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Business Intelligence, BI@Agora
面對產業上對於具備人工智慧與物聯網能力的人才需求迫切，國立高雄科技大學商業智慧學院 與 台灣微軟 ( Microsoft Taiwan ) 合作建置BI商業智慧體驗中心，將商業智慧 & 大數據分析核心單元融入沉浸式展示與體驗...
Introduction to Data Science
Analyzing and Visualizing Data
Essential Statistics for Data Analysis using Excel
Essential Statistics for Data Analysis
Programming
Database Administration, Application and Manipulation
Business Analytics and Decision Making
IBM中心特別邀請到Dr. Shu-Ping Chang前來為我們演講，歡迎有興趣的老師與同學免費報名參加！
演講標題：Intelligent IoT (iIOT) the 3rd Platform – the rise of AI
演 講 者：Dr. Shu-Ping Chang
時 間：2019/11/13(三) 11:00 ~ 12:00
地 點：交大工程四館824
活動報名網址： https://forms.gle/juZTt8yqJagwx3op9
聯絡方式：曾紫玲 Tel：03-5712121分機54599 Email：tzuling@nctu.edu.tw
Abstract:
Big Data is now commodity due to advance of information technologies. However, IoT solutions that work for billions of devices won’t necessarily scale to hundreds of billions of devices. The hot topic of the recent rising of artificial intelligence (AI) has brought new question of its impacts and influences.
Is AI a necessity as human society progress or just a pure scientific interest? By considering the industrial trends and technologies advancement, we wish to shed some light into this area and its potential directions. Most importantly, what does AI development really means in its essence?!
Biography:
Education:
Dr. Chang has a Ph.D. in Computer and Information Sciences from University of Minnesota with special focus in Computer Communication and System. He has a Master of Science degree from University of Minnesota with major in Computer and Information Sciences and minor in Electrical Engineering. His master project is in the field of Computer Vision. He also has a Bachelor of Sciences degree, first place honor, from National Chiao-Tung University, Republic of China, in Communication Engineering.
Experience :
Dr. Shu-Ping Chang works at AI First, T.J. Watson Research Center as a software development manager for the IBM System S (Streams) Laboratory, a cluster for distributed computing research and development. IBM System S project, the base of IBM Big Data product IBM Streams, uses streams processing architecture for massive information computing and management as decision making support.
His primary function is System S Laboratory management, prototype systems development and cluster system administration automation especially in faults management. Dr. Chang has more than 25 years research and product development experiences in the Computer and Information technology arena. He has broad and in-depth knowledge in computer system hardware architecture and software structure in Big Data platforms and prototype research and development, computer communication, relational database, internet-based solutions and cloud computing.
ในวิชา "วิทยาการคำนวณ" ระดับชั้น ม. 5
ได้ดึงวิชา data science (วิทยาศาสตร์ข้อมูล)
มาปูพื้นฐานให้เด็กๆ ได้เรียนกันแล้ว นับว่าเป็นโชคดี
เพราะวิชาพวกนี้เป็นของสูง กว่าจะสัมผัสก็คงตอนป.ตรี โท เอก
...Continue ReadingIn the subject of ′′ Calculation Theology ′′ class. 5
Pulled data science (data science)
Let's master the foundation for kids to learn. It's considered lucky.
Because these subjects are high to touch. It's probably in the middle of the year. Tri To Aek
Which I will review the content to read roughly. The content is divided into 4 chapters.
.
👉 ++++ Chapter 1-Information is valuable +++++
.
Data science in the textbook. Used by Thai name as ′′ Information Science ′′
This chapter will mention Big Data or big data with lots of valuable information.
And so much role in this 4.0 s both public and private sector.
.
If you can't imagine when you played Google search network, you'll find a lot of information that you can use in our business. This is why data science plays a very important role.
.
It's not surprising that it makes the Data Scientist s' career (British name data scientist) play the most important role and charming and interesting profession of the 21th century.
.
Data science, if in the book, he defines it
′′ Study of the process, method or technique to process enormous amounts of data to process to obtain knowledge, understand phenomena, or interpret prediction or prediction, find out patterns or trends from information.
and can be analysed to advise the right choice or take decision for maximum benefit
.
For Data science work, he will have the following steps.
- Questioning my own interest.
- Collect information.
- Data Survey
- Data Analysis (analyze the data)
- Communication and Results Visualization (Communicate and visualize the results)
.
🤔 Also he talks about design thinking... but what is it?
Must say the job of a data scientist
It doesn't end just taking the data we analyzed.
Let's show people how to understand.
.
The application design process is still required.
To use data from our analytics
The word design thinking is the idea. The more good designer it is.
Which Data Scientists Should Have To Design Final Applications
Will meet user demand
.
👉 ++++ Chapter 2 Collection and Exploration +++++
.
This chapter is just going to base.
2.1 Collection of data
In this chapter, I will talk about information that is a virtual thing.
We need to use this internet.
2.2 Data preparation (data preparation)
Content will be available.
- Data Cleaning (data cleansing)
- Data Transformation (data transformation)
In the university. 5 is not much but if in college level, you will find advanced technique like PCA.
- Info Link (combining data)
2.3 Data Exploration (data exploration)
Speaking of using graphs, let's explore the information e
Histogram graph. Box plot diagram (box plot). Distributed diagram (scatter plot)
With an example of programming, pulls out the plot to graph from csv (or xls) file.
2.4 Personal Information
For this topic, if a data scientist is implementing personal data, it must be kept secret.
.
Where the issues of personal information are now available. Personal Data Protection is Done
.
.
👉 ++++ Chapter 3 Data Analysis ++++
.
Divided into 2 parts:
.
3.1 descriptive analysis (descriptive analytics)
Analyzing using the numbers we've studied since
- Proportion or percentage
- Medium measurement of data, average, popular base.
Correlation (Correlation) relationship with programming is easy.
.
.
3.2 predictive analysis (predictive analytics)
.
- numeric prediction is discussed. (numeric prediction)
- Speaking of technique linear regression, a straight line equation that will predict future information.
Including sum of squared errors
Let's see if the straight line graph is fit with the information. (with programming samples)
- Finally mentioned K-NN (K-Nearest Neighbors: K-NN) is the closest way to finding K-N-Neighborhood for classification (Category)
*** Note *****
linear regression กับ K-NN
This is also an algorithm. One of the machine learning (machine learning, one branch of AI)
Kids in the middle of the day, I get to study.
.
.
👉 +++ Chapter 4 Making information pictured and communicating with information +++
.
This chapter doesn't matter much. Think about the scientist after analyzing what data is done. The end is showing it to other people by doing data visualization. (Better summoning)
.
In contents, it's for example using a stick chart, line chart, circular chart, distribution plan.
.
The last thing I can't do is tell a story from information (data story telling) with a message. Be careful when you present information.
.
.
.
*** this note ***
😗 Program language which textbooks mentioned and for example.
It's also python and R language
.
For R language, many people may not be familiar.
The IT graduate may be more familiar with Python.
But anyone from the record line will surely be familiar.
Because R language is very popular in statistical line
And it can be used in data science. Easy and popular. Python
.
But if people from data science move to another line of AI
It's deep learning (deep learning)
Python will be popular with eating.
.
.
#########
😓 Ending. Even I wrote a review myself, I still feel that.
- The university. 5 is it going to be hard? Can a child imagine? What did she do?
- Or was it right that I packed this course into Big Data era?
You can comment.
.
But for sure, both parents and teachers are tired.
Because it's a new content. It's real.
Keep fighting. Thai kids 4.0
.
Note in the review section of the university's textbook. 4 There will be 3 chapters. Read at.
https://www.facebook.com/programmerthai/photos/a.1406027003020480/2403432436613260/?type=3&theater
.
++++++++++++++++++++
Before leaving, let's ask for publicity.
++++++++++++++++++++
Recommend the book ′′ Artificial Intelligence (AI) is not difficult ′′
It can be understood by the number. End of book 1 (Thai language content)
Best seller ranked
In the MEB computer book category.
.
The contents will describe Artificial Intelligence (A) in view of the number. The end.
Without a code of dizzy
With colorful illustrations to see, easy to read.
.
If you are interested, you can order.
👉 https://www.mebmarket.com/web/index.php?action=BookDetails&data=YToyOntzOjc6InVzZXJfaWQiO3M6NzoiMTcyNTQ4MyI7czo3OiJib29rX2lkIjtzOjY6IjEwODI0NiI7fQ&fbclid=IwAR11zxJea0OnJy5tbfIlSxo4UQmsemh_8TuBF0ddjJQzzliMFFoFz1AtTo4
.
Personal like the book. You can see this link.
👉 https://www.dropbox.com/s/fg8l38hc0k9b0md/chapter_example.pdf?dl=0
.
Sorry, paper book. I don't have it yet. Sorry.
.
✍ Written by Thai programmer thai progammerTranslated
