Application of Data Science Techniques

This 4 week online course will teach you how to analyze data in real-world scenarios using machine learning techniques such as Reinforcement Learning, Computer Vision, Neural Networks, Clustering techniques, and Bayesian classifiers. You will also explore techniques for automating machine learning workflows.

Date

May 13 – June 10, 2024

Duration

4 weeks, 5 hours per week

PRICE

US rate $1,088; Int'l rate $1,000

FUTURE DATES

Aug 19 – Sept 16, 2024 and Nov 11 - Dec 16, 2024

CEUs

2.0 CEUs

Description Goes Here

Course Number

ADSTxB

Description Goes Here

TOPICS COVERED

Future of AI/ML in business
Naïve Bayes Clustering
Time series analysis
AI/ML low code / no code
Reinforcement learning
Demand forecasting
ML vision with Google
NLP – as it relates to reliability, topic modeling
ML in IoT applications
Dimensionality reduction (PCA, auto-encoding), how to find value in a highly dimensional dataset
Imbalanced data sets, how to address more focused on anomaly detection
Best practices for implementing machine learning on Google Cloud

Why this training is relevant

Discover how Data Science skills can unlock the potential of generative AI:

Foundation for Generative AI: Neural networks, especially in natural language processing, underpin generative AI. Knowledge on data science and specifically on Machine Learning (ML) provides an essential understanding for creating generative AI solutions.
Application of MLOps to Generative AI: MLOps practices are vital in the evolving use of generative AI.
Performance Improvement with ML Techniques: Classical ML techniques (data pre-processing, feature extraction, and anomaly detection) enhance generative AI performance.
Development of New Generative AI Models: Classical ML methods like reinforcement learning and Bayesian inference aid in creating generative AI models. These models adapt from feedback and adhere to specified constraints.
Not All Problems Are Solved by Generative AI: Some problems, notably supervised learning types such as regression, still require classical ML approaches.
Importance of Classical ML in Generative AI: Classical ML metrics (e.g., accuracy, precision, recall) gauge performance. Classical ML aids in debugging generative AI models. Identifying crucial features in model predictions using classical ML techniques is crucial.

TARGET AUDIENCE

Data Scientists
Data Analysts
Data Engineers
Business Analysts and staff
BI Developers
Anyone who wants to or needs to understand machine learning/AI techniques for data analysis, forecasting, prediction and algorithmic learning.

PREREQUISITES

Knowledge knowledge of data science principles and fundamental algorithms
Working knowledge of implementing several of the most frequently used AI/ML models
Foundational understanding of data science ethics exercises, knowledge checks and assignments
Data science lifecycle activities and outcomes

LEARNING OUTCOMES

Understand and apply data science principles and algorithms.

Implement some of the most frequently used advanced AI/ML models through hands-on exercises and projects.

Use Python/R AI/ML techniques and libraries by working on exercises and projects that utilize these tools.

Understand the capabilities and utilize use BigQuery, AutoML, and Vertex AI for ML development.

Understand MLOps to maintain machine learning models in production reliably and efficiently.

Grasp the fundamentals of Reinforcement Learning, exploring state-of-the-art algorithms and methodologies, and apply them in practical scenarios to optimize decision-making processes

OTHER CLOUD DATA ANALYTICS TRANSFORMATION COURSES

Upon successful completion of all "Fundamentals of Data" courses, you will be rewarded with a program certificate on Cloud Data Analytics Transformation, in addition to each individual course certificate.

Application of Data Visualization

Application of Data Engineering

Fundamentals of Data Visualization

Fundamentals of Data Engineering

Fundamentals of Data Science

MIT INSTRUCTORS

John Williams

Professor of Information Engineering and Civil and Environmental Engineering at MIT

Abel Sanchez

Executive Director, Research Scientist, Laboratory for Manufacturing and Productivity, MIT

The MIT xPRO Learning Experience

We bring together an innovative pedagogy paired with world class faculty.

Learn by doing

Practice process and methods through labs, simulations, assessments, case studies, and tools.

Learn from others

Connect with an international community of professionals while working on projects based on real-world examples.

Learn on demand

Access all of the content online and watch videos on the go.

Reflect and Apply

Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.

Demonstrate your success

Earn a Professional Certificate from MIT xPRO and Continuing Education Units (CEUs).

Learn from the best

Gain insights from MIT faculty and industry experts and connect with your course team and faculty over discussion forums and live virtual office hours.

HOW TO ENROLL

Boeing employees: All courses on this page are eligible for the Learning Together Program (LTP). Please check your eligibility to participate and then follow these instructions to enroll. Note: All vouchers must be submitted 5 business days prior to the course start date.

Review LTP policy and process details on Worklife > Browse Menu > Career > Learning Together Program.
Identify the MIT xPRO course(s) you would like to enroll in.
Courses may be added to the LTP system within 60 days of the course start date (U.S.), or up to one year in advance (ILHE – International Locally Hired Employee).
Drop Policy: Course refunds are given (no questions asked) through the first Monday of each course. You must cancel your course in the LTP system as well as with MIT xPRO. Follow instructions on Worklife to cancel in LTP system. To drop with MIT, please email support@xpro.mit.edu.
Go to My LTP from LTP Worklife portal. Select Massachusetts Institute of Technology as the school for enrollment. Complete all required fields on the LTP online enrollment.
U.S. employees will populate a tuition Voucher for billing purposes for each course by way of the LTP enrollment.
Employees outside the U.S. will populate a Sponsorship Letter for billing purposes. Please note:
- International Locally Hired Employees (ILHEs) must personally pay upfront (corp card is not permitted) and request reimbursement from LTP.
- Effective October 1, 2023, ILHEs from India will be asked to add 18% to the tuition price representing the tax levy that India is imposing on all online educational programs from US universities.
Create an account at xpro.mit.edu or login with an existing account. Use one of the supported browsers: Chrome, Safari or Firefox.
U.S. employees must upload the Boeing LTP Voucher to xpro.mit.edu/boeing/upload to authorize payment to be made to school. After you successfully upload your Voucher, you will be asked to confirm your course selection.
ILHE participants must complete this form and upload along with your Sponsorship Letter. Note: ILHE participants will need to upload the school's document of tuition cost for each course and the local payment center will process reimbursement.
You will need to submit your certificate of completion to the LTP system to confirm grade requirements are met and to close out each course.

Important Note:

If errors are found on the Vouchers -- for example, the course start date is incorrect -- MIT xPRO will identify the error and request that you resubmit the Voucher with the correct information.

If you have any questions about the enrollment process or if your Voucher is not accepted, please visit the MIT xPRO Support Center. For LTP-related questions, call or submit an electronic ticket via Worklife > Get Support.