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Applied Statistics for Data Science

This course provides a rigorous, hands-on overview of statistical modeling for data science. You'll cover probability fundamentals and hypothesis testing, as well as advanced topics in regression and foundational machine learning. Along the way you’ll apply your skills to real-life projects in online gaming, business analysis, and telecommunications. At the end of the course, you'll be well equipped to dive deeper into an advanced career in data science.

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Debjyoti Paul
Data Scientist at Amazon
Real-world projects that teach you industry skills.
Learn alongside a small group of your professional peers
Part-time program with 2 live events per week:
Next Cohort
January 23, 2023
4 weeks
US$ 400
or included with membership

Course taught by expert instructors

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Debjyoti Paul

Data Scientist at Amazon

Debjyoti Paul is currently a Data Scientist at Amazon with a wealth of experience working across different domains from search engine, ad ranking, banking, fraud and moderation. Prior to Amazon, Debjyoti worked in Decision Science at HSBC and Machine Learning at Microsoft.

The course

Learn and apply skills with real-world projects.

A custom sampler for use in modeling a specific situation.
    • Basic Probability Theory, Axioms, Bayes' Therem
    • Probability Distribution
    • Central Tendencies, Correlation, Covariance
    • Real-life Use Cases
    • Counting and Pigeon Hole with Application
    • Python Review
    An A/B test for an online gaming company.
      • Law of Large Numbers
      • Central Limit Theorems - How is a census done?
      • Dealing and Testing Hypotheses
      • Choosing the Right Statistical Test
      • Limitations of Hypothesis Testing
      Click prediction model for an online retail business.
        • Linear Regression Applications
        • Logistic Regression
        • Generalized Linear Models
        • ANOVA for Linear Regression
        Customer segmentation and generating synthetic data for modeling using generative models.
          • Estimating probability distribution using Gaussian Mixture Model
          • Expectation Maximization
          • Connection of Gaussian Mixture Model with Popular clustering technique - KMeans
          • Variational Inference and Variation Autoencoder

          Real-world projects

          Work on projects that bring your learning to life.
          Made to be directly applicable in your work.

          Live access to experts

          Sessions and Q&As with our expert instructors, along with real-world projects.

          Network & community

          Core reviews a study groups. Share experiences and learn alongside a global network of professionals.

          Support & accountability

          We have a system in place to make sure you complete the course, and to help nudge you along the way.

          This course is for...

          Data Analysts/Business Analysts looking to further their career into Data Science

          Other software engineers seeking to transition into data science and statistics related roles


          Foundation in basic statistics (mean, media, mode, and basic summary statistics)

          Knowledge of Python programming (variables, functions, lists, loops)

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