Introduction to Data Science and Machine Learning -

Introduction to Data Science and Machine Learning

Get a competitive advantage with data science and machine learning

Path to highly demanded jobs
Hands on project based learning
Leads to Microsoft Azure Data Scientist Certification

Overview

Data science is an exciting discipline, which leverages Machine Learning and Artificial Intelligence to enable decision makers to turn raw data into understanding, insight, and actionable options. With the enormous volume and variety of data being created and collected daily, Data Science is one of today’s fastest growing and critically important fields for businesses, organizations, and government. Data Scientists are in demand by both industry and the public sector with robust job growth expected well into the next decade.

Target Audience:

Information Architects, Data Analysts, Statisticians, Developers, Business Intelligence professionals, Business Analysts, Big Data specialists, Coders, Web Developers, learners interested in Predictive Analytics and anyone looking to expand their skills and / or advance their career by learning these valuable and in demand knowledge areas.

Scholarships Available

For scholarship information, click here.

Level 1 – Introduction to Data Science, Data Modeling and Statistics Using Python

Course Description:

This hands on, project-based course is an introduction to Data Science and Machine Learning using the Python programming language. Students will learn the fundamentals of problem solving and statistical algorithms, as well as how to use Jupyter Notebooks within the Anaconda development environment. The course aims to serve as a foundation for building real world applications with Machine Learning capabilities and as a starting point for a career as a well-rounded data practitioner.

See complete course outline, registration information and schedule for level 1 here

Objective:

After taking this class, students are expected to:

Understand fundamentals of the Python programming language and create scripts that interact with data sets and machine learning models,
Interact with data sets in various formats and create meaningful visualizations based on business requirements,
Understand the basics of Python-based machine learning models and when to select the appropriate algorithms based on business requirements,
Gain proficiency with the Anaconda Data Science Platform and Jupyter Notebooks

Course Outline:

Class Introduction and Course Topics Review
Overview with Data Sets, Python and Jupyter Notebooks
Transforming and Visualizing Datasets (Including CSV and JSON)
Implement Data Wrangling Techniques
Introduction to Statistical Analysis
Implement Regression, Classification and Clustering Models

Spring 2024

Location: Online (virtual/remote)
Dates: May 7– June 6 (Tuesday / Thursday evenings)
Time: 6:00 pm to 9:00 pm
Catalogue #: CE-COMP 2272
Class #: 3922
Cost: $ 875.00

Level 2 – Deep Learning

Course Description:

This hands on, project-based course is an extension of the Data Science and Machine Learning Level 1 course with a focus on implementing deep learning algorithms using the Python programming language and the PyTorch and Tensorflow deep learning libraries. Students will implement algorithms to solve problems related to classification, image recognition and natural language processing using the Anaconda and Google Colab development environments. This course will provide strong foundational knowledge for a career as a data scientist and will prepare the student to pass the Microsoft Certification Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

See complete course outline, registration information and schedule for level 2 here

Objective:

After taking this class, students are expected to:

Understand the foundations of how Deep Learning is associated with human brain function
Implement classification models using Deep Learning algorithms
Implement image recognition models using Deep Learning algorithms
Implement natural language processing models using Deep Learning algorithms
Gain additional experience with Jupyter Notebooks and Python
Gain foundational knowledge that can be used to prepare for Microsoft Exam DP-100

Course Outline:

Understanding Deep Learning Fundamentals
Understanding Deep Neural Networks
Implement a Classification Model Using Deep Learning
Understanding Model Loss, Optimizers and Learning Rates
Understanding Convolutional Neural Networks
Understanding Recurrent Neural Networks
Understanding Transfer Learning
Understanding the NLTK Libraries
Implement Deep Learning Models for Image Recognition and Natural Language Processing

Summer 2024

Location: Online (virtual/remote)
Dates: June 11 – July 18 (Tuesday / Thursday evenings – skip 7/2 & 7/4)
Time: 6:00 pm to 9:00 pm
Catalogue #: CE-COMP 2272
Class #: 3923
Cost: $ 875.00

Level 3 – Project Implementation on Azure

Course Description:

This hands on, project-based course is an extension of the Level 1 and Level 2 Data Science and Machine Learning courses and uses the Microsoft Azure Data Science cloud-based platform. This course will implement Data Science and Machine Learning solutions by creating Azure workspaces and resources, utilizing the Azure Machine Learning Studio development environment, and using the Azure Machine Learning SDK and CLI interfaces. This course will provide a strong foundational knowledge for a career as a data scientist and will prepare the student to pass the Microsoft Certification Exam DP-100: Designing and Implementing a Data Science Solution on Azure.

See complete course outline, registration information and schedule for level 3 here

Objective:

After taking this class, students are expected to:

Create Azure ML Workspaces and Related Resources
Use Azure ML Studio to create visually create ML pipelines, build Jupyter Notebooks, and run ML experiments
Use the Azure Python SDK to implement ML projects
Use the Azure CLI to implement ML projects
Optimize and Monitor Azure ML models and experiments
Gain foundational knowledge that can be used to prepare for Microsoft Exam DP-100

Course Outline:

Create Azure ML Workspace Resources (User Interface and CLI)
Create and Manage Azure ML Registries
Explore Azure ML Studio
Configuring ML Workspaces (Compute Resources, Datastores and Datasets)
Set up Git and Azure ML Integration for Source Control
Select Appropriate Azure Storage Resources
Create Azure ML Compute Targets
Implement Azure ML Notebooks
Design and Implement Azure ML Pipelines Using the Visual Designer
Create and Configure Azure ML “No Code” Data Science Experiments
Create and Configure Azure AutoML Experiments
Optimize Models Using Hyperparameter Tuning
Configure and Deploy Models and Monitor Results
Model Evaluation and Responsible AI Guidelines

Summer 2024

Location: Online (virtual/remote)
Dates: July 23– August 22 (Tuesday / Thursday evenings)
Time: 6:00 pm to 9:00 pm
Catalogue #: CE-COMP 2273
Class #: 3924
Cost: $ 875.00

If you enroll in the 3 sections you will receive a discount of $150.

FAQs

What is Data Science?

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision-making.
The goal of “R Programming for Data Science” is to help you learn the most important tools in R that will allow you to do data science. As you progress through this course, you’ll learn how to approach a variety of data science challenges, using the best parts of R.

Why is Data Science Important?

Data is one of the important assets in every organization because it helps business leaders make decisions based on facts, statistical numbers and trends The importance of data science is based on the ability to take existing data that is not necessarily useful on its own and combine it with other data points to generate insights an organization can use to learn more about its customers and audience.

Today’s data science teams are expected to answer many questions. Business demands better prediction and optimization based on real-time insights

With the volume and variety of social, mobile and device data, along with new technologies and tools, data science today plays a broader role than ever before. Business considers data science and AI to be a technology-enabled strategy.

Are there jobs available in Data Science?

The short answer is yes. Data science is one of the fastest growing fields today and is expected to continue into the next decade. As most of the fields are emerging continuously, the importance of data science is increasing rapidly. Data science has influenced various areas. Its effect can be observed in multiple sectors such as the retail industry, healthcare, government, financial and education.

It has become an important part of almost every sector. It provides the best solutions that help to fulfill the challenges of the ever-increasing demand and maintainable future. As the importance of data science is increasing day by day, the need for a data scientist is also growing. If you have the skills, there are jobs available not to mention those currently in technical careers (e.g. programming) climbing the career ladder with additional skills such as a data science practitioner.

What about non-technical or leadership roles in Data Science?

As the growth of data accelerates, so does the importance of data science and the teams of data scientists formed to turn this data into useful information, insight and knowledge. While companies prepare for big data integration, business leaders need to adapt their roles as team leaders for their data science employees. Your data science team should have the expertise to process data with freedom, but business leaders still need to understand the basic structures of what’s happening to create value from that data.

Why is this important for you or your organization? A New Era of Business Leader
Put into context in today’s business environment, there’s no situation where it’s okay to say as the leader, I don’t know what’s going on but my team does and that’s good enough. Yet many business leaders don’t know the most basic principles of data science. Business leaders (managers, directors, executives, vice presidents, etc.) don’t need to know the intimate details of data science processes but as the line between big data and business operations disappear, it’s more important than ever for business leaders to speak (understand) a little data science. This translates into to having some basic foundational knowledge.

Why it’s important to understand the basics:
Data science can be good storytelling but it is still science. Telling a story can often obscure the facts or make links where there aren’t any. Having the foundational knowledge or basic proficiency can help you avoid:

Getting taken – manipulating the data, not telling the whole story, targeted information gaps, all this things could make it easier to coerce or persuade you into a bad decision
Asking the wrong questions – data pulls are only as good as the questions you’re asking. Data must be evaluated regularly and that requires starting with the right question(s).
Replicating bias – data is neutral, but it’s aggregation and results are often the product of our preconceived ideas. Understanding the basics of data science helps you sort our the messiness of data in the real world.

How to Register:

You will need the Class # when speaking with a representative.

Office hours for registration are Monday – Thursday 8:30 a.m. to 7:15 p.m.Friday 8:30 a.m. to 4:30 p.m. (in summer, 9:00 a.m. – 12:00 noon)Saturday 9:00 a.m. to 3:30 p.m. (in summer, closed some Saturdays)

For course questions, please contact:

Romina Ganopolsky, Program Specialist, Professional Development Center: Call 914-606-5685 or email romina.ganopolsky@sunywcc.edu