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 includehands-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.
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:
- Regression analysis
- K-Means Clustering
- Principal Component Analysis
- Train/Test and cross validation
- Bayesian Methods
- Decision Trees and Random Forests
- Multivariate Regression
- Multi-Level Models
- Support Vector Machines
- Reinforcement Learning
- Collaborative Filtering
- K-Nearest Neighbor
- Bias/Variance Tradeoff
- Ensemble Learning
- Term Frequency / Inverse Document Frequency
- Experimental Design and A/B Tests
…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.
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.
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!
¿Cuáles son los requisitos?
- You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
- Some prior coding or scripting experience is required.
- At least high school level math skills will be required.
- This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them.
¿Qué voy a aprender en este curso?
- Extract meaning from large data sets using a wide variety of machine learning, data mining, and data science techniques with the Python programming language.
- Perform machine learning on «big data» using Apache Spark and its MLLib package.
- Design experiments and interpret the results of A/B tests
- Visualize clustering and regression analysis in Python using matplotlib
- Produce automated recommendations of products or content with collaborative filtering techniques
- Apply best practices in cleaning and preparing your data prior to analysis
¿A quién está dirigido?
- Software developers or programmers who want to transition into the lucrative data science career path will learn a lot from this course.
- Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you’ll need some prior experience in coding or scripting to be successful.
- If you have no prior coding or scripting experience, you should NOT take this course – yet. Go take an introductory Python course first.