New Online Data Summit Coming Fall 2019

A new online conference focused on cloud data technologies is coming this fall. It is not just a conference or webinar, it will be an interactive online platform. The focus of the event is data in the cloud (migrating, storing and machine learning). You can pre-register for the conference now.

Some of the topics from the summit include:

  • Data Science
  • IoT
  • Streaming Data
  • AI
  • Data Visualization

Here is an excerpt from the website:

The public cloud has drastically changed systems design, enabled microservices, and lowered the barrier to entry for big data & analytics.

Learn from companies which have migrated data platforms from on-premise to the cloud. See how they were redesigned to take advantage of endless storage and compute power.

Immerse yourself with the platforms which make modern Data Science and Machine Learning possible. Join your peers to see how their data platforms knocked down the old barriers and transformed how they work

What: Cloud Data Summit
Where: Online (a new conference platform)
When: October 16-17, 2019
How to pre-register: Register Online

I hope to see you there.

Course Launch – Intro to Azure ML Studio with Regression

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:

  • Linear Regression
  • Linear Correlation
  • Feature Selection
  • Splitting Data
  • Evaluating a model

Use the code BLOGREADER to save 30%.

Getting Your First Job in Data Science

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

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

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

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.

What’s next?

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.


How to get a Data Science Job

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.

Data Science Papers – Summer 2019 edition

Looking for a few academic data science papers to study? Here are a few I have found interesting. The are not all from the past 12 months, but I am including them anyhow.

My Experience Taking Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure

I took and passed DP-100 during the beta period. I recorded a live video talking about my experience. Below is that section of the live video. Also, here are the main topics:

  • Azure ML Studio
  • Machine Learning
  • Python
  • High-level knowledge of Azure Products

Also, if you want a checklist to prepare for the exam, I have created one, it is free.

Data Science News for May 2019

Here is the latest data science news for May 2019.

From Data Science 101

General Data Science

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