Course: Python for Data Science
Modules:
Welcome to python for data science, Expert Talk - A data scientist's experience with Python, What is python?, Working with Jupyter notebooks, Introduction to the problem, Solution approach - Preparing tables and charts, Solution approach -  Gaining Insights, Solution Approach - Airline traffic analysis, Solution summary, Expert Talk - Why Python is the language of choice for data science professionals, Introduction to the Problem, Exploring the Problem
Introduction, Diving into CSV Data, Data inspection, Finding missing data in the POS data, Deleting missing data and saving the cleaned data set, Lab data and problem, A note on assessments, Basic data structures - lists and dictionaries, Basic data structures - series, Creating a data frame using lists, dictionaries and series, Slicing with precision, Changing the indices and saving the new DataFrame, Navigating data insights, Selecting data that match certain criteria, Selecting data that match multiple criteria, Expert Talk - Understanding your data, What are the unique products in the POS data set?, Finding specific values in the data, How much did we sell per category?, Finding totals and averages by brand and by category, Grouping by multiple attributes, Displaying aggregated data in a pivot table, Expert talk - How insights and data analysis guide each other, Working with dates, How much did we sell each month?, What is the monthly average of sales?, Were there specific dates when sales were high?, What if we have more than one dataset?, Merging some simple data sets, Merging POS data with the online data, Walkthrough - How to approach a graded assignment, Summary
Introduction, Expert Talk - Why EDA is a superpower, Finding the average of the data, Understanding the spread of the data, Quantiles - how to understand and visualize them, Exploring variability in the POS data, What shape is my data?, Understanding the distributions of features in the POS data, Understanding Data Distributions, Some other common shapes of data - Part I, Some other common shapes of data - Part I, Some other common shapes of data - Part II, Some other common shapes of data - Part III, What chance of revenue falls in a given range, How are the features related to each other? - Part I, How are the features related to each other? - Part I, How are the features related to each other? - Part II, How are the features related to each other? - Part II, Visualizing categorical features, Visualizing proportions, Expert Talk - Power of visualization & its importance in storytelling, Using boxplots to compare revenues across segments in the POS data, Making better visuals - Part III, Communicating insights better by creating multiple subplots within the same plot, Comparing the distribution of revenue for each sector by overlaying their KDE plots, Sampling our data - Part I, Sampling our data - Part II, Introduction to hypothesis testing - Part I, Introduction to hypothesis testing - Part II, Hypothesis testing using Z - Test - Part I, Hypothesis testing using Z - Test - Part II, Hypothesis testing using t - Test, Hypothesis testing using Chi-square test, Summary
Introduction, Expert Talk  - Handling missing data, What to do with missing values?, Missing values in the POS data, Missing values within a hierarchy, Missing values within a hierarchy (contd.), What if parts of the hierarchy are also missing?, Finishing up missing value treatment in the POS data, Missing values - another simpler example, Working with categoric features, Transforming features - binning and discretization, Transforming features - binning and discretization (contd.), Encoding categoric features - one-hot and label encoding, Encoding features in the POS data, Finishing up the encoding and saving the encoded data, What is data normalization and why do we need it?, Data normalization using min-max scaling, Data normalization using z-score scaling, Other types of data transformation, Applying log transformation to the online data, Finding outlying data, Removing outliers by dropping them, How to deal with outliers - imputation, How to deal with outliers - capping, Summary
Introduction, Reducing the dimensionality of data sets, Exploring the features of the obesity data set, What is Principal Component Analysis(PCA)?, Applying PCA to the obesity data, Creating a transformed version of the data through feature engineering, Expert Talk - Gen AI in Python, Introduction to Gen AI in Python for Data science, Some quick data analysis using PandasAI, Some quick data visualization using PandasAI, Summary