Applied Data Science with R - Data Science Interview Handbook

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All LessonsFree Lessons (7)

Are You Ready to Become a Data Scientist?

Data Science and YOU!Data Science Process Pipeline Advancements in Data Science

Python Basics

Introduction to Python Variables Decision Making Loops Functions List and Tuple Dictionary Classes and Methods

Python Libraries

NumPy SciPy Pandas Data Visualization Scikit-learn TensorFlow

More Data Science Tools

Cloud ML Engines

Data Structures and Algorithms - I

Why Data Structures and Algorithms are Important Array Linked List Stack Queue Trees Hash Tables

Data Structures and Algorithms - II

Greedy Algorithms Divide and Conquer Backtracking Dynamic Programming

Statistics and Probability

Data Exploration Correlation Basics of Probability Conditional Probability Random Variable Normal and Binomial Distribution

Feature Engineering

The Need for Feature Engineering Numerical Features Categorical Features Date and Time Features Missing Data Putting Everything Together!

Basics of Machine Learning

Types of ML Problems Measuring ML Model Performance Improving ML Model Performance

Regression

Simple Regression Multiple Regression Regularized Regression Nonparametric Regression Regression Model Assessment

Classification

Linear Classifiers Logistic Regression Naïve Bayes Decision Trees Random Forest Adaboost Classification Model Assessment

Unsupervised Learning

Nearest Neighbors KMeans Clustering Probabilistic Clustering Hierarchical Clustering

Advanced Topics in Machine Learning

Neural Network and Deep Learning Issues in Deep Learning Recommendation Engines Natural Language Processing

Conclusion

This is The Beginning!Mega Quiz

Applied Data Science with R

Practice Mock Interview