Data-Driven Modelling and Predictive Analytics by Alex Khang (.PDF)

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Data-Driven Modelling and Predictive Analytics in Business and Finance by Alex Khang, Rashmi Gujrati, Hayri Uygun, R.K. Tailor, Sanjaya Singh Gaur
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Overview: Data-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent. Data analytics is the process of examining, cleaning, transforming and interpreting data to uncover meaningful patterns, trends and insights. It involves the use of statistical and computational techniques to analyze data and extract valuable information. Data analytics can be categorized into four main types: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. The commonly used tools and applications in data analytics include the following: • Excel: Microsoft Excel is a widely used tool for data analysis due to its user-​friendly interface and familiar spreadsheet format. • SQL: Structured Query Language (SQL) is essential for working with databases. SQL allows you to extract, manipulate, and analyze data from relational databases using queries. • Python: Python is a versatile programming language commonly used in data analytics. It offers numerous libraries and frameworks for data manipulation (e.g., Pandas), numerical computation (e.g., NumPy), and data visualization (e.g., Matplotlib and Seaborn). Python also has powerful Machine Learning libraries such as Scikit-​learn and TensorFlow.
Genre: Non-Fiction > Tech & Devices

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