Special offers now — see discounted courses.
day
:
hour
:
min
:
sec
See special offers
Python for Data Science and Machine Learning Essential Training Part 1

Python for Data Science and Machine Learning Essential Training Part 1

7h 35mIntermediate2026-03-30

Authors

Lillian Pierson, P.E.

Lillian Pierson, P.E.

Engineer, CEO, and Head of Product at Data-Mania

Course details

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.

Skills covered

Data Science FoundationsMachine LearningPythonEssential TrainingArtificial Intelligence (AI)Programming LanguagesData ScienceOpen SourceSoftware Development

Concepts

Introduction

  • Data science life hacks
  • How to use Codespaces with this course

Introduction to the Data Professions

  • Introduction to the data professions
  • Data science careers - Identifying where and how you'll thrive
  • Why to use Python for analytics
  • High-level course road map

Data Preparation Basics

  • Intro to data preparation
  • Numpy and pandas basics
  • Filtering and selecting
  • Treating missing values
  • Removing duplicates
  • Concatenating and transforming
  • Grouping and aggregation

Data Visualization 101

  • Importance of visualization in data science
  • The three types of data visualization
  • Selecting optimal data graphics
  • Communicating with color and context

Practical Data Visualization

  • Introduction to the matplotlib and Seaborn libraries
  • Defining elements of a plot
  • Plot formatting
  • Creating labels and annotations
  • Visualizing time series
  • Creating statistical data graphics in Seaborn
  • Creating standard data graphics

Exploratory Data Analysis

  • Simple arithmetic
  • Generating summary statistics
  • Summarizing categorical data
  • Spearman rank correlation and Chi-square
  • Extreme value analysis for outliers
  • Multivariate analysis for outliers
  • Pearson correlation analysis

Getting Started with Machine Learning

  • Cleaning and treating categorical variables
  • Transforming data set distributions
  • Applied machine learning - Starter problem

Data Sourcing via Web Scraping

  • Introduction of web scraping
  • Python requests for automating data collection
  • BeautifulSoup object
  • NavigableString objects
  • Data parsing
  • Web scraping in practice
  • Asynchronous scraping

Collaborative Analytics with Streamlit

  • Introduction to Streamlit
  • Environment setup
  • Create basic charts
  • Line charts in Streamlit
  • Bar charts and pie charts in Streamlit
  • Create statistical charts

Conclusion

  • Portfolio and career readiness
  • Next steps

Related courses

Related learn paths

About us

LyndaKade is a leading learning platform that helps people learn business, software, technology, and creative skills to achieve personal and professional goals.

Phone numberAparat ChannelTelegram SupportTelegram ChannelInstagram Page

All rights to this site belong to LyndaKade.

Terms of Service|Privacy Policy

نماد الکترونیک enamad در صورت اتصال با آی‌پی داخل کشور، نمایش داده خواهد شد.
logo-samandehi - لوگو ساماندهی
zarinpal
zibal