IBM
Introduction to Data Science Specialization
IBM

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.

Romeo Kienzler
Polong Lin
Alex Aklson

Instructors: Romeo Kienzler

91,678 already enrolled

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Get in-depth knowledge of a subject
4.7

(12,830 reviews)

Beginner level
No prior experience required
1 month
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
4.7

(12,830 reviews)

Beginner level
No prior experience required
1 month
at 10 hours a week
Flexible schedule
Learn at your own pace

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

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Taught in English

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Specialization - 4 course series

What is Data Science?

Course 111 hours4.7 (72,640 ratings)

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

Category: Data Science
Category: Big Data
Category: Machine Learning
Category: Deep Learning
Category: Data Mining

Tools for Data Science

Course 218 hours4.5 (29,106 ratings)

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

Category: Data Science
Category: Python Programming
Category: Github
Category: Rstudio
Category: Jupyter notebooks

Data Science Methodology

Course 36 hours4.6 (20,367 ratings)

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

Category: Data Science
Category: Data Analysis
Category: CRISP-DM
Category: Methodology
Category: Data Mining

Databases and SQL for Data Science with Python

Course 420 hours4.7 (20,506 ratings)

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

Category: Python Programming
Category: Cloud Databases
Category: Relational Database Management System (RDBMS)
Category: SQL
Category: Jupyter notebooks

Instructors

Romeo Kienzler
IBM
10 Courses694,960 learners

Offered by

IBM

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Degree credit eligible

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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