Hello 👋,

This repository hosts all projects/assignments completed by Neetin Verma as part of my Springboard Data Science Career Track program 2020/2021 using different python libararies used to solve different Data Science problems. I have a passion for learning and sharing my knowledge.

Table of Contents

Data Acquisition:

1. SQL queries : Answer questions using a country club database

https://github.com/neetinds/Springboard/blob/master/SQL_Mini_Pro/SQLMiniProject.sql

2. API & Requests package: Answer questions about Basilea Pharmaceutica AG (ticker PK5_UADJ) stock on the Frankfurt Stock Exchange using the Quandl API

https://github.com/neetinds/Springboard/blob/master/api_miniproject/api_data_wrangling_mini_project.ipynb

Statistics:

3. Experimental design & Hypothesis testing & Normality test

Frequentist Inference Part 1: Sampling for the Normal distribution, sampling distributions, Central Limit Theorem, and confidence intervals
https://github.com/neetinds/Springboard/blob/master/frequentist_inference/dataFrequentist%20Inference%20Case%20Study%20-%20Part%20A.ipynb

4. Frequentist Inference Part 2: Frequentist hypothesis testing using a hospital's medical charges dataset

https://github.com/neetinds/Springboard/blob/master/frequentist_inference/dataFrequentist%20Inference%20Case%20Study%20-%20Part%20B.ipynb

Projects: Ski Resort Mini Project:

5. Data Wrangling

https://github.com/neetinds/Springboard/blob/master/Ski_Resort/Step%20Two%20-%20Data%20Wrangling/02_data_wrangling.ipynb

6. Exploratory Data Analysis

https://github.com/neetinds/Springboard/blob/master/03_exploratory_data_analysis.ipynb

7. Preprocessing & Training

https://github.com/neetinds/Springboard/blob/master/Ski_Resort/Step%20Four%20-%20Preprocessing%20%26%20Training/04_preprocessing_and_training.ipynb

8. Modeling

 https://github.com/neetinds/Springboard/blob/master/Ski_Resort/Step%20Five%20-%20Modeling/05_modeling.ipynb

9. London Borough: Which boroughs of London have seen the greatest increase in housing prices, on average, over the last two decades?

    Data Science projects generally adhere to the four stages of Data Science Pipeline:

1. Sourcing and loading

2. Cleaning, transforming, and visualizing

3. Modeling

4. Evaluating and concluding

Notebook:

https://github.com/neetinds/Springboard/blob/master/London%20Boroughs/London%20Boroughs%20Notebook.ipynb

10. Generating HTML Report: Using Pandas Profiling on NASA Meteorites Data.

https://github.com/neetinds/Springboard/blob/master/meteorites.ipynb

Output HTML Report:

https://github.com/neetinds/Springboard/blob/master/example.html