Professional Certification

in Python Data Science

Start your data science journey with Python
and power up your career today!

Acquire in-demand skillsets and use them efficiently
with the most popular data science language, Python.

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Python Data Science



Training Program

Start your data science journey
with Python and power up
your career today!

Registrations starts on 1st April 2022

Limited Funding Available. Scholarships Available for Individuals.
Hurry Up. Talk to Us Now.
Offer Ends In










Registrations starts on 1st April 2022

Limited Funding Available.
Scholarships Available for Individuals.
Hurry Up.
Talk to Us Now.
Offer Ends In








Learn the fundamentals of Python Data Science and kickstart your career in one of the hottest professions of the decade.

This course includes the fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library.
The course also introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively.
By the end of this course, participants will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
Key Learning Outcomes

Upon completion, participants should be able to demonstrate each of the following outcome:-

  • Translate fundamental programming concepts such as loops, conditionals, etc into Python code.
  • Understand the key data structures in Python.
  • Understand how to write functions in Python and assess if they are correct via unit testing.
  • Know when and how to abstract code (e.g., into functions, or classes) to make it more modular and robust.
  • Produce human-readable code that incorporates best practices of programming, documentation, and coding style.
  • Use NumPy perform common data wrangling and computational tasks in Python.
  • Use Pandas to create and manipulate data structures like Series and DataFrames.
  • Wrangle different types of data in Pandas including numeric data, strings, and datetimes.
Course Modules Covered in the Python Data Science program
Module 1 - An Overview of Python

An Overview of Python

  1. What is Python?
  2. Interpreted languages
  3. Advantages and disadvantages
  4. Downloading and installing
  5. Which version of Python
  6. Where to find documentation
  7. Python Comments
  8. Output to the screen
  9. Running Python Scripts
  10. Structure of a Python script
  11. Using the interpreter interactively
Module 2 - Getting Started

Getting Started

  1. Using variables
  2. Assigning value to multiple variables
  3. Expression
  4. Math operators
  5. String types: normal, raw and Unicode
  6. String operators
  7. Command line parameters
  8. Reading from the keyboard
Module 3 - Decision & Flow Control,

Decision & Flow Control

  1. About flow control
  2. Indenting is significant
  3. The if statements
  4. The nested if statements
  5. The elif statements
  6. The for loops
  7. The while loops
  8. Loop Controls - break and continue
  9. The range() function
  10. Arrays
Module 4 - Defining Functions

Defining Functions

  1. Syntax of function definition
  2. Formal parameters
  3. Global versus local variables
  4. Passing parameters and returning values
  5. Passing list of parameters
  6. Variable length arguments
  7. Lambda functions
  8. Passing function to another function
  9. Returning function
  10. Inner functions
    Module 5 - Working with Files

    Working with Files

    1. Text file I/O overview
    2. Opening a text file
    3. Reading text files
    4. Raw (binary) data
    5. Writing to a text file
    6. Opening Excel File
    7. Reading from Excel File
    8. Writing data into Excel File
    Module 6 - Sequence


    1. List overview
    2. List methods
    3. Tuple overview
    4. Tuple methods
    5. Dictionary overview
    6. Dictionary methods
    7. Set overview
    8. Set methods
    9. Fetching values
    10. Fetching keys
    11. Testing for existence of elements
    12. Deleting elements
    13. Set Operators
    Module 7 - Python Classes

    Python Classes

    1. About o-o programming
    2. Defining classes
    3. Class methods and data
    4. Constructors
    5. Objects
    6. Instance methods
    7. Instance data
    8. Destructors
    9. Interfaces
    10. Inheritances
    Module 8 - Errors and Exception Handling

    Errors and Exception Handling

    1. Dealing with syntax errors
    2. Exceptions
    3. Handling exceptions with try/except
    4. Cleaning up with finally
    Module 9 - Using Modules

    Using Modules

    1. What is a module?
    2. The import statement
    3. Function aliases
    4. Packages
    5. Installing Packages from PYPI
    6. Standard Modules – sys
    7. Standard Modules – math
    8. Standard Modules – time
    Module 10 - Regular Expressions

    Regular Expressions

    1. RE Objects and Pattern matching
    2. Parsing data
    3. Subexpressions
    4. Complex substitutions
    5. RE tips and tricks
    Module 11 - Standard Library

    Highlights of the Standard Library

    1. Working with the operating system
    2. Grabbing web pages
    3. Sending email
    4. Using glob for filename wildcards
    5. math and random
    6. Accessing dates and times with datetime
    7. Working with compressed files
    Module 12 - Databases

    Accessing Databases

    1. Selecting Data
    2. Inserting and Updating Data
    3. Deleting data
    4. Generic database API based on MySQL
    5. Using the Object Relational Mapper (SQLAlchemy)
    6. Working with NoSQL databases
    Module 13 - Data Distribution

    Data distribution

    1. Center
    2. Spread
    3. Shape – Symmetry, Number of peaks, Skewness, Uniform
    4. Unusual Features – Gaps, Outliers
    5. Measures of central tendency - Mean, Median, Mode, Midrange
    6. Measures of spread - Range, Variation, Standard deviation, Interquartile range
    7. Measures of shape - Empirical rule, Chebyshev's rule, Skewness, Kurtosis
    8. Measures of relative position – Quartiles, Percentiles, Midquartile
    Module 14 - Extract data from Website

    Extract data from Website - Beautiful soup

    1. Installing Beautiful Soup
    2. Installing a parser
    3. Making the soup
    4. Kinds of objects
    5. Navigating the tree
    6. Managing the tree
    7. Searching the tree
    8. Append the tree
    9. Insert inside the tree
    10. Extract, decompose, replace with,
    11. wrap and unwrap
    12. Pretty-printing
    13. Non-pretty printing
    14. Output formatters
    15. Get Text
    16. Output Encoding
    17. Unicode
    Module 15 - Selenium IDE

    Selenium IDE

    1. Selenium Overview
    2. Selenium IDE Introduction
    3. Downloading and Installing Selenium IDE
    4. Recording and Running a Simple Test
    5. Selenium IDE – Features
    6. Installing Useful Tools for Writing Tests
    7. Selenium Concepts
    Module 16 - Selenium Webdriver

    Selenium Webdriver

    1. Introduction to selenium webdriver
    2. Advantages of webdriver
    3. Downloading and configuring Webdriver
    4. Converting Selenium IDE test to programming language (Python)
    5. Detailed discussion about webdriver commands
    6. Handling different browsers
    7. Create our own methods in Webdriver
    8. Using RC commands from webdriver project
    Module 17 - Python for Data Analysis – NumPy

    Python for Data Analysis – NumPy

    1. Introduction
    2. Ndarray Object
    3. Data Types
    4. Array Attributes
    5. Array Creation Routines
    6. Array from existing data
    7. Numerical ranges
    8. Array Indexing and Slicing
    9. Advanced Indexing
    10. Iterating over Array
    11. Array Manipulation
    12. Arithmetic Operators
    13. Binary Operators
    14. String Functions
    15. Mathematical Functions
    16. Statistical Functions
    Module 18 - Python for Data Analysis – Pandas

    Python for Data Analysis – Pandas

    1. Introduction to Pandas
    2. Series
    3. DataFrames
    4. Missing Data
    5. Group By
    6. Merging Joining and Concatenating
    7. Operations
    8. Data Input and Output
    Module 19 - Python for Data Visualization

    Python for Data Visualization

    1. Matplotlib
    2. Seaborn
    3. Distribution Plots
    4. Categorical Plots
    5. Matrix Plots
    6. Grids
    7. Regression Plots
    8. Pandas Built-in Data Visualization
    9. Plotly
    10. Cufflinks
    11. Geographical Plotting
    12. Choropleth Maps
    Module 20 - Python for Data Analysis – SciPy

    Python for Data Analysis – SciPy

    1. Introduction
    2. Basic functions
    3. Special functions
    4. Integration
    5. Optimization
    6. Interpolation
    7. Fourier transforms
    8. Signal Processing
    9. Linear Algebra
    10. Sparse Eigenvalue Problems with ARPACK
    11. Compressed Sparse Graph Routines
    12. Spatial data structures and algorithms
    13. Statistics
    14. Multidimensional image processing
    Our Training Methodology
    Your Instructor
    Thayanithy Jegan
    Principal Data Scientist
    CTO & Co-Founder of Thulija Technologies,
    HRDF Certified Trainer and Consultant

    A seasoned technology professional with over 17 years of industry experience as a software developer, solutions architect and technology consultant for major organizations. He is the Principal Lead Data Scientist at Thulija Academy.
    Thayanithy Jegan has trained executives and developers in some of the leading organizations in Asia such as Maxis, IFCA, SWIFT, PSDC, DHL, Standard Chartered, Infineon Technologies, Siemens and Bank Negara to name a few. His expertise is highly sought after to help executives break into various technology stacks and as well as data science, big data, and artificial intelligence in Malaysia and regionally.
    He has led major projects with clients such as Suruhanjaya Syarikat Malaysia (SSM), MYCOID, Kementerian Kerja Raya, Ministry of Education (MOE), Universiti Malaya, Perfisio Solutions, Kementerian Perdagangan Antarabangsa dan Industri, amongst others. He has also served as a Consultant for MIMOS Berhad, a Research and Development organisation that functions as an advisor to the Malaysian Government on technologies, policies and strategies relating to IT.
    Program Key Highlights

    48 hours of Remote Online Learning
    Additional Coaching Hours
    Live Hands-on Projects
    Certified by International Body
    Mentorship with Industry Experts
    Designed for Beginners & Professionals

    Get Professionally Certified

    Upon successfully completing this program, participants will be awarded the Professional Certification in Python Data Science by International Council for Technology Certifications (ICTC).
    This award is a validation to the efforts taken to master the domain expertise that will set you apart from your competition.
    Be a part of the global network of data science professionals and join the community across sectors.
    Get in Touch With Us Today!

    This training program is suitable for anyone who intends to enter into the field of Data Science. This program is being conducted in Malaysia and can be joined by anyone, anywhere in the world remotely.
    Program Fee

    MYR 6800 per pax.
    Funding price MYR 3700 per pax .

    Funding Schemes for Companies who are claiming from their HRDF levy or from the MDEC MyWiT scheme.

    Limited scholarships available for early self applying individual applicants.

    Find out how you can qualify for a scholarship.

    Enquire NOW on the various funding options available.

    One-time fee. One year access to course materials and resources.
    Our Participants Work In

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