# 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