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With over 10K hours of videos and 14 certification programs featuring leading data experts, we provide a wealth of knowledge and resources for those looking to learn and advance in the field of data science.
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Our course on Introduction to Python is designed for those with little to no programming experience who want to learn the basics of the Python programming language. In this course, you will learn the fundamentals of programming, including data types, variables, loops, and control structures. You will also learn how to work with libraries and modules, as well as how to read and write data to and from files.
Data visualization is a crucial skill for any data scientist, as it allows you to effectively communicate complex data insights to a wide audience. In our course on Data Visualization, you will learn how to create clear and effective visualizations using a variety of platforms including Python, Tableau, TIBCO Spotfire, and more.
Natural language processing (NLP) is a field of artificial intelligence that deals with the interaction between computers and human language. In our course on Natural Language Processing, you will learn how to use Python and popular NLP libraries such as NLTK and spaCy to process and analyze text data.
Deep learning is a subfield of machine learning that has achieved impressive results in a wide range of applications, including image and speech recognition, natural language processing, and more. In our course on Deep Learning, you will learn how to build and train deep neural networks using popular libraries such as TensorFlow and PyTorch.
Neural networks are a powerful tool for machine learning, allowing computers to learn and make decisions based on data input. In our course on Neural Networks, you will learn how to build, train, and evaluate neural networks using Python and popular libraries such as TensorFlow and PyTorch.
Data engineering is a crucial field in data science, as it involves the process of collecting, storing, and preparing data for analysis. In our course on Data Engineering, you will learn how to design and build efficient data pipelines using Python and popular tools such as Apache Spark and Apache Airflow.
Feature engineering is the process of creating and selecting relevant features for a machine learning model. In our course on Feature Engineering, you will learn how to design and construct features that can improve the performance of your model.
Synthetic data is artificially generated data that can be used for machine learning and artificial intelligence (AI) applications. In our course on Synthetic Data and AI, you will learn how to generate and use synthetic data to train and evaluate machine learning models.
Time series data is a type of data that is collected over a period of time and typically includes a time stamp. In our course on Time Series Data, you will learn how to analyze and forecast time series data using Python and popular libraries such as Pandas and Statsmodels.
Blockchain is a decentralized, distributed ledger technology that has gained significant attention in recent years due to its potential to revolutionize a wide range of industries. In our course on Blockchain, you will learn the fundamentals of blockchain technology and how to build applications using popular platforms such as Ethereum.
Data scientist is now the highest-paying job for students graduating with a bachelor’s degree in the United States and is projected to grow from a $138.9 billion market in 2020 to a $229.4 billion market by 2025. However, experiences with data are rare in today’s K-12 classrooms and curriculum. This course will introduce K-12 students, educators, and parents to foundational data science concepts and examples of data science careers across industries with a combination of workshops and presentations.
Launching a career in data science can be a challenging but rewarding journey. In our course on Launching a Career in Data Science, you will learn how to navigate the process of finding and securing a data science job, and how to succeed in your role once you get there.
As you progress in your data science career, it’s important to continue learning and growing to stay competitive and advance in your field. In our course on Leveling Up Your Career in Data Science, you will learn how to take your data science skills to the next level and achieve your career goals.
As you progress in your data science career, it’s important to continue learning and growing to stay competitive and advance in your field. In our course on Leveling Up Your Career in Data Science, you will learn how to take your data science skills to the next level and achieve your career goals.