Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!
If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists in the tech industry - and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them.
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning and data mining techniques real employers are looking for, including:
<ul><li>Regression analysis</li>
<li>K-Means Clustering</li>
<li>Principal Component Analysis</li>
<li>Train/Test and cross validation</li>
<li>Bayesian Methods</li>
<li>Decision Trees and Random Forests</li>
<li>Multivariate Regression</li>
<li>Multi-Level Models</li>
<li>Support Vector Machines</li>
<li>Reinforcement Learning</li>
<li>Collaborative Filtering</li>
<li>K-Nearest Neighbor</li>
<li>Bias/Variance Tradeoff</li>
<li>Ensemble Learning</li>
<li>Term Frequency / Inverse Document Frequency</li>
<li>Experimental Design and A/B Tests</li>
</ul> ...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster.
If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. I think you'll enjoy it!

Introduction

Windows Setup Instructions

Mac Setup Instructions

Linux Setup Instructions

Python Basics, Part 1

Python Basics, Part 2

Python Basics, Part 3

Python Basics, Part 4

Intro to Pandas

Types of Data

Mean, Median, Mode

Using mean, media, and mode in Python

Variation and Standard Deviation

Probability Density Function; Probability Mass Function

Common Data Distributions

Percentiles and Moments

A Crash Course in matplotlib

Data Visualization with Seaborn

Covariance and Correlation

Exercise: Conditional Probability

Exercise Solution: Conditional Probability

Bayes' Theorem

Linear Regression

Polynomial Regression

Multiple Regression

Multi-Level Models

Supervised vs. Unsupervised Learning, Train / Test

Using Train/Test to Prevent Overfitting

Bayesian Methods: Concepts

Implementing a Spam Classifier with Naive Bayes

K-Means Clustering

Clustering People by Income and Age

Measuring Entropy

Windows: Installing Graphviz

Mac: Installing Graphviz

Linux: Installing Graphviz

Decision Trees: Concepts

Decision Trees: Predicting Hiring Decisions

Ensemble Learning

[Activity] XGBoost

Support Vector Machines (SVM) Overview

Using SVM to Cluster People

User-Based Collaborative Filtering

Item-Based Collaborative Filtering

Finding Movie Similarities

Improving the Results of Movie Similarities

Making Movie Recommendations to People

Improving the Recommender's Results

K-Nearest-Neighbors: Concepts

Using KNN to Predict a Rating for a Movie

Dimensionality Reduction; Principal Component Analysis

PCA Example with the Iris Data Set

Data Warehousing; ETL and ELT

Reinforcement Learning

Hands-On with Q-Learning

Understanding a Confusion Matrix

Measuring Classifiers (Precision, Recall, F1, ROC, AUC)

Bias / Variance Tradeoff

K-Fold Cross Validation

Data Cleaning and Normalization

Cleaning Web Log Data

Normalizing Numerical Data

Detecting Outliers

Feature Engineering and the Curse of Dimensionality

Imputation Techniques for Missing Data

Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE

Binning, Transforming, Encoding,  Scaling, and Shuffling

Important Spark Installation Notes

Installing Spark - Part 1

Installing Spark - Part 2

Spark Introduction

Spark and the Resilient Distributed Dataset (RDD)

Introducing MLLib

Decision Trees in Spark

K-Means Clustering in Spark

TF / IDF

Searching Wikipedia with Spark

Using the Spark 2 DataFrame API for MLLib

Deploying Models to Production

A/B Testing Concepts

T-Tests and P-Values

Hands-On with T-Tests

Determining How Long to Run an Experiment

A/B Test Gotchas

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Data Science and Machine Learning with Python - Hands On!

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Data Science and Machine Learning with Python - Hands On!

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