Amazon and Coursera have teamed up to create Getting Started with AWS Machine Learning. Topics covered are:

- Machine Learning
- Computer Vision
- NLP
- Amazon Sagemaker

Happy Learning!

Amazon and Coursera have teamed up to create Getting Started with AWS Machine Learning. Topics covered are:

- Machine Learning
- Computer Vision
- NLP
- Amazon Sagemaker

Happy Learning!

A number of new impactful open source projects have been released lately.

- Pythia – from Facebook for deep learning with vision and language, “such as answering questions related to visual data and automatically generating image captions “
- InterpretML – from Microsoft, ” package for training interpretable models and explaining blackbox systems “
- ML framework for Julia – from Alan Turing Institute, MLJ is a machine learning toolbox for Julia
- Plato – a conversational AI platform from Uber

Is the list missing a project released in 2019? If so, please leave a comment.

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.

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.

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.

Recently updated, is the** March 2019 Machine Learning Study Path. **It contains links and resources to learn Tensorflow and Scikit-Learn.

If you are interested in details on the study path and how to best use the resources. There is a livestream on Facebook, Sunday March 17 on the Math for Data Science Facebook page.

**Machine Learning for Kids **is a site for children and teachers to explore machine learning with the Scratch Programming language. It includes numerous lessons and tutorials for building fun programs which incorporate machine learning.

University can be a great way to learn data science. However, many universities are very expensive, difficult to get admitted, or not geographically feasible. Luckily, a few of them are willing to share data science, machine learning and deep learning materials online for everyone. Here is just I small list I have come across lately.

**MIT Deep Learning**– Lecture notes, slides and guest talks about deep learning and self driving cars**Introduction to Artificial Intelligence from UC Berkeley**– lecture notes, slides, homework from the Fall 2018 course**Deep Learning Course from University of Paris-Saclay**– lecture notes and Python code**Stanford Machine Learning**– cheatsheets from Stanford’s CS 229 machine learning course, translated in multiple languages

Do you have any favorite university resources? If so, please leave a comment.

Christopher Bishop, a Technical Fellow at Microsoft Research, has released his textbook** ****Pattern Recognition and Machine Learning** as a free PDF download.

The book is a bit older, published in 2006, but it stills contains some great information. Some of the topics covered include:

- Probability Distributions
- Regression
- Classification
- Neural Networks
- Ensemble Models
- K-Means Clustering
- and many more….

Happy Reading!

%d bloggers like this: