课程
Introduction to Python for Finance
- 基础技能水平
- 4.7+
- 3.6K
Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.
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Build Python skills to elevate your finance career. Learn how to work with lists, arrays and data visualizations to master financial analyses.
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Learn about Excel financial modeling, including cash flow, scenario analysis, time value, and capital budgeting.
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Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.
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Build on top of your Python skills for Finance, by learning how to use datetime, if-statements, DataFrames, and more.
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Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
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Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
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Using Python and NumPy, learn the most fundamental financial concepts.
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Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
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Learn to implement custom trading strategies in Python, backtest them, and evaluate their performance!
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Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
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In this course, youll learn how to import and manage financial data in Python using various tools and sources.
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You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.
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Learn essential finance math skills with practical Excel exercises and real-world examples.
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Learn how to build a graphical dashboard with Google Sheets to track the performance of financial securities.
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Learn how to calculate meaningful measures of risk and performance, and how to compile an optimal portfolio for the desired risk and return trade-off.
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Learn basic business modeling including cash flows, investments, annuities, loan amortization, and more using Google Sheets.
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Learn key financial concepts such as capital investment, WACC, and shareholder value.
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Discover how to use the income statement and balance sheet in Power BI
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In this Power BI case study you’ll play the role of a junior trader, analyzing mortgage trading and enhancing your data modeling and financial analysis skills.
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Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
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Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.
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Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.
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Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
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Learn business valuation with real-world applications and case studies using discounted cash flows (DCF).
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Apply financial analysis in KNIME with real-world data, enhancing data preparation and workflow skills.
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Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
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Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
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Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
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Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.
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Learn how to build an amortization dashboard in Google Sheets with financial and conditional formulas.
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数据科学是一个专注于从数据中获取信息的专业领域。数据科学家使用编程技能、科学方法、算法等来分析数据,形成可操作的洞察。
你需要学习 Python 或 R 等编程语言,掌握数学和统计学原理。数据分析方法和数据科学工具的知识也是必不可少的。学习数据科学有很多方法。除了正式的教育途径,如学位或大学学习,还有很多其他资源可以帮助你按自己的节奏学习。除了在线课程和教程,还有书籍、视频等。
除了数学和统计学知识,数据科学家还需要 Python、R 和 SQL 等语言的编程技能。此外,数据科学需要处理大型数据集的能力、数据可视化、数据整理和数据库管理知识。机器学习和深度学习技能也很有用。
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这里有几个需要考虑的因素。首先,数据科学学位的竞争可能很激烈,通常需要持续的高分。同样,数据科学所需的许多技能需要大量的学习和耐心。掌握所有必要的基础知识可能需要几个月的时间,还需要大量的实践经验才能获得入门级职位。
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随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。