Data Analyst Nanodegree is a condensed course that includes all aspects of data analysis. Since I was dealing with a lot of data for my PhD thesis, I enrolled into this nanodegree. It was the most challenging course that I followed and required a big effort to succesfully graduate.

This Nanodegree has nine parts and seven projects.

##### Part 1 - Welcome to Nanodegree

Orientation part, to explain how the Nanodegree works.

##### Part 2 - Analyze Chopstick Length

This part gives an overview of a data study, and uses a discussion about chopstick lengths as an example.

###### Project: Analyze Chopstick Length

##### Part 3 - Statistics

This is a comprehensive part that includes an introduction to statistics.

###### Project: Test a Perceptual Phenomenon

##### Part 4 - Intro to Data Analysis

In this part a curated dataset is given to the student and he is expected to find out some insight by using data analysis processes.

###### Project: Investigate a Dataset

##### Part 5 - Data Wrangling

Creating the dataset is often the hard work for a data analyst. In this chapter, an online data source OpenStreetMap data is used and parsed by the student. It has two alternative projects, one that uses SQL database and one that uses mongoDB. I have done both options.

###### Project: Wrangle OpenStreetMap Data

##### Part 6 - Exploratory Data Analysis

This part includes an overview of EDA and introduces some methods of EDA.

###### Project: Explore and Summarize Data

##### Part 7 - Intro to Machine Learning

This part is an introduction to Machine Learning. It introduces all the basic concepts and the end project requires and understanding of how it works.

###### Project: Identify Fraud from Enron Email

##### Part 8 - Data Visualization

This part is about communicating the results of data exploration. In this part, D3.js and tableau is introduced. I used D3.js in a project later.

###### Project: Make Effective Data Visualization

##### Part 9 - Matrix Math & Numpy Refresher

This part is an optional part, that acts as a refresher for Matrix Math and Numpy.