How long does it take to complete this Specialization?

The Specialization consists of 4 courses. Suggested time to complete each course is 3-4 weeks. If you follow recommended timelines, it would take 3 to 4 months to complete the entire Specialization.

What background knowledge is necessary?

This Specialization is intended for learners wanting to build foundational skills in data science. No prior background in data science or programming is required.

Do I need to take the courses in a specific order?

In order to get the most out of this Specialization, it is recommended to take the courses in the order they are listed.

What will I be able to do upon completing the Specialization?

In this Specialization, learners will develop foundational data science skills to prepare them for a career or further learning that involves more advanced topics in data science.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Can I take the course for free?

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Introduction to Data Science Specialization

Introduction to Data Science Specialization

Launch your career in data science. Gain foundational data science skills to prepare for a career or further advanced learning in data science.

Instructors: Romeo Kienzler

Included with Coursera Plus

Learn more

4 course series

Get in-depth knowledge of a subject

4.7

(13,371 reviews)

Beginner level

No prior experience required

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

4 course series

Get in-depth knowledge of a subject

4.7

(13,371 reviews)

Beginner level

No prior experience required

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists
Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio
Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems
Write SQL statements and query Cloud databases using Python from Jupyter notebooks

Overview

Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.

This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.

You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You'll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

In addition to earning a Specialization completion certificate from Coursera, you’ll also receive a digital badge from IBM recognizing you as a specialist in data science foundations.

This Specialization can also be applied toward the IBM Data Science Professional Certificate.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

61 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from IBM

Specialization - 4 course series

All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets and tools like Jupyter, GitHub, and R Studio. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Creating and sharing a Jupyter Notebook containing code blocks and markdown
Devising a problem that can be solved by applying the data science methodology and explain how to apply each stage of the methodology to solve it
Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

What is Data Science?

Course 111 hours4.7 (76,818 ratings)View course

What you'll learn

Define data science and its importance in today’s data-driven world.
Describe the various 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.
Explain why data science is considered the most in-demand job in the 21st century.

Skills you'll gain

Data Science, Big Data, Cloud Computing, Deep Learning, Machine Learning, Digital Transformation, Data Analysis, Artificial Intelligence, Data-Driven Decision-Making, Data Mining, and Data Literacy

Tools for Data Science

Course 218 hours4.5 (30,126 ratings)View course

What you'll learn

Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools
Utilize languages commonly used by data scientists like Python, R, and SQL
Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features
Create and manage source code for data science using Git repositories and GitHub.

Skills you'll gain

Jupyter, GitHub, R Programming, Git (Version Control System), Machine Learning, Data Visualization Software, Version Control, IBM Cloud, Computer Programming Tools, Cloud Computing, Python Programming, R (Software), Other Programming Languages, Statistical Programming, Big Data, Open Source Technology, Data Science, Query Languages, and Development Environment

Data Science Methodology

Course 36 hours4.6 (20,946 ratings)View course

What you'll learn

Describe what a data science methodology is and why data scientists need a methodology.
Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study.
Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study.
Determine appropriate data sources for your data science analysis methodology.

Skills you'll gain

Predictive Modeling, Business Analysis, Data Cleansing, Data Collection, Data Quality, Data Modeling, Feature Engineering, Jupyter, User Feedback, Data Science, Software Development Methodologies, Data Mining, Data Processing, Stakeholder Engagement, Peer Review, Decision Tree Learning, Data Transformation, Data Analysis, and Business Requirements

Databases and SQL for Data Science with Python

Course 418 hours4.7 (22,318 ratings)View course

What you'll learn

Analyze data within a database using SQL and Python.
Create a relational database and work with multiple tables using DDL commands.
Construct basic to intermediate level SQL queries using DML commands.
Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins.

Skills you'll gain

SQL, Pandas (Python Package), Data Analysis, Data Manipulation, Relational Databases, Databases, Jupyter, Transaction Processing, Query Languages, Stored Procedure, and Python Programming

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Get a head start on your degree

When you complete this Specialization, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹

This Specialization has ACE® recommendation. It is eligible for college credit at participating U.S. colleges and universities. Note: The decision to accept specific credit recommendations is up to each institution.

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Instructors

Romeo Kienzler

IBM

10 Courses792,755 learners

Polong Lin

IBM

6 Courses367,906 learners

Alex Aklson

IBM

21 Courses1,342,464 learners

Offered by

IBM

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Frequently asked questions

Upon completion of the program, you will receive an email from Acclaim with your IBM Badge recognizing your expertise in the field. Some badges are issued almost immediately after completion of the badge activities, while others may take 1-2 weeks before they are issued. Once issued, you will receive a notification email from admin@youracclaim.com with instructions for claiming the badge. Learn more about IBM Badges

Data science is the process of collecting, storing, and analyzing data. Data scientists use data to tell compelling stories to inform business decisions. Learn more about what data science is and what data scientists do in the IBM Course, "What is Data Science?"

An understanding of data science and the ability to make data driven decisions is useful in any career, but some careers specifically require a data science background. Some examples of careers in data science include:

- Business Intelligence Analyst

- Data Analyst

- Data Architect

- Data Engineer

- Data Scientist

- Machine Learning Engineer

- Marketing Analyst

- Operations Analyst

- Quantitative Analyst

Yes. The Introduction to Data Science Specialization has secured a credit recommendation from the American Council on Education's (ACE) ACE Credit Recommendation, which is the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 7 college credits for completing the program. This aims to help open up additional pathways to learners who are interested in higher education and prepare them for entry-level jobs.

Introduction to Data Science Specialization

What you'll learn

Overview

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise