Advanced Certificate Course In Data Science With Placement

Data Science is a multidisciplinary field that involves using statistical and computational methods to extract insights from large and complex data sets. The goal of data science is to use data to inform decision-making and solve real-world problems in various industries such as healthcare, finance, marketing, and more. A typical Data Science course covers a broad range of topics, including statistics, probability theory, programming, machine learning, data visualization, data engineering, and big data technologies. Students will learn how to clean, process, and analyze data using programming languages such as Python and R, and how to build machine learning models to make predictions and classify data.



The benefits of taking a Data Science course include:


High Demand for Data Scientists: Data Science is a rapidly growing field with a high demand for skilled professionals. Taking a Data Science course can help you acquire the skills and knowledge needed to land a job in this field.


Career Growth: A Data Science course can help you grow your career, as it provides you with the skills and knowledge to work in various industries and take on leadership roles.


Lucrative Salary: Data Science is one of the highest-paying fields in the tech industry. Taking a Data Science course can lead to a high-paying job with excellent career growth opportunities.


Solving Real-World Problems: Data Science is all about using data to solve real-world problems. By taking a Data Science course, you'll learn how to use data to make informed decisions and solve complex problems in various industries.


Continuous Learning: The field of Data Science is constantly evolving, and there is always something new to learn. Taking a Data Science course can help you stay up-to-date with the latest trends and technologies in the field. 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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