Best Data Science Course | Advanced Certificate

Data Science is a relatively new field that combines aspects of statistics, computer science, and information technology to examine large data sets in order to draw conclusions about them. Students learn skills that are applicable in a wide range of industries, including healthcare, finance, marketing, and more. In addition, the Data Science course provides an opportunity to work with real-world data sets, which can be an invaluable experience for those planning to pursue a career in this field. Some of the specific topics covered in a Data Science course include data mining, machine learning, and web analytics. With the rapid growth of big data, there is an increasing demand for individuals with these skills; thus, completing a Data Science course can certainly give your career a boost.


Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured. The benefits of data science are many and varied. Those who enroll in a data science course can expect to gain valuable skills and knowledge that will help them in their future careers. The course will cover topics such as statistics, machine learning, programming, data visualization, and more. By the end of the course, students will have a strong understanding of how to use data to solve real-world problems.

If you're considering a career in data science, then you may be wondering if you should join a data science course. While there are benefits to taking a course, such as learning from experienced instructors and gaining hands-on experience with data, it's ultimately up to you to decide whether or not a course is right for you. Ultimately, the decision of whether or not to join a data science course depends on your individual needs and goals. for more details, you can check our site: https://www.talentserve.org/course-datascience


 COURSE CONTENT


  • Introduction to Python

  • Basic Steps

  • NUMPY

  • Data Visualization

  • Pandas

  • Exceptions and Errors

  • Introduction to Artificial Intelligence and Machine Learning

  • Data Wrangling and Manipulation

  • Supervised Learning

  • Supervised Learning-Classification

  • Unsupervised learning

  • Machine Learning Pipeline Building

  • Decision Tree Analysis and Ensemble Learning

  • AI and Deep learning introduction

  • Artificial Neural Network

  • Deep Neural Network & Tools

  • Deep Neural Net optimization, tuning, interpretability

  • Convolutional Neural Net

  • Recurrent Neural Networks

  • Overfit and underfit

  • Transfer Learning

  • Working with Generative Adversarial Networks

  • Pytorch

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