Jupyter Notebooks are fantastic tools for learning any technical topic, from basic programming to data science all the way to advanced artificial intelligence. Like a web page, you can read and review learning content; and like a traditional programming environment, Notebooks can be “hands-on”. Notebooks invite you to experiment with the code and data cells alongside the equations and graphs.
But have you ever wished an instructor was sitting right next to you when you’re stuck on something, to “show you how she would solve it?”
Graffiti brings that instructor into the Notebook, and makes Udacity’s new C++ Nanodegree Program an amazing way for software engineers to learn the C++ programming language!
Jupyter Graffiti are recorded, interactive demonstrations that live inside your Notebooks.
Via Jupyter Graffiti, Udacity instructors show you exactly how to solve each problem. They walk you through the content, pointing, selecting, typing and executing code, adding and removing code cells, and more. As a student, you can play these recordings as many times as you want, or not at all, and pause and rewind them to see the instructor write and explain the code again.
Real-world projects are integral to every Udacity Nanodegree program. They become the foundation for a job-ready portfolio to help learners advance their careers in their chosen field. The projects in the Data Engineer Nanodegree program were designed in collaboration with a group of highly talented industry professionals to ensure learners develop the most in-demand skills. Every project in a Nanodegree program is human-graded by a member of Udacity’s mentor and reviewer network. These project reviews include detailed, personalized feedback on how learners can improve their work. Graduates consistently rate projects and project reviews as one of the best parts of their experience with Udacity.
The Project Journey
The projects will take you on a journey where you’ll assume the role of a Data Engineer at a fabricated data streaming company called “Sparkify” as it scales its data engineering in both size and sophistication. You’ll work with simulated data of listening behavior, as well as a wealth of metadata related to songs and artists. You’ll start-, working with a small amount of data, with low complexity, processed and stored on a single machine. By the end, you’ll develop a sophisticated set of data pipelines to work with massive amounts of data processed and stored on the cloud.
Throughout Women’s History Month we’ve introduced you to five incredible women over the past few weeks who have taken the idea of balance to a whole new level; showing us how, no matter what your calendar looks like, it is possible to balance your job, social, and extracurricular activities while pursuing career advancement and new skills.
Rebecca McDowall succeeded in changing careers and landing a new job at a top tech company, Accenture. Today, she is analyzing the healthcare sector, looking for ways to improve the data of today for a better healthcare industry of tomorrow. We had a chance to speak with her and learn more about her journey.
Have you always been interested in data?
In a way, yes. I graduated from university in 2016 with a degree in mathematics and statistics. Initially, I chose math purely based on my interest in statistics; I loved the real-world applicability and how you can find data in relatively any part of life. But, I didn’t love the mathematics portion of the degree and when I graduated and started looking for jobs, I felt pretty lost.
Were you interested in any industry or particular job?
My mother works in healthcare and I always had an awareness of how important, yet stressful her job could be. I had a keen interest in trying to work alongside healthcare, possibly healthcare economics. Though I looked, I couldn’t find any entry-level positions based on my degree, so I ended up taking a job as an audio typist for the histopathology department whilst trying to gain internship experience in healthcare economics
I knew this wasn’t what I wanted to be doing, but I also knew it was a job in the industry I was curious about. After a few months, I became frustrated; it was boring and didn’t offer any true advancements further into the industry. A friend had also shared a website that detailed jobs of today that might be replaced by technology in the future, and my job was one of them. This got me thinking: how was technology evolving and how could I take advantage of emerging skills?
Udacity is excited to announce the release of a new R Programming track within the popular Programming for Data Science Nanodegree program. Now students can choose to learn either Python or R as they begin their journey into data science. Both the Python and R tracks also include courses on SQL, Command Line, and GitHub.
Udacity’s Data Science track begins with programming as it’s an essential skill for most data science and analytics work. In terms of popularity in the data science field, R and Python dwarf most other programming languages, and much has been written comparing the two. In fact, if you Google “r vs. python” you’ll get over 55,000 results! So rather than give just one more opinion, we’ve pulled together some of the key takeaways on the topic to help you make a decision on which language to learn.
For both R and Python, I’ll begin by introducing the programming language then describing some of the most significant advantages to using it.
What is R?
The programming language R was first released by a group of statisticians in 1994 and has since become widely used by statisticians, researchers, and data analysts around the world. It was created “for statisticians, by statisticians,” and has a wide array of built-in functions and third-party libraries enabling data scientists to accomplish tasks at every step of the data science process.
The numbers paint a pretty compelling picture, and all the press articles certainly add to the sense of excitement. Everywhere you look, there’s another story about the incredible demand for skilled data scientists. Just two weeks ago, a story from Bloomberg noted that job listings for data scientists on Indeed.com have increased 75% in just three years. Glassdoor has named Data Scientist the best job in the U.S. three years in a row. IBM is predicting a demand increase of nearly 30% in the next two years.
In short, it is an incredible time to become a Data Scientist.
That’s easier said than done, of course. But that’s where Udacity comes in. No matter where your skills and experience are today, we offer a point-of-entry into the world of data, and at Udacity, every data learning path ends with you being ready for success in the field.
Whether you want to master data science programming with Python and SQL, launch a Data Analyst career, or explore business and predictive analytics, Udacity offers world-class programs, expert instructors, and the opportunity to start building relevant skills right away. We’ve partnered with industry leaders like Tableau, Kaggle, and IBM Watson, to ensure you’re learning the in-demand skills recruiters and hiring managers are looking for.
In this post, I’ll walk you through all the Nanodegree programs in our School of Data Science. I’ll show you what each covers, make clear how they connect, and help you choose the program that’s right for you. In the process, I’ll describe the projects you’ll build, and the skills you’ll learn.
This year, we are honored to celebrate Women’s History Month throughout the month of March and International Women’s Day on March 8 by introducing you to three of our students. In keeping with this year’s International Women’s Day theme of “balance for better,” we hope you find inspiration in each student’s journey, and their ability to balance busy lives with their desire to progress in their career and better the balance of women in tech roles.
Flavia Izquierdo began her career as a software engineer. She excelled in the role, working for a number of exciting companies in Spain and Germany. But, after six years in the industry, she began to feel unfulfilled with her work.
“I just didn’t feel like it was interesting enough and I wanted to change, but my next thought was—what can I do? I’m not that young.”
At the same time, Flavia had to consider what would be more fulfilling? One of her first thoughts: data. Whenever her projects involved working with data, she found herself drawn to the possibilities and conclusions one could draw. She let this interest lead her. After researching data analyst jobs and looking for relevant online learning options for gaining these skills, Udacity’s Data Analyst Nanodegree program stood out.
“It just felt right. The structure, the projects, content…and the fact that you started from the very beginning and built analytical skills from the base all the way up to data wrangling—it seemed like a program that gave you a well-rounded set of skills.”
As her passion for data grew, she realized trying to balance data study and work didn’t feel right for her. She wanted to invest even more time in the program, so took the leap and quit her job as a software engineer. Flavia spent the next six months steadily learning the industry skills she had seen on numerous data analyst job descriptions.
Start Learning According to a recent report by DOMO, the world produces 2.5 million terabytes of data per day. Data is quickly becoming the lifeblood of digital transformation, and companies are scrambling to re-invent themselves as data-driven organizations. That’s why, according to Indeed and Glassdoor, the ratio of data engineer to data scientist job openings is roughly four-to-one.
Companies can’t find enough data engineers to store, organize, and manage their ever-increasing amount of data.
Data engineers are responsible for making data accessible to all the people who use it across an organization. That could mean creating a data warehouse for the analytics team, building a data pipeline for a frontend application, or summarizing massive datasets to be more user-friendly.
Today, we are excited to announce the Data Engineer Nanodegree Program. Students who take this program will learn the technical skills required to become a data engineer. With the launch of this program, anyone with an Internet connection (and the relevant background and skills) will be able to enroll. Companies all over the world are looking for data engineers and our goal is to help anyone who wishes to land a job in the field can do so.