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

71,665 already enrolled

Included with Coursera Plus

Get in-depth knowledge of a subject
4.7

(7,840 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

(7,840 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: Machine Learning
  • Category: Jupyter
  • Category: Data Science
  • Category: Data Structures
  • Category: Data Wrangling
  • Category: Data Cleansing
  • Category: Data Transformation
  • Category: Matplotlib
  • Category: Exploratory Data Analysis
  • Category: Pandas (Python Package)
  • Category: Data Manipulation
  • Category: Dashboard

Details to know

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Taught in English

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

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

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

Data Analysis with Python

Course 316 hours

What you'll learn

  • Construct Python programs to clean and prepare data for analysis by addressing missing values, formatting inconsistencies, normalization, and binning

  • Analyze real-world datasets through exploratory data analysis (EDA) using libraries such as Pandas, NumPy, and SciPy to uncover patterns and insights

  • Apply data operation techniques using dataframes to organize, summarize, and interpret data distributions, correlation analysis, and data pipelines

  • Develop and evaluate regression models using Scikit-learn, and use these models to generate predictions and support data-driven decision-making

Skills you'll gain

Category: Regression Analysis
Category: Scikit Learn (Machine Learning Library)
Category: Data Manipulation
Category: Data Analysis
Category: Pandas (Python Package)
Category: Matplotlib
Category: Data Wrangling
Category: Data Pipelines
Category: Exploratory Data Analysis
Category: Predictive Modeling
Category: NumPy
Category: Statistical Analysis
Category: Data Cleansing
Category: Data Import/Export
Category: Feature Engineering
Category: Data Transformation
Category: Data Visualization
Category: Python Programming
Category: Data-Driven Decision-Making

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

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

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 Courses351,088 learners
Joseph Santarcangelo
Joseph Santarcangelo
IBM
35 Courses2,039,000 learners

Offered by

IBM

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