About Structured Thinking and Communication for Data Science Professionals

Structured Thinking and Communication is one of the most important skill data science managers and customers value today.

Sadly, there aren’t many resources which help people in this area. This course was created with an aim to address this need and provide people with frameworks and best practices on structured thinking and communications. Specifically, we will teach:

  • How to take ambiguous business problems and then break them into structured data science problems?

  • How to present your analysis and business insights in an impactful manner?

  • How to do clear and structured written communications which people can easily understand


Who should take this course?

Structured Thinking and Communication is a need for every data science professional today. It is a skill every data science leader wants in every member of their team. 

So, if you are a data professional currently solving business problems and communicating regularly with business stakeholders - you need to have Structured Thinking and Communications. This course will help you achieve that.


Course curriculum

  • 1
    Module #1: Introduction to Structured Thinking
  • 2
    Module #2: Introduction to the Course
  • 3
    Module #3: Structured Thinking for Data Science
    • 3.1 Case Study - Problem Solving without Structured Thinking
    • 3.2 Feedback on Case Study #1
    • 3.3 Case Study - Problem Solving using Structured Thinking
    • 3.4 Feedback on Case Study #2
  • 4
    Module #4: Role of Structured Thinking in Data Science Lifecycle
  • 5
    Module #5: Understanding and Defining the Problem Statement
    • 5.1 Importance of Defining the Problem Statement
    • 5.2 5-Step Framework
    • 5.3 TOSCAR Framework for Defining a Problem
    • 5.4 Examples using TOSCAR
    • 5.5 Decomposition
    • 5.6 Common Pitfalls to Avoid while Defining a Problem
    • Exercise on Problem Definition
    • 5.7 Framing the Problem Statement for the Course
  • 6
    Module #6: Hypothesis Building
    • 6.1 What is Hypothesis Building & Framework
    • 6.2 Why Hypothesis Building is Important and Who Should be Involved
    • 6.3 How to Build a Comprehensive Hypothesis Set
    • 6.4 Hypothesis Building Example
    • 6.5 Best Practices & Pitfalls
    • 6.6 Building Hypothesis for this Course’s Problem Statement
    • Exhaustive List of Hypotheses
    • Exercise – Generate hypothesis for the below problem statements
    • Write for Analytics Vidhya's Medium Publication
  • 7
    Module #7: Data Extraction and Cleaning
    • 7.1 Mapping Data Elements to Hypothesis
    • 7.2 Mapping Teams to Data Elements
    • 7.3 Framework CASED
    • 7.4 Data Pull and Clean
    • 7.5 Validating Hypothesis
    • 7.6 Summary
    • 7.7 Link Back to Business Problem
    • 7.8 Exercise
  • 8
    Module #8: Data Modelling
    • 8.1 What is a Predictive Algorithm?
    • 8.2 Modelling Framework TESTS
    • 8.3 Target Variable Discovery
    • 8.4 Evaluation Metric
    • 8.5 Sampling
    • 8.6 Train your Model
    • 8.7 Score new population
    • 8.8 Summary
    • 8.9 Link back to business problem
    • 8.10 Exercise
  • 9
    Module #9: Post-Modelling Steps
    • 9.1 From Model to Strategy
    • 9.2 Dashboards
    • 9.3 Link back to Problem Statement
    • Write for Analytics Vidhya's Medium Publication
  • 10
    Module #10: Structured Thinking for Communication
    • 10.1 Importance of Communication
    • 10.2 Pyramid Principle for Communication
    • 10.3 BONUS - Components of the Pyramid Principle (SCQA and MECE)
    • 10.4 Structured Email Writing
    • 10.5 Structured Note-Taking
    • 10.6 Introduction to Effective Presentations
    • 10.7 6-Step Framework for Building Effective Presentations
    • 10.8 BONUS - SCQA Framework for Presentation Introductions
    • 10.9 Tips and Best Practices for Building Presentations
    • 10. 10 The Art of Storytelling
    • 10.11 3-Step Storytelling Framework
    • 10.12 Structured Thinking for Blogging
    • Write for Analytics Vidhya's Medium Publication

Case Study - Bank with high Credit Charge offs

Help the bank solve the problem in structured manner

Smart Bank is facing high Credit charge offs and losses from its Credit Card Portfolio. You are the head of Credit Risk and want to solve this problem for the bank at any cost.
Case Study - Bank with high Credit Charge offs

Certificate of Completion

Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Certificate of Completion

Instructor(s)

  • Kunal Jain

    Founder & CEO

    Kunal Jain

    Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch.
  • Pranav Dar

    Senior Content Strategist and BA Program Lead, Analytics Vidhya

    Pranav Dar

    Pranav is the Senior Content Strategist and BA Program Lead at Analytics Vidhya. He has written over 300 articles for AV in the last 3 years and brings a wealth of experience and writing know-how to this course. He has a decade of experience in designing courses, creating content and writing articles that people love to read. Pranav is also an instructor on 14+ courses on Analytics Vidhya and is a passionate sports analytics blogger as well.
  • Tavish Srivastava

    Co-Founder and Chief Strategy Officer

    Tavish Srivastava

    Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of experience in markets including the US, India and Singapore and domains including Digital Acquisitions, Customer Servicing and Customer Management in the BFSI industry. Being an expert data science practitioner, Tavish has designed multiple corporate trainings focused on the practical aspects of Machine Learning and other emerging technologies.

FAQ

  • Who should take this course?

    This course is meant for any one looking to become better in problem solving or looking to improve their communications. This course covers methods and frameworks to solve problems in structured manner and ways to improve your written and verbal communications

  • When will the classes be held in this course?

    This is a self paced course, which you can take any time at your convenience over the 6 months after your purchase.

  • How many hours per week should I dedicate to complete the course?

    If you can put between 2 to 4 hours a week, you should be able to finish the course in 4 to 6 weeks.

  • Do I need to install any software before starting the course ?

    You will get information about all installations as part of the course.

  • What is the refund policy?

    The fee for this course is non-refundable.

  • Do I need to take the modules in a specific order?

    We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

  • Do I get a certificate upon completion of the course?

    Yes, you will be given a certificate upon satisfactory completion of the course.

  • What is the fee for this course?

    Fee for this course is INR 3,000

  • How long I can access the course?

    You will be able to access the course material for six months since the start of the course.

Customer Support for our Courses & Programs

We are there for your support when you need!

  • Phone - 10 AM - 6 PM (IST) on Weekdays (Mon - Fri) on +91-8368808185

  • Email [email protected] (revert in 1 working day)

  • Discussion Forum - answer in 1 working day