Advance Certificate course in Data Science | Learn With TalentServe

In recent years, data science has become one of the most sought-after skills in the job market. As businesses increasingly rely on data to make decisions, they need employees who can collect, analyze, and interpret that data. A Data Science course can teach you the skills you need to pursue a career in this field. In a Data Science course, you will learn how to use statistical techniques to analyze data sets, identify patterns, and build predictive models. You will also learn how to use programming languages like R and Python to clean, manipulate, and visualize data. 


The benefits of taking a Data Science course go beyond just improving your employability. A good Data Science course will also give you the opportunity to work on real-world projects with real data sets. This will provide you with a chance to put your newly acquired skills into practice and hone your craft. This course will cover the fundamental concepts of data science, including data wrangling, exploratory data analysis, predictive modeling, and communication of results. Students will learn to apply these methods to real-world datasets to derive actionable insights. 

The course will also cover the ethical considerations involved in working with data, as well as the impact of data science on society. The Data Science training course is designed for people who want to learn Data Science from scratch or improve their Data Science skills. The course starts with the basics of Python programming and moves on to cover various important topics in data manipulation, analysis, and visualization. By the end of this course, you will be well-versed in all the concepts related to data science and will be able to apply them in practice. 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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