About this Course:
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 and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.
This class requires you to know Python! If you do not have any programming background, you will not be eligible for this course.
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. At the end, you'll be given a final project to apply what you've learned!
This is the first module of the course. There are two modules all together. In this module, we will teach you the fundamental concepts of mathematics including statistics and probability to get you started on your journey to becoming a data scientist.
What am I going to get from this course?
• Develop using iPython notebooks
• Understand statistical measures such as standard deviation
• Visualize data distributions, probability mass functions, and probability density functions
• Visualize data with matplotlib
• Apply conditional probability for finding correlated features
• Use Bayes' Theorem to identify false positives
• Make predictions using linear regression, polynomial regression, and multivariate regression • Understand complex multi-level models
• Use train/test and K-Fold cross validation to choose the right model
• Build a spam classifier using Naive Bayes
• Use decision trees to predict hiring decisions