Python for Data Science: Certification by iTrain Malaysia

Python for Data Science

Duration: 4 Days (Beginner to Intermediate) + 3 (Advanced) |
HRDF Claimable!

Python for Data Science Course Overview

Python is a general-purpose programming language that is becoming more and more popular for analysing datasets and conducting data science processes. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike any other Python tutorial, this class will teach on various environments for project development to let you choose your best one. All the steps to construct a data science project will be taught starting from data importing, data cleaning, data analysing and ending with data visualization to get new insights. In summary, getting certified in Python for Data Science will give you a complete understanding on Python from the ground up.

Learning Outcomes

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

  • Understand all of the basics of Python
  • Develop and write code easily in Python
  • Deal with different sources of data
  • Analyse and visualize the data in order to get new insights from the data

Course Outline


Beginner to Intermediate (4 Days)

Day 1

  • What is Algorithm?
  • What is Programming?
  • The Natural Language of the Computer
  • Machine Language
  • Programming Language Levels
  • Translators

  • Identifiers, Lists, and Tuples
  • Dictionaries, Sets and Strings
  • Operators, Control Structures and Loops

Day 2

  • Installing and Running Jupyter
  • User Interface
  • Checkpoints
  • Function
  • Lambda and Map Function
  • Globals and Locals

  • List Comprehension
  • Generator Expressions
  • Exceptions Handling

  • Modules
  • Documentation
  • Packages and Namespaces

  • Create, Read, Update, Delete (CRUD) a File

Day 3

  • What is JSON and Why It is Important
  • Module, Serialization, Deserialization

  • What is Web Scraping
  • HTML Tags
  • BeautifulSoup Module
  • Webpage Scraping Phase
  • What is NumPy?
  • Ndarray Object, Data Types
  • Array Attributes, Array Creation and Routines
  • Indexing and Slicing
  • Array Manipulation
  • Mathematical Functions

  • Series, Dataframe
  • Data Importing, Pre-processing, and Grouping

Day 4

  • Line, Bar, Pie Graph
  • Histogram, Scatter Plot
  • Graph Attributes, Text Annotation

  • ML Algorithm Types
  • Main Steps in ML Projects
  • Introduction to Scikit Learn Module

Advanced (3 Days)

Day 1: Applied Machine Learning

  • What is Machine Learning?
  • Introduction to SK Learn

  • What is Dataset
  • Iris Dataset
  • Handwritten Digits Dataset
  • Dataset Distribution

  • What is Supervised Learning?
  • Key Classifiers Algorithms
    • K-Nearest Neighbors (KNN)
    • Support Vector Machine (SVM)
    • Decision Tree (DT)
  • Performance Metrics and Errors
  • Regression

  • What is Unsupervised Learning?
  • Key Clustering Algorithms
    • K-Means
    • Mean Shift
  • Principal Component Analysis
  • Dimensionality Reduction

  • Introduction to Neural Network
  • Multi-Layer Perceptron Classifier
  • Hidden Layers
  • Activation Function
  • Solver

Day 2: Applied Natural Language Processing (NLP)

  • What is NLP?
  • Basic Text Analysis with Python
  • Introduction to NLTK

  • Tokenize Words and Sentences
  • Stop Words
  • Regular Expressions
  • Stemming
  • Part-of-Speech (POS) Tagging

  • What is Corpus?
  • Popular NLTK Corpus
  • Build Your Own Corpus

  • Text Classification
  • NLTK and Scikit Learn
  • Save and Load the Model

Day 3: Social Network Analysis (SNA)

  • Why Networks Are Very Important
  • Graph
  • Nodes and Edges
  • Introduction to Networks Module

  • Clustering Coefficient
  • Distance Measures
  • Connected Component
  • Network Robustness

  • Degree and Closeness Centrality
  • Betweenness Centrality
  • Hubs and Authorities

  • Power Law
  • Small World Network
  • Link Prediction
  • Use Case

“Good course for beginners in Python programming language.”

Maxolvin Sintore, Technical Data Analyst, Sarawak Shell Berhad

“I’ll recommend this! A mind blowing experience and learning process.”

Quah Chen Nam, Senior Manager, Bursa Malaysia Berhad

“Trainer’s explanation has been very precise and helpful.”

New Ru Wee, Engineer

“The course is very good, quite easy to follow & interactive.”

Balqis, Technical Data Analyst, Sarawak Shell Berhad

“The instructor was helpful in providing clear and structured explanation to someone with no prior programming background like myself..”

Ng Pui Kye, Data Scientist, Zillionquest Sdn Bhd



Students will be given a Certificate of Attendance after successfully completing the course.

You bet it is! Our Certification Body for this course is iTrain Asia Pte Ltd, the region’s top Certifications Tech Provider headquartered in Singapore, with branch offices in Malaysia and Indonesia.

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

● Explain the workflow of data science and applying data science concepts with Python
● Analyzing and solving data science datasets with Python

This is a 4-day course for Beginner to Intermediate and 3-day course for Advanced at an instructor-led training centre.

Computers are provided for iTrain students. However participants can also use their own computers as long as it’s installed with the necessary applications.

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