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Python for Data Scientist and Machine Learning

at Hartmann Software Group - Lodo

Course Details
$2,090 20 seats left
Start Date:

Mon, Mar 16, 9:00am - Mar 20, 5:00pm (5 sessions)

Next start dates (1)

1624 Market St Ste 202
Btwn 16th St Mall & 17th St
Denver, Colorado 80202
Book at Office/Home
Class Level: Intermediate
Age Requirements: 18 and older
Average Class Size: 10
Teacher: HSG Instructors

What you'll learn in this python class:

Python for Data Scientist and Machine Learning Practitioners

This is a 5 - day course that provides a ramp - up to using Python for data science/machine learning. Starting with the basics, it progresses to the most important Python modules for working with data, from arrays, to statistics, to plotting results. The material is geared towards data scientists and engineers. This is an intense, hands - on, programming class. All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs which helps students retain the earlier material. 

Python for Programming, Scikit-Learn and Tensorflow is a practical introduction to a working programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world.


Students must have at least 1 year of hands on data science experience and must be comfortable working with a variety of machine learning algorithms. Students should also be comfortable working with files and folders, and should not be afraid of the command line in Linux, Windows, or MacOS.

Course Outline

The Python Environment

  • Starting
  • Python
  • If the interpreter is not in your PATHs
  • Using the interpreter
  • Trying a few commands
  • The help() function
  • Running a Python script
  • Python scripts on UNIX
  • Python editors and IDEs

Getting Started

  • Using variables
  • Keywors
  • Built-in functions
  • Strings
  • Single-quoted string literals
  • Triple-quoted string literals
  • Raw string literals
  • Unicode literals
  • String operators and expressions
  • Converting among types
  • Writing to the screen
  • String formatting
  • Legacy string formatting
  • Command line parameters
  • Reading from the keyboard

Flow Control

  • About flow control
  • What’s with the white space?
  • if andelif
  • Conditional expressions
  • Relational and Boolean operators
  • while loops
  • Alternateways to exit as loop

Lists and Tuples

  • About Sequences
  • Lists
  • Tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Functions for all sequences
  • Using enumerate()
  • Operators and keywords for sequences
  • The xrange()function
  • Nested sequences
  • List comprehensions
  • Generator expressions

Working with Files

  • Text file I/O
  • Opening a text file
  • The with block
  • Reading a text file
  • Writing a text file
  • Python for Scientists
  • “Binary” (raw, or non-delimited) data

Dictionaries and Sets

  • About dictionaries
  • When to use dictionaries
  • Creating dictionaries
  • Getting dictionary values
  • Iterating through a dictionary
  • Reading file data into a dictionary
  • Counting with dictionaries
  • About sets
  • Creating sets
  • Working with sets


  • Defining a function
  • Function parameters
  • Global variables
  • Variable scope
  • Returning values

Exception Handling

  • Syntax errors
  • Exceptions
  • Handling exceptions with try
  • Handling multiple exceptions
  • Handling generic exceptions
  • Ignoring exceptions
  • Using else
  • Cleaning up with finally
  • Re-raising exceptions
  • Raising a new exception
  • The standard exception hierarchy

OS Services

  • The os module
  • Environment variables
  • Launching external processes
  • Paths, directories, and filenames
  • Walking directory trees
  • Dates and times
  • Sending email

Pythonic Idioms

  • The Zen of Python
  • Common Python idioms
  • Packing and unpacking
  • Lambda functions
  • List comprehensions
  • Generators vs. iterators
  • Generator expressions
  • String tricks

Modules and Packages

  • What is a module?
  • The import statement
  • Where did the.pyc file come from?
  • Module search path
  • Zipped libraries
  • Creating Modules
  • Packages
  • Module aliases
  • When the batteries aren’t included


  • Defining classes
  • Instance objects
  • Instance attributes
  • Methods
  • __init__
  • Properties
  • Class data
  • Inheritance
  • Multiple Inheritance
  • Base classes
  • Special methods
  • Pseudo-private variables
  • Static methods

Developer Tools

  • Program development
  • Comments
  • pylint
  • Customizing pylint
  • Unit testing
  • The unittest module
  • Creating a test class
  • Establishing success or failure
  • Startup and Cleanup
  • Running the tests
  • The Python debugger
  • Starting debug mode
  • Stepping through a program
  • Setting breakpoints
  • Debugging command reference
  • Benchmarking


  • About XML
  • Normal approaches to XML
  • Which module to use?
  • Getting started with ElementTree
  • How ElementTree works
  • Creating a new XML Document
  • Parsing an XML Document
  • Navigating the XML Document
  • Using XPath
  • Advanced XPath


  • About iPython
  • Features of iPython
  • Starting iPython
  • Tab completion
  • Magic commands
  • Benchmarking
  • External commands
  • Enhanced help
  • Notebooks


  • Python’s scientific stack
  • numpy overview
  • Creating arrays
  • Creating ranges
  • Working with arrays
  • Shapes
  • Slicing and indexing
  • Indexing with Booleans
  • Stacking
  • Iterating
  • Tricks with arrays
  • Matrices
  • Data types
  • numpy functions


  • About scipy
  • Polynomials
  • Vectorizing functions
  • Subpackages
  • Getting help
  • Weave

A Tour of scipy subpackages

  • cluster
  • constants
  • fftpack
  • integrate
  • interpolate
  • io
  • linalg
  • ndimage
  • odr
  • optimize
  • signal
  • sparse
  • spatial
  • special
  • stats


  • About
  • pandas
  • Pandas architecture
  • Series
  • DataFrames
  • Data Alignment
  • Index Objects
  • Basic Indexing
  • Broadcasting
  • Removing entries
  • Time series
  • Reading Data


  • About matplotlib
  • matplotlib architecture
  • matplotlib Terminology
  • matplotlib keeps state
  • What else can you do?

Python Imaging Library

  • The PIL
  • Supported image file types
  • The Image class
  • Reading and writing
  • Creating thumbnails
  • Coordinate system
  • Cropping an
  • d pasting
  • Rotating, resizing, and flipping
  • Enhancing

A Tour of Scikit-Learn subpackages

  • Loading, Training and Testing Data
  • Procesing Data
    • Standardization
    • Normalization
    • Binarization
    • Encoding Categorical Features
    • Inputing Missing Values
    • Generating Polynomial Features
  • Creating a Model
    • Supervised Linear Estimators
      • Linear Regression
      • Support Machine Vectors (SVM)
      • Naive Bayes
      • KNN
    • Unsupervised Learning Estimators
      • Principle Component Analysis (PCA)
      • K Means
  • Model Fitting
    • Supervised Learning
    • Unsupervised Learning
  • Prediction
    • Supervised Estimators
    • Unsupervised Estimators
  • Model Performance Evaluation
    • Classification
      • Accuracy Score
      • Classification Report
      • Confusion Matrix
    • Regression Matrix
      • Mean Absolute Error
      • Mean Squared Error
      • R Score
    • Clustering Matrix
      • Adjusted Rand Index
      • Homogeneity
      • V-measure
    • Cross-Validation
  • Model Tuning
    • Grid Search
    • Randomized Parameter Optimization


  • Installation
  • Class and Function Exploration
  • Creating First Graph and Running Session
  • Managing Graphs
  • Lifecycle of a Node Value
  • Linear Regression
  • Convolutional Neural Network
    • Architecture
    • Convolutional Layer
    • CNN Architectures

HSG courses are taught by the experienced instructors who are proven experts in their field. Our instructors are highly knowledgeable, friendly, reliable and inspiring. They speak and teach industry's best practices and often customize classes to meet individual needs.

Students are encouraged to ask questions and participate in discussions and training-labs.

Still have questions? Ask the community.

Refund Policy
To cancel or reschedule your registrations without penalty or charge, please notify us at [email protected] 14 days or more before the first day of my class.


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Start Dates (2)
Start Date Time Teacher # Sessions Price
9:00am - 5:00pm HSG Instructors 5 $2,090
This course consists of multiple sessions, view schedule for sessions.
Tue, Mar 17 9:00am - 5:00pm HSG Instructors
Wed, Mar 18 9:00am - 5:00pm HSG Instructors
Thu, Mar 19 9:00am - 5:00pm HSG Instructors
Fri, Mar 20 9:00am - 5:00pm HSG Instructors
9:00am - 5:00pm HSG Instructors 5 $2,090
This course consists of multiple sessions, view schedule for sessions.
Tue, Apr 21 9:00am - 5:00pm HSG Instructors
Wed, Apr 22 9:00am - 5:00pm HSG Instructors
Thu, Apr 23 9:00am - 5:00pm HSG Instructors
Fri, Apr 24 9:00am - 5:00pm HSG Instructors

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School: Hartmann Software Group


​The solution to most IT related problems lies in a better understanding of the technology.

Founded in June of 2002, the Hartmann Software Group (HSG) is an IT training company specializing in complicated software development languages and technologies ranging from C++ to Weblogic/Oracle Application...

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