Practical Data Science with Jupyter

Module 1: Installing Software and System Setup

• Structure
• Objective
• System requirements
• Downloading Anaconda
• Installing the Anaconda on Windows
• Installing the Anaconda in Linux
• How to install a new Python library in Anaconda?
• Open your notebook – Jupyter
• Know your notebook

Module 2: Python Language Basics

• Language Semantics
• Indentation, not braces
• Everything is an object
• Comments
• Function and object method calls
• Variables and argument passing
• Dynamic references, strong types
• Attributes and methods
• Imports
• Scalar Types
  • Numeric types
  • Strings
  • Bytes and Unicode
  • Booleans
  • Type casting
  • None
  • Dates and times
• Control Flow
  • if, elif, and else
  • for loops
  • while loops
  • pass

Module 3: Built-In Data Structures, Functions, and Files

3.1 Built-In Data Structures, Functions, and Files

• Tuple
  • if, elif, and else
  • Tuple methods
• List
  • Adding and removing elements
  • Concatenating and combining lists
  • Sorting
  • Slicing
• Set
• Built-In Sequence Functions
  • enumerate
  • sorted
  • zip
  • Reversed
• List, Set, and Dictionary Comprehensions
  • Nested list comprehensions

3.2 Functions

• Namespaces, Scope, and Local Functions
• Returning Multiple Values
• Functions Are Objects
• Anonymous (Lambda) Functions
• Generators
  • Generator expressions
  • itertools module
• Errors and Exception Handling

Module 4: NumPy Basics: Arrays and Vectorized   Computation

4.1 The NumPy ndarray: A Multidimensional Array Object

• Creating ndarrays
• Data Types for ndarrays
• Arithmetic with NumPy Arrays
• Basic Indexing and Slicing
  • Indexing with slices
• Boolean Indexing
• Fancy Indexing
• Transposing Arrays and Swapping Axes

4.2 Pseudorandom Number Generation

4.3 Universal Functions: Fast Element-Wise Array Functions

4.4 Array-Oriented Programming with Arrays

• Expressing Conditional Logic as Array Operations
• Mathematical and Statistical Methods
• Methods for Boolean Arrays
• Sorting
• Unique and Other Set Logic

4.5 File Input and Output with Arrays

4.6 Linear Algebra

Module 5: Getting Started with pandas

5.1 Introduction to pandas Data Structures

• Series
• DataFrame
• Index Objects

5.2 Essential Functionality

• Reindexing
• Dropping Entries from an Axis
• Indexing, Selection, and Filtering
  • Selection on DataFrame with loc and iloc
  • Integer indexing pitfalls
  • Pitfalls with chained indexing
• Arithmetic and Data Alignment
  • Arithmetic methods with fill values
  • Operations between DataFrame and Series
• Function Application and Mapping
• Sorting and Ranking
• Axis Indexes with Duplicate Labels

5.3 Summarizing and Computing Descriptive Statistics

• Correlation and Covariance
• Unique Values, Value Counts, and Membership

Module 6: Data Loading, Storage, and File   Formats

6.1 Reading and Writing Data in Text Format

• Reading Text Files in Pieces
• Writing Data to Text Format
• Working with Other Delimited Formats
• JSON Data
• XML and HTML: Web Scraping
  • Parsing XML with lxml.objectify

6.2 Binary Data Formats

• Reading Microsoft Excel Files

Module 7: Data Cleaning and Preparation

7.1 Reading and Writing Data in Text Format

• Filtering Out Missing Data
• Filling In Missing Data

7.2 Data Transformation

• Removing Duplicates
• Transforming Data Using a Function or Mapping
• Replacing Values
• Renaming Axis Indexes
• Discretization and Binning
• Detecting and Filtering Outliers
• Permutation and Random Sampling
• Computing Indicator/Dummy Variables

7.3 Extension Data Types

7.4 String Manipulation

• Python Built-In String Object Methods
• Regular Expressions
• String Functions in pandas

7.5 Categorical Data

• Background and Motivation
• Categorical Extension Type in pandas
• Computations with Categoricals
  • Better performance with categoricals
• Categorical Methods
  • Creating dummy variables for modeling

Module 8: Data Visualization

•     Structure
•     Objective
•     Bar chart
•     Line chart
•     Histograms
•     Scatter plot
•     Stacked plot
•     Box plot