Data Analysis and Scientific Python

Python Refresher / Crash Course

• Introduction
• IDEs and Tools for data analysis
• Data types and strings
• Control structures
• Functions and functional programming
• Collections
• Object Oriented
• Modules and Packages

Introduction to Data Analysis

• Data Science
• Data Analysis and ML Packages overview

NumPy

• Python arrays and NumPy Arrays
• Multi-dimensional Arrays
• Array slicing
• Fancy Indexing
• Data types
• Array calculation methods
• Statistics methods
• Universal functions
• Broadcasting
• Universal function methods
• Linear algebra
• Linear regression with Numpy 
• Feature Engineering
• Feature selections
• Dummy variables
• Converting continuous variable to discrete

SciPy package

• Interpolation
• Curve fit
• Optimization
• Statistics
• Image processing
• Integration and more
• Interpolation
• FFT
• Linear algebra
• Scikits

Pandas

• Overview
• Series and Dataframes
• Working with data

Pandas – Series

• Understanding one-dimensional labeled arrays
• Create a Series from Python objects
• Using the read_csv() method
• Attributes
• Methods
• Arguments and Parameters
• Extracting Series values

 Pandas – DataFrames

• Understanding two-dimensional data structures
• Selecting columns from a DataFrame
• Adding new columns to a DataFrame
• Working with Nulls
• Sorting a DataFrame
• Filter a DataFrame – conditions and methods
• Retrieving rows by Index position
• Delete rows or columns from a DataFrame
• Rename Index labels or Columns in a DataFrame
• Common String methods

Using MultiIndex

• Understanding multiIndexes in Pandas
• Creating a multiIndex
• Extracting rows from a multiIndex
• Common methods

Group By

• Basic operations
• Retrieving groups
• Common methods
• Group by multiple columns
• Iterating through groups

Joining and Concatenating Data

• Join operations between DataFrame objects
• Combining together Pandas objects

Working with Dates

• Understanding Python’s datetime module
• Pandas Timestamp and DateTimeIndex objects
• Pandas DateOffset and TimeDelta objects
• Common methods

Pandas I/O API

• Object conversions
• Export DataFrame to csv
• Importing and exporting Excel files

Data visualization

• Overview
• Learning from the data

Matplotlib package

• Line plots
• Scalar plots
• Bar plots
• Histograms
• Interactive graphs and Events
• Animation

Seaborn

• Overview
• Statistics graphs
• Histograms and distributions
• Bar plots and Box-plot
• Heat map and correlation
• Grids

Data Preparation

• Data preparation and cleaning
• Dealing with Missing values
• Central tendency
• Mean/median/mode
• Bias
• Variance and standard deviation
• Standard scores
• Feature scaling – standardization and normalization

Machine Learning Basic Concepts

• Overview
• Why Learn
• Applications
• Machine Learning Process
• Learning Types:
  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement learning
• CRISP-DM
• Examples

• Basic understanding of Programming concepts