Data Science Fundamentals with Python and SQL

IBM via Coursera Specialization

Go to class Write review

Details

Go to class

Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

26 weeks long, 4 hours a week
Level

Beginner

Found in

Overview

Class Central Tips

Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field. The specialization consists of 5 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets. Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper in Data Science and work on more advanced Data Science projects. No prior knowledge of computer science or programming languages required. This program is ACE® approved—when you complete, you can earn up to 8 college credits, the equivalent of 2 associate degree-level courses.

Syllabus

Course 1: Tools for Data Science
- Offered by IBM Skills Network. What are some of the most popular data science tools, how do you use them, and what are their features? In ... Enroll for free.

Course 2: Python for Data Science, AI & Development
- Offered by IBM Skills Network. Kickstart your learning of Python for data science, as well as programming in general, with this ... Enroll for free.

Course 3: Python Project for Data Science
- Offered by IBM Skills Network. This mini-course is intended to for you to demonstrate foundational Python skills for working with data. The ... Enroll for free.

Course 4: Statistics for Data Science with Python
- Offered by IBM Skills Network. This Statistics for Data Science course is designed to introduce you to the basic principles of statistical ... Enroll for free.

Course 5: Databases and SQL for Data Science with Python
- Offered by IBM Skills Network. Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language ... Enroll for free.

Courses

3 reviews
4 weeks long, 20 hours worth of material
View details

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, JupyterLab, RStudio IDE, Git, GitHub, and Watson Studio. 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 Skills Network 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 Watson Studio and demonstrate your proficiency preparing a notebook, writing Markdown, and sharing your work with your peers.
1 review
6 weeks long, 37 hours worth of material
View details

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.
2 reviews
5 weeks long, 21 hours worth of material
View details

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

This course will take you from zero to programming in Python in a matter of hours—no prior programming experience necessary! You will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills.

By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python. You’ll gain a strong foundation for more advanced learning in the field, and develop skills to help advance your career.

This course can be applied to multiple Specialization or Professional Certificate programs. Completing this course will count towards your learning in any of the following programs:

IBM Applied AI Professional Certificate

Applied Data Science Specialization

IBM Data Science Professional Certificate

Upon completion of any of the above programs, in addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing your expertise in the field.