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Fundamentals of Data Science

Learn the fundamentals of data science solutions in the cloud. You will use the Jupyter Notebook paradigm with Google Colaboratory (Colab) to create Python and R data science and data management libraries to implement ML algorithms. Additionally, you will use BigQuery to create ML models for forecasting, including access to public datasets.

Date

May 6 – June 3, 2024

Duration

4 weeks, 5 hours per week

PRICE

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

 

 
FUTURE DATES

Jan 20 - Feb 17, 2025 and May 5 - June 2, 2025

CEUs

2.0 CEUs

Description Goes Here
Course Number

FDSxB

Description Goes Here

TOPICS COVERED

  • SQL basics
  • Exploring datasets
  • Data science notebooks – Jupyter (R and Python)
  • Data processing (R and Python)
  • Basic machine learning regression
  • K-Means
  • Data science ethics
  • Forecasting models
  • Case Study (Baseball, Are Rich Happy?, and UFO)

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

  • Basic understanding of data and connecting to data
  • Basic understanding of data visualization
  • Basic data science principles (data science lifecycle)
  • Google Platform basic navigation skills
  • Python intermediate knowledge
  • Google BigQuery basic knowledge
  • Probability and statistics knowledge

LEARNING OUTCOMES

Understand and apply data science principles and algorithms in various contexts, including creating forecasting models.

Gain proficiency in implementing common AI/ML models using Python/R and relevant libraries by completing hands-on exercises and activities.

understand how to use BigQuery for ML development, exploring, and processing datasets for insightful analysis.

Understand and apply the data science lifecycle.

Other Courses in This Program

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.

Fundamentals of Data Visualization

DVCB

Fundamentals of Data Engineering

DECB

Application of Data Visualization

DVCA

Application of Data Engineering

DECA

Application of Data Science

DSCA

MIT INSTRUCTORS

Cloud_John_Williams

John Williams

Professor of Information Engineering and Civil and Environmental Engineering at MIT

Cloud_Sanchez

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.

  1. Review LTP policy and process details on Worklife > Browse Menu > Career > Learning Together Program.
  2. Identify the MIT xPRO course(s) you would like to enroll in.
  3. 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).
  4. 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.
  5. 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.
  6. U.S. employees will populate a tuition Voucher for billing purposes for each course by way of the LTP enrollment.
  7. 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.
  8. Create an account at xpro.mit.edu or login with an existing account. Use one of the supported browsers: Chrome, Safari or Firefox. 
  9. 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.
  10. 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.
  11. 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.