Certified Data Science Specialist (CDSS) (อบรมเชิงปฏิบัติการพร้อมสอบประกาศนียบัตรในระดับสากล) :: Software Park Thailand, เขตอุตสาหกรรมซอฟต์แวร์ประเทศไทย

Certified Data Science Specialist (CDSS) (อบรมเชิงปฏิบัติการพร้อมสอบประกาศนียบัตรในระดับสากล)

TEPC01
Online
Fundamental
English | 0

Professional Certification Program

Program Certified by iTrain Asia Pte Ltd

โดยในหลักสูตรจะให้ความรู้เกี่ยวกับแนวทางและเทคนิคที่คุณจำเป็นต้องทราบในการทำ Data Science ตั้งแต่ขั้นตอนการเก็บข้อมูล, การจัดการข้อมูล, การวิเคราะห์ข้อมูล จนถึงขั้นตอนการนำข้อมูลมาช่วยตัดสินใจ เพื่อให้คุณสามารถเริ่มต้นและมีทักษะด้าน Data Science

เมื่อผู้เรียนได้ผ่านการเรียน และทดสอบความรู้และความสามารถตามกำหนดเกณฑ์ของหลักสูตร จะได้รับ E-Certificate และ Digital Badge ในระดับสากล

Course description

Time

Days :

5 Day(s)

Duration :

30 Hour(s)

Time :

09:00:00 - 16:00:00

Training Date :

25-29 November 2024

Status :

Open Register

Instructor

Name :

Dr.Tarun Sukhani

Principal Trainer

Language :

English

Venue

Venue :

Online by ClassIn

Type :

Online

Certified Data Science Specialist (CDSS) (อบรมเชิงปฏิบัติการพร้อมสอบประกาศนียบัตรในระดับสากล)

Certified Data Science Specialist (CDSS)

Introduction:

Our lives are flooded by large amount of information, but not all of them is useful data. Therefore it is essential for us to learn how to applying data science to every aspect of our daily life from personal finances, reading, lifestyle to making business decisions. Leveraging on this data to make our life easier, or unlock new economic value for a business, is what you are going to learn in this course.

This course is a hands-on guided course for you to learn the concepts, tools, and techniques that you need to begin learning data science. We will cover the key topics from data science to big data, and the processes of gathering, cleaning and handling data. This course is well balanced between theory and practical, and key concepts are taught using case studies references. Upon completion, participants will be able to perform the basic data handling tasks, collect and analyze data, and present them using industry standard tools.

Upon completion of this course, you will be able to:

Identify appropriate model for different data types.
Create your own data process and analysis workflow.
Define and explain the key concepts and models relevant to data science.
Differentiate key data ETL process, from cleaning, processing to visualization.
Implement algorithms to extract information from dataset.
Apply best practices in data science, and familiar with standard tools.

Who Should Attend:

The course is vendor-neutral. It is suitable for Department Head, Business Analyst, Market Analyst, Data Analyst, BI practitioner, IT Managers, Software Developer, System Analyst, Startups who are working with Data, and any Business / IT Executives who are interested in learning data science, or who want to begin their career as data scientist.

Target audience:

This workshop is intended for individuals who are interested in learning data science, or who want to begin their career as a data scientist.

Prerequisites:

All participants should have basic understanding of data, relations, and basic knowledge of mathematics.

EXAMINATION:

No. of Questions: 50 Questions
Duration - 2 hours
Exam Type - Multiple Choice Questions (MCQ)
Compulsory Passing Rate - 70%

Participants will be equipped and ready to perform the basic data handling tasks, collect and analyze data, and present them using industry standard tools.

DAY 1
Introduction to Data Science

What is Data?
Types of Data
What is Data Science?
Statistical thinking
Knowledge Check
Lab Activity

Data Processes

Extract, Transform and Load (ETL)
Data Cleansing
Aggregation, Filtering, Sorting, Joining
Data Workflow
Knowledge Check
Lab Activity

Data Quality

Raw vs Tidy Data
Key features of data quality
Maintenance of data quality
Data profiling
Data completeness and consistency

Life of a data scientist

Identify problem
Define question
Define ideal dataset
Obtain data
Analyze data
Interpret results
Distribute results
Knowledge Check

DAY 2

Beginning Databases

Types of Databases
Relational Databases
NoSQL
Hybrid database
Knowledge check
Lab activity

Structured Query Language (SQL)

Performing CRUD (Create, Retrieve, Update, Delete)
Designing a Real world database
Normalizing a table
Knowledge check
Lab Activity

Introduction to Python

Basics of Python language
Functions and packages
Python lists
Functional programming in Python
Numpy and Scipy
iPython
Knowledge check
Lab Activity

Lab: Exploring data using Python

DAY 3
Data Gathering

Obtain data from online repositories
Import data from local file formats (json, xml)
Import data using Web API
Scrape website for data
Knowledge check
Lab Activity
Instructor-led case study

Exploratory Data Analysis

What is EDA?
Goals of EDA
The role of graphics
Handling outliers
Dimension reduction Introduction to R
Features of R
Vectors
Matrices and Arrays
Data Frame
Input / Output

Lab: Exploring data using R

DAY 4
Introduction Text Mining

What is Text Mining?
Natural Language Processing
Pre-processing text data
Extracting features from documents
Using BeautifulSoup
Measuring document similarity
Knowledge check
Lab activity

Supervised Learning

What is prediction?
Sampling, training set, testing set.
Constructing a decision tree.
Knowledge check
Lab Activity

DAY 5
Presenting Data

Choosing the right visualization
Plotting data using Python libraries
Plotting data using R
Using Jupyter Notebook to validate scripts
Knowledge check
Lab activity

Data Analysis Presentation

Using Markdown language
Convert your data into slides
Data presentation techniques
The pitfall of data analysis
Knowledge check
Lab activity

Group presentation Lab: Mini Project

Big Data Landscape

What is small data?
What is big data?
Big data analytics vs Data Science
Key elements in Big Data (3Vs)
Extracting values from big data
Challenges in Big data

Big data Tools and Applications

Introducing Hadoop Ecosystem
Cloudera vs Hortonworks
Real world big data applications
Knowledge check
Group discussion

What’s next?

Preview of Data Science Specialist
Showing advanced data analysis techniques
Demo: Interactive visualizations

EXAMINATION:

No. of Questions: 50 Questions
Duration - 2 hours
Exam Type - Multiple Choice Questions (MCQ)
Compulsory Passing Rate - 70%

Payment can be made by:

Cash or Credit Card or Bank Cheque payable to
สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ or National Science and Technology Development Agency
(a post-dated cheque is not accepted) on the first day of the service or within the last day of the service.
Account transfer and send the proof of the payment (the deposit slip) via email [email protected]
- ธนาคารกรุงเทพ สาขาอุทยานวิทยาศาสตร์
  Saving Account Number: 080-0-00001-0
  Account Name: สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ
- ธนาคารกรุงไทย สาขาตลาดไท
  Saving Account Number: 152-1-32668-1
  Account Name: สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ

Notes:

Withholding tax (3%) is exempt.
Should you need to withdraw, you must send the notice of the withdrawal in writing no later than 7 working days before the commencement date. The cancellation less than 7 days will be subject to a fine of 40% of the fee.
Software Park Thailand reserves the rights to cancel courses due to unforeseen circumstances.

Contact Person

For more information, contact our course coordinator on:

เสกสรรค์ สังสุข (อิฐ)

Mr. Seksun Sungsook

: +662 583 9992 Ext. 81421

: +6681 913 1828

: [email protected]

You are encouraged to use the course schedule as a guide to plan your training. The schedule is accessible at www.swpark.or.th for more information.

32,000 THB .

กำหนดอบรมวันที่ 25 - 29 พฤศจิกายน 2567

เวลา 09.00 - 16.00 น. (Online ผ่าน ClassIn)

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สอบถามข้อมูลเพิ่มเติม โปรดติดต่อ เสกสรรค์ สังสุข

Tel: 02-583-9992 Ext. 81421, 081-913-1828

Email: [email protected] , TTD@swpark.or.th

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Course Detail :