Page Contents:

About

Prerequisites

This course has no prerequisites, but hands on experience with Python and data would be helpful.

What will you learn

After completing this course, students will be able to use Python and key libraries to manipulate data.

Topics

Python data analysis basics

  • Basics of Python for Data Analysis
    • Why learn Python for data analysis?
    • Python 2.7 v/s 3.4
    • How to install Python?
    • Running a few simple programs in Python
  • Python libraries and data structures
    • Python Data Structures
    • Python Iteration and Conditional Constructs
    • Python Libraries
  • Import/Export Data
    • Import Data from CSV
    • Export Data to CSV
    • Import Data from Web

Numpy

  • Numpy Array
  • Numpy Arithmetic
  • Numpy Sequence
  • Logical Indexing
  • Slicing
  • Reshape
  • Linear Algebra
  • Statistics

Visualisations with Matplotlib

  • Create Plots
  • Plot Styling
  • Subplots
  • Scatter Plot
  • Bar Chart
  • Histogram
  • Pie Chart
  • Contour Plot

Pandas

  • What is Pandas?
  • Introduction to series and dataframes
  • Pandas Series
  • Pandas Data Frame
  • Selecting Data
  • Data Filter
  • Data Concatenate
  • Data Join
  • Data Pivot
  • Pandas Statistics

Time Series

  • DateTime
  • Time Range
  • Time Series
  • Rolling Window
  • Plotting Time Series

Scikit-Learn

Overview of Scikit-Learn Supervised and Unsupervised Learning Classification Regression Clustering

Building a Predictive Model in Python

  • Logistic Regression
  • Decision Tree
  • Random Forest

Details

Time

9:30 am – 6:00pm

Location

This course is offered at the following Sakura offices:

  • Singapore
  • San Francisco.

Duration

1 day

What is provided

  • Lunch
  • Internet

What you need to bring

Your own laptop