Udacity Data Analyst Nanodegree

Discover insights from data via Python and SQL.

Skills Acquired (Summary)

Prerequisites

You'll need to install:

• Python (3.x or higher)
• Jupyter Notebook
• Numpy
• Pandas
• Matplotlib
• Seaborn

And additional libraries defined in each project.

Recommended:

• Anaconda

Project Overview

P0: Explore Weather Trends

The first chapter was an introduction to the following projects of the Data Analyst Nanodegree.

First chapter project was about weather trends - it required to apply (atleast) the following steps:

• Extract data from a database using a SQL query
• Calculate a moving average
• Create a line chart

I analyzed local and global temperature data and compared the temperature trends in three german cities to overall global temperature trends. After cleaning the data, I've created a function, which was supposed to handle all the tasks that are needed to plot the data - for example calculating the linear trend and calculating the rolling average. In addition, the function had other various options for the visualization to get various graphs.

Key findings:

• the average global temperature is increasing, with an also increasing tempo
• Berlin is the only city in Germany in this dataset which has a higher average temperature than the global average

P1: Investigate a Dataset (Gapminder World Dataset)

This chapter was all about the data analysis process as whole. From gathering to cleaning, assessing and wrangling to exploring and visualizing the data over the programming workflow and communication was everything included.

This project included therefore all steps of the typical data analysis process. This includes:

• posing questions
• gather, wrangle and clean data
• communicate answers to the questions
• assited through visualizations and statistics.

Out of the project:

This project will examine datasets available at Gapminder. To be more specific, it will take a closer look on the life expectancy of the population from different countries and the influences from other variables. It will also take a look on the development of these variables over time.

What is Gapminder? "Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development." (https://www.gapminder.org/about-gapminder/)

Here we were confronted with the full joy of a real-life dataset: from hard-to-analyze structure, missing, messy, dirty data to real and - after finally being done with data wrangling - the reward of interesting insights.

P2: Analyze A/B Test Results

Following chapter was filled with a lot of information. We talked about: Data Types, Notation, Mean, Standard Deviation, Correlation, Data Shapes, Outliers, Bias, Dangers, Probability and Bayes, Distributions, Central Limit Theorem, Bootstrapping, Confidence Intervals, Hypothesis Testing, A/B Tests, Linear Regression, Logistic Regression and more.. *heavy breathing

To goal of the project in this chapter was to get experience with A/B testing, it's difficulties and drawbacks of it. First of all, we learned what A/B testing is all about - including different metrics like the Click Through Rate (CTR) and how to analyze these metrics properly. And second of all, we learned about the drawbacks like the novelty effect or change aversion.

In the end we brought everything we've learned together to analyze this A/B test properly.

P3: Gather, Clean and Analyze Twitter Data (WeRateDogs™ (@dog_rates))

This chapter was a deep dive into the data wrangling part of the data analysis process. We learned about the difference between messy and dirty data, how tidy data should look like, about the assessing, defining, cleaning and testing process, etc. Moreover, we talked about many different file types and different methods of gathering data.

In this project we had to deal with the reality of dirty and messy data (again). We gathered data from different sources (for example the Twitter API), identified issues with the dataset in terms of tidiness and quality. Afterwards we had to solve these problems while documenting each step. The end of the project was then focused on the exploration of the data.

P4: Communicate Data Findings

The final chapter was focused on proper visualization of data. We learned about chart junk, uni-, bi- and multivariate visualization, use of color, data/ink ratio, the lief factor, other encodings, [...].

The task of the final project was to analyze and visualize real-world data. I chose the Ford GoBike dataset.

License

• Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License