Inhalt: Data science can be generally defined as the process of making data useful, and data engineering is a key part of how and why. If you think of data science like a race car, the data engineers are the pit crew. They're not driving the car, but they make the car much easier to drive. Data engineers make sure the data flow is running smoothly, monitor systems, anticipate problems, and repair the data pipeline whenever problems arise. They extract and gather data from multiple sources and load it into a single, easy-to-query database. In short, data engineers make data scientists' lives easier. In this course, Harshit Tyagi explains the fundamentals of data engineering. He covers key topics like data wrangling, database schema, and developing ETL pipelines. He also details several data engineering tools like Hive, Hadoop, Spark, and Airflow. By the end of this course, it should be abundantly clear why the data engineer is one of the most valuable people in a data-driven organization. Umfang: 01:03:51
Inhalt: There is a growing demand to harness the power of natural language processing (NLP) and deep learning models to be able to make sense of textual data and reduce the emotional intervention of humans in order to make better decisions. In this course, instructor Harshit Tyagi provides a complete guide to understanding NLP using recurrent neural networks (RNNs). Harshit begins by introducing you to word encodings and using TensorFlow for tokenization. He describes the important concept of word embeddings and shows you how to use TensorFlow to classify movie reviews and project vectors. Harshit discusses RNNs and long short-term memory (LSTM), then shows you how to improve the movie review classifier from earlier in the course. He concludes with a discussion of how you can train RNNs to predict the next word in a sentence, which in turn allows you to generate some original text. Umfang: 01:47:32
Inhalt: The power and versatility of Python-coupled with its large ecosystem of third-party packages-make it indispensable to data scientists. In this course, instructor Harshit Tyagi shares practical tips and techniques that can help you enhance your own Python data science workflow. Harshit covers how to work with IPython notebooks, including how to debug errors. He shows how to use NumPy to manipulate arrays, as well as how to work with pandas, the data manipulation and analysis tool. He provides tips for visualizing your data with Matplotlib, explaining how to add text to plots and annotate elements on a chart. Plus, get best practices for working with scikit-learn, as well as other machine learning tips. Umfang: 01:58:17
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