Python for Data Scientists

Course Summary

The Python for Data Scientists course focuses on Python's ecosystem for data manipulation, data analytics, and machine learning. The course begins by covering the fundamentals of Python, including data structures, loops, and list comprehensions. Next, it examines the Python ecosystem to import and manipulate data, create summaries and exploratory visualizations, and perform standard hypothesis tests.

Prerequisites:
* This course requires the participant to have some programming or scripting experience

Purpose	Learn how to use Python to explore and analyze data, run basic regression models, visualize data, and apply some basic machine learning models to data.
Audience	Business Analysts, Data Engineers, or Data Scientists who don't possess knowledge of Python.
Role	Business Analyst \| Data Engineer \| Data Scientist
Skill Level	Introduction
Style	Lecture \| Hands-on Activities \| Labs
Duration	3 Days
Related Technologies	DevOps \| Python \| Machine Learning

Learning Objectives

Utilize Python to read, manipulate, and clean data
Analyze and visualize data using Python
Perform predictive analysis using basic machine learning models

What You'll Learn:

In the Python for Data Scientists training course, you'll learn:

Introduction to the Anaconda Python Distribution
- Understand and utilize Jupyter notebooks
- Understand the data science and machine learning libraries that are included
Introduction to Python
- Dynamic typing
- Primitive datatypes
- Looping/list comprehensions
- Modules and packages
Introduction to Pandas
- Datatypes
- Import data
  - CSV
  - Excel
  - SQL
- Creating numerical summaries
- Exploring data
- Descriptive statistics
- Basic probability distributions (Gaussian/normal, Poisson, Chi-Squared, binomial, exponential) including generating random numbers and finding critical values
- Standard hypothesis testing, e.g., t-tests, z-tests, ANOVA, chi-square tests, as well as basic non-parametric tests like Wilcoxon signed-rank and rank-sum tests
- Dummy variables
- Linear regression
- Logistic regression
- Evaluating regression models
- Simulating data from probability distributions
- Permutation tests and the bootstrap
- Creating publication-quality graphics
Introduction to SciKit-Learn
- Supervised vs. Unsupervised learning
- Classification vs. Regression
- Linear Regression
- Decision Trees
- Support Vector Machines
- Ensemble Models
- Evaluating Models
- Fine-Tuning Your Models

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.

Expert Practitioners

Industry experts that bring their battle scars into the classroom.

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!

Dive in and learn more

When transforming your workforce, it's important to have expert advice and tailored solutions. We can help. Tell us your unique needs and we'll explore ways to address them.

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Python for Data Scientists

Course Summary

Purpose

Audience

Role

Skill Level

Style

Duration

Related Technologies