data structures are designed to make real-world data analysis significantly easier, but this might not be immediately the case for those who are just getting started with it.

Here are just a few of the things that pandas does well:
-	Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
-	Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
-	Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
-	Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
-	Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
-	Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
-	Intuitive merging and joining data sets
-	Flexible reshaping and pivoting of data sets
-	Hierarchical labeling of axes (possible to have multiple labels per tick)
-	Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
-	Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.

<h3>*Course Format and Hands-on exercise: 
</h3>4 Weekend courses: 1.5 hours teaching + 1.5 hours hands-on in-class project 
Hands-on Exercises: We will prepare in-class project every week. Students will have opportunities to work with assistance from TAs.
Course Project: Portfolio analysis on S&amp;P 500 stock market data
Highlights
1.	Four-week course on analyzing financial data from excellent instructors and TAs.
2.	A fully hands-on experience with 4 in-class projects and 1 final project with real-world data.
3.	Build your own stock market analysis tool from scratch.
4.	Networking with YiDu AI community and get to know thousands of talented AI researchers and data scientists from big companies (Facebook, Shopee, Grab, etc) and top-tier research institutes.
 
Week 1: Introduction to Big Data Analysis
Key Points:
1.	Overview of stock market data analysis with python
2.	Introduction to numpy array
3.	Pandas series and index object
4.	Pandas dataframe basics
Hands-on:
1.	Demo on using numpy array and pandas to get insight from data
 
Week 2: Data Manipulation with Pandas
Key Points:
1.	Pandas dataframe operation
1.	Filter dataframe by condition
2.	Insert and remove rows/columns
3.	Best practice: avoid chained index
2.	Advanced pandas dataframe
1.	Sort
2.	Rank
3.	Find Kth largest
4.	Summary statistics and accumulations
5.	String operations
6.	GroupBy operation
7.	Binning functions: cut and qcut
Hands-on: Full data manipulation workflow
1.	Importing data
2.	Cleaning data
3.	Merging and concatenating data
4.	Data Aggregation
 
Week 3: Data Visualisation and Regular Expression
Key Points:
1.	Data visualisation with matplotlib
1.	Line chart
2.	Scatter Chart
3.	Bar Chart
4.	Histogram
2.	Advanced visualisation with seaborn
3.	Regular expression fundamentals
1.	Patterns
2.	Find first match
3.	Find all matches
4.	Group
Hands-on:
1.	Visualise stock market statistics data with matplotlib and seaborn
2.	Demo on handling complicated data with regular expression
 
Week 4: Time-series Data Analysis
Key Points:
1.	Managing time series data with pandas
1.	Indexing and slicing time series
2.	Normalisation
3.	Financial time series - return and risk
4.	Financial time series - correlation and covariance
2.	Stock data analysis
1.	Trend analysis
2.	Risk analysis
Hands-on:
 	Analyse real-world stock market data and visualise trend/risk information
 
Project: Portfolio analysis on S&amp;P 500 stock market data

After the 4 weeks course, you are expected to:
1.	Understand basic knowledge of numpy and pandas
2.	Efficiently extract information from complicated data with regular expression
3.	Learn how to play with various data via data cleaning, data merging and data aggregation.
4.	Master end-to-end data analysis procedure
5.	Have insights on how to visualise data and explain to stakeholders
6.	Learn how to handle financial and time-series data

<h3>About the Instructor
</h3>Dr. Xu Nuo - NUS phd. Senior data engineer in Grab. Three years’ experience in python and big data engineer.

<h3>About Provider
</h3>Established in 2017, YiDu AI is one of the largest machine learning communities in Singapore. We have brought to you 20+ machine learning seminars and 10+ industrial talks. As an NUS Enterprise Incubatee, we will keep bringing you the best content, supported by the broader university network.

<h3>Wang He, Software Engineer @Finbook
</h3>I took the beginner course. The course gives beginners a solid start with enough real examples and hands-on practice. Besides, the course also provides enough technical and theoretical depth for those with some prior knowledge to explore. Instructors are all experienced industrial data science professionals and PhD students in math/data science. They can explian the things and answer your questions clearly. In general a well designed course. I would highly recommend this course to you!
 
<h3>Yang Zhiyue, Quantitative Analyst
</h3>The course covers a broad range of well selected machine learning topics, elaborated by dedicated lecturers and teaching assistants. Together with the theory, interesting and well designed projects are provided. What excited me the most are the course competition on Kaggle and industry talks, from which I benefit significantly. Strongly recommended to those who aim at a data or quant oriented career.
 
<h3>Wang Xiaobai, Software Engineer
</h3>The beginner course is a smooth gateway for learner who are not in AI industry or did’t not study AI in school.
From basic algorithm to mechine learning thinking, this course write a clear roadmap for who are interest in AI but first time travle in “AI jungle”.
I was so excited to training my first model during the course,and it’s also a memorable experience to competite with other trainee on Kaggle
Hope you can also join the course and start your AI journey.
<h3>Yu Yiming, Senior risk consultant, AIR Worldwide
</h3>Yidu AI’s beginner course is very suitable for professionals like me who has already a busy schedule of work. It has a perfect balance of theory and practice. You get to learn and built real life application of ML on day one. The instructors, TA and your classmates are also a big asset of the program. I would highly recommend it to anyone who has enough stats background and is interest in Machine learning and big data.
<h3>Xuan, Data Analyst
</h3>This is a great short training. I gained practical data science skills through hands-on projects and mathematics teaching. Teachers were passionate PhD student from NUS who are really helpful and detail-driven. The course material was succinct and self-explanatory. The projects were representative. I still review these materials from time to time. I would highly recommend the training to those who hope to have a head start in data science with several weeks.

<h3>Target Participants
</h3>Anyone who is interested in Python and Data Science.
FAQs
•	What do I need to attend this course?
Please bring your own computer and your ID card (for entrance of the classroom). 
•	How can I get a refund if I decide not to enroll? 
Unfortunately, once enrolled you will not be able to get a refund due to need to cover our cost of venue, instructors, and other logistics. We seek your understanding. 
 
<h3>CONTACT
</h3>Email: yiduai.sg@gmail.com 
Wechat Official Account (微信公众号): onedegree_ai

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Python Data Analytics Course: Pandas and Data Visualization

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