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IBM Data Science Professional Certificate

📄 Summary

Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate will help anyone interested in pursuing a career in data science or machine learning develop career-relevant skills.

It’s a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certificate – no prior knowledge of computer science or programming languages required – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist.

The program consists of 9 online courses that will provide you with the latest job-ready tools and skills, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets.

Upon completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science.

In addition to earning a Professional Certificate from Coursera, you'll also receive a digital badge from IBM.

📑 Main Topics

  • Describe what is data science, the various activities of a data scientist’s job, and methodology to think and work like a data scientist .
  • Develop hands-on skills using the tools, languages, and libraries used by professional data scientists.
  • Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python.
  • Apply various data science skills, techniques, and tools to complete a project using a real-world data set and publish a report for stakeholders.

🔑 Key Skills Learned

  • Data Science.
  • Deep Learning.
  • Machine Learning.
  • Big Data.
  • Data Mining.
  • Python Programming.
  • Jupyter notebooks.
  • Rstudio.
  • Methodology.
  • CRISP-DM.
  • Data Analysis.

🏆 Certificates

To verify the certificates, click the images to follow the links.

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