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macroeconomic_indicators

p1-investigate-datasets

Analysis of datasets from "Gapminder World" to gain insights into countries' environment and economy from their consumption of energy. The analysis covers steps ranging from gathering and wrangling data to EDA and drawing conclusions.

p2-ab-testing

Analysis of mock A/B Test Results by an e-commerce company. Application of probability, hypothesis testing, sampling distribution, two-sample z-test, and logistic regression to determining whether the company should implement the new web page it developed to increase users' conversion rate

p3-analyze-tweet-data

Wrangling and analysis of Tweets from WeRateDogs (@dogrates) with Python in Jupyter Notebook. Project focuses on gathering, assessing and cleaning data. Various methods, including Python's Requests and Tweepy packages for performing a GET Request and querying Twitter API, were used to collect Tweets and relevant data available online.

p4-prosper-loan-data-visualization

Exploratory and explanatory data analysis of Prosper loan data with Python in Jupyter Notebook for demonstrating the significance of data visualization techniques. Visuals are created with matplotlib and seaborn throughout data analysis process from systematically exploring the dataset to deriving insights and communicating key findings.

pdsnd_github

Python code for analyzing bike-share data in Chicago, New York City, and Washington. Computes and displays descriptive statistics based on user-input in Terminal.

udemy-dbt-course