Python Programming

On this course you will start Python programming language from the beginning, but it is of great help to have basic programming concepts and knowledge. With the right instructor training programming does not need to be difficult, programming is structured and logic. This training is 100% in practice “Hands On”. We learn by doing.

Python is a general-purpose programming language gaining a lot of ground rapidly in the last few years. Python is a popular language for data science and machine learning. Companies process data from different sources to gain more insights to their data. In this course you will learn data processing from different sources and ways to store and manipulate data, and helpful data science.

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Course Details

  • 🕒 Schedule: Twice a week, evenings only

  • ⏱️ Duration: This course is 45 hours from 2.5 hours each session.

  • 💶 Price: €300

  • 👨‍🏫 Format: Instructor-led, hands-on training

  • 📦 Includes: This course includes a project coded by the students and the instructor.

Course Requirements

  • 📈 Level: Intermediate

  • 🧠 Prerequisites: Basic knowledge of Microsoft Excel is required

  • 👨‍🎓 Recommended for: Students or professionals with prior experience in data entry, spreadsheets, or general computer use

Chapter 1

 

  • General introduction
    • Python programming introduction, history and usage
    • Installation, configuration and first python program
    • Variables and basic data types (strings, lists, dictionaries, …)
    • Input / Output operations
    • Basic operators
  • Python knowledge
    • Control Flow ( if-else conditions, For and While loops, …)
    • Boolean and binary operations
    • Lists, tuples, sets, dictionaries
    • Functions
    • String methods
    • List & dictionary comprehension
    • Data conversion – date functions
    • Packages

 

Chapter 2

  • Object Oriented Programming
    • Intro to Object-Oriented programming (OOP)
    • Objects & Class
      • Attributes and methods
    • Polymorphism and Inheritance
    • Exceptions
    • Generators
    • Read and write files

 

Chapter 3

  • Numpy and Pandas, HTTP, JSON format
    • Introduction to Numpy and Pandas
    • Connection between Numpy and Pandas
    • HTTP protocol – Import data from online sources
    • JSON format – Import data from online sources
  • Advancing with Pandas and data manipulation
    • Indexing
    • Cut
    • Filter
    • Revision

 

Chapter 4

  • Matplotlib, data visualization
    • Data visualization: scatter plots, line plots, box plots, bar charts,and histograms with matplotlib
    • Interpreting picture structure
    • Modifying charts, important attributes and arguments

 

 

Chapter 5

  • Summary and Project
    • Recall of all the knowledge gained
    • Project with real data sets
    • Conclusion

 

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Outcomes

Collecting data from different sources like MS Excel, databases etc, cleaning, transforming, modeling and visualizing the data on inteligent dashboards.

 

Prepare for a career as a Data Scientist

  • Receive professional-level training from CollegeToCareer instructors.
  • Demonstrate your proficiency in portfolio-ready projects
  • Earn an employer-recognized certificate.

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