Beginning Data Science with Python

Data science is an interdisciplinary field that combines statistical analysis, programming, and domain knowledge to extract valuable insights and make data-driven decisions. Python has emerged as one of the most popular programming languages for data science due to its simplicity, versatility, and rich ecosystem of libraries. Whether you're a complete beginner or an experienced programmer looking to dive into data science, learning Python is an essential first step.

In this course, we will explore the fundamentals of data science using Python. We will cover key concepts such as data manipulation, exploratory data analysis, data visualization, and machine learning. Through hands-on examples and practical exercises, you will gain a solid foundation in Python programming and learn how to apply it to real-world data problems. By the end of this guide, you will have the knowledge and skills to start your journey in data science and unlock the power of Python for data analysis and predictive modeling. So, let's embark on this exciting adventure and begin our data science journey with Python!

The course is suitable for data analysts, data science or finance students or managers require doing computations on large datasets.

You'll Learn

Module One   

Intalling Python

  • Python on Windows
  • Python on Mac OS

Working with variables and data

  • Naming and using variables
  • Numbers, strings and boolean
  • Stripping whitespaces
  • Concatenating strings
  • Stripping whitespaces
  • Tabs and newlines
  • Integers and floats
  • Writing comments
  • Strings manipulation

Working with Lists and Tuples

  • Accessing Elements in a List
  • Working with Elements (appending, adding, sorting, deleting )
  • For Loop
  • Looping through an entire list
  • Slicing and looping through a slice
  • Copying a list
  • Organizing a list
  • List comprehension
  • Vertical summation
  • Defining a tuple
  • Tuples and Lists conversion
  • Looping through a Tuple

If and Conditional Statements

  • Various conditional tests ( equality and inequality checking )
  • Conditional testing on a list
  • Boolean Expressions
  • if-else , if-elif-else
  • Using if statement with multiple lists
  • Nesting Blocks
  • While Loop using a list
  • While Loop using a flag
  • Flow control with break statement
  • Flow control with continue statement


  • Accessing values in a Dictionary
  • Adding new key-value pairs
  • Looping through all key-value pairs
  • Looping through all values in a dictionary

Getting user Input

  • Using input() function
  • Handling numberical input


  • Defining a function
  • Arguments and parameters
  • Working with positional arguments
  • Working with keyword arguments
  • Setting arguments' default values
  • Returning result from a function

Working with Modules

  • Storing functions in a module file
  • Importing an entire module
  • Importing individual functions

Using Python's standard modules

  • Math module
  • Datetime module
  • Random module
  • OS module

Working with files

  • Reading data from a txt or csv files
  • Merging and working data from a file
  • Writing to a file

Errors handling

  • Handling error using Exception
  • The try-except blocks
  • The else block
  • Displaying errors messages

PYD4083 - 廣東話 22 Aug enrol
PYD4082 - 廣東話 28 Aug enrol
PYD4093 - 廣東話 04 Sep enrol
PYD4091 - 廣東話 20 Sep enrol
PYD4101 - 廣東話 05 Oct enrol
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Module Two  

Data Preparation and loading

  • Data loading using Pandas
  • Working with problematic data
  • Dealing with big datasets

Data Processing with Pandas

  • Introduction to Data Structures
  • Series / DataFrame / Index Objects
  • Viewing and selecting data
  • Statistical and histogramming operations
  • String methods
  • Concatenating / joining / appending data
  • Arithmetic methods
  • Operations between DataFrame and Series
  • Function application and mapping
  • Sorting and Ranking
  • Doing common Excel tasks in Pandas
  • Doing Pivot Table in Pandas

Data Processing with Numpy

  • Array Creation - N-dimensions
  • Printing Arrays
  • Numpy DataTypes and conversion
  • numpy.ndarray methods
  • Basic arrays elementwise operations
  • Matrix product using dot function
  • Universal functions
  • Indexing, slicing and iterating
  • Shape manipulation
  • Copies and views
  • Data Processing Using Arrays

Data Visualization

  • pyPlot Basics
  • Knowing different chart elements
  • Charting with Pandas
  • Plotting a single interactive chart
  • Saving your charts
  • Subplots
  • Plotting multiple charts

Machine Learning

  • Supervised and unsupervised Learning
  • Training set and testing set
  • Dimensionality reduction
  • The PCA Decomposition
  • K-Nearest Neighbours Classifier
  • Linear Regression
  • Polynominal interpolation and curve fitting
  • Support Vector Machine