Computer Academy

BEGINNING DATA SCIENCE WITH PYTHON

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Course Information

Schedules
  • 3 Jan 2025 (Fri) - 22 Jan 2025 (Wed) 10:00 AM - 5:30 PM
  • 4 Jan 2025 (Sat) - 22 Jan 2025 (Wed) 9:40 AM - 5:30 PM
Registration period
25 Nov 2024 (Mon) - 4 Jan 2025 (Sat)
Price
HKD 7,380
(Enrolling this course on or before 30th November can receive early bird discount of $1220
)
Course Level
Study Mode
Duration
6 Day(s)
Language
Cantonese
Location
Room 603 Dominion Centre, 43-59 Queen's Road East Wanchai, Hong Kong
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Course Overview

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.

 

What You’ll Learn


Module One

  • Intalling Python
  • Working with variables and data
  • Working with Lists and Tuples
  • If and Conditional Statements
  • Dictionaries
  • Getting user Input
  • Functions
  • Working with Modules
  • Using Python's standard modules
  • Working with files
  • Errors handling

Module Two

  • Data Preparation and loading
  • Data Processing with Pandas
  • Data Processing with Numpy
  • Data Visualization
  • Machine Learning

 



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