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You are here:: Training/Seminar Training Schedule Introduction to Python Programming for Data Science

Introduction to Python Programming for Data Science

Categories : Data Technology
Posted by : juntima | Posted On : Wednesday, 10 January 2018 14:49
Training Date : 13 November 2018 - 15 November 2018
31 July 2018 - 02 August 2018
03 April 2018 - 05 April 2018
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กรุณา ล็อกอิน ก่อนลงทะเบียน หรือสร้างบัญชีผู้ใช้ใหม่ได้ ที่นี่
Time : 18 Hour(s)
Days : 3 Day(s)
Duration : 09:00 - 16:00
Fee : 12,000 THB (Excluded Vat 7%)
Language : Thai
Instructor : Mr.Veerasak Krisanapraphan
Objectives :

        ในช่วงทศวรรษที่ผ่านมา แนวโน้มทางด้านการศึกษาและอุตสาหกรรมได้ยกให้การเขียนโปรแกรมภาษา Python
กลายเป็นเครื่องมือที่สำคัญที่สุดเพียงหนึ่งเดียว สำหรับการคำนวณสถิติ การสร้างแผนภูมิภาพ และวิทยาศาสตร์ข้อมูล
Introduction to Python Programming for Data Science จะทำให้ผู้เข้าร่วมการฝึกอบรมได้รู้จักกับวิธีการเขียน
โปรแกรมภาษา Python เพื่อสำรวจข้อมูลจากความหลากหลายของแหล่งที่มา โดยการสร้างแบบจำลองเชิงอนุมาน
สร้างแผนภูมิกราฟ และการแสดงข้อมูลด้านอื่น ๆ    

Objectives: By the end of the course attendant will:    
• Master the use of the Python interactive environment    
• Expand Python capability by installing Python packages   
• Manage Python Environment using Python Environment Tool
• Explore and understand how to use the Python documentation    
• Read Structured Data into Python from various sources    
• Using Python for mathematical operations    
• Write user-defined Python functions    
• How and when to use control statements    
• Deal with missing data    
• Understand base Python graphics    
• Use Python for descriptive statistics    
• Use Python for inferential statistics
• Write multivariate models in Python
• Understand confounding and adjustment in multivariate models    
• Understand interaction in multivariate models  

Who Should Attend :

Data Scientist, Data Analysis Engineer, Data Engineer, BI Consultant, BI Programmer,
System Programmer, System Analyst, Development Manager, Project Manager or
Chief Technology Officer, Chief Innovation Officer.

Course Outline :

Day 1 :
     1. Overview    
        • History of Python    
        • Advantages and disadvantages    
        • Downloading and installing    
        • How to find documentation   

     2. Introduction    
        • Using the Python console    
        • Getting help    
        • Learning about the environment    
        • Writing and executing scripts    
        • Saving your work    

     3. Installing Python Packages    
        • Finding resources    
        • Installing resources

     4. Manage Python Environment
        • Using Environment packages
        • Virtual Environment (virtualenv)
        • Anaconda (conda)
        • Using Jupyter Notebook

     5. Data Structures, Variables    
        • Variables and assignment    
        • Data types    
        • Indexing, subsetting    
        • Viewing data and summaries    
        • Naming conventions    
        • Objects   

     6. Getting Data into the Python Environment using Panda   
        • Built-in data    
        • Reading data from structured text files    
        • Reading data using ODBC    

Day 2 :
     7. Control Flow    
        • Truth testing    
        • Branching    
        • Looping    
        • Vectorized calculations    

     8. Functions in Depth    
        • Parameters    
        • Return values    
        • Variable scope    
        • Exception handling    

     9. Handling Dates in Python    
        • Date and date-time classes in Python    
        • Formatting dates for modeling

     10. Descriptive Statistics    
        • Continuous data    
        • Categorical data

     11. Inferential Statistics    
        • Bivariate correlation    
        • T-test and non-parametric equivalents    
        • Chi-squared test    
        • Distribution testing    
        • Power testing

Day 3 :   
     12. Group By Calculations    
        • Split apply combine strategy 

     13. Basic Graphics and Matplotlib  
        • Base graphics system in Python    
        • Scatterplots, histograms, barcharts, box and whiskers, dotplots    
        • Labels, legends, Titles, Axes    
        • Exporting graphics to different formats    

     14. Linear Regression    
        • Linear models    
        • Regression plots    
        • Confounding / Interaction in regression    
        • Scoring new data from models (prediction)   

     15. Conclusion  

Payment Condition :

Payment can be made by:

    1. Cash or Credit Card or Bank Cheque payable to “Software Park Thailand #2” (a post-dated cheque is not accepted) on the first day of the service or within the last day of the service.
     2. Account transfer and send the proof of the payment (the deposit slip) via fax or email to fax no. 02-583-2884 or email

        2.1 Siam Commercial Bank, Chaengwattana Branch
              Saving Account Number: 324-2-56262-0
             Account Name: Software Park Thailand#2

        2.2 Krungsri Bank, Chaengwattana (Software Park) Branch
              Saving Account Number: 329-1-34850-3
             Account Name: Software Park Thailand#2


- 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:

Juntima  Klumchaun

Tel: +66-2583-9992 Ext. 1424

Fax: +66-2583-2884


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