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Big Data Analytics In Finance
What you'll learn
Understand the fundamentals of big data analytics and its relevance in finance.
Apply big data tools and techniques for risk assessment and management.
Develop strategies for customer segmentation using data-driven insights.
Implement effective fraud detection systems using advanced analytics.
Requirements
No prior big data experience required
Basic knowledge of finance and statistics
Familiarity with spreadsheets and a willingness to learn
Description
Upon Completing This Course, Learners Will Be Able To:Apply big data analytics techniques to enhance financial decision-makingUnderstand and implement data-driven strategies using big data tools and platforms to support critical financial decisions, including investment analysis, portfolio optimization, and budgeting. Learners will explore real-time data processing, data visualization, and predictive analytics in financial contexts.Develop effective risk management and fraud detection strategiesUtilize big data techniques such as anomaly detection, machine learning models, and behavioral analytics to identify and mitigate financial risks. Learners will design and evaluate systems that proactively detect fraud patterns, assess credit risks, and ensure regulatory compliance.Create targeted customer segmentation models using data insightsLeverage clustering algorithms, transaction data, and behavioral patterns to segment customers based on profitability, risk profiles, and product preferences. Learners will gain the ability to design data-driven marketing and personalized financial services strategies.Integrate financial data from multiple sources for holistic analysisLearn how to aggregate and clean data from internal systems, market feeds, and third-party sources to gain a comprehensive view of financial performance and customer behavior.Interpret and communicate insights from complex datasets to stakeholdersBuild dashboards and data visualizations that clearly convey analytical findings to financial managers, executives, or clients, enabling informed decision-making.
Finance professionals or students looking to expand into data analytics,Analysts aiming to integrate big data into financial decision-making,Anyone with a basic understanding of finance and a curiosity for data analytics
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Published 3/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 372.21 MB | Duration: 0h 33m
A Practical Guide to Big Data in Finance: Tools, Techniques, and Real-World Applications
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 372.21 MB | Duration: 0h 33m
A Practical Guide to Big Data in Finance: Tools, Techniques, and Real-World Applications
What you'll learn
Understand the fundamentals of big data analytics and its relevance in finance.
Apply big data tools and techniques for risk assessment and management.
Develop strategies for customer segmentation using data-driven insights.
Implement effective fraud detection systems using advanced analytics.
Requirements
No prior big data experience required
Basic knowledge of finance and statistics
Familiarity with spreadsheets and a willingness to learn
Description
Upon Completing This Course, Learners Will Be Able To:Apply big data analytics techniques to enhance financial decision-makingUnderstand and implement data-driven strategies using big data tools and platforms to support critical financial decisions, including investment analysis, portfolio optimization, and budgeting. Learners will explore real-time data processing, data visualization, and predictive analytics in financial contexts.Develop effective risk management and fraud detection strategiesUtilize big data techniques such as anomaly detection, machine learning models, and behavioral analytics to identify and mitigate financial risks. Learners will design and evaluate systems that proactively detect fraud patterns, assess credit risks, and ensure regulatory compliance.Create targeted customer segmentation models using data insightsLeverage clustering algorithms, transaction data, and behavioral patterns to segment customers based on profitability, risk profiles, and product preferences. Learners will gain the ability to design data-driven marketing and personalized financial services strategies.Integrate financial data from multiple sources for holistic analysisLearn how to aggregate and clean data from internal systems, market feeds, and third-party sources to gain a comprehensive view of financial performance and customer behavior.Interpret and communicate insights from complex datasets to stakeholdersBuild dashboards and data visualizations that clearly convey analytical findings to financial managers, executives, or clients, enabling informed decision-making.
Finance professionals or students looking to expand into data analytics,Analysts aiming to integrate big data into financial decision-making,Anyone with a basic understanding of finance and a curiosity for data analytics
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nitroflare.com:
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