This repository contains all of the code, projects and reports that I wrote as I pursued my Udacity - Data Analyst NanoDegree
I even wrote an article on LinkedIn about the same Yes! Udacity's Data Analyst Nano Degree is done. 🎈, so here is an excerpt from it. 🙂
I am extremely ecstatic to announce that I have graduated from Udacity’s Data Analyst Nano Degree Program!!! 🎊
Khushboo mentored me all the way through it, and this certainly would not have been possible without her help! Together we were able to fast track and complete the 4 month Nano Degree in just 2.
6 projects, 50-something quizzes, 2 months, and several notebooks later I was awarded this!
A huge thanks to Khushboo, Udacity, and everyone who helped me along the way.
A bit about the course, the Nano Degree was extremely well structured
- Introduction - I was introduced to Udacity, how the Nano Degree works and how the projects need to be submitted.
- Introduction to Data Analysis - I learned the data analysis process of questioning, wrangling, exploring, analyzing, and communicating data. and how to work with data in Python using libraries like NumPy and Pandas.
- Practical Statistics - I learned how to apply inferential statistics and probability to important, real-world scenarios, such as analyzing A/B tests and building supervised learning models.
- Data Wrangling - I was introduced to the data wrangling process of gathering, assessing, and cleaning data and how to use Python to wrangle data programmatically and prepare it for deeper analysis.
- Data Visualization - I applied sound design and data visualization principles to the data analysis process to use the analysis and visualizations to tell a story with data.
Over the course of the last 2 months, I carefully studied what Data Analysis truly is, how various features affect the outcomes, the types of features, the types of datasets, the problems which we face while working with data in real life, how to solve them and so much more!
Section 3 - the one in the middle proved to be one of the most tedious and difficult sections in the entire Nano Degree - it included several statistical concepts like Confidence Intervals, Probability and Inferential Statistics which felt like getting more than what I bargained for - but after about 2 weeks of persistent studying I was able to complete it.
I understood how NumPy and Pandas, helped to keep things in order, how their utility functions make things very easy. I learned how I was missing out on a whole lot by not using Jupyter notebooks until now. I learned how to use MatPlotLib to create convincing visuals and graphs.
I tried to wrap my head around complicated mathematical concepts - the different measures of Spread, Mean, Median, Mode, Probability and Outcomes, Confidence Intervals, P-Values among other things.
It was an amazing endeavor, and the projects which I created every week, made all the times that I stayed up late studying worth it!
🎉