Python for Data Science and Machine Learning Essential Training Part 1

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Lillian Pierson, P.E.

7:44:23

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01 - Introduction

01 - Data science life hacks.mp4

00:41

02 - What you should know.mp4

00:59

03 - How to use Codespaces with this course.mp4

03:05

02 - 1. Introduction to the Data Professions

01 - Introduction to the data professions.mp4

13:43

02 - Data science careers Identifying where and how youll thrive.mp4

04:57

03 - Why to use Python for analytics.mp4

06:24

04 - High-level course road map.mp4

01:34

03 - 2. Data Preparation Basics

01 - Intro to data preparation.mp4

03:20

02 - Numpy and pandas basics.mp4

04:03

03 - Filtering and selecting.mp4

13:33

04 - Treating missing values.mp4

18:04

05 - Removing duplicates.mp4

07:50

06 - Concatenating and transforming.mp4

13:15

07 - Grouping and aggregation.mp4

05:48

04 - 3. Data Visualization 101

01 - Importance of visualization in data science.mp4

03:43

02 - The three types of data visualization.mp4

08:42

03 - Selecting optimal data graphics.mp4

06:57

04 - Communicating with color and context.mp4

06:48

05 - 4. Practical Data Visualization

01 - Introduction to the matplotlib and Seaborn libraries.mp4

17:49

02 - Creating standard data graphics.mp4

10:25

03 - Defining elements of a plot.mp4

12:28

04 - Plot formatting.mp4

15:01

05 - Creating labels and annotations.mp4

18:49

06 - Visualizing time series.mp4

08:23

07 - Creating statistical data graphics in Seaborn.mp4

14:51

06 - 5. Exploratory Data Analysis

01 - Simple arithmetic.mp4

08:14

02 - Generating summary statistics.mp4

09:39

03 - Summarizing categorical data.mp4

10:19

04 - Pearson correlation analysis.mp4

14:53

05 - Spearman rank correlation and Chi-square.mp4

14:48

06 - Extreme value analysis for outliers.mp4

13:59

07 - Multivariate analysis for outliers.mp4

07:47

07 - 6. Getting Started with Machine Learning

01 - Cleaning and treating categorical variables.mp4

10:48

02 - Transforming data set distributions.mp4

06:55

03 - Applied machine learning Starter problem.mp4

08:52

08 - 7. Data Sourcing via Web Scraping

01 - Introduction of web scraping.mp4

02:25

02 - Python requests for automating data collection.mp4

09:37

03 - BeautifulSoup object.mp4

19:04

04 - NavigableString objects.mp4

11:00

05 - Data parsing.mp4

13:57

06 - Web scraping in practice.mp4

13:12

07 - Asynchronous scraping.mp4

20:36

09 - 8. Collaborative Analytics with Streamlit

01 - Introduction to Streamlit.mp4

05:14

02 - Environment setup.mp4

02:48

03 - Create basic charts.mp4

09:06

04 - Line charts in Streamlit.mp4

08:42

05 - Bar charts and pie charts in Streamlit.mp4

11:31

06 - Create statistical charts.mp4

08:35

10 - Conclusion

01 - Next steps.mp4

01:10

Description

Python for Data Science and Machine Learning Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts-giving you even more hands-on, real-world Python experience. In part one, instructor Lillian Pierson takes you step by step through a data science and machine learning project: a web scraper that downloads and analyzes data from the web. Along the way, she introduces techniques to clean, reformat, transform, and describe raw data; generate visualizations; remove outliers; perform simple data analysis; and generate web-based graphs using Streamlit. By the end of this course, you'll have acquired basic coding experience that you can take to your organization and quickly apply to your own custom data science and machine learning projects.

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

Lillian Pierson, P.E.

Instructor's Courses

I have 17 years of experience launching and developing technology products and delivering strategic consulting services. Many of the products I’ve managed have been e-learning products that educate learners on how to apply data science, data strategy, and business strategy to increase profits for their companies. To date these products have been consumed by 1.7 MM+ learners and have generated over $6 MM in revenue for my clients. I have launched over 40 products globally, delivered in 4 different languages. My products & go-to-market strategies have supported organizations as large as Walmart, Amazon, Microsoft, Dell & the US Navy. In fact, over the last 10 years I’ve supported 10% of Fortune 100 companies. Besides my extensive business background, I’m also an accomplished data scientist & engineer, having held licensure as a Professional Engineer since 2014. Over the past 10 years, I've especially enjoyed my work in: ▪ Go-To-Market Strategy & Product Marketing Management: Defining MMPs, product & service launch planning & execution, driving direct growth through "Acquisition, Activation, Retention, Revenue & Referral" funnels ▪ Product Management & Leadership: Managing product roadmaps, prioritizing product backlogs, managing products through their entire lifecycle, from conception to execution, hypothesis testing, UX design, website & product analytics, Python / R, SQL, ML / AI, experimentation, A/B Testing ▪ Business Leadership: Managing cross-functional teams, managers & stakeholders, representing the voice-of-customers, working with engineering teams ▪ Tech Startup Mentoring: Mentored 14 data startup founders to earn $2.8 MM+ in cum. revenue in the first year of businesses ▪ Data Science Instructor: Over 1.7 MM+ learners - using Python to demonstrate & explain machine learning algorithms & recommendation systems ▪ Earned & maintained accredited licensure as a Professional Engineer Of course, the above is not all I did - I also ran the business, oversaw customer happiness management, managed team members / managers within Data-Mania & worked directly with clients to ensure our collaborations exceeded their expectations. I am most passionate about product growth & marketing for tech startups. You can take a peek at my CV by going to my website here: https://www.data-mania.com/blog/lillian-pierson-cv/ If you'd like to discuss the possibility of me supporting your company & team, I'd love to chat with you inside my LinkedIn direct messages (it is set to "Open"). Just click the "Message" button on my profile and I will see you there!

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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.