IBM
Applied Data Science Specialization
IBM

Applied Data Science Specialization

Get hands-on skills for a career in data science. Learn Python, analyze and visualize data. Apply your skills to data science and machine learning.

Dr. Pooja
Joseph Santarcangelo
Saishruthi Swaminathan

Instructors: Dr. Pooja +4 more

70,426 already enrolled

Included with Coursera Plus

Get in-depth knowledge of a subject
4.7

(7,797 reviews)

Beginner level
No prior experience required
Flexible schedule
2 months at 10 hours a week
Learn at your own pace
Build toward a degree
Get in-depth knowledge of a subject
4.7

(7,797 reviews)

Beginner level
No prior experience required
Flexible schedule
2 months at 10 hours a week
Learn at your own pace
Build toward a degree

What you'll learn

  • Develop an understanding of Python fundamentals

  • Gain practical Python skills and apply them to data analysis

  • Communicate data insights effectively through data visualizations

  • Create a project demonstrating your understanding of applied data science techniques and tools

Skills you'll gain

  • Category: Seaborn
  • Category: Jupyter
  • Category: Data Visualization
  • Category: Matplotlib
  • Category: Dashboard
  • Category: Exploratory Data Analysis
  • Category: Data Analysis
  • Category: Data Science
  • Category: Data Cleansing
  • Category: Python Programming
  • Category: Predictive Modeling
  • Category: Data Manipulation

Details to know

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Taught in English
Recently updated!

May 2025

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

What you'll learn

  • Learn Python - the most popular programming language and for Data Science and Software Development.

  • Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.

  • Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

  • Access and web scrape data using APIs and Python libraries like Beautiful Soup.

Skills you'll gain

Category: Data Analysis
Category: Data Pipelines
Category: Scikit Learn (Machine Learning Library)
Category: Data Manipulation
Category: Descriptive Statistics
Category: Regression Analysis
Category: Machine Learning Methods
Category: NumPy
Category: Data Import/Export
Category: Data Cleansing
Category: Exploratory Data Analysis
Category: Data Science
Category: Pandas (Python Package)
Category: Predictive Modeling
Category: Data Visualization
Category: Statistical Analysis
Category: Data Wrangling
Category: Python Programming

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

Data Analysis with Python

Course 315 hours

What you'll learn

  • Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

  • Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

  • Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

  • Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Skills you'll gain

Category: Data Analysis
Category: Box Plots
Category: Scatter Plots
Category: Plotly
Category: Heat Maps
Category: Histogram
Category: Data Presentation
Category: Seaborn
Category: Pandas (Python Package)
Category: Matplotlib
Category: Geospatial Information and Technology
Category: Interactive Data Visualization
Category: Data Visualization
Category: Dashboard
Category: Python Programming
Category: Data Visualization Software

What you'll learn

  • Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story

  • Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble

  • Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps

  • Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library

Skills you'll gain

Category: Web Scraping
Category: Github
Category: Data Analysis
Category: Machine Learning Methods
Category: Data-Driven Decision-Making
Category: Plotly
Category: Data Collection
Category: Data Presentation
Category: Exploratory Data Analysis
Category: Pandas (Python Package)
Category: Data Science
Category: Predictive Modeling
Category: Statistical Modeling
Category: Data Wrangling

What you'll learn

  • Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders 

  • Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation

  • Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors

  • Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model 

Skills you'll gain

Category: Web Scraping
Category: Jupyter
Category: Data Analysis
Category: Pandas (Python Package)
Category: Data Science
Category: Data Manipulation
Category: Matplotlib
Category: Data Processing
Category: Data Collection
Category: Dashboard
Category: Python Programming
Category: Data Visualization Software

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

 
ACE Logo

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. 

Instructors

Dr. Pooja
Dr. Pooja
IBM
4 Courses339,728 learners
Joseph Santarcangelo
Joseph Santarcangelo
IBM
34 Courses1,936,073 learners

Offered by

IBM

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