LetsPy

This repo contains codes/slides for LetsPy talk.

Topics

• Refresh the basics of Python language - which was covered on 1st Day
  • list
  • tuple
  • dictionary
  • sets
• Introduction to Data Science
  • What is Data Science
  • Business Intelligence Vs Data Science
  • Lifecycle of Data Science
  • Case study
• Introduction to Pandas
  • Installation !pip install pandas
  • Covering the basics of library's main data structures - dataframes and series.
  • Working with DataFrame - dives a bit deeper into the functionality of DataFrames which shows how to inspect, select, filter, merge, combine, and group your data.
  • Using pandas with Titanic dataset - Kaggle's beginner friendly problem statement so that one should apply the learnings of the first two parts in order to do basic analysis.
• Introduction to Numpy
  • Installation !pip install numpy
  • Numpy 1D Array
  • Numpy 2D Array
  • Multidimensional Array
  • Array concatenation & splitting & subarrays
  • Difference between dynamic-type list and fixed-type (Numpy-style) array
• Introduction to matplotLib
  • Installation - !pip install matplotlib
  • Plot two dataframe columns as a scatter plot
  • Plot column values as a bar plot
  • Line plot with multiple columns
  • Save plot to file
  • Bar plot with group by
  • Stacked bar plot with group by, percentage counts
  • Stacked bar plot with two-level group by
  • Stacked bar plot with two-level groupby, percentages normalized to 100%
  • Stacked bar plot with a single groupby, percentages normalized to 100%
  • Plot a histogram of column values
  • Date histogram
• Introduction to Scikit-learn - Optical Recognition of Handwritten Digits using Scikit - Learn Beginner Friendly ML project.
  • Installation !pip install -U scikit-learn
  • This tutorial will help folks, to understand basics of ML - i.e. how to do Exploratory Data Analysis with the help of matplotlib and Principal Component Analysis (PCA).
  • Preprocess the data with normalization + split the data into training and test sets.
  • If time permits, will do KMeans on top of this.

Sources:-

• Introduction to Data Science
• Introduction to MatplotLib
• Introduction to Scikit-Learn
• Scikit-Learn Example
• Sklearn Example