Introduction to Python for Data Science-Welcome to the Course
Program introduction
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Welcome to python for data science
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Course syllabus
Expert Talk - A data scientist's experience with Python
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Introduction to Python for Data Science-Kickstarting with Python and Jupyter
What is python?
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Working with Jupyter notebooks
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Installation guide
Working effectively with Jupyter notebooks
Introduction to Python for Data Science-How Python Solved a Business Problem
Disclaimer
Introduction to the problem
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Solution approach - Preparing tables and charts
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Solution approach - Gaining Insights
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Solution Approach - Airline traffic analysis
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Solution summary
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Introduction to Python for Data Science-The Business Problem for the Course
The Global Problem Statement
Expert Talk - Why Python is the language of choice for data science professionals
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Introduction to the Problem
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Exploring the Problem
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Tell us what you think!
Data wrangling with Python-Getting started with a data set
Introduction
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Diving into CSV Data
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Data inspection
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Finding missing data in the POS data
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Deleting missing data and saving the cleaned data set
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Data cleaning with python
Lab data and problem
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Data wrangling with Python-DataFrame Fundamentals and Indexing
Basic data structures - lists and dictionaries
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Basic data structures - series
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Creating a data frame using lists, dictionaries and series
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Python statistics fundamentals
Slicing with precision
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Changing the indices and saving the new DataFrame
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Data wrangling with Python-Exploring and Querying Columns
Navigating data insights
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Selecting data that match certain criteria
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Selecting data that match multiple criteria
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Expert Talk - Understanding your data
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Data wrangling with Python-Manipulation and Grouping of Data
What are the unique products in the POS data set?
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Finding specific values in the data
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How much did we sell per category?
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Finding totals and averages by brand and by category
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Grouping by multiple attributes
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Displaying aggregated data in a pivot table
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Data wrangling with Python-Date Time Operations in Python
Expert talk - How insights and data analysis guide each other
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Working with dates
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How much did we sell each month?
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What is the monthly average of sales?
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Were there specific dates when sales were high?
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Working with dates
Data wrangling with Python-Putting Together Data from Multiple Sources
What if we have more than one dataset?
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Merging some simple data sets
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Merging POS data with the online data
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Resources - Datasets and Jupyter notebooks
Summary
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Exploratory data analysis-Descriptive Statistics
Introduction
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Expert Talk - Why EDA is a superpower
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Finding the average of the data
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Understanding the spread of the data
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Quantiles - how to understand and visualize them
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Exploring variability in the POS data
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What shape is my data?
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Understanding the distributions of features in the POS data
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Exploratory data analysis-Understanding Data Distributions
Understanding Data Distributions
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Some other common shapes of data - Part I
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Some other common shapes of data - Part I
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Some other common shapes of data - Part II
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Some other common shapes of data - Part III
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What chance of revenue falls in a given range
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Exploratory data analysis-Relationships Between Features
How are the features related to each other? - Part I
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How are the features related to each other? - Part I
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How are the features related to each other? - Part II
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How are the features related to each other? - Part II
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Visualizing categorical features
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Visualizing proportions
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Exploratory data analysis-Enhancing Data Visualizations
Expert Talk - Power of visualization & its importance in storytelling
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Using boxplots to compare revenues across segments in the POS data
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Making better visuals - Part III
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Communicating insights better by creating multiple subplots within the same plot
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Comparing the distribution of revenue for each sector by overlaying their KDE plots
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Exploratory data analysis-Comparing Two Groups: Hypothesis Testing
Sampling our data - Part I
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Sampling our data - Part II
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Introduction to hypothesis testing - Part I
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Introduction to hypothesis testing - Part II
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Hypothesis testing using Z - Test - Part I
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Hypothesis testing using Z - Test - Part II
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Hypothesis testing using t - Test
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Hypothesis testing using Chi-square test
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Datasets and Jupyter notebooks
Summary
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Data pre-processing-Treating Missing Data
Introduction
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Expert Talk - Handling missing data
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What to do with missing values?
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Missing values in the POS data
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Missing values within a hierarchy
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Missing values within a hierarchy (contd.)
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What if parts of the hierarchy are also missing?
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Finishing up missing value treatment in the POS data
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Missing values - another simpler example
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Data pre-processing-Data Transformation
Working with categoric features
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Transforming features - binning and discretization
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Transforming features - binning and discretization (contd.)
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Encoding categoric features - one-hot and label encoding
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Encoding features in the POS data
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Finishing up the encoding and saving the encoded data
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Data pre-processing-Understanding Data Normalization and Outlier Detection
What is data normalization and why do we need it?
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Data normalization using min-max scaling
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Data normalization using z-score scaling
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Other types of data transformation
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Applying log transformation to the online data
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Finding outlying data
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Removing outliers by dropping them
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How to deal with outliers - imputation
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How to deal with outliers - capping
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Data pre-processing
Resources - Datasets and Jupyter notebooks
Summary
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Feature Engineering-Creating Derived Features in Python
Introduction
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Reducing the dimensionality of data sets
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Exploring the features of the obesity data set
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What is Principal Component Analysis(PCA)?
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Applying PCA to the obesity data
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Creating a transformed version of the data through feature engineering
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Complete guide to Feature Engineering
Resources - Datasets and Jupyter notebooks
Feature Engineering-Gen AI in Python
Expert Talk - Gen AI in Python
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Introduction to Gen AI in Python for Data science
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Some quick data analysis using PandasAI
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Some quick data visualization using PandasAI
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Summary
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