Data Science Career

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  1. Learn Python

An introduction to programming using a language called Python. Learn how to read and write code as well as how to test and “debug” it. Designed for students with or without prior programming experience who’d like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac.

  1. Learn Machine Learning

Google’s fast-paced, practical introduction to machine learning, featuring a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

  1. Introduction to Deep Learning

MIT’s introductory program on deep learning methods with applications in language, and more!

  1. Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

  1. Learn Data Analysis

A focus on several techniques that are widely used in the analysis of high-dimensional data.

  1. Learn Excel and PowerBI

Excel is a Microsoft application that has been in use since 1985. You might be surprised that in addition to simple spreadsheets, Excel can enable modern analytics and business intelligence. In this Learning Path, you will learn how to modernize and empower data conversations within your organization using Excel & Power BI together with MS Teams, and SharePoint. You’ll gain awareness of the business intelligence landscape, technology capabilities, and their roles.

  1. Learn Data Visualization

Learn basic data visualization principles and how to apply them using ggplot2.

  1. Learn PowerBI

Browse this collection by role specialization to find recommended training for developing your skills as a Data Analyst.

  1. Learn Tableau

Discover self-paced, guided learning paths curated by experts.

  1. Learn Statistics

Welcome to the Statistics 101 course, taught by Murtaza Haider, Assistant Professor at Ryerson University. Statistics is one of the most challenging topics to learn, but Murtaza brings a gentle introduction to statistics in practice. Learn about descriptive statistics, variance, probability, correlation, and data visualization. This course ends with a fully-guided statistics exercise exploring the “hot” topic of: do good looking professors get better teaching evaluations? A free trial of SPSS Statistics is included in this course.


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Elyse Y. Robinson Elyse Y. Robinson, an enterprising entrepreneur, is the mastermind behind Taxes and Services, a multifaceted holding company that doubles as her accounting firm. Her ventures encompass an array of innovative projects. One of her key initiatives is Switch Into Tech, a dynamic weekly newsletter that doubles as a platform for advertising monthly career seminars, offering weekly tech-related freebies, and promoting her latest podcast episodes of Nobody Wants To Work Tho. Additionally, Elyse shares her insights through her blog at Data.gal, where she delves into various data-related topics. Her literary contribution includes authoring Nube: Switch Into A Cloud Career, a guide for navigating cloud technology careers. Furthermore, she has established FullTuitionScholarships.org, an invaluable resource aimed at helping individuals avoid college debt. Elyse’s passions extend beyond her businesses; she is deeply enamored with Mexico, has an insatiable appetite for research, and is dedicated to assisting others in transitioning into technology careers.