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2020-12-12 17:15:10
30-Day Money-Back Guarantee

This course includes:

  • 28 mins on-demand video
  • 136 articles
  • 133 coding exercises
  • Full lifetime access
  • Access on mobile and TV
Development Data Science NumPy

130+ Exercises - Python Programming - Data Science - Pandas

Improve your Python programming and data science skills and solve over 130 exercises in Pandas!
Bestseller
Rating: 4.5 out of 54.5 (8 ratings)
2,303 students
Created by Paweł Krakowiak
Last updated 1/2021
English
English
30-Day Money-Back Guarantee

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

18 sections • 142 lectures • 42m total length

  • Info
    00:16
  • Preview02:55
  • Preview03:47
  • Google Colab - Intro
    09:57
  • Preview04:07
  • Introduction to Spyder
    02:56
  • Anaconda installation - Linux
    04:39

  • A few words from the author
    00:20

  • Exercise 0
    1 question
  • Solution 0
    00:01

  • Exercise 1
    1 question
  • Solution 1
    00:02
  • Exercise 2
    1 question
  • Solution 2
    00:04
  • Exercise 3
    1 question
  • Solution 3
    00:04
  • Exercise 4
    1 question
  • Solution 4
    00:04
  • Exercise 5
    1 question
  • Solution 5
    00:04
  • Exercise 6
    1 question
  • Solution 6
    00:06
  • Exercise 7
    1 question
  • Solution 7
    00:05
  • Exercise 8
    1 question
  • Solution 8
    00:06
  • Exercise 9
    1 question
  • Solution 9
    00:05
  • Exercise 10
    1 question
  • Solution 10
    00:05

  • Exercise 11
    1 question
  • Solution 11
    00:04
  • Exercise 12
    1 question
  • Solution 12
    00:04
  • Exercise 13
    1 question
  • Solution 13
    00:05
  • Exercise 14
    1 question
  • Solution 14
    00:03
  • Exercise 15
    1 question
  • Solution 15
    00:06
  • Exercise 16
    1 question
  • Solution 16
    00:07
  • Exercise 17
    1 question
  • Solution 17
    00:07
  • Exercise 18
    1 question
  • Solution 18
    00:06
  • Exercise 19
    1 question
  • Solution 19
    00:07
  • Exercise 20
    1 question
  • Solution 20
    00:02

  • Exercise 21
    1 question
  • Solution 21
    00:01
  • Exercise 22
    1 question
  • Solution 22
    00:02
  • Exercise 23
    1 question
  • Solution 23
    00:02
  • Exercise 24
    1 question
  • Solution 24
    00:04
  • Exercise 25
    1 question
  • Solution 25
    00:04
  • Exercise 26
    1 question
  • Solution 26
    00:03
  • Exercise 27
    1 question
  • Solution 27
    00:03
  • Exercise 28
    1 question
  • Solution 28
    00:02
  • Exercise 29
    1 question
  • Solution 29
    00:04
  • Exercise 30
    1 question
  • Solution 30
    00:03

  • Exercise 31
    1 question
  • Solution 31
    00:02
  • Exercise 32
    1 question
  • Solution 32
    00:02
  • Exercise 33
    1 question
  • Solution 33
    00:09
  • Exercise 34
    1 question
  • Solution 34
    00:12
  • Exercise 35
    1 question
  • Solution 35
    00:12
  • Exercise 36
    1 question
  • Solution 36
    00:02
  • Exercise 37
    1 question
  • Solution 37
    00:02
  • Exercise 38
    1 question
  • Solution 38
    00:03
  • Exercise 39
    1 question
  • Solution 39
    00:02
  • Exercise 40
    1 question
  • Solution 40
    00:03

  • Exercise 41
    1 question
  • Solution 41
    00:05
  • Exercise 42
    1 question
  • Solution 42
    00:11
  • Exercise 43
    1 question
  • Solution 43
    00:07
  • Exercise 44
    1 question
  • Solution 44
    00:08
  • Exercise 45
    1 question
  • Solution 45
    00:05
  • Exercise 46
    1 question
  • Solution 46
    00:05
  • Exercise 47
    1 question
  • Solution 47
    00:10
  • Exercise 48
    1 question
  • Solution 48
    00:11
  • Exercise 49
    1 question
  • Solution 49
    00:10
  • Exercise 50
    1 question
  • Solution 50
    00:10

  • Exercise 51
    1 question
  • Solution 51
    00:03
  • Exercise 52
    1 question
  • Solution 52
    00:04
  • Exercise 53
    1 question
  • Solution 53
    00:04
  • Exercise 54
    1 question
  • Solution 54
    00:05
  • Exercise 55
    1 question
  • Solution 55
    00:05
  • Exercise 56
    1 question
  • Solution 56
    00:06
  • Exercise 57
    1 question
  • Solution 57
    00:05
  • Exercise 58
    1 question
  • Solution 58
    00:05
  • Exercise 59
    1 question
  • Solution 59
    00:04
  • Exercise 60
    1 question
  • Solution 60
    00:04

  • Exercise 61
    1 question
  • Solution 61
    00:02
  • Exercise 62
    1 question
  • Solution 62
    00:02
  • Exercise 63
    1 question
  • Solution 63
    00:03
  • Exercise 64
    1 question
  • Solution 64
    00:03
  • Exercise 65
    1 question
  • Solution 65
    00:02
  • Exercise 66
    1 question
  • Solution 66
    00:04
  • Exercise 67
    1 question
  • Solution 67
    00:04
  • Exercise 68
    1 question
  • Solution 68
    00:05
  • Exercise 69
    1 question
  • Solution 69
    00:06
  • Exercise 70
    1 question
  • Solution 70
    00:04

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 '100+ Exercises - Python Programming - Data Science - NumPy'
  • basic knowledge of Pandas

Description

------------------------------------------------------------------------------

RECOMMENDED LEARNING PATH

------------------------------------------------------------------------------

  • 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

  • 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

------------------------------------------------------------------------------

COURSE DESCRIPTION

------------------------------------------------------------------------------

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.

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.

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:

  • people who want to improve their Python programming skills
  • people who want to improve their data science skills
  • people who want to prepare for an interview

Instructor

Paweł Krakowiak
Data Scientist, Securities Broker
Paweł Krakowiak
  • 4.6 Instructor Rating
  • 1,084 Reviews
  • 23,484 Students
  • 31 Courses

EN

Data Scientist/Python Developer/Securities Broker

Founder at e-smartdata[.]org.

A big fan of new technologies, especially in the areas of artificial intelligence, big data and cloud solutions.

A graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science in the Big Data specialization.

A graduate of Master's Degree in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science of the University of Lodz.

Stockbroker license holder with experience in teaching at a university.

Lecturer at the GPW Foundation (technical analysis, behavioral finance and portfolio management).

The main areas of interest are artificial intelligence, machine learning, deep learning and financial markets.

PL

Data Scientist, Securities Broker

Założyciel platformy e-smartdata[.]org

Miłośnik nowych technologii, szczególnie w obszarze sztucznej inteligencji, big data 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.

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 sztuczna inteligencja, uczenie maszynowe, uczenie głębokie i rynki finansowe.

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