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Data Science Principles from Harvard Business School Online

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Duration

4 Weeks

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Course Mode

Self Study

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Assignments

Included

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Accreditation

Available

Program Overview

World-class faculty.
Edge-of-your-seat online learning.
Global peer collaboration and networking.
Real-world, case-based learning.

Pre-requisites

Our easy online application is free, and no special documentation is required. All applicants must be at least 18 years of age, proficient in English, and committed to learning and engaging with fellow participants throughout the course. We confirm enrollment eligibility within one week of your application.

What you will learn?

Understand the modern data science landscape and technical terminology for a data-driven world.
Recognize major concepts and tools in the field of data science and determine where they can be appropriately applied.
Appreciate the importance of curating, organizing, and wrangling data.
Explain uncertainty, causality, and data quality—and the ways they relate to each other.
Predict the consequences of data use and misuse and know when more data may be needed or when to change approaches.

About this Specialization

Data Science Principles is a Harvard Online course in collaboration with Harvard Business School Online that gives you an overview of data science with a code- and math-free introduction to prediction, causality, data wrangling, privacy, and ethics.
 
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Syllabus

Module 1: Data 101    

Case Studies

  • Flu Detection

Takeaways

  • Explain why data collection is important

  • Identify factors that may affect data quality

  • Recognize that not all data is numerical

  • Explain how the organization of data can affect the information you are able to extract from it

Key Exercises

  • List sources of data

  • Discuss what can be done with data

  • Categorize data by various factors

  • Determine whether data is high-quality or not


Module 2: Predictions and Recommendations 

Case Studies

  • Predicting Sepsis

Takeaways

  • Understand the basic structure of a predictive algorithm

  • Identify where human decisions shape predictive systems

  • Evaluate the success of a predictive system

Key Exercise

  • Examine how weather forecasts work

  • Use data to create a prediction

  • Sort types of training data

  • Simulate a predictive system


Module 3: Cause and Effect   

Key Exercise

  • The Google Tax

Takeaways

  • Explain why it is important to establish causal relationships

  • Identify barriers to establishing causal relationships in a variety of settings

  • Identify why randomization can help establish a causal relationship but also create other problems

Key Exercises

  • Classify relationships based on correlation or causation

  • Examine the relationship between variables

  • Identify potential common causes for correlated events


Module 4: Data Governance and Privacy         

Case Studies

  • Privacy and Facial Recognition

Takeaways

  • Explain why data privacy is important

  • Describe what can constitute a violation of privacy

  • Critique existing privacy policies

  • Create a set of ethical tenets to guide data work at their own organizations

Key Exercises

  • Formulate data privacy guidelines

  • Discuss the risks of data re-identification

  • Evaluate existing data privacy policies for ethics


Module 5: Beyond the Spreadsheet

Case Studies 

  • Burning Glass and Text Data

Takeaways

  • Identify sources of non-numerical data

  • Explain why it would be useful to use non-numerical data

  • Describe the differences in approach for supervised and unsupervised learning

  • Identify use cases for neural networks

Key Exercises

  • Perform a sentiment analysis

  • Determine what types of data an algorithm cannot read

  • Examine how computers intake visual and audio data

  • Experiment with facial recognition

 

Module 6: Data Science Ecosystems  

Case Studies

  • Harvard Link

Takeaways

  • Explain the importance of data transformation and wrangling

  • List the common technologies used within data science ecosystems

  • Describe the connection between data science tasks, software tools, and hardware tools

  • Identify potential sources of bottlenecks in the data science process

Key Exercises

  • Identify and order the lifecycle of data

  • Define what "the cloud" is

  • Estimate the size of various data streams


Module 7: The Road Ahead

Case Studies 

  • Healthcare Prioritization

Takeaways

  • Recognize a problem that an algorithm might be able to solve

  • Recognize the challenges created by using data science tools in ways outside their intended use

  • Identify steps within the data science process that need auditing

Key Exercises

  • Choose types of data to ingest into an algorithm

  • Evaluate the risks of solely using an algorithm to make decisions

  • Discuss how algorithms can reinforce biases

  • Create a set of guidelines to evaluate projects

Institute Information

Established in 2001 and adeptly run by the Sringeri Mutt with the benign blessings of Sri Sri Bharathitheertha Mahaswamigal, the College believes in keeping a proactive approach for the overall development of the students.

ASIET is the first self-financing technical education centre to be awarded the ISO 9001: 2008 certification. Our Alumni are prestigious and many occupy responsible positions across prestigious organizations in India and abroad. The institute is affiliated to the APJ Abdul Kalam Technological University, accredited by NBA and approved by AICTE.

Harvard Business School Online launched as HBX in 2014 to deepen the School’s impact and broaden its reach, all while staying true to the HBS mission: to educate leaders who make a difference in the world. The nuance? Now we could reach those leaders wherever they are—in the world, in their careers, and in their lives. Since, HBS Online has educated 100,000-plus learners from more than 175 countries via our innovative online platform.

About Institution

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Contact Us

+91 85901 36067

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