The 2024 Pandas Bootcamp: Advanced Data Analysis with Python

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1 - Introduction

1 -Welcome to the Course!.mp4

04:52

2 - IMPORTANT Example Files (and Exercise Solutions!).html

2 - Python Crash Course

1 -What Is Programming.mp4

06:27

1 - Note to Students PLEASE READ.html

2 -The Programming Environment.mp4

12:28

3 -Values and Types.mp4

08:18

3 - The Programming Environment - Exercises.html

4 -Functions.mp4

10:16

5 -Expressions.mp4

10:03

6 -Expressions in Colab.mp4

04:47

7 -Variables.mp4

13:03

7 - Expressions in Colab - Exercises.html

8 -Naming Variables.mp4

06:18

8 - Variables - Exercises.html

9 -Errors.mp4

06:54

10 -Comments.mp4

05:28

11 -Text Cells.mp4

19:44

12 -Colab Tips and Pitfalls.mp4

14:14

12 - Text Cells - Exercises.html

13 -Objects, Attributes, and Methods.mp4

09:06

14 -Modules and Libraries.mp4

12:14

14 -STEM Salaries.csv

15 -Lists.mp4

11:57

16 -Tuples.mp4

09:16

17 -Dictionaries.mp4

17:03

18 - Data Structures - Exercises.html

3 - Working with DataFrames

1 -Introducing DataFrames.mp4

10:08

1 - IMPORTANT DOWNLOAD EXAMPLE DATASETS.html

2 -Introducing the Example Datasets.mp4

02:39

2 - Introducing DataFrames - Exercises.html

3 -DataFrames and the read csv Method - Part I.mp4

10:46

4 -DataFrames and the read csv Method - Part II.mp4

04:33

5 -Providing DataFrame Column Names.mp4

05:49

5 - DataFrames and the read csv Method - Exercises.html

6 -Inspecting DataFrames.mp4

08:16

6 - Providing DataFrame Column Names - Exercises.html

7 -Data Types and the info Method.mp4

11:15

7 - Inspecting DataFrames - Exercises.html

8 -Renaming Columns.mp4

07:10

8 - Data Types and the info Method - Exercises.html

9 -Dropping Columns.mp4

06:53

9 - Renaming Columns - Exercises.html

10 -Selecting Columns.mp4

04:24

10 - Dropping Columns - Exercises.html

11 - Selecting Columns - Exercises.html

4 - Working with Series

1 -Series 101.mp4

09:04

2 -Converting Series with to numeric.mp4

10:47

2 - Series 101 - Exercises.html

3 -Converting Series with to datetime.mp4

06:08

3 - Converting Series with to numeric - Exercises.html

4 -Adding Columns (Series) to DataFrames.mp4

09:54

4 - Converting Series with to datetime - Exercises.html

5 -Creating Derived Columns.mp4

16:00

5 - Adding Columns (Series) to DataFrames - Exercises.html

6 -The assign Method.mp4

12:20

6 - Creating Derived Columns - Exercises.html

7 - The assign Method - Exercises.html

5 - Basic Data Analysis with Pandas

1 -The sum Method.mp4

12:25

2 -The count Method.mp4

10:12

2 - The sum Method - Exercises.html

3 -Mean and Median.mp4

12:33

3 - The count Method - Exercises.html

4 -Standard Deviation and the describe Method.mp4

12:47

4 - Mean and Median - Exercises.html

5 -Using describe on Non-Numeric Fields.mp4

10:47

5 - Standard Deviation and the describe Method - Exercises.html

6 -The unique and nunique Methods.mp4

10:31

6 - Using describe on Non-Numeric Fields - Exercises.html

7 -The value counts Method.mp4

06:43

7 - The unique and nunique Methods - Exercises.html

8 - The value counts Method - Exercises.html

6 - Indexing and Sorting

1 -The iloc Method.mp4

14:10

2 -Indexing Basics.mp4

13:51

2 - The iloc Method - Exercises.html

3 -The loc Method.mp4

06:44

3 - Indexing Basics - Exercises.html

4 -Sorting by Index.mp4

11:19

4 - The loc Method - Exercises.html

5 -Sorting By Columns.mp4

15:51

5 - Sorting by Index - Exercises.html

6 -Dropping Rows By Index.mp4

09:21

6 - Sorting By Columns - Exercises.html

7 - Dropping Rows By Index - Exercises.html

7 - Selecting Data with Criteria

1 -Filtering DataFrames with a Boolean Series.mp4

11:33

2 -Applying Other Logical Conditions.mp4

11:07

2 - Filtering DataFrames with a Boolean Series - Exercises.html

3 -The between and isin Methods.mp4

11:15

3 - Applying Other Logical Conditions - Exercises.html

4 -Combining Conditions Using the & Operator.mp4

17:58

4 - The between and isin Methods - Exercises.html

5 -Combining Conditions Using the Operator.mp4

06:03

5 - Combining Conditions Using the & Operator - Exercises.html

6 -Combining And & Or Logic.mp4

18:18

6 - Combining Conditions Using the Operator - Exercises.html

7 -Negation.mp4

11:15

7 - Combining And & Or Logic - Exercises.html

8 -The isna Method.mp4

15:49

8 - Negation - Exercises.html

9 - The isna Method - Exercises.html

8 - Updating DataFrames

1 -Updating DataFrame Values with loc.mp4

09:17

2 -Replacing DataFrame Values.mp4

09:45

2 - Updating DataFrame Values with loc - Exercises.html

3 -Updating Values with Boolean Masks.mp4

15:55

3 - Replacing DataFrame Values - Exercises.html

4 -Removing Null Values.mp4

14:06

4 - Updating Values with Boolean Masks - Exercises.html

5 -Replacing Null Values.mp4

09:02

5 - Removing Null Values - Exercises.html

6 -Identifying Duplicate Data.mp4

09:32

6 - Replacing Null Values - Exercises.html

7 -Removing Duplicate Data.mp4

10:46

8 - Identifying and Removing Duplicate Data - Exercises.html

9 - Combining Datasets

1 -Stacking Datasets Vertically I.mp4

10:47

2 -Orders 2021.csv

2 -Orders 2022.csv

2 -Orders 2023.csv

2 -Stacking Datasets Vertically II.mp4

08:38

3 -Fetching Excel Data Into Pandas.mp4

10:01

3 -Orders.xlsx

3 -Order Details 2021.csv

3 -Order Details 2022.csv

3 -Order Details 2023.csv

3 - Stacking Datasets Vertically - Exercises.html

4 -Customers.csv

4 -Joining DataFrames Horizontally I.mp4

10:10

4 -Orders 2021.csv

4 -Order Details.xlsx

4 - Fetching Excel Data Into Pandas - Exercises.html

5 -Joining DataFrames Horizontally II.mp4

08:44

6 -Customers.csv

6 -Left and Right Joins.mp4

12:40

6 -Orders 2021.csv

6 -Order Details 2021.csv

6 -Products.csv

6 - Joining DataFrames Horizontally - Exercises.html

7 -Full Outer Joins.mp4

07:41

8 -Combining More Than Two Tables.mp4

10:58

8 -Customers.csv

8 -Orders 2021.csv

8 -Order Details 2021.csv

8 -Order Details 2022.csv

8 -Order Details 2023.csv

8 -Products.csv

8 - Outer Joins - Exercises.html

9 -Customers.csv

9 -Orders 2022.csv

9 -Order Details 2022.csv

9 -Products.csv

9 - Combining More Than Two Tables - Exercises.html

10 - Grouping and Aggregation

1 -Grouping and Aggregation 101.mp4

14:55

2 -Applying Multiple Aggregations.mp4

14:09

2 - Grouping and Aggregation 101 - Exercises.html

3 -Grouping By Multiple Columns.mp4

08:49

3 - Applying Multiple Aggregations - Exercises.html

4 -The transform Method.mp4

14:35

4 - Grouping By Multiple Columns - Exercises.html

5 -Pythonic Pivot Tables.mp4

14:37

5 - The transform Method - Exercises.html

6 - Pythonic Pivot Tables - Exercises.html

11 - Working with String Data

1 -upper, lower, and capitalize.mp4

07:44

2 -The len Method.mp4

04:16

2 - upper, lower, and capitalize - Exercises.html

3 -Regular Expressions 101.mp4

15:03

3 - The len Method - Exercises.html

4 -Matching Digits with Regular Expressions.mp4

06:49

4 - Regular Expressions 101 - Exercise.html

5 -The contains Method.mp4

14:06

5 - Matching Digits with Regular Expressions - Exercises.html

6 -The replace Method I.mp4

08:21

6 - The contains Method - Exercises.html

7 -The replace Method II.mp4

08:32

8 - The replace Method - Exercises.html

12 - Working with Datetime Data

1 -Using Datetime Values as Criteria.mp4

14:39

2 -The datetime Module I.mp4

10:06

2 - Using Datetime Values as Criteria - Exercises.html

3 -The datetime Module II.mp4

08:29

4 -Date Math in Pandas.mp4

14:44

4 - The datetime Module - Exercises.html

5 -The shift Method I.mp4

12:20

5 - Date Math in Pandas - Exercises.html

6 -The shift Method II.mp4

09:45

7 -Calculating rolling Averages.mp4

13:42

7 - The shift Method - Exercises.html

8 - Calculating rolling Averages - Exercises.html

13 - Data Visualization with Pandas

1 -Data Visualization 101.1.mp4

13:11

2 -Data Visualization 101.2.mp4

05:27

3 -Bar Plots.mp4

12:00

3 - Data Visualization - Exercises.html

4 -Scatter Plots.mp4

16:09

4 - Bar Plots - Exercises.html

5 -Customizing Plot Appearance.mp4

07:19

5 - Scatter Plots - Exercises.html

6 -Customizing Plot Axes.mp4

14:15

7 - Customizing Plots - Exercises.html

14 - Functional Programming in Python

1 -Apply-ing Functions to Data Analysis.mp4

04:38

2 -If Statements in Python.mp4

10:35

3 -Incorporating Multiple Logical Conditions.mp4

11:19

4 -Incorporating And and Or Logic.mp4

13:22

5 -Functions in Python.mp4

10:04

5 - If Statements - Exercises.html

6 -Returning Values From Functions I.mp4

08:27

7 -Returning Values From Functions II.mp4

08:26

8 - Functions - Exercises.html

15 - Leveraging the map and apply Methods

1 -The map Method.mp4

09:49

2 -Using map with Custom Functions I.mp4

09:27

2 - The map Method - Exercises.html

3 -Using map with Custom Functions II.mp4

10:41

4 -The apply Method.mp4

10:07

4 - Using map with Custom Functions - Exercises.html

5 -Applying apply to Multiple Columns.mp4

08:08

5 - The apply Method - Exercises.html

6 - Applying apply to Multiple Columns - Exercises.html

16 - BONUS LESSON

1 - BONUS LESSON.html

Description

Master Pandas and Python with real-world datasets and 200+ hands-on exercises! Go from beginner to expert Data Analyst!

What You'll Learn?

Master the fundamentals of Python programming for data analysis
Leverage the powerful Pandas library to manipulate and analyze complex datasets
Fetch and import external datasets into Pandas from various sources
Handle missing data, duplicates, and data type conversions
Filter datasets with logical criteria using SQL-like operations
Merge and combine multiple datasets efficiently
Create insightful summary views by grouping and aggregating data
Pivot and reshape datasets for deeper insights
Manipulate string data and harness the power of regular expressions
Perform time series analysis and calculations on datetime data
Visualize your findings with various types of plots and charts
Transform data using custom Python functions and Pandas methods

Who is this for?

Anyone interested in mastering data analysis, regardless of background or experience

Excel or SQL users who want to scale up their data analysis capabilities

Experienced programmers looking to add powerful data analysis skills to their toolkit

Data analysts or scientists who want to transition to Python-based analysis

Business professionals seeking to leverage data for better decision-making

What You Need to Know?

No programming experience is required

All you need is a computer with internet access

More details

Description
Master data analysis with Python and Pandas: the most comprehensive AND effective course anywhere!
Welcome to the ultimate course on data analysis using Python and the powerful Pandas library. Whether you're a complete beginner or an experienced programmer looking to level up your analytical skills, this course is designed to take you from zero to data manipulation and analysis guru.

What makes this course special?
Proven success: Over 100,000 students have used my courses to master other data analysis tools like SQL, Excel, and Power BI
No prerequisites: Start from scratch or jump ahead if you're an experienced programmer
Step-by-step approach: I break down every concept step-by-step, never assuming knowledge of any concepts that haven't already been covered
Real-world problem-solving: This course is jam-packed with examples using real-world datasets, from house sales data to UFO sightings!
LOTS of practice: Literally hundreds(!) of exercises are integrated throughout the course, providing immediate reinforcement after each concept
Comprehensive coverage: We progress from basic Python programming to advanced data transformations with Pandas, covering every step in between

Here's what you'll learn:
Master the fundamentals of Python programming, specifically tailored for data analysis
Harness the full power of the Pandas library to manipulate and analyze complex datasets
Learn how to fetch and import external datasets into Pandas from various sources
Perform exploratory data analysis (EDA), including a range of statistical measures
Dive deep into indexing, sorting, filtering, and updating Pandas DataFrames
Learn how to handle missing data
Filter large datasets using SQL-like operations and logical criteria
Merge and combine multiple datasets efficiently
Create insightful summary views by grouping and aggregating data
Manipulate string data and harness the power of regular expressions
Perform time series analysis and calculations on datetime data
Create insightful visualizations to communicate your findings effectively
Apply functional programming concepts to streamline your data analysis
Transform data using custom Python functions and Pandas methods

By the end of this course, you'll have the skills to:
Confidently work with large datasets using Python and Pandas
Perform complex data transformations and analysis
Create insightful visualizations to communicate your findings
Apply functional programming concepts to data analysis
Tackle real-world data problems with ease

About your instructor: I'm Travis Cuzick, and I've taught over 100,000 students how to leverage data analysis tools like SQL, Excel, Power BI, and Python. With years of experience in teaching these skills, and applying them on the job, I've designed this course to be the most effective way to master data analysis with Python and Pandas.
Don't miss this opportunity to transform yourself into a Python-powered Data Guru. With the ever-increasing importance of data - and the ability to make sense of it -Â in today's world, the skills you'll gain from this course will only become more valuable over time, this course will equip you with a skill set that will only grow more valuable in the future.
Enroll now and take the first step towards mastering data analysis with Python and Pandas. I'll see you inside!
Who this course is for:
Anyone interested in mastering data analysis, regardless of background or experience
Excel or SQL users who want to scale up their data analysis capabilities
Experienced programmers looking to add powerful data analysis skills to their toolkit
Data analysts or scientists who want to transition to Python-based analysis
Business professionals seeking to leverage data for better decision-making

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language english
Training sessions 107
duration 18:46:13
Release Date 2025/01/16

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