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.
- 1.1 What is Structured Thinking?
- 1.2 Why is Structured Thinking Required?
- 1.3 Who needs Structured Thinking?
- 2.1 Instructor Introduction FREE PREVIEW
- 2.2 Methodology
- 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
- 4.1 Role of Structured Thinking in Data Science Lifecycle
- 4.2 Structured Thinking at Each Stage of the Data Science Lifecycle
- Exercise for Understanding the Data Science Lifecycle
- 5.1 Objective for Communication with Stakeholders
- 5.2 Types of Problem Statements
- 5.3 Problem Statement for this Course
- 5.4 Biases and Pitfalls
- 5.5 Convert Business Problem to Data Problem
- 5.6 Setting the Problem Statement for the Course
- 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
- 7.1 Mapping Hypothesis Set to Data Requirements
- 7.2 Feasibility Analysis / Frequency of Data
- 7.3 Normalization and Data Structuring
- 7.4 Data Cleaning
- 7.5 Validate Hypothesis through Exploration
- 7.6 Link back to Course’s Problem Statement
- 8.1 Select the Validation Set
- 8.2 Select the Right Model / Evaluation Metric
- 8.3 Build Model
- 8.4 Validating Models
- 8.5 Link back to Course’s Problem Statement
- 9.1 Build Strategy from Models
- 9.2 Business Metric v Statistical Metric
- 9.3 Finalizing Framework for Monitoring
- 9.4 Structuring Dashboarding
- 10.1 Importance of Communication
- 10.2 Pyramid Principle – Refresher
- 10.3 Structured Email Writing
- 10.4 Structured Note-Taking
- 10.5 Introduction to Effective Presentations
- 10.6 Framework of a Presentation
- 10.7 Bonus: SCQA Structure with Case Study
- 10.8 Tips & Best Practices for Building a Presentation
- 10.9 The Art of Storytelling
- 10.10 Storytelling Structure - Case Study
- 10.11 Structured Blogging
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 is a data scientist and Senior Editor for Analytics Vidhya. He has experience in data visualization and data science. Pranav has previously worked for a number of years in the learning and development field for a globally-known MNC. He brings a wealth of instructor experience to this course as he has taken multiple trainings on data science, statistics and presentation skills over the years. He is passionate about writing and has penned over 200 articles on data science for Analytics Vidhya.
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.
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.
Support for Structured Thinking and Communication for Data Science course can be availed through any of the following channels: