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What you'll learn?

  • A solid understanding of Data Science and Data Analytics.
  • The scope of Data Science and Data Analytics in e-commerce.
  • The Data Science and Data Analytics challenges in the e-commerce industry.
  • The solutions for the data science challenges in the e-commerce industry.
  • How Data Science and Data Analytics are being leveraged by e-commerce businesses.

Requirements

  • Must have a strong inclination to learn new concepts.
  • Doesn’t require any prior knowledge as this is a beginner-level course.
  • A laptop or a desktop computer with an internet connection.

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Description

Designed and curated by industry experts, this course is a beginner’s guide to get a good insight into the fundamental concepts of Data Science and Data Analytics and how online e-commerce businesses are benefiting from customer analytics and predictive analytics.

The course wraps up with a final discussion on the Data Science challenges in the e-commerce industry and how they can be overcome.

Who this course is for:

  • Business professionals who would like to have their own online e-commerce store.
  • Any MBA, B.Tech or any college graduate who wants to jumpstart their career in e-commerce.
  • Digital Marketing enthusiasts who would like to widen their existing knowledge in the digital space.

Course content

Total:60 Total hours: 2 Hours
  • Understanding Data Science and Data Analytics 3 MIN
  • The significance of Data Science and Data Analytics 2 MIN
  • An Overview of Data Science & Data Analytics In E-Commerce 2 MIN
  • Retain customers 2 MIN
  • Give product recommendations 1 MIN
  • Analyze customer sentiments 1 MIN
  • Predict the lifetime value of the customers 1 MIN
  • Detect Fraud 2 MIN
  • Recommendation engines 2 MIN
  • Market Basket Analysis 2 MIN
  • Warranty Analytics 2 MIN
  • Price Optimization 2 MIN
  • Inventory Management 2 MIN
  • Location of new stores 1 MIN
  • Customer sentiment analysis 1 MIN
  • Merchandising 1 MIN
  • Lifetime value prediction 2 MIN
  • Track Shopping Pattern Analysis 1 MIN
  • Personalized Customer Service 1 MIN
  • Predictive Analysis 1 MIN
  • Focus on Micro-Moments 1 MIN
  • Easy Online Payments 1 MIN
  • Guaranteeing the accuracy of the collected Data 1 MIN
  • Sourcing and collecting the data 1 MIN
  • Ensuring unbreakable data security 1 MIN
  • Drawing the insights requires time 1 MIN
  • Making the customer trust the retailers with their data 1 MIN
  • Market Basket Analytics: Ship Before You Shop 2 MIN
  • Predictive Enhancements In Pricing Models 2 MIN
  • Suggestive Search: The Game of Relevance 2 MIN
  • Customer Behavior Analysis 1 MIN
  • Optimized Supply Chain Management 3 MIN
  • What Is Data Analytics? 2 MIN
  • Why is Data Analytics Important? 2 MIN
  • What Is the Data Analytics Process? 3 MIN
  • What Is the Importance of Data Analytics in Research? 1 MIN
  • What is Data Analytics: Types of Data Analytics? 3 MIN
  • Artificial Intelligence and Machine Learning 2 MIN
  • How to Become a Data Analyst 2 MIN
  • Where is the data? 3 MIN
  • What can be done with data analysis? 6 MIN
  • The transformation 1 MIN
  • E-commerce 1 MIN
  • Manufacturing 2 MIN
  • Banking and Finance 2 MIN
  • Transport 2 MIN
  • Healthcare 1 MIN
  • Digital Marketing 3 MIN
  • Supply Chain Management 1 MIN
  • Merchant/Customer Fraud Detection 1 MIN
  • Merchant Analytics 1 MIN
  • Recommender Systems 1 MIN
  • Product specific analytics 1 MIN
  • Online Marketing Analytics 1 MIN
  • User Experience Analytics 1 MIN
  • Stages of data analysis 3 MIN
  • Types of e-commerce data analysis 6 MIN
  • Setting up e-commerce data analysis from A to Z 3 MIN
  • How Data Science Boost the Sales of the E-commerce Industry 8 MIN
  • Conclusion 2 MIN

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