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Professional Certificate in

E-commerce Analytics Course

With our E-commerce Analytics course, you will emerge as an HR professional ready to grab innumerable opportunities in the space of Self-Service Avenues, Talent Management, Payroll, and Reporting.
  • Get Trained by Trainers from ISB, IIT & IIM
  • 16 Hours of Intensive Classroom & Online Sessions
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  • Get Quiz Questions and Use Cases
E-commerce Analytics course - 360digitmg
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Academic Partners & International Accreditations
  • E-commerce Analytics certification course  with Microsoft
  • E-commerce Analytics certification course  certification with NASSCOM certificate
  • E-commerce Analytics certification course  with INNODATATICS certificate
  • E-commerce Analytics certification course  with TUV
  • E-commerce Analytics certification course with SUNY
  • E-commerce Analytics certification course  with NEF

With the advancement of the internet, E-commerce has boomed, and processing the large amount of data that is being generated has become paramount for the e-commerce industry to make informed decisions. E-commerce Analytics is referred to as a tool or strategy that is designed to analyze large amounts of data.
As e-commerce entirely exists in a virtual space it generates complex datasets about customer behavior. With the humongous amount of data generated, finding the right tool is vital for business development and survival in the competitive world.
Technology evolves rapidly and shopping trends change daily. So using the same tool for a longer period is not viable. To stay ahead in the market and make reliable and profitable insights, e-commerce analytics tools should be effective.

E-commerce Analytics

Program Cost

INR 12,000/-

Overview of E-commerce Analytics Course

With the majority of companies going online, brands are opting for a cloud-based commerce approach to deliver the best shopping experience to customers. Unlike previously, now the companies should provide seamless service across every touchpoint- social, mobile, web, and store. With the help of E-commerce Analytics, companies can track the online purchase activity of customers and can use pop-ups, gift cards, or discount coupons to re-direct the customers to their brand. With the 360DigiTMG E-Commerce Analytics program, you can learn to adopt tools that integrate with your unified commerce platform and can track and keep up with the rapidly evolving customer journey.

E-commerce Analytics Course Training Learning Outcomes

Machine Learning and Big Data Analytics have become game-changer in the E-Commerce domain. This Certification Program is a sui generis attempt to blend Machine Learning solutions for traditional customer retention problems and loyalty. Specifically designed to suit energy and resource professionals and data professionals who wish to understand the application of Big Data Analytics, Machine Learning, Neural Networks, and Deep Learning to E-Commerce industry data. This E-Commerce Analytics course is meant for professionals from the E-commerce industry as it provides a comprehensive picture of how Data Science and Artificial Intelligence can be leveraged to increase productivity and profits in decisions. Understanding the applications of Data Science, Machine Learning to the energy and resource industry will be the prime objective of this content-rich program.

Work with various information sources
Analyse structured and unstructured data using different tools and techniques
Develop an understanding of descriptive and predictive analytics
Apply data-driven, machine learning approaches for HR functions such as attrition prediction
Understanding how surveys work and perform analytics on them
Use ML techniques to create a personalized experience for employees
Use data visualisation concepts to represent data for easy understanding

Block Your Time

hr analytics course - 360digitmg

16 hours

Classroom Sessions

hr analytics course - 360digitmg

20 hours

Programming Videos

Who Should Sign Up?

  • IT Engineers
  • Data and Analytics Manager
  • Business Analysts
  • Data Engineers
  • Banking and Finance Analysts
  • Marketing Managers
  • Supply Chain Professionals
  • HR Managers
  • Math, Science and Commerce Graduates

E-commerce Analytics Course Modules

  • This module introduces the topic of E-commerce
  • Examples like business expansion
  • Basic information on AI and ML
  • Introduction to the various stages of analytics and using an historical example of the various stages can help understand the steps involved in analytics
  • Discussion on CRISP-ML (Q)
  • Explanation on the importance CRISP-ML (Q) in various Data Science related projects
  • Explanation on unsupervised and supervised learning
  • Explanation on the training , validation and testing stages of supervised learning
  • Understanding right fit, underfit and overfit scenarios in supervised learning
  • Understanding Hyperparameter tuning in the context of overfit
  • This module covers the various steps of EDA
  • Discussion on the 4 business moment decisions in EDA
  • Discussion on univariate, bivariate, and multivariate EDA steps
  • Explanation of the various pre-processing steps involved in any Machine learning or Artificial intelligence project
  • Brief introduction to feature engineering
  • Re-cap of unsupervised learning in ML
  • Understanding of clustering in layman’s terminology
  • Understanding K-means clustering from a technical perspective
  • Understanding the usage of elbow curve and silhouette scoring to decide on the ideal number of clusters
  • Re-cap on supervised learning
  • Understanding of nearest neighbor in ML
  • Understanding KNN algorithm and distance metrics
  • Understanding of early stopping point in training phase
  • Explanation of the need for odd number of neighbors when classifying
  • Understanding the decision making process for humans and correlating to the topic of Decision Trees
  • Understanding the various components of a decision tree
  • Understanding the idea behind root node and how it affects the overall tree
  • Understanding entropy and information gain concepts of decision tree to make logical choices on root node
  • Usage of hyperparamter tuning to optimize the tree
  • Introduction to the concept of line equation
  • Correlation of line equation in ML terms
  • Understanding of correlation and its importance in Linear regression
  • Differentiating between the equations of SLR and MLR
  • Explanation on the OLS concept
  • Re-cap on the topics covered before
  • Understanding the importance of AI
  • Understanding the reasoning behind the concept of neural networks
  • Explanation on the perceptron algorithm
  • Introduction to multilayer perceptron
  • Understanding the concept of weight calculations
  • Brief intro in gradient descent
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