Week One & Two:

Introduction to Data Science

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do, this field is data science. In these sessions, we will get an overview of what data science is today.

Defining Data Science

• What is data science?
• There are many paths to data science
• Any advice for a new data scientist?
• What is the cloud?
• "Data Science: The coolest Job in the 21st Century"

What do data science people do?

• A day in the life of a data science person
• R versus Python?
• Data science tools and technology
•  "Regression"

Data Science in Business

• How should companies get started in data science?
• Tips for recruiting data science people
• "The Final Deliverable"

Use Cases for Data Science

• Applications for data science
•  "The Report Structure"

Data Science People

• Things data science people say
• "What Makes Someone a Data Scientist?"

Week Three & Four:

Data Science Methodology

Learn emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just
passed.

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision-making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at
hand.

This session has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address the question at hand. Accordingly, in this session, you will learn:

• The major steps involved in tackling a data science problem.
• The major steps involved in practicing data science, from forming a concrete business or research problem to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
• How data scientists think!

From Problem to Approach

• Business Understanding
• Analytic Approach

From Requirements to Collection

• Data Requirements
• Data Collection

From Understanding to Preparation

• Data Understanding
• Data Preparation

From Modeling to Evaluation

•  Modeling
• Evaluation

From Deployment to Feedback

• Deployment
• Feedback

Week Five & Six:

Data Science Tools

In these sessions, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations, and how data scientists use these tools today.

With the tools hosted in the cloud, you will be able to test each tool and follow instructions to run simple code in Python or R. To complete the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio on Cloud and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers. 

This hands-on course will get you up and running with some of the latest and greatest data science tools.

You will Learn:

• How to use various data science and data visualization tools hosted on Skills Network Labs
• How to use Jupyter Notebooks including its features and why it's so popular
• Popular tools used by R Programmers including RStudio IDE
• IBM Watson Studio including its features and capabilities
• How to create and share a Jupyter Notebook

Introduction

• Languages of Data Science
• Data Science Tools
• Packages, APIs, Data Sets, and Models
• GitHub
• Jupyter Notebooks and JupyterLab
• RStudio IDE
• Watson Studio

Week Seven & Eight:

Big Data Fundamentals

How big is big and why does big matter and what does Apache Hadoop have to do with it? In this course, you will see the Big Data big picture and you will learn the
the terminology used in Big Data discussions.

Get answers to fundamental questions such as: What is Big Data? How do we tackle Big Data? Why are we interested in it? How does Big Data add value to
businesses?

1. Gain insights on how to run better businesses and provide better services to customers
2. Get recommendations on how to process big data on platforms that can handle the volume, velocity, variety, and veracity of Big Data
3. Learn why Hadoop is a great Big Data solution and why it's not the only Big Data solution

What is Big Data?

• Characteristics of Big Data
• What is the V’s of Big Data?
• The Impact of Big Data

Big Data - Beyond the Hype

• Big Data Examples
• Sources of Big Data
• Big Data Adoption

The Big Data and Data Science

• The Big Data Platform
• Big Data and Data Science
• Skills for Data Scientists
• The Data Science Process

Use Cases

• Big Data Exploration
• The Enhanced 360 View of a Customer
• Security and Intelligence
• Operations Analysis

Processing Big Data

• Ecosystems of Big Data
• The Hadoop Framework

Concluding Remarks!