Financial Modeling Assessment Analysis
Financial modelling assessment helps recruiters and hiring managers to assess the ability of job applicants for using SAS/Python/R-Programming for financial modelling.
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Program Overview
(Highly Recommended Certification Course Boosting the Employment Creation!!)
Financial Modeling is the process in which financial aspects are represented through designed models and mathematical tools for predicting the financial portfolios and assets of a business. The financial model basically performs calculations and gives recommendations based on the financial information given.
Financial modelling assessment helps recruiters and hiring managers to assess the ability of job applicants for using SAS/Python/R-Programming for financial modelling. Financial modeling assessment is created and validated by subject matter experts to assess & hire financial modeling expert as per the industry standards.
This Financial Modeling Course is useful for hiring as on following profile….
- Financial Modeling Analyst
- Financial Modelling Consultant
- Financial Modeling Specialist
- Finance Research Manager
Programme Highlights
- Post Graduate Program in Financial Modelling Assessment is a BIAL (A Unit of DECISIONTREE ENDEAVOUR) Employment Augmentation Learning Programme (EALP). DECISIONTREE ENDEAVOUR Employment Augmentation Learning Programme are well framed and recognized by the corporate sector.
- The programme is of three semesters (1 ½ Year), with classroom contact program usually conducted mostly on weekends or after business hours. You can pursue the programme without any career break.
- Emphasis on Experiential Learning including Case Studies, Simulations, spyder-jupyter labs, Group Assignments, etc.
- The Dissertation (Project Work) in the final semester enables students to apply concepts and techniques learnt during the programme.
- Participants who successfully complete the programme will become members of an elite & global community of DECISIONTREE ENDEAVOUR Alumni.
- Option to submit fee using easy-Semester wise with 0% interest. Enroll with only INR 800/- & Course Fees: 12500/- Per Semester.
Who this course is for
This course is geared towards the following:
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Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs.
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Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors.
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Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience.
Are there any course requirements or prerequisites?
- No prior experience is required. We will start from the very basics
- You’ll need to install Anaconda and Python. We will show you how to do that step by step.
What you’ll learn
Signing up for the course today could be a great step towards your career in professional Journey. Make sure that you take full advantage of this amazing opportunity!
See you on the inside!
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Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.
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Understand how to leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio.
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Understand the theory and intuition behind Capital Asset Pricing Model (CAPM), Markowitz portfolio optimization, and efficient frontier.
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Apply Python to implement several trading strategies such as momentum-based and moving average trading strategies.
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Understand how to use Jupyter Notebooks for developing, presenting and sharing Data Science projects.
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Learn how to use key Python Libraries such as NumPy for scientific computing, Pandas for Data Analysis, Matplotlib for data plotting/visualization, and Seaborn for statistical plots.
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Master SciKit-Learn library to build, train and tune machine learning models using real-world datasets.
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Apply machine and deep learning models to solve real-world problems in the banking and finance sectors such as stock prices prediction, security news sentiment analysis, credit card fraud detection, bank customer segmentation, and loan default prediction.
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Understand the theory and intuition behind several machine learning algorithms for regression tasks (simple/multiple/polynomial), classification and clustering (K-Means).
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Assess the performance of trained machine learning regression models using various KPI (Key Performance indicators) such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error intuition, R-Squared intuition, and Adjusted R-Squared.
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Assess the performance of trained machine learning classifiers using various KPIs such as accuracy, precision, recall, and F1-score.
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Understand the underlying theory, intuition and mathematics behind Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTM).
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Train ANNs using back propagation and gradient descent algorithms.
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Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance.
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Master feature engineering and data cleaning strategies for machine learning and data science applications.
Business Intelligence & Analytics Lab (BIAL)
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