Software Dataflow Components

Overview

Data science is the synthesis of domain knowledge, statistics, computer science, information technology and, many times, human intuition. This course provides the gate-way to becoming a data scientist. The entry-point to becoming a data scientist is knowledge of various statistical techniques used by data scientists referred to as exploratory data analysis, or EDA. While knowledge of the statistics of EDA is necessary, it is not sufficient.

Today’s data scientists are expected to be programmers or application developers. This course will deliver both the coverage of the necessary EDA statistics and of the programming/visualization environment provided by the Python programming language and the Apache Zeppelin IDE.

This course provides, through lecture and lab, key concepts from statistics that are relevant to data science. 

50% Lecture 50% Hands-on Labs

Target Audience

Individuals needing to be exposed to over 30 essential concepts in statistics needed by Data Scientist. Applications will be written in the Python programming language using the Apache Zeppelin environment.

Prerequisites

Experience with the Python programming language and the Zeppelin IDE is a prerequisite. It is suggested that a student new to programming and new to using Zeppelin first take the course ’Introduction to Python using Zeppelin’ as a prerequisite to this course.

Course Outline

Day 1: Exploratory Data Analysis
Day 2: Data and Sampling Distributions
Day 3: Regression and Prediction
Day 4: Classification and Introduction to Machine Learning
 

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