Python for data scientist2023-03-09T02:19:47+00:00

Python for Data Scientist
Course and Certification

Course and Certifications

Course and Certifications

Empower Your Data Analysis with Python

Your Path to Becoming a Cyber Security Expert

100% HRD Corp Claimable With No Upfront Payment

100% HRD Corp Claimable With No Upfront Payment

100% HRD Corp Claimable With No Upfront Payment

100% HRD Corp Claimable With No Upfront Payment

Course Overview

Python for Data Scientists is a course that offers a hands-on approach to gain an entry-level understanding of data analytics tools and techniques for scientific and mathematical computing by using Python. You will learn how to complete real-world tasks such as data analysis and visualization, as well as gain a fundamental understanding of machine learning algorithms. Through lectures and lab exercises, the concepts will be reinforced, and upon completion of the course, you will have the skills to apply Python to complete real-world tasks.

Course Objective

  • Learn the fundamentals of Python programming and how it is used in data science.
  • Understand and apply the five project management process groups: Initiating, Planning, Executing, Monitoring, and Controlling, and Closing.

  • Recognize control flow statements, conditional statements, relational and Boolean operators, while loops, and exception handling

  • Recognize and apply the tools and techniques used in each process group and knowledge area.

  • Discover how to use Python for data analysis and visualisation tasks, including file I/O, dictionaries, sets, functions, classes, modules, and XML and JSON, as well as an introduction to iPython, NumPy, SciPy, Pandas, and Matloplib.

Upcoming Training Dates

  • 13-17  Feb  2023

  • 6-10  March 2023
  • 10-14  April 2023
  • 8-12 May 2023

  • 19-23 June 2023
  • 8-12  July 2023



For limited time only!

30-Day Money-Back-Guarantee

Who should get certified:

  • Data Analysts and Data Scientists

  • Scientists and Engineers

  • Business Analysts
  • Software Developers

  • Data Engineers

  • Professionals from any fields looking to use Python for data analysis and visualization tasks


  • Comfortable working with files and folders and not afraid of the command line in Linux, Windows or Mac OS

  • Familiarity with Python

  • Basic knowledge of programming concepts
  • Basic knowledge of computer science

  • Basic knowledge of data science concepts
  • Basic math skills

Course Curriculum

Model 1: Introduction2023-02-07T00:28:39+00:00
  • Getting to know the participants
  • Introduction to PMI®
  • PMP® Certification – advantages
  • PMP® Certification – process and fees related information
  • PMP ® Application procedures – instructions on how to fill in the form
  • PMP® examination – information on the split of questions, question pattern
  • PMP® examination – tips on how to prepare and take the examination (this theme will be reiterated throughout the course)
  • PMP® – fulfilling the CCR requirements
  • Familiarization with the course outline
  • Familiarization with the protocols and timings
  • Expectation setting and clarifications
  • Introductory quiz – to assess the current level of familiarization of the participants with PMBOK® concepts and assess the gaps
Module 2: Introduction to Project Management2023-02-07T01:37:23+00:00
  • Using variables:
  • Python keywords
  • Built-in string functions in Python
  • Single quote for string literals
  • Triple quote for string literals
  • Python raw strings
  • Unicode literals
  • Operators and expressions
  • Convert among types
  • Write unicode characters
  • Formatted unicode string literals
  • Legacy codes in string formatting
  • Command line parameters
  • Reading keyboard input
Module 3: Control Flow2023-02-07T01:37:49+00:00
  • About control flow
    • What’s with the white space
    • If and Elif control flow statements
  • Conditional statements
  • Relational and Boolean operators
  • While loops
  • Alternate ways to exit from a loop
Module 4: Lists and Tuples2023-02-07T01:38:16+00:00

About Sequences Lists Tuples:

  • Indexing and slicing
  • Iterating through a sequence
  • Functions for all sequences
  • The enumerate() function
  • Operators and keywords for sequences
  • The xrange() function
  • Nested sequences
  • List comprehensions
  • Generator expressions
Module 5: Working with Files2023-02-07T01:38:45+00:00

Python files I/O:

  • File open
  • The with block
  • Read text files
  • Writing to text files
Module 6: Dictionaries and Sets2023-02-07T01:40:29+00:00
  • About dictionaries:
    • When to use dictionaries
    • Create dictionaries
  • Python dictionary get()
  • Iterate through a dictionary:
    • Read a file data into a dictionary
    • Count with dictionaries
  • About sets:
    • Create a set
    • Work with sets
Module 7: Functions2023-02-07T01:40:49+00:00
  • Define a function
  • Function parameters
  • Global variables
  • Variable scope
  • Return values
Module 8: Exception Handling2023-02-07T01:41:09+00:00
  • Try-except
  • Multiple exceptions handling
  • Generic exceptions handling
  • Ignore an exception
  • Using else clause:
    • Cleaning up with finally
    • Re-raise an exception
  • Raise a new exception
  • The standard exception class hierarchy
Module 9: OS Services2023-02-07T01:41:34+00:00
  • The OS module
  • Environment variables
  • Launch external processes:
    • Paths, directories, and file names
    • Walking directory trees
    • Dates and times
    • Send an e-mail
Module 10: Pythonic Idioms2023-02-07T01:41:54+00:00
  • The Zen of Python
    • Common Python idioms
    • Packing and unpacking
    • Lambda functions
  • List comprehensions
    • Generators vs. iterators
    • Generator expressions
    • String tricks
Module 11: Modules and Packages2023-02-07T01:42:15+00:00
  • What is a module?
  • The import statements
  • Where did the .pyc file come from?
  • The module search path
  • Tab completion
  • Zipped libraries
  • Create modules
  • Packages
  • Module aliases
  • Batteries not included
Module 12: Classes2023-02-07T01:42:35+00:00
  • Define classes
  • Instance objects
  • Instance attributes
  • Methods
  • The __init__ method
  • Properties
  • Class data
  • Inheritance
  • Multiple Inheritance
  • Base classes
  • Special methods
  • Pseudo-private variables
  • Static methods
Module 13: Pylint2023-02-07T01:42:57+00:00
  • Program development
  • Comments
  • Pylint
  • Customizing Pylint
  • Unit testing
  • The unittest module
  • Create a test class
  • Establish success or failure
  • Startup and Cleanup
  • Run the tests
  • Debugging
  • Start the debug mode
  • Step through a program
  • Set breakpoints
  • Debug a command reference
  • Benchmarking
Module 14: XML and JSON2023-02-07T01:43:18+00:00
  • About XML
  • Normal approaches to XML
  • Which module to use?
  • Get started with ElementTree
  • How ElementTree works
  • Create a new XML Document
  • Parse XML Documents
  • Navigate the XML Documents
  • Use XPath
  • Advanced XPath
Module 15: iPython2023-02-07T01:43:37+00:00
  • Features of iPython
  • Start iPython
  • Tab completion
  • Magic commands
  • Benchmarking
  • External commands
  • Enhanced help
  • Notebooks
Module 16: NumPy2023-02-07T01:44:30+00:00
  • Python’s scientific stack
  • NumPy overview
  • Create arrays
  • Create ranges
  • Work with arrays
  • Shapes
  • Slicing and indexing
  • Indexing with Booleans
  • Stacking
  • Iterating
  • Tricks with arrays
  • Matrices
  • Data types
  • NumPy functions
Module 17: SciPy2023-02-07T01:45:44+00:00
  • About SciPy
  • Polynomials
  • Vectorize functions
  • Sub-packages
  • Get help
  • Weave
Module 18: A Tour of SciPy Sub-packages2023-02-07T01:46:03+00:00
  • Clustering package
  • Constants
  • Legacy discrete Fourier transforms
  • Integration and ODEs
  • Interpolation
  • Input and output
  • Linear algebra
  • Multidimensional image processing
  • Orthogonal distance regression
  • Optimization and root finding
  • Signal processing
  • Sparse matrices
  • Spatial algorithms and data structures
  • Special functions
  • Statistical functions
Module 19: Pandas2023-02-09T03:18:56+00:00
  • About Pandas
    • Pandas
    • Architecture
    • Series
  • DataFrames
    • Data alignment
    • Index objects
    • Basic indexing
    • Broadcasting
    • Remove entries
    • Time series
  • Read data
Module 20: Matplotlib2023-02-07T01:46:46+00:00
  • About Matplotlib
    • Matplotlib architecture
    • Matplotlib terminology
    • Matplotlib keep state
    • What else can you do?
Module 21: Python Imaging Library2023-02-07T01:47:24+00:00
  • Image file types supported by PIL
  • The image class
  • Read and write
  • Create thumbnails
  • Coordinate system
  • Crop and paste
    • Rotate, resize, and flip
    • Enhance

Corporate Benefits

By upskilling your employees in Python for Data Scientist training, you can:

  • Improve data analysis and visualization for better insights and decision-making to achieve a better result and increased competitiveness.
  • Boost employee efficiency and productivity, resulting in a more powerful data science team.
  • Enhance your ability to adopt technology and stay current with trends to compete with many industries and organizations.
  • Increase your market competitiveness by assembling a strong data science team and developing technological capabilities.

HRD Corp Claimable

We are proud to be an authorized training provider of HRD Corp (Human Resources Development Corporation). Our certification training programs are eligible for funding under SBL-Khas Scheme.

HRD Corp provides training and upskilling opportunities for Malaysian employees who are:

  1. Malaysian citizens or permanent residents
  2. Employed in a company that contributes to HRD Corp (employer with 10 or more Malaysian employees, or employers from specific sectors)
  3. Registered with the HRD Corp portal and have an activated account
  4. Have not attended the same training program within the last 12 months
  5. Meet the specific requirements for the training program, such as qualifications, skills, or job-specific criteria.

Our dedicated team will help you every step of the way, from claim application to completion. Invest in your employees’ professional growth and development by enrolling them in our training today!



Excellent Training Centre! 

The Python for Data Scientist training has completely transformed the way I approach data analysis and modeling. I highly recommend it for anyone looking to enhance their data science skills.

Tan Cheng Wei
Data Scientist

A Life-Changing Experience! 

I have taken many data science courses in the past, but this Python for Data Scientist training has exceeded my expectations. The hands-on approach to learning was incredibly effective, and I feel confident using Python in my day-to-day work.

Raudhah Nadira
Quantitative Analyst

Fun and Informative Training! 

The instructor was top-notch and made the complex concepts of data science and Python approachable and understandable. I am now able to tackle complex data projects with ease and confidence.

Scientific Researcher


1. Why should I get certified in Python for Data Scientist?2023-02-07T03:37:47+00:00

Obtaining Python for Data Scientist certification can demonstrate your expertise in the field, increase your job opportunities and advancement potential, help you stand out in a competitive job market, and lead to potential career advancement and professional development opportunities.

2. What fundamental knowledge and skills must I have prior to enrolling in Python for Data Scientist?2023-02-07T03:39:37+00:00

It is sufficient for a student to have a basic understanding of programming concepts and math.

3. What software and tools will I need for the course?2023-02-07T03:40:11+00:00

The course will require a computer with Python installed and a text editor or integrated development environment (IDE). The course also requires some specific libraries such as NumPy, Pandas, and scikit-learn.

4. Will the course provide me with a certification?2023-02-07T03:40:36+00:00

Yes, you will receive a certificate to prove that you have completed your training.

5. Will there be an exam for this course?2023-02-07T03:40:56+00:00

There is currently no examination available for this course.

6. What is HRD Corp claimable course and how do I apply?2023-02-07T03:41:22+00:00

HRD Corp Claimable Course is a dedicated programme that provides funding and support to registered employers for employee retraining and upskilling in accordance with operational and business requirements.

To apply for this course under the HRD Corp programme, please fill out the form above, and our personnel will contact you personally.

7. How to claim training under HRD Corp?2023-02-07T03:41:43+00:00

To make a claim, your company must first be registered with HRD Corp and have contributed. After the training is completed, your company must submit a claim form to HRD Corp along with supporting documentation. There is no need to pay anything up front. HRD Corp will then process the claim and, if approved, reimburse you.

Our personnel will walk you through the entire claim application process from beginning to end.

8. How do I get in touch to learn more about the program?2023-02-07T03:42:03+00:00

Following your registration, our personnel will contact you to provide consultation, advice, and feedback. You can get in touch with the person in charge, or you can reach us at We are always happy to help.


Why Choose us

Reputable training facility with over 20 years of experience.

Experienced and knowledgeable instructors

High-quality, relevant, and up-to-date training materials

Convenient training that fits your hectic schedule

Good value for money that is worth the investment

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