Experience and background in software development.
Helpful to have some background in analytics or machine learning.
Some background in Python highly recommended though a brief intro is included.
Python has become a powerful language and environment for performing data science. It combines a robust, object-oriented language with a powerful library of data science packages, such as numpy, scipy, matlibplot, scikit-learn, and pandas. These tools together make python one of the best combinations of robust programming language together with great library support.
This course is designed for Data Analysts, Data Scientists, and Developers.
In this course, participants will learn:
- Quick Python primer
- Quick primer on data science algorithms
Python language Overview
Basics of Python language
ow to edit, run, and test python code
ntroducing the Anaconda distribution of Python.
Using Jupyter notebooks.
Series and Dataframes
Loading data using Pandas
NumPy and SciPy
Visualizing data with matlibplot
Doing Data Science with Scikit-learn
Building a Classifier
Big Data With PySpark
Introduction to Spark and PySpark
Using the Spark framework for Big Data
Using MLLib or Data Science in PySpark