Building Data Scientists
- 1. Building Data Scientists Machine Learning Mastery in Python Mitch Sanders Jan 10th 2018 Internal Use - Confidential
- 2. 2 of Y Internal Use - Confidential Trend #2 Non-Data Scientists will perform more fairly sophisticated analytics alongside data scientists Data Scientist Algorithm Coder Data Science Citizens Advanced Analytics Programmers Statisticians Business Analyst Coders Data Science continues to develop specialties - this means the mythical ‘full stack’ data scientist will disappear Trend #1 Data Scientist Data Engineer Algorithm Coder Data Storyteller Industry Trends for 2018 – How what we’re doing fits into the future the Context
- 3. 3 of Y Internal Use - Confidential the Course Machine Learning Mastery - Understand Your Data - Create Accurate Models - Work Projects End-To-End • 16 weeks – May-Oct., 2017 • 20+ class hours – 20% homework, 80% live coding • 17 notebooks – Python code templates • 4 Prerequisites – Coding, statistics, algorithms, thirst to learn • 1 Textbook – Machine Learning Mastery w/ Python -Dr. Jason Brownlee • 1 Teacher – Mitch Sanders w/ Assistant – Uday Waghmare • 14 Students – global: software engineers, adv. analysts, statisticians • Platform – Jupyter, Python 2.7, Anaconda • Code Repository – GitHub • NPS Survey – Survey Monkey, LTR = 90 • Awarded – “On the Spot”
- 4. 4 of Y Internal Use - Confidential the Content Prepare & Explore Model Improve Accuracy & Finalize Python ML Ecosystem SciPy Scikit-learn Crash Courses NumPy Matplotlib Pandas Load Libraries & Data Descriptive Statistics Attribute Data Types Class Distribution Correlation Analysis Skew of Univariates Pre Processing Rescale Standardize Normalize BinarizeFeature Selection Tree & Univariate Recursive -RFE Principle Comp. Analysis - PCA Feature Importance Resampling Split into Train/Test K-fold Cross Validation Leave One Out Repeated Random Evaluation Metrics For Classification For Regression Spot Check Classification Algorithms Linear – • Logistic Regression • Linear Discriminate Analysis (LDA) Non-linear – • K-Nearest Neighbor (KNN) • Naïve Bayes • Class & Regression Trees (CART) • Support Vector Machines (SVM) Compare Algorithms Spot Check Regression Algorithms Linear – LR, LASSO, ElasticNet (EN) Non-Linear – CART, SVR, KNN Automate w/ Pipelines Preparation Pipelines Feature Extraction Pipelines Modeling Pipelines Ensembles - Performance Improvements Boosting – • AdaBoost, • Gradient Boosting (GBM) Bagging – • Random Forest, Extra Trees • Voting Algorithm Parameter Tuning Parameters Grid Search Random Search Finalize Model Predict on Validation Data Create Standalone on Entire Data Save Model for Production Visualization Univariate Plots Multivariate Plots Case Studies #1 & #2 Key concepts – and flow – the 17 notebooks #1 #17
- 6. 6 of Y Internal Use - Confidential the Course Syllabus Python Ecosystem for Machine Learning • Python • SciPy • Scikit-learn • Python Ecosystem Installation • Summary Crash Course in Python and SciPy • Python Crash Course • NumPy Crash Course • Matplotlib Crash Course • Pandas Crash Course • Summary How To Load Machine Learning Data • Considerations When Loading CSV Data • Pima Indians Dataset • Load CSV Files with the Python Standard Library • Load CSV Files with NumPy • Load CSV Files with Pandas • Summary Understand Your Data With Visualization • Univariate Plots • Multivariate Plots • Summary Prepare Your Data For Machine Learning • Need For Data Pre-processing • Data Transforms • Rescale Data • Standardize Data • Normalize Data • Binarize Data (Make Binary) • Summary Feature Selection For Machine Learning • Feature Selection • Univariate Selection • Recursive Feature Elimination • Principal Component Analysis • Feature Importance • Summary Evaluate the Performance of Machine Learning Algorithms with Resampling • Evaluate Machine Learning Algorithms • Split into Train and Test Sets • K-fold Cross-Validation • Leave One Out Cross-Validation • Repeated Random Test-Train Splits • What Techniques to Use When • Summary Machine Learning Algorithm Performance Metrics • Algorithm Evaluation Metrics • Classification Metrics • Regression Metrics • Summary Spot-Check Classification Algorithms • Algorithm Spot-Checking • Algorithms Overview • Linear Machine Learning Algorithms • Nonlinear Machine Learning Algorithms • Summary Spot-Check Regression Algorithms • Algorithms Overview • Linear Machine Learning Algorithms • Nonlinear Machine Learning Algorithms • Summary Compare Machine Learning Algorithms • Choose The Best Machine Learning Model • Compare Machine Learning Algorithms Consistently • Summary Automate Machine Learning Workflows with Pipelines • Automating Machine Learning Workflows • Data Preparation and Modeling Pipeline • Feature Extraction and Modeling Pipeline • Summary Improve Performance with Ensembles • Combine Models Into Ensemble Predictions • Bagging Algorithms • Boosting Algorithms • Voting Ensemble • Summary
- 7. 7 of Y Internal Use - Confidential data science student questions - 1 “So you do Data Science work. What really does that involve? And how is that different than programming, statistical work or data engineering?” “I want to learn Data Science. Between R, Python and SAS, where should I start and what are the Pros and Cons of each?” “What is OOP (Object orientated programming) and Structured Programming and what’s the difference between them?" “What is main differences between Python 2.7 and Python 3.x versions? And why do so many developers stay with Python 2.7?” "What is the difference between Supervised Learning an Unsupervised Learning?" "What's different graphing might a univariate have compared to a bivariate analysis? Can you graph multivariate?" "How do you explain machine learning to an 8-year old child?" "What is Gradient Descent? "What is multicollinearity and how you can overcome it?"
- 8. 8 of Y Internal Use - Confidential data science student questions - 2 "What is the curse of dimensionality?" "What do you understand by Hypothesis in the content of Machine Learning?" "What's the difference between a Test Set and a Validation Set?" "What is cross-validation and what is it used for?" "What's difference between a Classification Regression Tree algoithm and a Random Forest? And when is one better than the other?" "What are the basic assumptions to be made for linear regression?" "Can you explain in simple language what is an Eigenvalue and Eigenvector?" "Do gradient descent methods always converge to same point?" "What's difference between continuous, ordinal and categorical variables?" "What is K-means? How can you select K for K-means?"
- 9. 9 of Y Internal Use - Confidential data science student questions - 3 "Why is naive Bayes so ‘naive’ ?" "OLS is to linear regression as Maximum likelihood is to logistic regression. Explain the statement." "What do you understand by Bias Variance trade off?" "Do you suggest that treating a categorical variable as continuous variable would result in a better predictive model?" "When does regularization becomes necessary in Machine Learning?" "Explain a model and its dimensions to an 8 year old." "How do you determine and deal with correlated features in your data set, how to reduce the dimensionality of data?" "During analysis, how do you treat missing values?" "What is Regularization and what kind of problems does regularization solve?"
- 10. Extras
- 11. 11 of Y Internal Use - Confidential the Data Scientist Roles Roles Defined by 3 different Data Science Authors Data Scientist Core Skills How To Build A Successful Data Science Team The seven people you need on your Big Data team Descriptions: Capture Data Engineer Handyman Expert in Dell EDW, D3, BO, Hana/BMS, other RDBMS, and ETL work Open Source Guru (plus Data Modeler) Hadoop stack, Cloudera, Linux, data structures and network Analyze Machine Learning Expert Data Modeler (plus all aspets of Data Engineer and Business Analyst) SQL, RDBMS, Teradata, Dell infrastructure Deep Diver Machine Learning, R, Python, SQL, ETL work, algorithm modeling, statistics Present Business Analyst Story Teller PowerPoint, Design, Tableau, understands customers business language and technical, artistic eye Snoop (plus Handyman skills) Enthusiastic, deeply creative, super savy in Dell envirionments, finds contacts and not hesitant to do work-arounds Privacy Wonk Dell policy meticulous, socially aware, foresees roadblocks
- 12. 12 of Y Internal Use - Confidential
- 13. 13 of Y Internal Use - Confidential
- 14. 14 of Y Internal Use - Confidential
Editor's Notes
- #3: https://www.datasciencecentral.com/profiles/blogs/6-predictions-about-data-science-machine-learning-and-ai-for-2018