130+ Exercises - Python Programming - Data Science - Pandas
What you'll learn
- solve over 130 exercises in Pandas
- deal with real programming problems in data science
- work with documentation and Stack Overflow
- guaranteed instructor support
Course content
- 00:16Info
- 00:11Requirements
- Preview02:55
- Preview03:47
- 09:57Google Colab - Intro
- Preview04:07
- 02:56Introduction to Spyder
- 04:39Anaconda installation - Linux
Requirements
- completed course '200+ Exercises - Programming in Python - from A to Z'
- completed course '210+ Exercises - Python Standard Libraries - from A to Z'
- completed course '150+ Exercises - Object Oriented Programming in Python - OOP'
- completed course '100+ Exercises - Python Programming - Data Science - NumPy'
- basic knowledge of Pandas
Description
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RECOMMENDED LEARNING PATH
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PYTHON DEVELOPER:
200+ Exercises - Programming in Python - from A to Z
210+ Exercises - Python Standard Libraries - from A to Z
150+ Exercises - Object Oriented Programming in Python - OOP
150+ Exercises - Data Structures in Python - Hands-On
100+ Exercises - Advanced Python Programming
100+ Exercises - Unit tests in Python - unittest framework
100+ Exercises - Python Programming - Data Science - NumPy
100+ Exercises - Python Programming - Data Science - Pandas
100+ Exercises - Python - Data Science - scikit-learn
250+ Exercises - Data Science Bootcamp in Python
SQL DEVELOPER:
SQL Bootcamp - Hands-On Exercises - SQLite - Part I
SQL Bootcamp - Hands-On Exercises - SQLite - Part II
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COURSE DESCRIPTION
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130+ Exercises - Python Programming - Data Science - Pandas
Welcome to the course 130+ Exercises - Python Programming - Data Science - Pandas where you can test your Python programming skills in data science, specifically in Pandas.
Some topics you will find in the exercises:
working with Series
working with DatetimeIndex
working with DataFrames
reading/writing files
working with different data types in DataFrames
working with indexes
working with missing values
filtering data
sorting data
grouping data
mapping columns
computing correlation
concatenating DataFrames
calculating cumulative statistics
working with duplicate values
preparing data to machine learning models
dummy encoding
working with csv and json filles
merging DataFrames
pivot tables
The course is designed for people who have basic knowledge in Python, NumPy and Pandas. It consists of 130 exercises with solutions.
This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.
If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today.
Who this course is for:
- everyone who wants to learn by doing
- everyone who wants to improve their Python programming skills
- everyone who wants to improve their data science skills
- everyone who wants to prepare for an interview
Instructor
![Paweł Krakowiak](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 1,904 Reviews
- 95,991 Students
- 42 Courses
EN
Python Developer/Data Scientist/Stockbroker
Founder at e-smartdata[.]org.
Big fan of new technologies!
Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization.
Graduate of MA studies in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics.
Stockbroker license holder (no 3073).
Lecturer at the GPW Foundation (technical analysis, behavioral finance and portfolio management).
PL
Data Scientist, Securities Broker
Założyciel platformy e-smartdata[.]org
Miłośnik nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python oraz rozwiązań chmurowych.
Absolwent podyplomowych studiów na Polsko-Japońskiej Akademii Technik Komputerowych na kierunku Informatyka, spec. Big Data.
Absolwent studiów magisterskich z matematyki finansowej i aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego.
Od 2015 roku posiadacz licencji Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073).
Wykładowca w Fundacji GPW prowadzący szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych.
Z doświadczeniem w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką.
Główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.