Python for Data Science: Python 3.7 Intermediate
 4 Courses  6h 2m 26s
 5 Books  19h 22m
 Includes Lab
 3 Courses  4h 1m 23s
 1 Course  1h 10m 56s
 5 Books  20h 22m
 Includes Lab
 2 Courses  3h 7m 57s
 3 Courses  4h 52m 16s
 Includes Lab
 2 Courses  1h 42m 19s
 2 Books  9h 53m
 Includes Lab
 2 Courses  3h 20m 12s
 2 Courses  3h 12m 2s
 3 Books  20h 37m
 Includes Lab
 11 Courses  17h 4m 44s
 8 Books  38h 47m
 Includes Lab
 1 Course  1h 7m 42s
 4 Courses  4h 55m 16s
GETTING STARTED
Analyzing Data Using Python: Data Analytics Using Pandas

1.Course Overview2m 45sNOW PLAYING

2.Creating a pandas Series11m 32sUP NEXT
GETTING STARTED
Python with Altair: An Introduction to Altair

1.Course Overview2m 19sNOW PLAYING

2.Setting up Altair6m 36sUP NEXT
GETTING STARTED
Python  Using Pandas to Work with Series & DataFrames

1.Course Overview1m 29sNOW PLAYING

2.Installing Pandas4m 49sUP NEXT
GETTING STARTED
Probability Distributions: Getting Started with Probability Distributions

1.Course Overview1m 47sNOW PLAYING

2.Getting Familiar with Statistics6m 12sUP NEXT
GETTING STARTED
Operations with petl: Introduction

1.Course Overview2m 30sNOW PLAYING

2.Installing petl and Creating a Data Table7m 55sUP NEXT
GETTING STARTED
Streaming Data Architectures: An Introduction to Streaming Data in Spark

1.Course Overview2m 29sNOW PLAYING

2.Introduction to Streaming data7m 18sUP NEXT
GETTING STARTED
Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

1.Course Overview2m 41sNOW PLAYING

2.Installing the Seaborn Module5m 6sUP NEXT
GETTING STARTED
Python & Matplotlib: Getting Started with Matplotlib for Data Visualization

1.Course Overview2m 13sNOW PLAYING

2.Installing Matplotlib10m 41sUP NEXT
GETTING STARTED
Python  Introduction to NumPy for Multidimensional Data

1.Course Overview2m 6sNOW PLAYING

2.Introduction to NumPy and the NumPy Ecosystem2m 11sUP NEXT
GETTING STARTED
Applied Predictive Modeling

1.Course Overview1m 30sNOW PLAYING

2.Overview of Predictive Modeling5m 55sUP NEXT
GETTING STARTED
Probability Distributions: Uniform, Binomial, & Poisson Distributions

1.Course Overview1m 55sNOW PLAYING

2.Generating Uniform Distributions7m 1sUP NEXT
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COURSES INCLUDED
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Digital badges are yours to keep, forever.BOOKS INCLUDED
Book
Thinking in Pandas: How to Use the Python Data Analysis Library the Right WayBook
Python Data Analytics: With Pandas, NumPy, and Matplotlib, Second EditionBook
Python Data Analytics: Data Analysis and Science Using Pandas, Matplotlib, and the Python Programming LanguageBook
Handson Data Analysis and Visualization with Pandas: Engineer, Analyse and Visualize Data, Using Powerful Python LibrariesBook
Pandas in 7 Days: Utilize Python to Manipulate Data, Conduct Scientific Computing, Time Series Analysis, and Exploratory Data AnalysisBOOKS INCLUDED
Book
Data Science Fundamentals for Python and MongoDBBook
Data Science Using Python and RBook
Python for Data Science for Dummies, 2nd EditionBook
Python for R Users: A Data Science ApproachBook
Practical Data Science with Python 3: Synthesizing Actionable Insights from DataBOOKS INCLUDED
Book
PySpark SQL Recipes: With HiveQL, Dataframe and GraphframesBook
Python for Data Science for Dummies, 2nd EditionBOOKS INCLUDED
Book
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Second EditionBook
Python Data Analytics: With Pandas, NumPy, and Matplotlib, Second EditionBook
Python Data Analytics: Data Analysis and Science Using Pandas, Matplotlib, and the Python Programming LanguageBOOKS INCLUDED
Book
Python Data Analytics: With Pandas, NumPy, and Matplotlib, Second EditionBook
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib, Second EditionBook
Data Science Fundamentals for Python and MongoDBBook
Data Analysis and Visualization Using Python: Analyze Data to Create Visualizations for BI SystemsBook
Data Science Using Python and RBook
Python for Data Science for Dummies, 2nd EditionBook
Python for R Users: A Data Science ApproachBook
Practical Data Science with Python 3: Synthesizing Actionable Insights from Data