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Statistics for Data Scientists
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. scientists referred to as exploratory data analysis, or EDA. While knowledge of the statistics of EDA is necessary, it is not sufficient. The entry-point to becoming a data scientist is knowledge of various statistical techniques used by data.
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.
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 DFHz course “Introduction to Python using Zeppelin” as a prerequisite to this course.
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.
FORMAT
50% Lecture 50% Hands-on Labs
TEXT BOOK
Practical Statistics for Data Scientists by Peter Bruce and Andrew Bruce.
DURATION
This is a 4 day class when taught on-site with ILT or via web-ex with VILT. It is also offered on a per-module basis for on-line self-enablement via our LMS, Brane.
AGENDA SUMMARY
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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