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Data Science

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Our program serves as the foundation for many well-known concepts of data science. We teach practical techniques and algorithms for extracting and studying useful knowledge from data. This course is not a theory class as we believe there are many ways to learn statistics and analytics concepts on your own. We are providing students with a set of practical tools for data science and knowledge on how to apply Python to solve linear algebra, statistics, and probability problems. This course is designed to fill the gap between theoretical academic research and the needs of the industry.

Monday, August 13, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am
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This two-day course provides an overview of how Python can be used in Data Science to manipulate, process, clean, and crunch data. It is an introduction to scientific computing in Python focusing data-intensive applications. Specifically, the class will review the essential Python libraries: NumPy, pandas, matplotlib, IPython, and SciPy. Audience Students wanting use Python in data analytics applications. Prerequisites Students should have taken an introductory Python course or have six months of Python programming experience.

Thursday, August 23, 2018 - 10:00am

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