Data science focuses on the extraction of knowledge and insights from large and complex datasets. It involves the use of a range of techniques and methods, such as statistical analysis, machine learning, and data visualization, to identify patterns, trends, and relationships in data. Data scientists use these techniques and methods to analyze data from a variety of sources, including sensors, social media, and databases, and to uncover insights that can be used to inform decision-making and improve operations.
Data science is a multidisciplinary field that combines elements of computer science, statistics, and domain-specific expertise, such as in business, healthcare, or finance. Data scientists typically have strong skills in coding, statistics, and problem-solving, as well as a deep understanding of the domain in which they are working.
Overall, data science is a field that plays a critical role in driving insights and decision-making in many organizations and is becoming increasingly important in the era of big data and advanced analytics.
Here you can find a selection of data science courses to help you in your knowledge management efforts. This selection of courses covers everything from the basics of data science to more advanced topics.
Introduction to data analytics for accounting professionals
This course covers the foundations of data analytics and how to conduct and apply this to projects in your organization. Whether the data you’re looking at is financial or non-financial data, structured or unstructured, you need to understand the language of data analytics so that you can communicate effectively with colleagues and add value when using data analytics in your organization. By completing this course, you will be in a better position to ask the right questions, add greater value, and improve the quality of services to your stakeholders.
IBM AI Engineering
Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data-driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.
You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
Machine learning with Python
Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.
Introduction to Deep Learning and Neural Networks with Keras
This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks.
Deep neural networks with pytorch
The course will teach you how to develop deep learning models using Pytorch, starting with Pytorch’s tensors and Automatic differentiation package.
AI Capstone Project with Deep Learning
In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model.