Course Design By
Nasscom & Wipro
Comprehensive Python and mathematics training
Industry-experienced trainers
Interactive group discussions and debates
100% placement guarantee
From Scratch to Mastery: Our experienced trainers take you through the fundamentals, starting with expressions and variables. Gain a deep understanding of essential data structures like lists, tuples, dictionaries, and sets.
Beyond Basics: Move beyond the basics as our trainers delve into the utilization and implementation of advanced concepts such as loops, functions, objects, and classes.
Hands-On Data Manipulation: Work seamlessly with data using Pandas and NumPy libraries. Acquire practical skills that bridge the gap between theory and real-world application.
Comprehensive Learning: This training doesn't just provide theoretical knowledge but also offers hands-on practical experience.
Study Materials: As part of our commitment to your learning journey, we provide study materials that complement the course content, ensuring you have the resources you need.
we train you to get hired.
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Course Design By
Nasscom & Wipro
Course Offered By
Croma Campus
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14-Jun-2025*
16-Jun-2025*
11-Jun-2025*
14-Jun-2025*
16-Jun-2025*
11-Jun-2025*
You will get certificate after
completion of program
You will get certificate after
completion of program
You will get certificate after
completion of program
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we train you to get hired.
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Gain a competitive edge with industry-focused Python for Data Science courses at Croma Campus, ensuring practical skills and real-world applications.
Basic knowledge of programming and a strong desire to delve into the world of data science are recommended prerequisites for the Python for Data Science course.
Anyone with an interest in data science, including students, professionals, and enthusiasts, can register for the Python for Data Scientist course.
Experienced and industry-qualified instructors at Croma Campus will guide you through the Python for Data Scientist Course.
The course duration varies, but typically, it is designed to be completed within a reasonable timeframe, considering both learning depth and efficiency.
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