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

Upon successfully completing these courses, you will have the practical knowledge and experience to take start tackling Data Science problems and challenges.

¿Necesito realizar los cursos en un orden específico?

Sí, se recomienda encarecidamente realizar los cursos en el orden sugerido, ya que se basan en conceptos de los cursos anteriores.

¿Obtendré créditos universitarios por completar la Especialización?

No hay créditos universitarios asociados a esta especialización en este momento.

Where can I find more information on ACE credit recommendations?

Please see Coursera’s ACE Recommendations FAQ.

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.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Data Science Fundamentals with Python and SQL Specialization

Data Science Fundamentals with Python and SQL Specialization

Build the Foundation for your Data Science career. Develop hands-on experience with Jupyter, Python, SQL. Perform Statistical Analysis on real data sets.

Instructors: Murtaza Haider

Included with Coursera Plus

Learn more

5 course series

Get in-depth knowledge of a subject

4.6

(3,243 reviews)

Beginner level

Recommended experience

2 months at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

5 course series

Get in-depth knowledge of a subject

4.6

(3,243 reviews)

Beginner level

Recommended experience

2 months at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio
Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy
Statistical Analysis techniques including Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression
Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables

Overview

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® recommended—when you complete, you can earn up to 8 college credits.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

55 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 - 5 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. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Extracting and graphing financial data with the Pandas data analysis Python library
Generating visualizations and conducting statistical tests to provide insight on housing trends using census data
Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

Tools for Data Science

Course 118 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, R Programming, GitHub, Git (Version Control System), Machine Learning, Data Visualization Software, Data Science, Development Environment, IBM Cloud, Cloud Computing, Version Control, Python Programming, Computer Programming Tools, Open Source Technology, Query Languages, R (Software), Other Programming Languages, Big Data, and Statistical Programming

Python for Data Science, AI & Development

Course 225 hours4.6 (42,562 ratings)View course

What you'll learn

Develop a foundational understanding of Python programming by learning basic syntax, data types, expressions, variables, and string operations.
Apply Python programming logic using data structures, conditions and branching, loops, functions, exception handling, objects, and classes.
Demonstrate proficiency in using Python libraries such as Pandas and Numpy and developing code using Jupyter Notebooks.
Access and extract web-based data by working with REST APIs using requests and performing web scraping with BeautifulSoup.

Skills you'll gain

Python Programming, Pandas (Python Package), Data Structures, Web Scraping, NumPy, Application Programming Interface (API), Data Manipulation, JSON, Object Oriented Programming (OOP), Data Processing, Scripting, Restful API, Automation, Data Import/Export, Programming Principles, Computer Programming, Data Analysis, and Jupyter

Python Project for Data Science

Course 38 hours4.5 (4,779 ratings)View course

What you'll learn

Play the role of a Data Scientist / Data Analyst working on a real project.
Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis.
Apply Python fundamentals, Python data structures, and working with data in Python.
Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook.

Skills you'll gain

Python Programming, Data Manipulation, Data Analysis, Web Scraping, Matplotlib, Pandas (Python Package), Data Processing, Jupyter, Data Collection, Dashboard, and Data Science

Statistics for Data Science with Python

Course 414 hours4.5 (447 ratings)View course

What you'll learn

Write Python code to conduct various statistical tests including a T test, an ANOVA, and regression analysis.
Interpret the results of your statistical analysis after conducting hypothesis testing.
Calculate descriptive statistics and visualization by writing Python code.
Create a final project that demonstrates your understanding of various statistical test using Python and evaluate your peer's projects.

Skills you'll gain

Statistical Hypothesis Testing, Probability, Probability & Statistics, Correlation Analysis, Regression Analysis, Probability Distribution, Descriptive Statistics, Statistical Analysis, Matplotlib, Statistics, Data Analysis, Scientific Visualization, Data Visualization, Pandas (Python Package), Data Science, Statistical Methods, Exploratory Data Analysis, and Jupyter

Databases and SQL for Data Science with Python

Course 518 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), Databases, Data Analysis, Jupyter, Relational Databases, Data Manipulation, Query Languages, Transaction Processing, Python Programming, and Stored Procedure

Earn a career certificate

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

Build toward a 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.

Learn more

Instructors

Murtaza Haider

IBM

3 Courses52,542 learners

Romeo Kienzler

IBM

10 Courses792,755 learners

Joseph Santarcangelo

IBM

36 Courses2,185,735 learners

Rav Ahuja

IBM

56 Courses4,347,424 learners

Offered by

IBM

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Frequently asked questions

The specialization requires 36-48 hours of effort to complete. Working 10-12 hours a week, it can be completed within 1-2 months. Working 2-3 hours a week it can be completed in 4-6 months.

Just basic computer literacy and willingness to self-learn online. No prior knowledge of computer science or programming languages required.

It is recommended that you complete the first 2 courses in order before the remaining courses.

Yes. The IBM Data Science Fundamentals with Python and SQL Specialization recently secured a credit recommendation from the American Council on Education's (ACE) Credit Recommendation, which is the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 8 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.

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credlybadge, which contains the ACE®️credit recommendation. Once claimed, you will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed.

Data Science Fundamentals with Python and SQL Specialization

What you'll learn

Overview

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise