Data Analytics with Python

  • Because Python is now one of the most popular programming languages!

  • Because it is simple and easy to learn, and it is a good way to get introduced to the basics of programming!

  • Because there are many Python libraries for data science and machine learning!

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Intro to Python

Beginner

Learn basic programming skills and first practical experience with the so-popular Python programming language!

 

Monday and Wednesday, 19:00 - 21:00 (+/- 30 min)

 

WHAT?

You will learn how to program concepts, solve problems, and how to search for solutions and ideas. Building a computer program is NOT rocket science!

 

OUTLINE:

1. Introduction
2. Operators & Precedence
3. Variables
4. Sequential programming
5. Conditional Operations
6. Strings and String Operations
7. Iterative programming
8. Using and Writing Functions
9. Structuring Files & Importing
10. Nested Lists
11. Reading and Writing to Files
12. Dictionaries, Sets, Tuples, etc.
13. Using Libraries

 

After this course:

  • You have a basic understanding of what you can do with Python

  • You have written a simple program with Python

  • You know how to use the internet to research and solve simple problems

 

Entry requirements

  1. Watch the How Computers Work YouTube series

  2. Register on dataquest.io and finish the first module of the course Python for Data Science: Fundamentals

  3. Answer a short quiz on the day of the interview

 

WHAT AFTER?

  • Data Analytics with Python

  • Online Courses

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Data Analytics with Python

Intermediate

Analyze data to win essential insights into people’s behavior and trends.

 

Monday and Wednesday, 19:00 - 21:00

 

WHAT?

You will learn how to analyze datasets and visualize your conclusions. You will also get an introduction to machine learning.

 

OUTLINE:

  1. Data Analysis

    1. Basic and composite built-in data types

    2. Introduction to series

    3. DataFrames. Indexing and slicing

    4. Missing values

    5. String processing and regex

    6. Groupby and aggregation

    7. Joins & concat/unions

  2. Visualization

    1. Types of Plots

    2. Intro to Matplotlib and Seaborn

    3. Customize your plots

  3. Introduction to ML

    1. Introduction to the ML Pipeline

    2. Inspection of dataset and classification workflow

    3. Logistic regression

    4. Decision trees and random forests

    5. Clustering

After this course:

  • Build a Data Science Pipeline with Python

  • Know about Data Science Tools (pandas, sklearn, ...)

  • Get the Data Science mindset

  • Tell a Data Science Story

 

Entry requirements:

 

Desirable skills:

  • Statistical knowledge

  • Linear Algebra

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