What are the ethical considerations regarding the privacy and control of consumer information and big data, especially in the aftermath of recent large-scale data breaches?

This course provides a framework to analyze these concerns as you examine the ethical and privacy implications of collecting and managing big data. Explore the broader impact of the data science field on modern society and the principles of fairness, accountability and transparency as you gain a deeper understanding of the importance of a shared set of ethical values. You will examine the need for voluntary disclosure when leveraging metadata to inform basic algorithms and/or complex artificial intelligence systems while also learning best practices for responsible data management, understanding the significance of the Fair Information Practices Principles Act and the laws concerning the "right to be forgotten."

This course will help you answer questions such as who owns data, how do we value privacy, how to receive informed consent and what it means to be fair.

Data scientists and anyone beginning to use or expand their use of data will benefit from this course. No particular previous knowledge needed.

This is your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required.

The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data. Predicting future trends and behaviors allows for proactive, data-driven decisions. During the class learners will acquire new skills to apply predictive algorithms to real data, evaluate, validate and interpret the results without any pre requisites for any kind of programming. Participants will gain the essential skills to design, build, verify and test predictive models.

You Will Learn
• How to design Data Science workflows without any programming involved
• Essential Data Science skills to design, build, test and evaluate predictive models
• Data Manipulation, preparation and Classification and clustering methods
• Ways to apply Data Science algorithms to real data and evaluate and interpret the results

What you will learn
Understand Python language basics and how they apply to data science.
Practice iterative data science using Jupyter notebooks on IBM Cloud.
Analyze data using Python libraries like pandas and numpy.
Create stunning data visualizations with matplotlib, folium, and seaborn.
Build machine learning models using scipy and scikitlearn.
Demonstrate proficiency in solving real life data science problems.

What you will learn
Fundamental R programming skills
Statistical concepts such as probability, inference, and modeling and how to apply them in practice
Gain experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
Become familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
Implement machine learning algorithms
In-depth knowledge of fundamental data science concepts through motivating real-world case studies

Gain an overview of data science fundamentals.

What you will learn
Learn about various tools used by Data Scientists and become experienced in using some of them like Jupyter notebooks.
Develop an understanding of the key steps involved in tackling a data science problem.
Learn to follow a methodology to think and work like a Data Scientist.
Write SQL to query databases and explore relational database concepts.
Complete hands-on labs and projects to apply their newly acquired skills and knowledge.