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Certificate Program in Data Science for Finance (CPDSF)
Build your career in Data Science for Finance
Live Online Instructor-led Weekend Program
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Indian Institute of Quantitative Finance
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Quick Facts
- Program Duration
- Program Schedule
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CPDSF Program | Data Science for Finance Course Highlights
- Comprehensive & Exhaustive Modules
- Weekly Classroom Style Live Sessions
- Python Practice Labs - Python Data Science Libraries
- Front-to-Back Data Science Applications Development
- Financial Datasets Mining Use Case Studies
- Tech Stack - Big Data Platforms & Visualization Tools
- BFSI Industry Focused - Capstone Project
About Certificate Program in Data Science for Finance
DATA SCIENCE BFSI CAREER ROLES
- Big Data Scientist
- Big Data Startgetist
- Big Data Analytics Expert
- Synthetic Data Expert
- Big Data Engineer
- Big Data Platform Expert
- Big Data Owner
STATISTICAL SKILLS
- Statistical Skills
– Descriptive & Inferential Statistics - Probability Skills
- Random Events, Experiments & Expectations - Exploratory data analytics Skills
- New Age unstructured data mining - Programming Skills
- Data Analytics in Python
- Data Visualization in Python
- Data Visualization in Business Intelligence Tools
CPDSF - KEY LEARNING OUTCOMES
- Big Data Science
- Structured & Unstructured Datasets
- Data Diagnostics & Distribution
- Data De-noising
- Data Mining & Exploration
- Data Manipulations & Transformations
- Data Augmentation & Synthesizing
- Data Visualization & Storytelling
CPDSF Course Outline
Module 1 - Applied Programming in Python
3 Weeks
- Python Toolsets & Libraries –
- Pandas, Numpy, Scipy,
- Matplotlib, Seaborn, Bokeh, Plotly - Business Intelligence Tools & Software
- Tableau, Power BI, Qlik
Module 2 - Applied Statistics & Probability
3 Weeks
- Multivariate Statistics
- Distribution Families – Discrete & Continuous
- Sampling Estimation & Central Limit Theorems
- Decision Theory & Science
- Random Experimental & Probabilistic Framework
- Bayesian Theory, Hypothesis Testing, Inference and Confidence Intervals
Module 3 - Big Data Mining & Manipulation
2 Weeks
- 7 V’s of Big Data - Volume, Velocity, Variability, Variety, Veracity, Value, Visualization
- Structured & Unstructured Datasets – Data Massaging & Manipulations
- High Dimensional Data Handling
- Financial Datasets - Risk, Treasury, Front Office Pricing & Valuation, Trading, Climate & Sustainability
Module 4 - Exploratory Data Analytics (EDA)
5 Weeks
- Data Extraction, Exploration & Error Handling
- Data Cleansing, Transformation & Aggregation
- Data De-noising, Distribution Fitting & Descriptive Statistics
- Data Augmentation , Generation & Synthetization
- Data Statistical Inference & Insights
- Data Visualization & Storytelling
Module 5 - Capstone Project
1 Month
- BFSI industry application project supervised by a BFSI SME expert industry mentor
| Batch | Start Date | Fee | Mode | Time |
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Why Choose CPDSF Course?
AI- ML Data Science Applications in Finance Podcast Series
AI- ML Data Science in Finance
Faculty
SANJAY BHATIA
Background
- Director in UBS - Risk Modelling & Analytics, Model Risk Management & Control, Chief Risk Office (CRO) Function
- MBA-Finance & MSc in Machine Learning & Artificial Intelligence from Liverpool John Moores University (LJMU)
- Post-Graduate Diploma in Machine Learning & Artificial Intelligence from IIIT-Bangalore
- Domain SME on Credit Risk , Derivatives Counterparty Credit Risk, Derivative Pricing, Stochastic Modelling, Stress Testing
ML Expertise (Teaching ML for Quantitative Finance & Risk Management)
- Financial Prediction (Regression & Classification ) - Lasso/Ridge Regression, CART Decision Trees, Ensemble Learning (Bagging & Boosting) & Support Vector Machines (SVM)
- Financial Time Series Forecasting - (Recurrent) Neural Networks, RNN-LSTM, RNN-GRU, Hybrid-RNN-LSTM-GRU
- Financial Instrument Pricing - Non-Linear & High Dimensional Derivative Pricing using Neural Networks
- ML Model Optimization – Hyperparameters Tuning K-Fold Cross-Validation, Stochastic Gradient Descent, Convergence etc.
- Regulatory & Industry ML Adoption, Challenges & Use Cases – Model Explainibility, Performance Evaluation & Testing
Prof Rituparna Sen
Background
- - Associate Professor at the Applied Statistics Division, Indian Statistical Institute, Bangalore.
- - She worked as Assistant Professor at the University of California at Davis from 2004–2011
- - She has also taught courses in Chennai Mathematical Institute and Madras School of Economics.
- - An elected member of the International Statistical Institute
- - A council member of the International Society for Business and Industrial Statistics.
- - She has been awarded the Young Statistical Scientist Award by the International Indian Statistical Association, the Best - Student Paper Award by the American Statistical Association and the Women in Mathematical Sciences award by Technical University of Munich, Germany.
ML Expertise (Teaching ML Statistics for Computational Finance)
- - Authored A Book on Computational Finance with R
- - Authored 30+ Research Papers & Articles
- - Editor: Journal of Applied Stochastic Models in Business and Industry
- - Member of CAIML (Center for Artificial Intelligence and Machine Learning) at ISI
- - Associate Editor of several other journals
- - Guided several masters students on theses in the ML area
Dr. Arindam Chaudhuri
Background
- Worked as post doctoral fellow with Department of Computer Science, University of Copenhagen and Department of Computer Science, Technical University of Berlin.
- Worked as researcher with Siemens Research Labs Amsterdam and Samsung Research Labs at New Delhi & Bangalore.
- His current research interests include business analytics, artificial intelligence, machine learning, deep learning.
- He has published 4 research monographs and 60 articles in international journals and conference proceedings.
- He has served as reviewer for several international journals and conferences.
ML Expertise (Teaching ML Statistics for Computational Finance)
- Implemented Machine Learning Methods - Support Vector Machines, Artificial Neural Networks, Deep Learning Networks, Clustering, Genetic Algorithms and Evolutionary Computing with basic mathematical foundations of probability theory, fuzzy sets, rough sets, possibility theory and a variation of these for solution of various business problems.
- Integrated various artificial intelligence methods to form different soft computing frameworks such as neuro-fuzzy, fuzzy-genetic, neuro-genetic and rough-neuro-fuzzy-genetic.
- Successfully applied these methods for different categories of industrial problems such as decision theory, time series forecasting and prediction, image compression, sentiment analysis, recommendation systems, social networks analytics in order to achieve better results
Ritesh Chandra
Background
- - B. Tech. (IIT-Kanpur), PGDM (IIM-Calcutta), CFA
- - 15 years banking experience in risk management with domestic and MNC banks
- - Member, Board of Studies in the area of Finance at IMT-CDL, Ghaziabad
ML Expertise
- - Passionate about teaching. Has been conducting workshops / training programmes for the last 8 yrs in areas of Quantitative Finance, Financial Management, Risk Management and Machine Learning
- - Has contributed to a book on Applications of Blockchains in Financial Services industry
- - Has worked as visiting faculty with several institutions.
Rupal
Background
- B.Tech from IIT, Kanpur and Executive MBA from IIM Kozhikode.
- Currently working as Vice President, Fixed Income at one of the largest International Bank for their Corporate Investment Banking Division.
- Prior to this he was working as Assistant Vice President at Credit Suisse, Investment Banking Division.
- Regularly worked as an internal trainer in the organizations that he has worked in.
ML Expertise (Teaching ML Application for Financial Systems)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning & Data Science.
- His areas of interest are fixed income pricing, financial analytics and statistical learning
Dr. Hari
Background
- M.Sc. in Mathematics from B.H.U. Varanasi.
- Awarded four gold medals because of his outstanding performance in B.H.U.
- He has completed his Ph.D. in Mathematics from Indian Institute of Science, Bangalore.
- He has also worked with Deep Value (Algorithm trading firm).
- Currently involved with one of the world's leading analytics product company.
ML Expertise (Teaching ML Application for Algorithmic Trading)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning for Trading.
- ML applications for Algorithmic Trading, High Frequency Trading & Quantitative Analytics
Admission Process in Data Science for Finance Course
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Send Your Application
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Get on a call with a counsellor
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Wait for Application Acceptance
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Pay the fee & join the upcoming batch
Finance your Study
Educational Loans
We are very happy to help you progress to greater heights in your career in every way possible. Education loans available at 0% interest for full time Indian residents. Easy EMI plans available.
Student Aid
Encourages the full time students to enter this domain, benefits, if you are still pursuing formal education.
Get Answers
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Will this data science course cover python in detail?
Yes, CPDSF covers Python programming and coding from scratch to a level required for Data Science. It covers all data manipulations, analytics and visualization in Python with potent libraries and functions in Pandas, Numpy, Scipy, Matplotlib, Seaborn, Statsmodels etc. -
How important is SQL for data science?
SQL empowers data scientists to efficiently query and handle vast datasets. It is a handy skill for data extraction, data aggregation and data mapping. -
What is the role of certified data scientist in finance?
A certified data scientist has the required skills and core competencies to pursue a broader career options in finance across varied data science job role families like- • BIG Data Scientist
- • BIG Data Strategist
- • BIG Data Analytics Expert
- • Synthetic Data Expert
- • BIG Data Engineer
- • Big Data Platform Expert
- • Big Data Owner
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How much does a certified data scientist earn in finance?
It varies a lot across potential employers and job role families but for a given experience level (like entry, mid, lateral careers), a certified data scientist earns at least 70%-150% more than a like-for-like BFSI IT engineer or data analyst role family. -
What skills does a certified data scientist need?
A certified data scientist requires below must-to-have skills and core competencies Descriptive and Inferential- • Statistical Skills - Descriptive & Inferential statistics
- • Probability Skills - Probability Estimation & Random Event Prediction Skills
- • Exploratory Data Analytics Skills
- • Data Mining & Manipulation Skills – Convention Structured & New Age Unstructured Data
- • Programming Skills
- - Data Analytics in Python
- - Data Visualization in Python
- - Data Visualization in Business Intelligence Tools
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Will you learn coding in the course?
Yes, CPDSF exhaustive course coverage holistically prepares the aspirants to become full-fledged Data Scientist for BFSI Financial domain by broadly imparting below know-how skill-set: -
What will I learn at IIQF to help me practically at work?
Yes, CPDSF exhaustive course coverage holistically prepares the aspirants to become full-fledged Data Scientist for BFSI Financial domain by broadly imparting below know-how skill-set:- • Big Data Science
- • Structured & Unstructured Datasets
- • Data Diagnostics & Distribution
- • Data De-Noising & De-Trending
- • Data Mining & Exploration
- • Data Manipulations & Transformations
- • Data Augmentation & Synthesizing
- • Data Visualization & Storytelling
-
How to Become a Data Scientist in the Investment Industry?
To become a data scientist in investment industry and broader BFSI sector, one has to have the know-how of the domain specific data science use cases like handling market datasets which are characterized as noisy (low signal-to-noise ratio), high dimensional and time varying (heteroskedastic). This involves performing data mining and manipulations on capital market data for various asset classes (equity, fixed income, interest rates, forex, commodity, alternatives etc.) to gain statistical insights into the variable distributions, variable significance, variable time correlations, covariations with other variables, variable tail behaviour, latent variables etc. It also requires one to hone statistical, probability, programming and moreover big data exploration, analytics & visualization skills. CPDSF provides an exhaustive coverage of these bouquet of skills for data science in a specialized domain-centric way to deep-dive into the use-cases and applications for investment finance. -
What data science and coding knowledge do I need to take this certificate course?
CPDSF is designed to cover from scratch all the required coding and programming skills in the de-facto data science language i.e., Python as well as a know-how of big data platforms and data visualization using python libraries & business intelligence toolsets. Hence, there are no prerequisites for the course (while some prior understanding would be advantageous) given that all coding skills are imparted as part of the preparatory primer modules within the course. -
Can I download the Excel files and financial modelling templates for the course?
Upon enrolling for CPDSF, a candidate would be provided with the curated materials (case studies, problem sets, datasets, codes, required libraries, functions etc.) to get them started and going into front-to-back designing and development of data science applications for various financial sub-domains like investment, risk, trading, financial economics, financial instrument valuation/pricing, business/portfolio strategy etc.How can I persuade my employer to take this course for me?
IIQF CPDSF certification is a BFSI domain centric, application focussed and holistic course for working professionals in finance to get a practical hand-on into areas of data science as specifically applied for finance.Can I email the instructor if I have any questions?
A candidate would be provided with a direct access to the industry and academic faculty instructors through various channels and forums which allows them to regularly and seamlessly raise any doubts, questions, clarifications, seek further information and also request for a personalized mentoring guidance while being on their CPDSF learning journey.What sets your Data Science in Finance course apart from other similar offerings?
There are multiple data science courses in the market yet most of them are very generic and theory oriented which falls short of even coming any way near to addressing the specialized use cases for the challenging and diversified financial domain. CPDSF is distinctively designed to offer:- • Class room style learning through live lectures
- • Teaching instructors are industry subject matter experts and academic researchers.
- • Rigorous coverage of the statistics, probability and mathematics behind data science.
- • Financial application and use case centric leaning for varied sub-domains like risk, accounting, financial economics, investments, trading, financial regulation etc.
- • Implementation and Prototyping practice labs to build real skills in Python and other toolsets.
- • Access to comprehensive and curated materials – user case library, case studies, problem sets, datasets, python guides on set-up & libraries, python codes/utility functions etc.
- • Career coaching and counselling support for candidate profiling, CV building, skill mapping, job filtering/hunting and interview preparations.
- • Industry placement support for the Indian Banks, Global Banks, Investment Funds, Fintech firms, Consulting firms, Product companies, IT service companies.
How does data science play a role in shaping financial decisions?
As per the World Economic Forum (WEF), by 2025, 463 Exabyte’s of data per day will be created which a decade back, was only around 1 Exabyte of data per day. An Exabyte is a 1 byte followed by 18 zeros!! This makes a strong case for data science in the data-intensive financial world as the big banks, funds and other premier financial institution would be required to perform big data mining at a very large scale (more than ever before) to remain relevant and gain a competitive advantage over challenger Fintech firms. For driving their business strategies for mainstream/ alternative investments, trading, portfolio management, product engineering, climate risk, the big street financial firms need to harness the potential of modern unstructured data (social media, textual, satellite imaging, videos data) for augmenting it with conventional structured (numerical) data. So data science is becoming decision science as these firms are building huge capabilities in data science driven predictive and prescriptive analytics for data-drive decision making.What are the core concepts covered in the Data Science for Finance course?
IIQF CPDSF certification comprehensively covers:- • Financial Risk Data Mining & Exploration
- - Analytical Processing of Structured & Unstructured Datasets
- - Financial Risk Data Diagnostics, Distribution & De-noising
- - Financial Risk Data Manipulation & Transformations
- • Financial Risk Data Generation & Patterns Detection
- - Alternative Data Sourcing for Risk, Finance, Trading & Treasury
- - Financial Risk Data Augmentation & Synthesizing
- • Financial Risk Data Inference & Insights
- - Financial Risk Data Inferencing & Visualization
- - Financial Risk Data Storytelling & Decision Science
Can beginners with no prior data science or finance knowledge enrol in this course?
Yes, as IIQF CPDSF is intended and designed to build skills ground-zero-up by providing all the necessary background and basic know-what, know-why and know-how as starting points for somebody coming from a different domain or even a fresh starter.What programming languages and tools are essential for data science in finance?
Broadly speaking, the below technical toolsets are required for data science in finance:- • Python Toolsets & Libraries
- - Pandas, Numpy, Scipy
- - Matplotlib, Seaborn, Bokeh, Plotly
- • Business Intelligence Tools & Software>
- - Tableau or Power BI or Qlik
- • Big Data Platforms & Infrastructure
How are real-world financial datasets utilised in the course curriculum?
CPDSF uses actual financial market time series data (across asset classes), credit default and recovery data (from external rating agencies), fraud data, sentiment data (capturing market expert opinions) to name a few that are used to build the data science applications for financial use cases.Are there any specific projects or case studies focused on data science applications in finance?
CPDSF bakes in data science applications for financial use cases for example,- • Data mining for Housing Price Index or Equity Price Index construction
- • Data mining for credit rating, default and recovery prediction.
- • Text mining for creating sentiment indicators in the models
- • And many others
What are the career prospects after completing the Data Science in Finance course?
CPDSF prepares the aspirants for wider span of data science career roles in Finance like:- • BIG Data Scientist
- • BIG Data Strategist
- • BIG Data Analytics Expert
- • Synthetic Data Expert
- • BIG Data Engineer
- • Big Data Platform Expert
- • Big Data Owner
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