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2021-01-01 20:12:07
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Development Data Science

Master Data Science with Python in 10 Hours | 2021

Learn Python, its libraries for Data Science (Pandas,Matplotlib,Seaborn) and data pre-processing for Machine Learning!
Rating: 4.5 out of 54.5 (229 ratings)
20,387 students
Created by Berk Hakbilen, Tarik Sahin
Last updated 1/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Importance of Data Science and It's applications
  • Python - data types, functions, if conditions, loops and functions
  • Numpy library - a faster alternative to store data!
  • Pandas library - import, clean and modify large data sets
  • Matplotlib library - visualize data
  • Seaborn library- create impressive statistical plots
  • Communicate conclusions from data effectively using plots
  • and many more!

Course content

7 sections • 62 lectures • 10h 41m total length

  • Preview01:07
  • Preview05:56

  • Anaconda Installation
    02:33
  • Jupyter Notebook Walk-through
    07:41
  • Lecture Materials - All the Jupyter Notebooks
    00:11

  • Numbers and Basic Operations
    12:29
  • Strings
    17:51
  • Format Print
    11:17
  • Lists
    07:56
  • Tuples
    04:52
  • Dictionaries
    10:17
  • If Conditions
    11:25
  • While and For Loops
    23:04
  • Functions - Part I
    15:17
  • Functions - Part II
    15:17
  • Exercises I - Python Exercises Overview
    05:58
  • Solutions I - Python Solutions
    15:53

  • Preview04:28
  • Numpy: Arrays
    17:52
  • Numpy: Arithmetical Operations
    04:32
  • Numpy: Selection and Indexing - Part I
    07:22
  • Numpy: Selection and Indexing - Part II
    10:55
  • Numpy: Some Built-in Functions
    14:17
  • Exercises II: Numpy Exercises Overview
    04:06
  • Solutions II: Numpy Solutions
    11:49

  • Preview03:16
  • Data Structures: Series - Part I
    04:27
  • Data Structures: Series - Part II
    20:49
  • Data Structures: DataFrames - Part I
    13:15
  • Data Structures: DataFrames - Part II
    06:55
  • Methods to Modify Data: Reindex - Part I
    03:46
  • Methods to Modify Data: Reindex - Part II
    14:04
  • Methods to Modify Data: Drop
    10:30
  • Methods to Modify Data: Sort
    10:58
  • Some Statistical Methods
    09:02
  • Methods: Dealing with Missing Data
    10:30
  • Hierarchy - Multi Index
    16:40
  • Methods: Restructure Data - Pivot
    07:03
  • Combine DataFrames: Merge
    12:29
  • Combine DataFrames: Concatenation and Join
    09:31
  • Methods to Modify Data: Replace
    10:28
  • Group Data: Groupby
    11:41
  • Some Useful Built-in Functions
    15:25
  • Correlating Data: Cross Tabulation
    06:49
  • Exercises III: Pandas Exercises Overview (using Netflix data)
    09:12
  • Solutions III: Pandas Solutions
    27:50

  • Preview03:46
  • Matplotlib: Creating Plots - Part I
    16:06
  • Matplotlib: Creating Plots - Part II
    17:44
  • Matplotlib: Some Plot Types
    10:52

  • Preview02:12
  • Seaborn Notebook: Overview
    02:04
  • Seaborn: Distribution Plots
    15:25
  • Seaborn: Categorical Plots - Part I
    09:58
  • Seaborn: Categorical Plots - Part II
    11:26
  • Seaborn: Matrix Plots
    07:46
  • Seaborn: Grid Plots
    12:40
  • Seaborn: Regression and Grid Plots - lmplot
    06:56
  • Seaborn: Customizing Plots
    10:42
  • Exercises IV: Seaborn Exercises Overview
    08:36
  • Solutions IV: Seaborn Solutions
    26:02
  • Congratulations!
    00:12

Requirements

  • A computer with internet connection

Description

With this course, you will learn the basics of Python and its most popular libraries for Data Science such as Numpy, Pandas, Matplotlib, Seaborn.  You will learn all the important tools and knowledge for Data Science with more than 60 lectures, practice all your new skills with 4 big exercises sections, including more than 85 exercise questions and you will do all of this using one of the most popular programming languages: PYTHON!

Data pre-processing is a very important stage of the work flow of Machine Learning. With this course, you will learn how to import, check, clean data in terms of data pre-processing for Machine Learning, also visualize data and communicate your results using impressive plots. This course will help you jump start your career or take your first big step into the world of Data Science and Machine Learning which are very popular fields with many attractive job opportunities!

Don't miss this train and take your place in the world of DATA!

Who this course is for:

  • Anyone who wants to learn Python and Data Science
  • Beginner Python developers curious about data science

Instructors

Berk Hakbilen
Computational Engineer
Berk Hakbilen
  • 4.5 Instructor Rating
  • 296 Reviews
  • 23,664 Students
  • 2 Courses

I am an engineer with Bachelor's Degree in Aerospace Engineering and Master's Degree in Computational Engineering working in software and method development for CAE systems. Throughout my education and professional career, I have leveraged Data Engineering and Machine Learning to find solutions to engineering problems. Also, working on a start up to provide an innovative solution to an environmental problem using Deep Learning algorithms to help overcome one of the best environmental challenges we are facing on earth! As an instructor, I am also passionate about sharing my knowledge and experience with my students!

Tarik Sahin
Computational and Machine Learning Engineer
Tarik Sahin
  • 4.5 Instructor Rating
  • 296 Reviews
  • 23,664 Students
  • 2 Courses

He has done his Bachelor in Aerospace Engineering in Turkey and his master's in Computational Engineering in Germany. He worked in the industry and as a student assistant in the Ruhr University Bochum in the fields of software development, numerical methods, data analytics. He has written his Master's Thesis in MTU Aero Engines in Munich, whose topic is related to software development and mesh optimizations. Currently, he is writing and publishing articles in Artificial Intelligence, Machine Learning, Deep Learning and Data Science fields also he is working on a start-up that can be introduced as Recycling with Machine Learning.


Tarık Şahin Orta Doğu Teknik Üniversitesi Havacılık ve Uzay Mühendisliği bölümünü bitirdi. Lisans süresinde Türkiye'de tanınmış bir çok şirkette mühendis ve stajyer olarak çalıştı. Daha sonra Yüksek lisansını yapmak için Almanya Ruhr Universitaet Bochum Computational Engineering bölümüne gitti. Yüksek lisans süresinde yazılım geliştirme, veri analitiği, numerik hesaplamalar üzerine üniversitede asistan olarak çalıştı. Daha sonra Münih'teki bir uçak motoru fabrikasında yazılım geliştirme üzerine yüksek lisans tezini yazdı. Şimdi ise yapay zeka alanlarında bir start-up üzerine çalışmalar yürütmektedir.   

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