Learn how to code in Python Take your career to the next level Work with Python's conditional statements, functions, sequences, and loops Work with scientific packages, like NumPy Understand how to use the data analysis toolkit, Pandas Plot graphs with Matplotlib Use Python to solve real-world tasks Get a job as a data scientist with Python Acquire solid financial acumen Carry out in-depth investment analysis Build investment portfolios Calculate risk and return of individual securities Calculate risk and return of investment portfolios Apply best practices when working with financial data Use univariate and multivariate regression analysis Understand the Capital Asset Pricing Model Compare securities in terms of their Sharpe ratio Perform Monte Carlo simulations Learn how to price options by applying the Black Scholes formula Be comfortable applying for a developer job in a financial institution You'll need to install Anaconda. You'll need to install Anaconda. Do you want to learn how to use Python in a working environment? Are you a young professional interested in a career in Data Science? Would you like to explore how Python can be applied in the world of Finance and solve portfolio optimization problems?

But wait, hasn't there been a mathematical method for optimizing portfolios around for some years? Right, it's called the Modern portfolio theory (MPT) by economist Harry Markowitz, introduced in a 1952 essay, for which he was later awarded a Nobel Memorial Prize in Economic Sciences. The simple idea of the model is diversification in investing: owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return. And how can we make it AI?

Reinforcement learning is arguably the coolest branch of artificial intelligence. It has already proven its prowess: stunning the world, beating the world champions in games of Chess, Go, and even DotA 2. Using RL for stock trading has always been a holy grail among data scientists. Stock trading has drawn our imaginations because of its ease of access and to misquote Cardi B, we like diamond and we like dollars . There are several ways of using Machine Learning for stock trading. One approach is to use forecasting techniques to predict the movement of the stock and build some heuristic based bot that uses the prediction to make decisions.

Property technology, often abbreviated as proptech, is becoming increasingly commonplace around the Asia Pacific (APAC) in recent years, and certain factors are set to see proptech truly takeoff. An excellent example of proptech disrupting traditional property investor tools is the AI-powered platform, OfficeBlocks. According to the Urban Land Institute and PwC Emerging Trends in Real Estate Asia Pacific (APAC) 2020 report, awareness of and investment in proptech strategies are growing rapidly. And the recent world events have made things difficult for the property investor market, with travel restrictions and other restrictions limiting the possibilities. But using the Market Intelligence App within the OfficeBlocks platform, a photo of a property can be uploaded, and the industry-first AI and big data tool will send out the rental estimates and valuation of the property to an email address within minutes.

Modern portfolio theory (MPT) is a hypothesis about investment theory that Harry Markowitz published in 1952. Since that time, Markowitz's theory has been one of the most influential forces in finance for both academics and practitioners. Markowitz asserted that risk-averse investors could construct portfolios of assets that maximize return for a given level of risk. The application of MPT allows investors to create an optimal portfolio of assets for any particular level of risk. Depending on the individual's risk tolerance, they should invest in the return-maximizing set of assets.