Introduction to Data Science

Overview

Courses

• What is Data Science?

  3.0 (1)

  12 hours a week, 4 weeks long

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  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 this course, we will meet some data science practitioners and we will get an overview of what data science is today.

• Data Science Methodology

  (0)

  12 hours a week, 4 weeks long

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  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 course 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 course, 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!
  
  LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

• Tools for Data Science

  1.0 (2)

  12 hours a week, 4 weeks long

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  What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
  
  LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

• Databases and SQL for Data Science

  2.0 (1)

  12 hours a week, 4 weeks long

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  Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. A working knowledge of databases and SQL is a must if you want to become a data scientist.
  
  The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.
  
  The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.
  
  No prior knowledge of databases, SQL, Python, or programming is required.
  
  Anyone can audit this course at no-charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.
  
  LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.