DataAnalysisPython
Projects for Udacity Data Analyst Nano Degree
Install / Use
/learn @VenessaM/DataAnalysisPythonREADME
Data Analyst Nanodegree
Mu Yuhong Created on 20-May-2019
The repository contains projects for Udacity's Data Analyst Nanodegree.
I took this online course at the end of my MBA journey to advance analytics skills in Python and SQL. This course focuses on Data Wrangling, Data Visualization, A/B Hypothesis Testing, and Regression analysis.
Part 1: Investigate a Dataset -Introduction to Data Analysis
Learn the data analysis process of wrangling, exploring, analyzing, and communicating data. Work with data in Python, using libraries like NumPy and Pandas.
Project - Explore Weather Trends
In this project, you'll get familiar with SQL, and learn how to download data from a database. You’ll analyze local and global temperature data and compare the temperature trends where you live to overall global temperature trends.
Project - Investigate a Dataset
You will choose one of Udacity's curated datasets and investigate it using NumPy and Pandas. Go through the entire data analysis process, starting by posing a question and finishing by sharing your findings.
Investigate a dataset: Investigating European Soccer Database(Draft Version)
Part 2: Practical Statistics
Learn how to apply inferential statistics and probability to real-world scenarios, such as analyzing A/B tests and building supervised learning models.
Project - Analyze A/B Test Results
Analyze the results of an A/B test run by an e-commerce website to decide whether the company should implement new page, keep the old page, or perhaps run the experiment longer to increase user conversion rate.
Selected topics of Practical Statistics
- 2.1 Descriptive Statistics Part 1 and Part 2
- 2.2 Admission Case Study- Learn to ask the right quesiton, as you learn about Simpson's Paradox.
- 2.3 Probability
- 2.4 Binomial Distribution
- 2.5 Conditional Probability
- 2.6 Bayes Rule
- 2.7 Python Probability Practice
- 2.8 Normal Distribution Theory
- 2.9 Sampling Distribution and the Central Limit Theorem
- 2.10 Confidence Intervals
- 2.11 Hypothesis Testing and A/B Tests
- 2.12 Regression
- 2.13 Multiple Linear Regression
- 2.14 Logistic Regression
Part 3: Wrangle and Analyze Data
Learn the data wrangling process of gathering, assessing, and cleaning data. Learn to use Python to wrangle data programmatically and prepare it for analysis.
Project - Wrangle and Analyze Data
Real-world data rarely comes clean. Using Python, you'll gather data from a variety of sources, assess its quality and tidiness, then clean it. You'll document your wrangling efforts in a Jupyter Notebook, plus showcase them through analyses and visualizations using Python and SQL.
Part 4: Data Visualization
Learn to apply visualization principles to the data analysis process. Explore data visually at multiple levels to find insights and create a compelling story.
Project - Communicate Data Findings
You will use Python’s data visualization tools to systematically explore a selected dataset for its properties and relationships between variables. Then, you will create a presentation that communicates your findings to others.
