IBM
IBM Data Science Professional Certificate
IBM

IBM Data Science Professional Certificate

Prepare for a career as a data scientist. Develop in-demand skills and hands-on experience to get job-ready in as little as 5 months. No prior experience required.

Dr. Pooja
Romeo Kienzler
Joseph Santarcangelo

Instructors: Dr. Pooja

321,869 already enrolled

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Earn a career credential that demonstrates your expertise
4.6

(70,747 reviews)

Beginner level
No prior experience required
5 months
Approx. 10 hours a week
Self-paced, no deadlines
Earn degree credit

What you'll learn

  • Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles

  • Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL

  • Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines

  • Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers

Details to know

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Taught in English

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Prepare for a career in Data Science

  • Receive professional-level training from IBM
  • Demonstrate your proficiency in portfolio-ready projects
  • Earn an employer-recognized certificate from IBM
  • Qualify for in-demand job titles: Data Scientist, Junior Data Scientist, Data Architect
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$138,000+
median U.S. salary for Data Science
¹
69,000+
U.S. job openings in Data Science
¹

Get exclusive access to career resources upon completion

  • Soft skills training

    Get free access to IBM’s People and Soft Skills Specialization

  • Resume review

    Improve your resume and LinkedIn with personalized feedback

  • Interview prep

    Practice your skills with interactive tools and mock interviews

  • Career support

    Plan your career move with Coursera’s job search guide

¹Lightcast™ Job Postings Report, United States, 7/1/22-6/30/23. ²Based on program graduate survey responses, United States 2021.

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Professional Certificate - 10 course series

What is Data Science?

Course 111 hours4.7 (69,008 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: Model Selection
Category: Data Analysis
Category: Python Programming
Category: Data Visualization
Category: Predictive Modelling

Tools for Data Science

Course 218 hours4.5 (28,418 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 (20,049 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: Python Programming
Category: Numpy
Category: Pandas

Python for Data Science, AI & Development

Course 425 hours4.6 (35,904 ratings)

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: Python Programming
Category: Dashboards and Charts
Category: dash
Category: Data Visualization
Category: Matplotlib

Python Project for Data Science

Course 58 hours4.5 (4,061 ratings)

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: Github
Category: Jupyter Notebook
Category: K-Means Clustering
Category: Methodology
Category: Data Science Methodology

Databases and SQL for Data Science with Python

Course 620 hours4.6 (19,609 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

Data Analysis with Python

Course 715 hours4.7 (17,911 ratings)

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: Machine Learning
Category: regression
Category: Hierarchical Clustering
Category: classification
Category: SciPy and scikit-learn

Data Visualization with Python

Course 819 hours4.5 (11,604 ratings)

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: Data Science
Category: Data Analysis
Category: Python Programming
Category: Pandas
Category: Jupyter notebooks

Machine Learning with Python

Course 913 hours4.7 (15,550 ratings)

What you'll learn

  • Describe the various types of Machine Learning algorithms and when to use them 

  • Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 

  • Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

  • Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

Skills you'll gain

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

Applied Data Science Capstone

Course 1013 hours4.7 (7,042 ratings)

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

Instructors

Dr. Pooja
IBM
4 Courses278,595 learners
Romeo Kienzler
IBM
10 Courses654,755 learners
Joseph Santarcangelo
IBM
28 Courses1,434,003 learners

Offered by

IBM

Get a head start on your degree

When you complete this Professional Certificate, you can earn college credit if you are admitted and enroll in one of the following online degree programs.¹

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Degree credit eligible

This Professional Certificate 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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