Bertelsmann 2019Scholarship Content OverviewJune 2019

Objective: provide a technical overview of the 3 scholarship focus areas so that Bertelsmann Program Administrators have a deeper understanding of the material that will be covered in each scholarship track.

Data● Data challenge course overview● Data Analyst Nanodegree program overview

AI● Intro to Deep Learning with PyTorch challenge course overview● Deep Learning Nanodegree program overview

Cloud● Cloud DevOps challenge course overview● Cloud DevOps Nanodegree program overview

Agenda

Data Track

Data Challenge Course

Become familiar with key skills for a career in Data Analytics and Data Science

● Descriptive Statistics○ Compute and interpret values like: Mean, Median, Mode, and Standard Deviation○ Compute simple probabilities

● SQL○ Extract and analyze data stored databases○ Join tables together, and perform aggregations

● Python○ Represent and store data using Python data types and variables○ Use conditionals and loops to control the flow of your programs○ Define lambda expressions to quickly create anonymous functions

Data Analyst Nanodegree Program

Problems Addressed

Organizations have more data than they can processData is not useful by itself

Target Personas

Experienced spreadsheet analysts looking to automate cleaning and analysis tasksCurrent Business Analysts

Technology Python - Pandas, Numpy, MatplotlibSQL

Key Skills Interpret Data Visualization, Descriptive Statistics, Data WranglingRelational Databases, Hypothesis Testing

Example Projects

Investigate NYSE DataAnalyze Experiment Results (A/B Testing)

Wrangle and Analyze DataCommunicate Data Findings

Program Details

Program length: 4 months part time (1 term)Practitioner level

DAND Project - Explore Weather Trends

OverviewStudents select a city to compare temperature trends over the last 200 years to global averages

Students willExtract data using SQL queryCalculate Moving Average in Excel or NumpyCreate a line chart in Excel or MatplotlibAnalyze and observe trends in the data

Ex. NYC has always been warmer thanglobal average

DAND Project - Analyze Experiment Results | A/B testing

OverviewAnalyze the results of an A/B experiment for a website and recommend which version of the web page to use

Students willDescribe and clean the dataset using PandasDefine a hypothesis to measure successCalculate p-value to evaluate the hypothesisProvide a recommendation based on the experiment

Deep Learning Track

Deep Learning Challenge Course

Learn the fundamentals of Deep Learning and build your own Neural Network with PyTorch

● Introduction to Deep Learning○ Neural networks and gradient descent○ Build a neural network to predict student admissions

● Introduction to PyTorch○ Build your first neural network with PyTorch to classify images of clothing○ Use Transfer Learning to train a state-of-the-art image classifier

● Neural Networks○ Build Convolutional Neural Networks (CNN) for computer vision applications○ Build Recurrent Neural Network (RNN) natural language processing

● Sentiment Prediction with RNN

● Deploying PyTorch Models with Torch Script

Deep Learning Nanodegree Program

Problem Addresses

Traditional Machine Learning models require high touch parameter tuningSome data concepts are too complex or abstract to represent simply

Target Personas

Software Engineers interested AIMachine Learning Engineers

Technology Python, PyTorch

Program Details

Program length: ~4 months part time Practitioner level

Key Skills Neural Networks, Gradient Descent, CNN, Transfer LearningRNN, LSTM, GAN

Example Projects

Cancer detection in MRI imagesAutomated Video CaptioningImage classificationSentiment Analysis

OverviewBuild a pipeline to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed.

Students willUse transfer learning to detect dogs (and humans) in an image

Write a Convolutional Neural Network from scratch, defining the model architecture and loss function

Train, Validate, and Test your model

DLND Project - Dog Breed Classifier

OverviewStudents are provided a data image set of celebrity faces and are tasked with generating new realistic face images. This model is call a Generative Adversarial Network (GAN)

Students willDefine a CNN in PyTorch

Use the CNN model to create a Discriminator and a Generator function

Tune hyperparameters and train the model

Generate realistic facial images

DLND Project - Generate Faces

Cloud DevOps

Cloud DevOps - Challenge Course

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● Linux Command Line Basics:○ Learn the basics of the command line interface of a Linux server: the terminal and shell

● Version Control with Git & GitHub:○ Learn about the benefits of version control and install the version control tool Git

● Cloud Fundamentals:○ Fundamentals of cloud computing:compute power, security, storage, networking, messaging, and

management services in the cloud with AWS

● Deploy Infrastructure as Code (IAC):○ Setup and deploy your first server using CloudFormation, AWS’ tool for Infrastructure as Code.○ Convert business requirements into infrastructure diagrams ○ Implement virtual private network and subnets to provide inbound and outbound internet access ○ Use routing table to route the traffic within our virtual private cloud○ Deploy a web server into an autoscaling group○ Implement load-balancer to increase the capacity of your app○ Implement security groups and understand the concept of least-privilege as it applies to network

traffic.

Cloud DevOps Engineer Nanodegree

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Problem Addressed

Enterprise software architecture is evolving to rely on cloud technologiesContinuity, performance, and reliability are expectations of any modern software

Target persona

Enterprise DevOps EngineerCloud EngineerSoftware Developer

Technology Technology: AWS: EC2, VPC, S3, DynamoDB, RDS, SQS, SNS, CLI, LambdaTravis CI, Docker, Kubernetes, CloudWatch, Route 53

Key Skills Infrastructure as Code, High Availability, Networking Infrastructure, CI/CD, Containerization, Serverless, Orchestration, Autoscaling

Applications Cloud Migration of existing enterprise applicationsBuilding foundational infrastructure for rapid application development

OverviewThe software development pushed the latest version of Udagram code to an S3 storage bucket.You have been tasked with deploying the application, along with the necessary supporting software into its matching infrastructure.

Students willDeploy web servers for a highly available web app using CloudFormation.

Deploying the networking components, servers, security roles and software from code for automation

Demonstrate Industry Best Practices

Cloud DevOps Project - Deploy a High-Availability Web App

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OverviewThe Data Science team is ready to deploy a Machine Learning Model to production as a microservice to run predictions in real time. You are tasked with deploying the microservice.

Students willTest your project code using linting

Deploy your containerized application using Docker and make a prediction

Create, configure, and deploy a Kubernetes cluster

Upload a complete Github repo with CircleCI to indicate that your code has been tested

CloudDev Project - Serverless Application Development

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