Hey peeps! Here is a course for all the engineering students out there who want to pave their way towards being a data scientist. So, gear up and tighten the seat belts. Let’s get into the world of data science.

In this course you will:

• Meet people who work in data science
• Explore definitions of data science
• Learn about data science in a business context
• Discover some use cases and applications of data science

Syllabus

Module 1 – Defining Data Science

• What is Data Science?
  • Fundamentals of Data Science
  • The Many Paths to Data Science
  • Advice for New Data Scientists

Module 2 – What Data Scientists Do

• A Day in the Life of a Data Scientist
  • Old problems, new problems, Data Science solutions
  • Data Science Topics and Algorithms
  • Cloud for Data Science

Module 3 – Data Science in Business

• Foundations of Big Data
  • How Big Data is Driving Digital Transformation
  • What is Hadoop?
  • Data Science Skills and Big Data
  • Data Scientists at New York University

Module 4 – Use Cases for Data Science

• What is the Difference?
  • Neural Networks and Deep Learning
  • Applications of Machine Learning

Exercise – Computer Vision with IBM Watson

Module 5 – Data Science in Business

• How Data Science is Saving Lives
  • How Companies Should Get Started in Data Science
  • Applications of Data Science

Module 6 – Careers and Recruiting in Data Science

• How Can Someone Become a Data Scientist
  • Recruiting for Data Science
  • Careers in Data Science
  • High School Students and Data Science Careers

MODULE 1:

Learning Objectives

In this module you will:

• Hear from data science professionals to learn what data science is.
• Learn about the many paths to data science.
• Hear from data science professionals as they give advice to anyone who is passionate about data science.
• Learn some statistics about the field of data science, the demand for data scientists, and some of the qualities of excelling data scientists.
• Learn why data science is the sexiest job of the 21st century.

SUMMARY:

In this module, you have learned:

• Data science is the study of large quantities of data, which can reveal insights that help organizations make strategic choices.
• There are many paths to a career in data science; most, but not all, involve a little math, a little science, and a lot of curiosity about data.
• New data scientists need to be curious, judgemental and argumentative.
• Why data science is considered the sexiest job in the 21st century, paying high salaries for skilled workers.

MODULE 2:

Learning Objectives

In this module you will:

• Hear from data scientists as they share with you what a typical day in the life of a data scientist looks like.
• Hear from data scientists as they share with you what tools, algorithms, and technologies they use on a daily basis.
• Hear from data scientists as they try to explain the term “cloud”.
• Learn about data science, data scientists, and how each is defined.

In this module, you have learned:

• The typical workday for a Data Scientist varies depending on what type of project they are working on.
• Many algorithms are used to bring out insights from data. 
• Accessing algorithms, tools, and data through the Cloud enables Data Scientists to stay up-to-date and collaborate easily.

MODULE 3:

In this module, you have learned:

• How Big Data is defined by the Vs: Velocity, Volume, Variety, Veracity, and Value.
• How Hadoop and other tools, combined with distributed computing power,  are used to handle the demands of Big Data.
• What skills are required to analyse Big Data. 
• About the process of Data Mining, and how it produces results.

MODULE 4:

Learning Objectives

In this module you will:

• Hear from Norman White, the Faculty Director of the Stern Centre for Research Computing, at New York University.
• Hear from Norman White as he talks about data science and what skills are required for anyone interested in pursuing a career in this field.
• Hear from Norman White as he explains some of the popular data science tools and algorithms.
• Hear from Norman White as he gives advice to high schools students, in particular, and anyone, in general, who are looking to start a career in data science.
• Learn about data mining, and the steps the comprise the process of mining a given dataset.
• Learn about regression and what questions can be put to regression analysis.

SUMMARY:

In this module, you have learned:

• The differences between some common Data Science terms, including Deep Learning and Machine Learning.
• Deep Learning is a type of Machine Learning that simulates human decision-making using neural networks.
• Machine Learning has many applications, from recommender systems that provide relevant choices for customers on commercial websites, to detailed analysis of financial markets.
• How to use regression to analyze data.

MODULE 5:

Learning Objectives

In this module you will:

• Learn about what companies need to do in order to start with data science.
• Learn about some of the qualities that differentiate data scientists from other professionals.
• Learn about some applications of data science.
• Learn about analytics and what important role data scientists play in this process.
• Learn about story-telling and the importance of an effective final deliverable.
• Learn about the main components of an effective final deliverable.
• Apply what you learned about data science to answer open-ended questions.
• Demonstrate your understanding of the readings to define what data science and data scientist mean.
• Demonstrate your understanding of the readings to answer a question about the final deliverable of data science project.

Summary:

In this module, you have learned:

• Data Science helps physicians provide the best treatment for their patients, and helps meteorologists predict the extent of local weather events, and can even help predict natural disasters like earthquakes and tornadoes.
• That companies can start on their data science journey by capturing data. Once they have data, they can begin analysing it.
• Some ways that data is generated by consumers. 
• How businesses like Netflix, Amazon, UPs, Google, and Apple use the data generated by their consumers and employees.
• The purpose of the final deliverable of a Data Science project is to communicate new information and insights from the data analysis to key decision-makers.

MODULE 6:

Learning Objectives

In this module you will:

• Learn about what companies need to do in order to start with data science.
• Learn about some of the qualities that differentiate data scientists from other professionals.
• Learn about some applications of data science.
• Learn about analytics and what important role data scientists play in this process.
• Learn about story-telling and the importance of an effective final deliverable.
• Learn about the main components of an effective final deliverable.
• Apply what you learned about data science to answer open-ended questions.
• Demonstrate your understanding of the readings to define what data science and data scientist mean.
• Demonstrate your understanding of the readings to answer a question about the final deliverable of data science project.

SUMMARY:

In this module, you have learned:

• The length and content of the final report will vary depending on the needs of the project.
• The structure of the final report for a Data Science project should include a cover page, table of contents, executive summary, detailed contents, acknowledgments, references, and appendices.
• The report should present a thorough analysis of the data and communicate the project findings.