Statistics for Risk Modeling (SRM)

30,000.00

SRM – Statistics for Risk Modeling  – Regular Lectures
Faculty: Shivangee Agarwal
Course Duration: 200+ hours

Category:

This course is meticulously crafted to prepare candidates for the Statistics for Risk Modeling (SRM) Exam, which evaluates your ability to utilise various statistical methods and models to analyse data effectively.

Key Topics:

  • Regression and Time Series Models: Techniques for predicting and analyzing trends over time.
  • Principal Components Analysis: Methodology for reducing dimensionality in data.
  • Decision Trees: Tools for making decisions based on data characteristics.
  • Cluster Analysis: Techniques for classifying and grouping data.
  • Model Selection and Validation: Processes for choosing the best models and ensuring their accuracy and reliability.

This course ensures that you are well-prepared to demonstrate your ability to apply these statistical methods and models, pivotal for successful risk modelling and actuarial work.

WHAT YOU WILL GET

Course Deliverables

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Recorded lectures
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Soft copy of resources
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Access to Student dashboard
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100% Placement & Training Assistance
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Technical support
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Practice papers
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Chapter wise MCQ Bank
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Live Industry expert sessions
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Live Concept classes
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Hard copies of revision note book & Flashcards
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Doubt solving sessions
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Course extension (2 more terms)
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Interactive Q & A Forum

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FACULTY

Meet The Instructor


Shivangee Agarwal is a Qualified Actuary with a Master’s in Data and Business Analytics from IIM Indore. She secured All India Rank 1 in CT7 – Business Economics and holds a B.Com (Hons in Finance) from St. Xavier’s College, Kolkata. Over 6+ years, she has taught 2500+ students in Actuarial and Data Analytics domain, becoming a favourite coach for CS2, CB2, CP2, and CP3 papers. She has expertise in Excel and R Programming. Her inspiring journey led her to conduct workshops and lectures at various colleges like Lady Shri Ram College, St. Xavier’s College, SRCC, Mithibai College, IIM Indore and IBS Hyderabad.

PLAYLIST

Demo Class Videos