Data Science Foundations: Python Scientific Stack

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Miki Tebeka

2:21:37

103 View

01. Introduction

001. The Python scientific stack.mp4

00:50

002. What you should know.mp4

00:20

003. Setting up.mp4

00:30

02. Visual Studio Code

004. Working with VS Code.mp4

01:48

005. Using code cells.mp4

01:43

006. Extensions to Python language.mp4

01:14

007. Understanding markdown cells.mp4

01:29

03. NumPy Basics

008. NumPy overview.mp4

01:40

009. NumPy arrays.mp4

03:06

010. Slicing.mp4

01:31

011. Boolean indexing.mp4

03:00

012. Understanding broadcasting.mp4

01:41

013. Understanding array operations.mp4

02:26

014. Understanding ufuncs.mp4

02:19

015. Challenge Working with an image.mp4

01:06

016. Solution Working with an image.mp4

00:48

04. pandas

017. pandas overview.mp4

01:19

018. Loading CSV files.mp4

02:54

019. Parsing time.mp4

01:46

020. Accessing rows and columns.mp4

03:47

021. Calculating speed.mp4

03:11

022. Displaying speed box plot.mp4

02:31

023. Challenge Taxi data mean speed.mp4

00:23

024. Solution Taxi data mean speed.mp4

00:51

05. Installing Packages

025. Introduction to Python packages.mp4

02:46

026. Using environments.mp4

02:10

027. Managing dependencies.mp4

01:25

028. Challenge Creating requirements.mp4

00:22

029. Solution Creating requirements.mp4

00:20

06. folium and Geo

030. Creating an initial map.mp4

01:29

031. Drawing a track on a map.mp4

02:41

032. Using geo data with Shapely.mp4

03:58

033. Challenge Drawing the running track.mp4

00:16

034. Solution Drawing the running track.mp4

00:46

07. NYC Taxi Data

035. Examining data.mp4

01:30

036. Loading data from CSV files.mp4

01:31

037. Working with categorical data.mp4

02:28

038. Working with data Hourly trip rides.mp4

03:57

039. Working with data Rides per hour.mp4

03:15

040. Working with data Weather data.mp4

03:03

041. Challenge Graphing taxi data.mp4

00:38

042. Solution Graphing taxi data.mp4

02:26

08. scikit-learn

043. scikit-learn introduction.mp4

01:13

044. Regression.mp4

05:21

045. Understanding train test split.mp4

01:58

046. Preprocessing data.mp4

02:57

047. Composing pipelines.mp4

01:47

048. Saving and loading models.mp4

01:34

049. Challenge Hand-written digits.mp4

01:12

050. Solution Hand-written digits.mp4

01:10

09. Plotting

051. Matplotlib overview.mp4

00:58

052. Using styles.mp4

01:30

053. Customizing pandas output.mp4

02:58

054. pandas plotting.mp4

02:20

055. Using Matplotlib with pandas.mp4

01:58

056. Interactive plots.mp4

01:44

057. Other plotting packages.mp4

02:52

058. Challenge Stocking data bar charts.mp4

00:27

059. Solution Stocking data bar charts.mp4

01:26

10. Other Packages

060. Other packages overview.mp4

01:07

061. Going faster with Numba.mp4

02:15

062. Understanding deep learning.mp4

04:59

063. Working with image processing.mp4

02:49

064. Understand NLP.mp4

03:20

065. Working with bigger data.mp4

04:16

11. Development Process

066. Development process overview.mp4

00:45

067. Understanding source control.mp4

04:38

068. Code review.mp4

02:11

069. Testing overview.mp4

02:33

070. Testing example.mp4

01:42

12. Conclusion

071. Next steps.mp4

00:23

Description

Join instructor Miki Tebeka as he dives into the Python scientific stack and shows you how to use it to solve problems. Miki covers the major packages used throughout the data science process: numpy, pandas, matplotlib, scikit-learn, and others. He also guides you through how to load data, analyze data, run models, and display results.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.

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Python

Data Science

Miki Tebeka

Instructor's Courses

Teaching effective hands-on workshops all of the world. Consultant solving hard problem with the right tools (Java and C++ are *not* the right tools ;). Book author, LinkedIn learning Author, open source contributor and convention organizer, meetup co host and coding for fun in my spare time. Specialties: Python & Scientific Python (Expert), Go (Expert), C/C++, Clojure, JavaScript, bash, ... Information retrieval - tokenization, summarization, clustering, search ... Concurrency - Multi process, multi threaded, Hadoop ... Web development - REST APIs, jQuery, JavaScript, CSS, (X)HTML Assemblers, Linkers, Debugger, Simulators SCM tools (git, Mercurial, Perforce, subversion, CVS, ClearCase) Linux, OS X and Windows Functional Programming, OOD, OOP Databases - SQL (BigQuery, PostgreSQL, MySQL, Oracle) and NoSQL (Redis, MongoDB, CouchDB)

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