What Is Data Communication? Basics to Know
November 22, 2024
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Prepare for a career as a data scientist. Build job-ready skills – and must-have AI skills – for an in-demand career. Earn a credential from IBM. No prior experience required.
Instructors: IBM Skills Network Team
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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
Add to your LinkedIn profile
Prepare for a career in the high-growth field of data science. In this program, you’ll develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry-level data scientist in as little as 4 months. No prior knowledge of computer science or programming languages is required.
Data science involves gathering, cleaning, organizing, and analyzing data with the goal of extracting helpful insights and predicting expected outcomes. The demand for skilled data scientists who can use data to tell compelling stories to inform business decisions has never been greater.
You’ll learn in-demand skills used by professional data scientists including databases, data visualization, statistical analysis, predictive modeling, machine learning algorithms, and data mining. You’ll also work with the latest languages, tools,and libraries including Python, SQL, Jupyter notebooks, Github, Rstudio, Pandas, Numpy, ScikitLearn, Matplotlib, and more.
Upon completing the full program, you will have built a portfolio of data science projects to provide you with the confidence to excel in your interviews. You will also receive access to join IBM’s Talent Network where you’ll see job opportunities as soon as they are posted, recommendations matched to your skills and interests, and tips and tricks to help you stand apart from the crowd.
This program is ACE® and FIBAA recommended —when you complete, you can earn up to 12 college credits and 6 ECTS credits.
Applied Learning Project
This Professional Certificate has a strong emphasis on applied learning and includes a series of hands-on labs in the IBM Cloud that give you practical skills with applicability to real jobs. You'll also have the option to learn how generative AI tools and techniques are used in data science.
Tools you’ll use: Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio
Libraries you’ll use: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc.
Projects you’ll complete:
Extract and graph financial data with the Pandas Python library
Use SQL to query census, crime, and school demographic data sets
Wrangle data, graph plots, and create regression models to predict housing prices with data science Python libraries
Create a dynamic Python dashboard to improve US domestic flight reliability
Apply machine learning classification algorithms to predict whether a loan case will be paid off
Train and compare machine learning models
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.
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.
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.
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.
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.
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.
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
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
Job-ready foundational machine learning skills in Python in just 6 weeks, including how to utilizeScikit-learn to build, test, and evaluate models.
How to apply data preparation techniques and manage bias-variance tradeoffs to optimize model performance.
How to implement core machine learning algorithms, including linear regression, decision trees, and SVM, for classification and regression tasks.
How to evaluate model performance using metrics, cross-validation, and hyperparameter tuning to ensure accuracy and reliability.
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
Leverage generative AI tools, like GPT 3.5, ChatCSV, and tomat.ai, available to Data Scientists for querying and preparing data
Examine real-world scenarios where generative AI can enhance data science workflows
Practice generative AI skills in hand-on labs and projects by generating and augmenting datasets for specific use cases
Apply generative AI techniques in the development and refinement of machine learning models
Describe the role of a data scientist and some career path options as well as the prospective opportunities in the field.
Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.
Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.
Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
When you complete this Professional Certificate, 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
When you complete this Professional Certificate, 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
Illinois Tech
Degree
O.P. Jindal Global University
Degree · 12 - 24 months
Heriot-Watt University
Degree · 18 months - 8 years
Illinois Tech
Degree · 12-24 months
Illinois Tech
Degree · 12-15 months
Ball State University
Degree · 24 months
University of London
Degree · 3 – 6 years
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
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.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
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Taking the IBM Data Science Professional Certificate boosted my confidence and command over data and data visualization. Data visualization was one of the most exciting features of the program supported by excellent hands-on material and labs. It has also opened a door to new opportunities.
Learning from India
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Taking the IBM Data Science Professional Certificate boosted my confidence and command over data and data visualization. Data visualization was one of the most exciting features of the program supported by excellent hands-on material and labs. It has also opened a door to new opportunities.
Learning from India
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Upon completion of this program, you will receive an email from Coursera with directions on how to claim your IBM Badge through Acclaim. Learn more about IBM Badges
Data science is the process of collecting, storing, and analyzing data. Data scientists use data to tell compelling stories to inform business decisions. Learn more about what data science is and what data scientists do in the first course of this Professional Certificate, "What is Data Science?"
An understanding of data science and the ability to make data driven decisions is useful in any career, but some careers specifically require a data science background. Some examples of careers in data science include:
- Business Intelligence Analyst
- Data Analyst
- Data Architect
- Data Engineer
- Data Scientist
- Machine Learning Engineer
- Marketing Analyst
- Operations Analyst
- Quantitative Analyst
This is a self-paced Professional Certificate that you can complete on your own schedule in less than 6 months.
This Professional Certificate is open to everyone with any work and academic background. No prior computer programming experience is necessary, however, it may be helpful to be familiar with computers and high school math. For the last few courses, knowledge of calculus and linear algebra is an asset but not an absolute requirement.
We highly recommend taking at least the first two courses in the order shown, as each course builds on content from prior courses.
If you have already completed some of the courses in this program, either individually or as part of another Specialization or Professional Certificate, they will be marked as "Complete." You do not need to take those courses again, and will be able to finish the Professional Certificate more quickly. You will only need to complete the courses that you have not yet completed.
As a Coursera learner who completes this Professional Certificate, you will have special access to join IBM’s Talent Network. Our Talent Network members receive all of the tools you need to land a dream job with IBM - sent directly to your inbox! You will get job opportunities as soon as they are posted, recommendations to apply that match directly to your skills and interests, and tips and tricks to help you stand apart from the crowd.
If you have already completed some of the courses in this Professional Certificate, either individually or as part of another Specialization, they will be marked as "Complete". You do not have to take those courses again, and will be able to finish the Professional Certificate more quickly. You will only need to complete the courses that you have not yet completed.
Yes. The IBM Data Science Professional Certificate secured a credit recommendation from the American Council on Education’s (ACE) Credit Recommendation, as well as the European Credit Transfer and Accumulation System (ECTS) from the Foundation for International Business Administration Accreditation (FIBAA) – industry standards for translating workplace learning into college credit. 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 Credly badge, which contains the ACE®️ credit or ECTS 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.
For ACE recommendations, please see Coursera’s ACE Recommendations FAQ. For ECTS recommendations, please see Coursera’s ECTS Recommendations FAQ.
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.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.
¹ Median salary and job opening data are sourced from Lightcast™ Job Postings Report. Data for job roles relevant to featured programs (2/1/2024 - 2/1/2025)
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