Data Science for Non-Data Scientists

Duration (in days): 

3

Description:

Most enterprises have a lot of data, but don't fully utilize the knowledge present in the data. 

This course aims at getting engineers and other college graduates to understand the opportinuties in the data and also some of the best tools to process the data.

We will start with some fundamental math and statistics and move to contemporary tools and algorithms.

Objectives:

  • Understand the value in your data

  • Understand fundamental (high-school level) math required to understand machine learning and fundamental data science

  • Learn how to covert domain models into useful input models for machine learning

  • Learn to use some of the contemporary tools (e.g., Spark MachineLearning, Tensorflow, Various Python libraries)

  • Learn some of the most common machine learning algorithms

Prerequisites:

Most enterprises have a lot of data, but don't fully utilize the knowledge present in the data. 

This course aims at getting engineers and other college graduates to understand the opportinuties in the data and also some of the best tools to process the data.

We will start with some fundamental math and statistics and move to contemporary tools and algorithms.

Audience

Software engineers, data engineers, software architects, and technical minded managers

Outline

Introduction

  • What is data science?

  • What is machine learning?

  • What data is useful?

  • A few case studies that illustrate the value of data

  • Goals of this course

Introduction to Python

  • Python fundamentals

  • Introduction to NumPy

  • Data manipulation using Pandas

  • Visualization with Mathplotlib

  • First example of machine learning in Python

Introduction to Computational Thinking

  • Optimization problems

  • Graph-theoretic models

  • Stochastic thinking

  • Random walks

  • Monte Carlo simulation

  • Confidence intervals

  • Let's talk statistics

  • Confidence intervals

  • Experimental data

Machine Learning

  • What is machine learning, really?

  • Classes of algorithms

  • Clustering

  • Classification

  • Neural nets

  • Common mistakes

  • Best practices

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