PROGRAM OVERVIEW
Artificial intelligence (AI) and advanced analytics change the way financial institutions interact with their customers and run their organizations. These institutions know that advanced analytics has the potential to inform business strategy and improve operational performance. The ability to structure, manipulate, interpret, and extract insights from data is central to an organization’s survival. Data Analytics methods, can be used to automate and optimize financial decisions, according to methods that are already used in the area. However, it is also possible, when using advanced analytics techniques, to incorporate machine learning and AI algorithms to develop predictions in an innovative and yet unexplored way in the market: this is what will generate a great competitive advantage for companies in the financial market.
Hence there is a growing demand for professionals specializing in data analytics in the field of financial services. The role of a data analyst is dynamic, complex, and driven by various skills. These skills range from a basic understanding of financial statement data and non-financial metrics linked to financial performance to a deeper dive into business and financial modelling.
This course introduces an overview of financial data analytics. You will learn why, when, and how to apply financial data analytics in real-world situations. You will explore techniques to analyse your business-derived data and evaluate the risk-reward trade-off expounded in modern theories. While most of the focus will be on the prices, returns, and risk of corporate stocks, the analytical techniques can be leveraged in other domains. This multifaceted specialization will equip the people interested in entering the dynamic world of data and business analysis and gaining more profound technical knowledge in Finance and Quantitative Modelling.
This course equips you with data analysis skills in short supply and can be utilized in algorithmic trading, machine learning, and AI applications. You will develop core finance, investments, and data analytics skills as you study. You will also be taught how to analyse data and get a thorough understanding of core financial theory and investment practices.
KEY BENEFITS
The Program is aimed at providing:
- Gain a deep understanding of financial services’ theories
- Develop knowledge and skills in analyzing data to inform investment decisions
- Identify where and how to apply advanced data analytics
- Familiar with the business problems with the data-driven decision-making process
- Explanation of how data is used for performance evaluation
- Describe ARIMA, SARIMAX, RNN, LSTM, etc.
- Offering forward-looking strategic insights based on accurate and detailed forecasts, rather than traditional historical financial reporting.
- Mitigating risk by using facts to make important business decisions with more confidence.
- Reducing costs while making more productive.
WHO SHOULD ATTEND THIS PROGRAM
DOWNLOAD COURSE CATALOGUE
- Course Overview and Objectives
- Introduction Data Analytics
- Statistical Analysis and Data Analytics for finance
- Contemporary issues and development of data analytics
- Applications of data analytics in finance and investment
- Understanding Key Financial Statements
- Financial Analysis Techniques
- Financial Statement Analysis
- Corporate Finance
- Principles of Risks Analysis and Financial Risk Management
- Analysis of Financial Risks
- Market Risks
- Credit Risks
- Operational Risks
- Integrated Risk Management
- Quantitative Asset Allocation and Portfolio Risk Management
- Data Basics
- Types of Data
- Big Data
- Databases and Other Tools
- Data Process (Crisp-DM)
- Business Understanding
- Data Understanding
- Data Preparation
- How Much Data Do You Need?
- Examples of Data Science in Finance
- What Is Machine Learning?
- The Machine Learning Process
- Applying Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Semi-supervised Learning
- Common Types of Machine Learning Algorithms
- Naïve Bayes Classifier
- K-Nearest Neighbor
- Linear Regression
- Decision Tree
- Ensemble Modelling
- K-Means Clustering
- Genetic Algorithem
- What Is Deep Learning
- Difference Between Deep Learning and Machine Learning
- Artificial Neural Networks (ANNs)
- Recurrent Neural Network
- Convolutional Neural Network (CNN)
- Generative Adversarial Networks (GANs)
- Deep Learning Use Cases
- Deep Learning Hardware
- When to Use Deep Learning?
- Forecasting in Practice
- Subjective Forecasting
- Business Forecasting and Time Series Data
- Introduction to Financial Analytics
- Forecasting Performance Measurements: Distance
- Forecasting Performance Measurements: Metrics
- Introduction to Forecasting
- Average Method
- Naive Method
- Linear Regression
- Moving Averages
- Exponential Smoothing
- Simple Exponential Smoothing
- Holt's Exponential Smoothing
- Holt-Winter's Forecasting Model
- Autoregression
- Examples
- Stationarity
- ARIMA
- SARIMAX
- RNN
- LSTM
Portfolios in Practice
- Introduction
- Expected Returns
- Risk of a Security
- Efficient Frontier
- Portfolio Weights
- Capital Allocation Line
- Diversification
Introduction to Algorithmic Trading
- Trend Following Strategy
- Backtesting
- Example
Conclusion
- Fraud Prevention
- Risk Management
- Credit Allocation
- Customer Analytics
- Algorithmic Trading
Be the first to add a review.
Related Courses
