
Data Analytics with Python
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Because Python is now one of the most popular programming languages!
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Because it is simple and easy to learn, and it is a good way to get introduced to the basics of programming!
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Because there are many Python libraries for data science and machine learning!
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:
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You have a basic understanding of what you can do with Python
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You have written a simple program with Python
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You know how to use the internet to research and solve simple problems
Entry requirements
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Watch the How Computers Work YouTube series
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Register on dataquest.io and finish the first module of the course Python for Data Science: Fundamentals
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Answer a short quiz on the day of the interview
WHAT AFTER?
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Data Analytics with Python
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Online Courses
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:
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Data Analysis
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Basic and composite built-in data types
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Introduction to series
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DataFrames. Indexing and slicing
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Missing values
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String processing and regex
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Groupby and aggregation
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Joins & concat/unions
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Visualization
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Types of Plots
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Intro to Matplotlib and Seaborn
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Customize your plots
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Introduction to ML
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Introduction to the ML Pipeline
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Inspection of dataset and classification workflow
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Logistic regression
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Decision trees and random forests
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Clustering
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After this course:
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Build a Data Science Pipeline with Python
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Know about Data Science Tools (pandas, sklearn, ...)
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Get the Data Science mindset
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Tell a Data Science Story
Entry requirements:
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Register on dataquest.io and finish the following modules:
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Answer a short quiz on the day of the interview
Desirable skills:
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Statistical knowledge
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Linear Algebra