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BigData Analysis with PySpark Bharti Motwani | Associate Professor | University of Maryland, USA Ideal for business analysts, this session will provide practical examples of how to use PySpark to solve business problems. Finally, you’ll discuss a stack that offers an improved UX that frees up time for tasks that matter.
5. Text Analytics and NaturalLanguageProcessing (NLP) Projects: These projects involve analyzing unstructured text data, such as customer reviews, social media posts, emails, and news articles. NLP techniques help extract insights, sentiment analysis, and topic modeling from text data.
As a programming language it provides objects, operators and functions allowing you to explore, model and visualise data. The programming language can handle BigData and perform effective data analysis and statistical modelling.
Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, NaturalLanguageProcessing , Statistics and Mathematics. It is critical for knowing how to work with huge data sets efficiently.
Let’s look at five benefits of an enterprise data catalog and how they make Alex’s workflow more efficient and her data-driven analysis more informed and relevant. A data catalog replaces tedious request and data-wranglingprocesses with a fast and seamless user experience to manage and access data products.
Dealing with large datasets: With the exponential growth of data in various industries, the ability to handle and extract insights from large datasets has become crucial. Data science equips you with the tools and techniques to manage bigdata, perform exploratory data analysis, and extract meaningful information from complex datasets.
B BigData : Large datasets characterised by high volume, velocity, variety, and veracity, requiring specialised techniques and technologies for analysis. DataWrangling: The cleaning, transforming, and structuring of raw data into a format suitable for analysis.
Over the past decade, data science has undergone a remarkable evolution, driven by rapid advancements in machine learning, artificial intelligence, and bigdata technologies. By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow.
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