Next Level Python for Data Science | Working with Libraries, Frameworks, and Visualization Tools

This course explores using Python for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing using Big Data. In this course you ll learn about essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, along with frameworks like TensorFlow and Spark. It also covers visualization tools like matplotlib, PIL, and Seaborn.

Python Review

• Python Language
• Essential Syntax
• Lists, Sets, Dictionaries, and Comprehensions
• Functions
• Classes, Modules, and imports
• Exceptions

iPython

• iPython basics
• Terminal and GUI shells
• Creating and using notebooks
• Saving and loading notebooks
• Ad hoc data visualization
• Web Notebooks (Jupyter)

NumPy

• NumPy basics
• Creating arrays
• Indexing and slicing
• Large number sets
• Transforming data
• Advanced tricks

SciPy

• What can SciPy do?
• Most useful functions
• Curve fitting
• Modeling
• Data visualization
• Statistics

SciPy subpackages

• Clustering
• Physical and mathematical Constants
• FFTs
• Integral and differential solvers
• Interpolation and smoothing
• Input and Output
• Linear Algebra
• Image Processing
• Distance Regression
• Root-finding
• Signal Processing
• Sparse Matrices
• Spatial data and algorithms
• Statistical distributions and functions
• C/C++ Integration

pandas

• pandas overview
• Dataframes
• Reading and writing data
• Data alignment and reshaping
• Fancy indexing and slicing
• Merging and joining data sets

matplotlib

• Creating a basic plot
• Commonly used plots
• Ad hoc data visualization
• Advanced usage
• Exporting images

The Python Imaging Library (PIL)

• PIL overview
• Core image library
• Image processing
• Displaying images

seaborn

• Seaborn overview
• Bivariate and univariate plots
• Visualizing Linear Regressions
• Visualizing Data Matrices
• Working with Time Series data

SciKit-Learn Machine Learning Essentials

• SciKit overview
• SciKit-Learn overview
• Algorithms Overview
• Classification, Regression, Clustering, and Dimensionality Reduction
• SciKit Demo

TensorFlow Overview

• TensorFlow overview
• Keras
• Getting Started with TensorFlow

PySpark Overview

• Python and Spark
• SciKit-Learn vs. Spark MLlib
• Python at Scale
• PySpark Demo

RDDs and DataFrames

• DataFrames and Resilient Distributed Datasets (RDDs)
• Partitions
• Adding variables to a DataFrame
• DataFrame Types
• DataFrame Operations
• Dependent vs. Independent variables
• Map/Reduce with DataFrames

Spark SQL

• Spark SQL Overview
• Data stores: HDFS, Cassandra, HBase, Hive, and S3
• Table Definitions
• Queries

Spark MLib

• MLib overview
• MLib Algorithms Overview
• Classification Algorithms
• Regression Algorithms
• Decision Trees and forests
• Recommendation with ALS
• Clustering Algorithms
• Machine Learning Pipelines
• Linear Algebra (SVD, PCA)
• Statistics in MLib

Spark Streaming

• Streaming overview
• Integrating Spark SQL, MLlib, and Streaming

Before attending this course, you should have:

• A solid data analytics and data science background
• Python experience

Topics are covered in-depth and are geared for experienced students who have taken one of the prerequisite courses below or have practical hands-on experience.

Data Scientists, Data Engineers, and Software Engineers who are experienced with basic Python and data science.