IBM Introduction to Data Science

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Learning Experience9.2
Content Rating9.1

IBM data science foundation will provide you with the key foundational skills for your career in data science or further advanced learning in the field.

Last updated on March 15, 2021 8:33 pm

About this Course

Launch your career in data science. Gain foundational data science skills to prepare for a career or further advanced learning in data science. Interested in learning more about data science, but don’t know where to start? The IBM Data Science foundation is a 4-course Specialization that 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. 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.

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 you will learn from this course

  • 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.

Syllabus

There are 4 Courses in this IBM Data Science foundation Specialization

Course 1. IBM Data Science foundation: What is 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 this course, we will meet some data science practitioners and we will get an overview of what data science is today.

Course 2. Tools for Data Science

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.

In this course of the IBM Data Science foundation, 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.

Course 3. IBM Data Science foundation: Data Science Methodology

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.

Course 4. Databases and SQL for Data Science with Python

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.

Project for IBM Data Science foundation

You will utilize tools like Jupyter, GitHub, R Studio, and Watson Studio to complete hands-on labs and projects throughout the Specialization. Using new skills and knowledge gained through the program, you’ll also work with real world data sets and query them using SQL from Jupyter notebooks.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

$39.00

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Add to compare
  • Coursera
  • IBM
  • Microdegree
  • Self-paced
  • Beginner
  • 3+ Months
  • Paid Course (Paid certificate)
  • English
  • Python
  • None Pre-requisite
  • Cloud Databases Data Science Data Science with 'Python' SQL for Data Science
Learning Experience
9.2
Content Rating
9.1
PROS: Perfect introduction to data science Gives you a good idea and overview of different tools Step-by-step guide of how to create a data science project The entire program is well structured and has good hands-on assignments.
CONS: Videos need to be updated for changes to Watson Studios Support from IBM on their cloud services should also be improved.

Description

About this Course

Launch your career in data science. Gain foundational data science skills to prepare for a career or further advanced learning in data science. Interested in learning more about data science, but don’t know where to start? The IBM Data Science foundation is a 4-course Specialization that 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. 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.

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 you will learn from this course

  • 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.

Syllabus

There are 4 Courses in this IBM Data Science foundation Specialization

Course 1. IBM Data Science foundation: What is 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 this course, we will meet some data science practitioners and we will get an overview of what data science is today.

Course 2. Tools for Data Science

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.

In this course of the IBM Data Science foundation, 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.

Course 3. IBM Data Science foundation: Data Science Methodology

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.

Course 4. Databases and SQL for Data Science with Python

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.

Project for IBM Data Science foundation

You will utilize tools like Jupyter, GitHub, R Studio, and Watson Studio to complete hands-on labs and projects throughout the Specialization. Using new skills and knowledge gained through the program, you’ll also work with real world data sets and query them using SQL from Jupyter notebooks.

Note: Your review matters

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • Coursera
  • IBM
  • Microdegree
  • Self-paced
  • Beginner
  • 3+ Months
  • Paid Course (Paid certificate)
  • English
  • Python
  • None Pre-requisite
  • Cloud Databases Data Science Data Science with 'Python' SQL for Data Science

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IBM Introduction to Data Science
IBM Introduction to Data Science

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