Core Statistics for datascience

Core Statistics for Datascience

Outline for Talk

Part 1:

• Introduction to Colab & Introduction to Data Science
• Data science key facts
• Data science libraries

Part 2:

Notebooks in this repo [EDA, Feature Engineering and Predictive Modeling Theory]:

• Data Science Workflow
• Trends Supervised Learning
• Clustering
• GMM
• PCA
• Recommendations
• Network Analysis

Part 3 (Bonus):

• Doing ML in the cloud: walk through census project AWS Sagemaker

Additional Related Topics from Noah Gift

His most recent books are:

• Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018)
• Python for DevOps (O'Reilly, 2020).

His most recent video courses are:

• Essential Machine Learning and A.I. with Python and Jupyter Notebook LiveLessons (Pearson, 2018)
• AWS Certified Machine Learning-Specialty (ML-S) (Pearson, 2019)
• Python for Data Science Complete Video Course Video Training (Pearson, 2019)
• AWS Certified Big Data - Specialty Complete Video Course and Practice Test Video Training (Pearson, 2019)
• Building A.I. Applications on Google Cloud Platform (Pearson, 2019)
• Pragmatic AI and Machine Learning Core Principles (Pearson, 2019)
• Data Engineering with Python and AWS Lambda (Pearson, 2019)

His most recent online courses are:

• Microservices with this Udacity DevOps Nanodegree (Udacity, 2019)
• Command Line Automation in Python (DataCamp, 2019)
• AWS Certified Cloud Practitioner 2020-Real World & Pragmatic.