What is Data Science

Week 1
Week 2
Week 3

Week 1

Learning Objectives:

Define data science and its importance in today’s data driven world.
List some paths that can lead to a career in data science.
Summarize advice given by seasoned data science professionals to data scientists who are just starting out.
Articulate why data science is considered the most in-demand job in the 21st century.
Describe what a typical day in the life of a data scientist looks like.
Define some of the commonly used terms in data science.
Summarize the impact of cloud technologies on data science.
Identify some of the key qualities of a successful data scientist.

Summary:

The typical work day 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.

Week 2

Learning Objectives:

Define Big Data and its distinguishing characteristics, such as velocity, volume, veracity, and value
Describe how Hadoop and other big data tools, combined with distributed computing power, are triggering digital transformation.
List some of the skills required to be a data scientist and analyze big data. List some of the skills required to analyze big data.
Explain what data mining is.
Summarize the importance of establishing goals, data selection, preprocessing, transformation, and storage of data in preparation for data mining.
Explain the difference between deep learning and machine learning.
Describe regression and how it might be used to predict market behavior and trend analysis.

Big Data and Data Mining

Summary:

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.

Deep Learning and Machine Learning

Data Science is the process and method for extracting knowledge and insights from large volumes of disparate data. It involves mathematics, statistical analysis, data visualization machine learning and more. It could use machine learning algorithms and deep learning models. It’s a broad term encompasses the entire data processing methodology.

AI includes everything that allows computers to learn how to solve problems and make intelligent decision.

Both AI and Data Science can involve the use of Big Data.

Summary:

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.

IBM Cloud

Create an account on IBM Cloud

Week 3

Learning Objectives:

Describe the application of data science in healthcare.
Explain how companies can start on their data science journey.
Describe some of the ways in which data is generated by consumers.
Describe how businesses such as Netflix, Amazon, UPS, Google, and Apple are using data generated by their consumers and employees.
Compare some of the qualities that differentiate data scientists from qualities of other data professionals.
Articulate the purpose of the final deliverable of a data science project and the role of storytelling in the final deliverable.
Describe what the final report of a Data Science project should cover and how it should be structured for best results.
Demonstrate your understanding of data science by articulating what data scientists do and what a data science report contains. Evaluate assignments submitted by fellow learners. Award points, provide constructive feedback, and offer ideas and suggestions that fellow learners can apply right away.

Data Science and Business

Reading The Final Deliverable about United States Economic Forecast by Deloitte 2014.

Careers and Recruiting in Data Science

Data Scientists need programming, mathematics, and database skills, many of which can be gained through self-learning.
Companies recruiting for a Data Science team need to understand the variety of different roles Data Scientists can play, and look for soft skills like storytelling and relationship building as well as technical skills.
High school students considering a career in Data Science should learn programming, math, databases, and, most importantly practice their skills.

The Report Structure

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, acknowledgements, references and appendices.
The report should present a thorough analysis of the data and communicate the project findings.

Reading: Structure of A Report

Cover Page
Executive Summary
Table of Contents
Introduction
Methodology
Results
Discussion
Conclusion
Appendix

To be a data scientist writer:

Have you told readers, at the outset, what they might gain by reading your paper?
Have you made the aim of your work clear?
Have you explained the significance of your contribution?
Have you set your work in the appropriate context by giving sufficient background (including a complete set of relevant references) to your work?
Have you addressed the question of practicality and usefulness?
Have you identified future developments that might result from your work?
Have you structured your paper in a clear and logical fashion?

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