Skip to content

VenessaM/DataAnalysisPython

Repository files navigation

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.

Explore Weather 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.

Analyze A/B Test Results

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.

Wrangle and Analyze Data

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.

Communicate Data Findings


Cert


Other Resources:

Other Projects(Kaggle)