10 Best Free Data Science Certification Courses in 2023

10 Best Free Data Science Certification Courses

Are you looking for some interesting career opportunities after your bachelor’s or masters? Ever thought of being a Data Scientist? If numbers and data interest you, you can make a living out of it! Then here is a list of 10 Free Data Science Certification Courses. A Data Scientist position requires expert data science knowledge and an understanding of the basic tools and technologies required.

It can be a stand-out career option for both new graduates and working professionals! The certification courses listed below are ideal for anyone with an analytical mind and any educational background.

In recent times a data scientist is the highest-ranking employee in any analytics firm and is ranked among the 50 best jobs for the year 2021. So, if your interests are inclined towards solving business problems with a good data analytic strategy, make your choices and grab the opportunities below!

List of Free Data Science Certification Courses

If you want to learn thoroughly about Data Science, then here is the opportunity to learn 10 Free Data Science Certification Courses that you can choose from.

1. IBM Data Science Professional Certificate (Coursera)

The basic structure of the curriculum consists of nine online courses that provide an adequate understanding of Python, SQL, data analysis and visualization, open-source tools, statistical knowledge, etc. Besides this, the course provides real-time experience on the IBM Cloud with actual data sets and tools.

You will have developed a portfolio of data science projects after finishing these courses, giving you the confidence to dive into an exciting career in data science. Additionally, a digital badge from IBM will be provided to appreciate your mastery of data science.

Course Highlights:

  • A basic introduction to Data Science: Thought process and daily tasks of a data scientist.
  • Understanding how data scientists utilize tools, languages, and professional real-time data.
  • Understanding how Python import and cleans data sets, analyze and display data, and create machine learning models and pipelines.
  • Procedures to complete a project and produce a report using diverse data science skills, methodologies, and tools.

Course Duration: 2-3 months

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2. Data Science Math Skills (Coursera)

This course is basically for learners who have basic math abilities but lack complete algebra or pre-calculus knowledge. This course helps in gaining data science arithmetic skills and provides the fundamental math on which data science is built, which can be understood with ease.

This is a beginner’s level introductory course that can be taken before going on to more difficult topics. At the end of the course, learners will grasp the language, notation, ideas, and algebra principles that all data scientists must know.

Course Highlights:

  • Understanding Set theory, including Venn diagrams
  • Concepts of Tangent lines to a curve and the idea of instantaneous rate of change
  • Properties of natural log function, exponents, and logarithms
  • Understanding Bayes’ theorem and concepts of probability theory

Course Duration: 1 Month

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3. Data Science for Business Innovation (Coursera)

The course is a compilation of the data science skills that executives and middle managers require to encourage data-driven innovation. It includes beginners’ level introductory lectures on topics such as data valuation, managing big data, machine learning, etc. Besides proper theoretical knowledge, this course has exposure to the practical application of different real-time concerns. Additionally, this course covers data science vocabulary and ideas, tools and methodologies, uses case studies, and shares success stories.

Besides the general introduction to data science, this course attempts to explore the concepts of supervised, unsupervised, and, semi-supervised approaches. The course also introduces the importance and effect of scalable, cloud-based computing platforms as well as NoSQL data formats and technologies.

Course Highlights:

  • Introduction to data science
  • Ideas on how the overall knowledge of data science helps businesses achieve better results
  • Understanding machine learning techniques: fundamental notions and intuitions

Course Duration: 7 hours

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4. SQL for Data Science (Coursera)

This course revolves around the foundations of SQL and data analysis so that learners begin to understand data before data science applications. It will help one to construct basic and sophisticated queries to select data from the tables. Gradually, the learners will be working with various sorts of data including strings and, integers and finally be an expert to compile, filter, and narrow down the findings.

This course benefits the learners in many ways including learning the construction of new tables and being able to feed data into them, using popular operators and sorting data, utilisation of case statements, and finally exploring the data-related subjects. All these skills will help in solving real-world projects.

Course Highlights:

  • Understanding how to filter, sort, and summarise data, using SQL commands
  • Using the UNION operator, how to create an analysis table from several searches.
  • Understanding how to manipulate texts, dates, and numeric data with functions to combine data from various sources into fields that are formatted correctly for analysis.

Course Duration: 14 hour

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5. Data Science, Machine Learning, Data Analysis, Python & R (Udemy)

This course is specially structured by two experts in the field of data science to share their knowledge and experience, and assist the learners in understanding complicated theories, algorithms, and code libraries straightforwardly for easy learning. This course is specifically designed for those who take interest in Python and R developers and are thinking about stepping into the field of data science.

Furthermore, the course is jam-packed with hands-on activities based on real-world scenarios. As a result, it’s not only the theory but also practice where one can create models. Additionally, this course also contains Python and R code templates that can be downloaded easily to create one’s projects.

Course Highlights:

  • Introduction to Data Science
  • Understanding how Python and R are used to analyse data
  • Understanding the role of Python and R to visualise data
  • Concepts of Artificial Intelligence and Machine learning

Course Duration: 8 hours 6 minutes

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6. Python Crash Course for Data Science and Machine Learning (Udemy)

This course is sixth on the list of Free Data Science Certification Courses and is for anyone who wants to experience the practical side of data science. It introduces the JupyterLab tool and Jupiter notebooks that help the learners to master the Python basics and syntax for constructing data science projects. The course also provides a summary exercise and a comprehensive solution at the end to practice Python for testing the acquired skills from the course.

Additionally, this course also explores the concepts of Machine Learning which is in high demand in the hi-tech sector and slowly gaining traction in a variety of other fields. As a result, a data scientist is regarded in the top ten most sought-after occupations on the planet!

Course Highlights:

  • Understanding Python programming language
  • Learning JupyterLab for Jupiter notebooks
  • Understanding IF and For-Loop Statements
  • Concepts of Data Science Project Libraries

Course Duration: 1 hour 39 minutes

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7. Introduction to Data Science for Complete Beginners (Udemy)

This course is designed for data science enthusiasts and aspirants. The course is designed in a way to bridge the gap between theory and practice. As we see, most of the data science courses push more towards practical exposure thereby the proper theoretical understanding goes for a toss. Hence, this course is specifically designed for those who want accurate knowledge in this field

The course briefs the general overview of this field and provides a solid basis for comprehending the most essential topics before stepping into advanced data science courses. For beginners, it is the most appropriate choice and for experts, this course serves as a revision of the important concepts.

Course Highlights:

  • Introduction to data science and who are data scientists
  • Concept of Machine learning and the role of Machine learning Engineer
  • Introduction to Kaggle
  • Various books for Data Science

Course Duration: 1 hour 56 minutes

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8. R for Data Science: A Practical Introduction (SkillShare)

This is a great course for those who want hands-on practical experience in the field of data science in just about half an hour. R claims to be one of the popular data science platforms as one with very little coding knowledge can benefit and learn from the course.

The key content of the course includes working with a hypothetical dataset from a company’s sales force and learning what particular data HR requires to hire the best workers. It also introduces how well summary data can be obtained for various employee subgroups and also teaches how to construct simple graphs for better visualizations. Lastly, the course highlights how can the findings be well recorded and a final report is created that is simple to update as and when data changes.

Course Highlights:

  • Introduction to R programming
  • Concepts of Data Analysis and Business Analytics
  • Introduction to basic statistics in the field of Data Science

Course Duration: 34 minutes

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9. Data Science and Business Analytics with Python (SkillShare)

In different types of fields, knowledge of Business Analysis and data sciences has become the need of the hour. For arriving at a proper conclusion, interpreting results and prior to taking important decisions, concepts of analytics come in handy. As Python has become the right hand of Data Science, this course revolves around the concepts of Python.

This course is titled intermediate level and assumes a pre-understanding of Python syntax; however, the course will address some of the fundamentals to provide a shared learning environment.

Course Highlights:

  • Understanding how Data is loaded from files (such as Excel tables) and databases (e.g., SQL servers)
  • Concepts of Data Analysis and data cleaning
  • Understanding Machine Learning
  • Concepts about validation of models and churn analysis
  • Understanding report creation and data visualisation

Course Duration: 4 hours 3 minutes

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10. Intro to Data Science (Udacity)

The course is at an intermediate level and completely focuses on understanding the fundamental concepts of data sciences. It touches on all basic topics such as data analysis, data manipulation, understanding of statistics, etc. all required in knowing the world of data sciences.

The course believes in approaching all themes succinctly rather than focussing on a single topic in depth. This will help the learner in being an overall expert in data sciences and apply the fundamental data science approaches smoothly.

Course Highlights:

  • Understanding concepts of data analysis and data manipulation
  • Basic understanding of statistics
  • Understanding Information Visualization and Data Communication
  • Concept of Machine Learning

Course Duration: 2 months

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