Python Data Science
Course: PYTHONDS
Duration: 3 Days
Level: I
On our website at:
http://www.verhoef-training.com/courses/PYTHONDS.html
Course Summary
Python has emerged as a popular and effective language in the world of data science. The dynamic nature of the language, the relative simplicity of the syntax, and the abundance of fast and powerful libraries have all been important contributory factors in this growth.
This course takes a detailed look at the most popular Python libraries for numeric processing, statistical analysis, machine learning, and visualization. We also show how to make use of common Python data types and algorithms to achieve real-world tasks.
Topics Covered In This Course
Python Quick Start
- Python Essentials
- Language Fundamentals
- Functions
- Data Structures
Getting Started with NumPy
- Setting the Scene
- NumPy Arrays
- Manipulating Array Elements
- Manipulating Array Shape
NumPy Techniques
- NumPy Universal Functions
- Aggregations
- Broadcasting
- Manipulating Arrays using Boolean Logic
- Additional Techniques
Getting Started with Pandas
- Introduction to Pandas
- Creating a Series
- Using a Series
- Creating a DataFrame
- Using a DataFrame
Pandas Techniques
- Universal Functions
- Merging and Joining Datasets
- A Closer Look at Joins
Working with Time Series Data
- Introduction to Time Series Data
- Indexing and Plotting Time Series Data
- Testing Data for Stationarity
- Making Data Stationary
- Forecasting Time Series Data
- Scaling Back the ARIMA Results
Introduction to Machine Learning
- Machine Learning Concepts
- Classification
- Clustering
Getting Started with Scikit-Learn
- Scikit-Learn Essentials
- A Closer Look at Datasets
Understanding the Scikit-Learn API
- Introduction
- Scikit-Learn API Essentials
- Performing Linear Regression
Going Further with Scikit-Learn
- Introduction
- Understanding Naive Bayes Classification
- Naive Bayes Example using Scikit-Learn
Case Study
- Worked example of a real-world data science problem
What You Can Expect
- Using NumPy and Pandas for efficient data manipulation
- Using Matplotlib and Seaborn for visualization
- Working with time series data
- Machine learning concepts
- Using Scikit-Learn for machine learning
Who Should Take This Course
Data Analysists, Software Engineers, Employees assigned to review data Managers
Recommended Prerequisites
Some familiarity with Python or another contemporary language would be beneficial
Training Style
50% lecture and 50% Labs
Related Courses
| Code |
Course Title |
Duration |
Level |
|
PYTH3I |
Python Programming Introduction |
3 Days |
I |
Details |
PYTHONA |
Advanced Python Development For Data Science |
4 Days |
II |
Details |
Every student attending a Verhoef Training class will receive
a certificate good for $100 toward their next public class taken
within a year.
You can also buy "Verhoef Vouchers" to get a discounted rate for a
single student in any of our public or web-based classes.
Contact your account manager or our sales office for details.
