Coursera – Introduction to Data Science – Specialization (by IBM)

Coursera – Introduction to Data Science – Specialization (by IBM)
English | Tutorial | Size: 1.17 GB


Launch your career in data science. Gain foundational data science skills to prepare for a career or further advanced learning in data science.

What you’ll Learn:
• Describe what data science and machine learning are, their applications & use cases, and
various types of tasks performed by data scientists
• Gain hands-on familiarity with common data science tools including JupyterLab, R Studio,
GitHub and Watson Studio
• Develop the mindset to work like a data scientist, and follow a methodology to tackle
different types of data science problems
• Write SQL statements and query Cloud databases using Python from Jupyter notebooks

Skills you’ll Gain:
• Data Science
• Python Programming
• Cloud Databases
• SQL
• Relational Database Management System (RDBMS)

Specialization – 4 Course Series:
Interested in learning more about data science, but don’t know where to start? This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field.

This Specialization will introduce you to what data science is and what data scientists do. You’ll discover the applicability of data science across fields, and learn how data analysis can help you make data driven decisions. You’ll find that you can kickstart your career path in the field without prior knowledge of computer science or programming languages: this Specialization will give you the foundation you need for more advanced learning to support your career goals.

You’ll grasp concepts like big data, statistical analysis, and relational databases, and gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. You’ll complete hands-on labs and projects to learn the methodology involved in tackling data science problems and apply your newly acquired skills and knowledge to real world data sets.

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