| 1 - DataFrames and Series.html | 102.4 B | ||
| 1 - Introduction.mp4 | 71.8 MB | ||
| 10 - Conditional filtering.mp4 | 15.8 MB | ||
| 11 - Useful inbuilt functions.mp4 | 7.1 MB | ||
| 12 - Grouping.mp4 | 15 MB | ||
| 13 - Concatenating and merging.mp4 | 130.5 MB | ||
| 14 - Basic plotting.mp4 | 42.2 MB | ||
| 15 - Colab Notebook with all code for this lecture.txt | 102.4 B | ||
| 15 - Introduction of the dataset.mp4 | 6.4 MB | ||
| 16 - Fixing issues and cleaning the dataset.mp4 | 139 MB | ||
| 17 - Dealing with missing values Homeplanet.mp4 | 110.6 MB | ||
| 18 - Dealing with missing values CryoSleep.mp4 | 98.5 MB | ||
| 19 - Dealing with missing values Cabins.mp4 | 68.8 MB | ||
| 2 - Prerequisites.mp4 | 10.5 MB | ||
| 20 - Inspecting remaining columns.mp4 | 69.6 MB | ||
| 21 - Imputing missing values using KNearest Neighbour Algorithm.mp4 | 79 MB | ||
| 22 - Colab Notebook with all code for this lecture.txt | 102.4 B | ||
| 22 - Creating a new feature 1 Family Count.mp4 | 63.8 MB | ||
| 23 - Creating a new feature 2 Age Group.mp4 | 39.4 MB | ||
| 24 - Creating a new feature 3 Spend Group.mp4 | 13.5 MB | ||
| 25 - Creating a new feature 4 Personality.mp4 | 28.8 MB | ||
| 26 - Colab Notebook with all code for this lecture.txt | 102.4 B | ||
| 26 - Countplots.mp4 | 61.2 MB | ||
| 27 - Histograms and Scatterplots.mp4 | 91.6 MB | ||
| 28 - Why you shouldnt just rely on summary statistics.mp4 | 9.8 MB | ||
| 29 - Colab Notebook with all code for this lecture.txt | 102.4 B | ||
| 29 - Pearsons Correlation Coefficient.mp4 | 22 MB | ||
| 3 - Colab Setup.mp4 | 84.2 MB | ||
| 3 - Google Colab.txt | 0 B | ||
| 30 - Student ttest.mp4 | 26.1 MB | ||
| 31 - ChiSquare test.mp4 | 38 MB | ||
| 31 - vid2-chi2.mp4 | 67.6 MB | ||
| 32 - Expanding Your Data Analysis Skills What We Havent Covered Yet.html | 8.6 KB | ||
| 33 - Essential Statistical Concepts Every Data Analyst should know.html | 12 KB | ||
| 4 - Why Python Pandas.html | 5.7 KB | ||
| 5 - Before you start.html | 716.8 B | ||
| 6 - Colab Notebook with all code for this lecture.txt | 102.4 B | ||
| 6 - Creating a DataFrame.mp4 | 79.2 MB | ||
| 7 - Selecting Columns.mp4 | 12.7 MB | ||
| 8 - Indexing.mp4 | 27.6 MB | ||
| 9 - Applying Functions.mp4 | 53.6 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 43 total files | |||
Data Analysis Masterclass: Real-World Pandas Coding
https://DevCourseWeb.com
Published 9/2024
Duration: 4h21m | Video: .MP4, 1920x1080 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 1.54 GB
Genre: eLearning | Language: English
Master Pandas: Real-World Data Cleaning, Feature Engineering, Visualisation, Statistical Analysis Challenges
What you'll learn
Be proficient in using the pandas library which includes data loading, exploring, cleaning and a variety of data manipulation techniques.
Develop proficiency in identifying and handling missing or inconsistent data, including techniques for folling, interpolating, and removing missing values.
Learn how to create new features (feature engineering) from existing data to enhance the dataset and improve the quality of the analysis.
Gain the ability to create insightful visualisations using pandas integrated plitting capabilities and additional libraries like Seaborn.
Understand how to perform various statistical tests to analyse the data, including descriptive statistics, correlation, and hypothesis testing.
Apply all the cocepts learned through practical, hands-on experience using a real-world dataset from Kaggle.
Requirements
Basic understanding of Python (data types: familiarity with fundamental data types such as integers, floats, strings, lists and dictoinaries; variables: understanding how to declate and use variables; loops: basic knowledge of for loops; conditional statements: basic knowledge of if-statements; functions: ability to write and call functions, including passing arguments and returning values).
Only a computer and an internet connection needed, as well as a Google account.
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