University of London
IBM
Data Science Foundations Specialization
5,589 enrolled
University of London
IBM

Data Science Foundations Specialization

Unlock Academic & Career Success with Data Science. Build the foundational knowledge and hands-on skills you need to forge new career opportunities, with no technical experience required.

Included with Coursera Plus

Get in-depth knowledge of a subject

(292 reviews)

Beginner level
No prior experience required
Flexible schedule
3 months at 10 hours a week
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject

(292 reviews)

Beginner level
No prior experience required
Flexible schedule
3 months at 10 hours a week
Earn a career credential
Share your expertise with employers

Overview

  • Foundational knowledge and practical understanding of data science that unlocks academic and career opportunities

  • Basic hands-on skills in Python, R, SQL, and tools like GitHub and Jupyter Notebooks, including their essential features and uses in data science

  • Foundational data science processes, including data collection, simple model building, and algorithm concepts using flowcharts and pseudocode.

  • Basic data analysis with Python, using libraries like Pandas and Numpy, creating simple dashboards, and working with clustering algorithms.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English
117 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 University of London

Specialization - 8 course series

What you'll learn

  • In this course you learn how Data Science is applied in the real world, what we mean by data, and what we mean by machine learning.

Skills you'll gain

Category: Machine Learning
Category: Unsupervised Learning
Category: Data Science
Category: Data Analysis
Category: Applied Machine Learning
Category: Machine Learning Algorithms

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 Analysis
Category: Data Storage
Category: Analytics
Category: Data-Driven Decision-Making
Category: Data Literacy
Category: Artificial Intelligence
Category: Data Mining
Category: Data Management

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: Jupyter
Category: R Programming
Category: Machine Learning
Category: Application Programming Interface (API)
Category: GitHub
Category: Git (Version Control System)
Category: Data Science
Category: Data Visualization Software
Category: Collaborative Software
Category: Data Management
Category: Open Source Technology
Category: Restful API
Category: Data Visualization
Category: Query Languages
Category: Python Programming
Category: Computer Programming Tools
Category: Cloud API
Category: Data Analysis Software
Category: IBM Cloud
Category: Statistical Programming

What you'll learn

  • In this course you will learn the history of algorithms, discretisation and pseudocode and Euclidean algorithm in pseudocode.

Skills you'll gain

Category: Pseudocode
Category: Computer Science
Category: Algorithms
Category: Data Structures
Category: Computational Thinking
Category: Program Development

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

Category: Pandas (Python Package)
Category: Web Scraping
Category: Python Programming
Category: Data Structures
Category: NumPy
Category: Object Oriented Programming (OOP)
Category: Data Manipulation
Category: Programming Principles
Category: Application Programming Interface (API)
Category: Computer Programming
Category: Data Import/Export
Category: Jupyter
Category: Data Collection
Category: Data Processing
Category: Data Analysis

What you'll learn

  • In this course you will engage in a variety of mathematical and programming exercises while completing a data clustering project.

Skills you'll gain

Category: NumPy
Category: Data Manipulation
Category: Scatter Plots
Category: Pandas (Python Package)
Category: Data Analysis
Category: Unsupervised Learning
Category: Python Programming
Category: Descriptive Statistics
Category: Machine Learning Algorithms
Category: Data Visualization Software
Category: Statistical Analysis
Category: Statistics
Category: Jupyter
Category: Plot (Graphics)

What you'll learn

  • In this course you will tackle a prediction problem: forecasting the number of bicycles that will be rented on a given day.

Skills you'll gain

Category: Predictive Modeling
Category: Regression Analysis
Category: Correlation Analysis
Category: Data Collection
Category: Exploratory Data Analysis
Category: Data Science
Category: Probability & Statistics
Category: Time Series Analysis and Forecasting
Category: Forecasting
Category: Statistical Modeling

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

Category: Python Programming
Category: Data Manipulation
Category: Data Analysis
Category: Dashboard
Category: Web Scraping
Category: Data Science
Category: Data Structures
Category: Jupyter
Category: NumPy
Category: Pandas (Python Package)
Category: Interactive Data Visualization
Category: Data Processing
Category: Data Capture

Earn a career certificate

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

Instructors

Romeo Kienzler
IBM
10 Courses773,260 learners
Robert Zimmer
University of London
5 Courses13,715 learners
Joseph Santarcangelo
IBM
35 Courses2,069,496 learners
Alex Aklson
IBM
21 Courses1,303,572 learners
Rav Ahuja
IBM
56 Courses4,044,616 learners
Azim Hirjani
IBM
1 Course286,156 learners
Aije Egwaikhide
IBM
6 Courses734,289 learners

Offered by

IBM

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