My solutions to problems on Dataquest
- Python Basics: Files and Loops, Booleans and If Statements
- Challenge: Files, Loops, and Conditional Logic
- List Operations, Dictionaries, Introduction to Functions
- Debugging Errors
- Guided Project: Using Jupyter notebook
- Guided Project: Explore U.S. Births
- Modules and Classes
- Error Handling
- List Comprehensions
- Challenge: Modules, Classes, Error Handling, and List Comprehensions
- Variable Scopes, Regular Expressions
- Dates in Python
- Guided Project: Exploring Gun Deaths in the US
- NumPy, Pandas
- Working with Missing Data
- Guided Project: Analyzing Thanksgiving Dinner
- Line, Bar plots, Scatter plots, Histograms and Box plots
- Guided Project: Visualizing Earnings Based On College Majors
- Storytelling through data visualisation
- Guided Project: Visualizing The Gender Gap In College Degrees
- Conditional plots
- Visualising geographic data
- Data Cleanning: Combining, Analyzing and Visualising
- Guided Project: Analysing NYC High School Data
- Guided Project: Star Wars Survey
- Command Line Basics
- Command Line Python Scripting
- Working with Jupyter Console
- Piping and Redirecting output
- Data Cleaning and Exploration Using Csvkit
- Git and Version Control
- SQL, Summary Statistics, Querying SQLite from Python
- Joins in SQL
- Building Complex Queries
- Table Relations and Normalization
- Guided Project: Designing and Creating a Database
- Using PostgreSQL
- Indexing
- APIs and Web Scraping
- Sampling
- Visualising and Comparing Frequency Distributions
- Guided Project: Ivestigating Fandango Movie Ratings
- Averages and Variability
- Z-Scores
- Guided Project: Finding best Markets to Advertise in
- Probability
- Significance Testing
- Chi-squared tests
- Guided Project: Winning Jeopardy
- KNN, Model Performance
- Hyperparameter Optimization
- Cross Validation
- Guided Project: Predicting Car Prices
- Calculus for Machine Learning: Linear and Non-linear functions, Limits
- Linear Algebra for ML
- Linear Regression for ML, Feature Selection, Gradient Descent
- Guided Project: Predicting House Prices
- Logistic Regression
- K-Means clustering
- Introduction to neural networks
- Guided Project: Predicting the stock Market
- Decision Trees
- Guided Project: Predicting bike rentals
- Machine Learning Project
- Memory and Unicode
- Algorithms
- Binary Search
- Data Structures
- Recursion and Advanced Data Structures
- Guided Project: Investigating Airplane Accidents
- Python Programming: Lambda Functions, Parellel processing
- Kaggle Fundamentals
- Naive Bayes for Sentiment Analysis
- K-Nearest Neighbors
- Introduction to Natural Language Processing
- Introduction to spark, spark dataframes and spark sql
- Guided Project: Transforming Hamlet Data into a dataset