Next Level Python for Data Science / Intermediate | Working with NumPy, Pandas, SciKit Learn, SciPy, Spark, TensorFlow, Streaming & More

Course Objectives

Skills-Focused, Hands-On Learning: This course is about 50% hands-on lab to 50% lecture ratio, combining engaging instructor presentations, demos and practical group discussions with extensive machine-based student labs and project work. Throughout the course students will learn to write Python scripts and apply them within a scientific framework working with the latest technologies listed in the agenda.

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development.  Working in a hands-on learning environment led by our expert practitioner, students will learn:

• How to work with Python in a Data Science Context
• How to use NumPy, Pandas, and MatPlotLib
• How to create and process images with PIL
• How to visualize with Seaborn
• Key features of SciPy and Scikit Learn
• How to interact with Spark using DataFrames
• How to use SparkSQL, MLlib, and Streaming in BigData

Course Prerequisites

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.  Attending students are required to have a background in basic Python development skills.

Course Agenda

Session: Python for Data Science

Lesson: Python Review (Optional)

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

Lesson: iPython

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

Lesson: numpy

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

Lesson: scipy

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

Lesson: A tour of 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

Lesson: pandas

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

Lesson: matplotlib

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

Lesson: The Python Imaging Library (PIL)

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

Lesson: seaborn

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

Lesson: SciKit-Learn Machine Learning Essentials

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

Lesson: TensorFlow Overview

• TensorFlow overview
• Keras
• Getting Started with TensorFlow

Session: Python on Spark

Lession: PySpark Overview

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

Lesson: 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

Lesson: Spark SQL

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

Lesson: 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

Lesson: Spark Streaming

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

Course Materials

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the course environment software and set up files, project files (or code, if applicable) and solutions required for the hands-on work.