Online courses are a great way to share knowledge with others; that is why I have decided to launch a few courses. The first course is Intro to Azure ML Studio – Regression. This is a smaller course and should take about 2 hours to complete.
Azure ML Studio is a drag-and-drop interface for doing machine learning.
Topics are all based upon Azure ML Studio, and they include:
Getting your first data science job might be challenging, but it’s possible to achieve this goal with the right resources.
Before jumping into a data science career, there are a few questions you should be able to answer:
How do you break into the profession?
What skills do you need to become a data scientist?
Where are the best data science jobs?
First, it’s important to understand what data science is. To do data science, you have to be able to process large datasets and utilize programming, math, and technical communication skills. You also need to have a sense of intellectual curiosity to understand the world through data. To help complete the picture around data science, let’s dive into the different roles within data science.
The Different Data Science Roles
Data science teams come together to solve some of the hardest data problems an organization might face. Each individual of the team will have a different part of the skill set required to complete a project from end to end.
Data scientists are the bridge between programming and algorithmic thinking. A data scientist can run a project from end-to-end. They can clean large amounts of data, explore data sets to find trends, build predictive models, and create a story around their findings.
Data analysts sift through data and provide helpful reports and visualizations. You can think of this role as the first step on the way to a job as a data scientist or as a career path in of itself.
Data engineers typically handle large amounts of data and lay the groundwork for data scientists to do their jobs effectively. They are responsible for managing database systems, scaling data architecture to multiple servers, and writing complex queries to sift through the data.
The Data Science Process
Now that you have a general understanding of the different roles within data science, you might be asking yourself “what do data scientists actually do?”
Data scientists can appear to be wizards who pull out their crystal balls (MacBook Pros), chant a bunch of mumbo-jumbo (machine learning, random forests, deep networks, Bayesian posteriors) and produce amazingly detailed predictions of what the future will hold.
Data science isn’t magic mumbo-jumbo though, and the more precise we get about to clarify this, the better. The power of data science comes from a deep understanding of statistics,algorithms, programming, and communication skills. More importantly, data science is about applying these skill sets in a disciplined and systematic manner. We apply these skill sets via the data science process. Let’s look at the data science process broken down into 6 steps.
Step 1: Frame the problem
Before you can start solving a problem, you need to ask the right questions so you can frame the problem.
Step 2: Collect the raw data needed for your problem
Now, you should think through what raw data you need to solve your problem and find ways to get that data.
Step 3: Process the data for analysis
After you collect the data, you’ll need to begin processing it and checking for common errors that could corrupt your analysis.
Step 4: Explore the data
Once you have finished cleaning your data, you can start looking into it to find useful patterns.
Step 5: Perform in-depth analysis
Now, you will be applying your statistical, mathematical and technological knowledge to find every insight you can in the data.
Step 6: Communicate the results of the analysis
The last step in the data science process is presenting your insights in an elegant manner. Make sure your audience knows exactly what you found.
If you worked as a data scientist, you would apply this process to your work every day.
Before you jump into data science and working through the data science process, there are some things you need to learn to become a data scientist.
Most data scientists use a combination of skills every day. Among the skills necessary to become a data scientist include an analytical mindset, mathematics, data visualization, and business knowledge, just to name a few.
In addition to having the skills, you’ll need to then learn how to use the modern data science tools. Hadoop, SQL, Python, R, Excel are some of the tools you’ll need to be familiar using. Each tool plays a different role in the data science process.
If you’re ready to learn more about data science, take a deeper look at the skills necessary to become a data scientist, and how to get a job in data science, download Springboard’s comprehensive 60-page guide on How to get your first job in data science.
About Springboard: At Springboard, we’re building an educational experience that empowers our students to thrive in technology careers. Through our online workshops, we have prepared thousands of people for careers in data science.
Avrim Blum, John Hopcroft, and Ravindran Kannan wrote the book, Foundations of Data Science (PDF download). It is free and available for download. It can be useful for academic work or in business. It covers topics such as:
Microsoft Build 2019 – This is a huge conference hosted by Microsoft for the developer community. Many of the presentation are available to watch online. Not all are data science/AI related, but many are.
Google I/O 2019 Videos – Google’s big annual conference. Nearly all of the sessions are recorded. There are lots of AI talks and demos.
AWS DeepRacer – Learn reinforcement learning by programming an autonomous car and competing in races.
The exam can be broken down into 4 components: Machine Learning, Azure ML Studio, Azure Products, and Python. Below is a breakdown of the topics I remember from the exam.
These are topics which would be covered in a traditional machine learning course. Here are some of the specific topics I remember.
Evaluation of Linear Regression
Evaluation of Classification
Fisher’s exact test
Deep learning – high-level, what is is for
Neural Networks (RNN vs CNN vs DCN vs GAN)
Azure ML Studio
Azure ML Studio is a major focus of the exam, so you need to be fluent in how to use it. Questions ranged from the basics of how to import data all the way to specifics about certain modules.
missing data questions
There were a number of questions from this category. The question would present you a scenario problem and ask which products would be useful for solving the problem. The questions did not go very deep into any of the products, but you will need to know the purpose of these products.
Azure Machine Learning Service
Blob storage – specifically how to get data in/out
Azure Cognitive Services (high level)
Data Science Virtual Machine
Python was the language of choice for the exam, so focus on it.
Azure Machine Learning SDK for Python
Not on the exam
The following topics were not covered on my exam. The exam questions are pulled from a pool of questions, so it is possible these topics may be cover on a different person’s exam. In any case, these are definitely not major portions of the exam.