CertNexus Certified Data Science Practitioner Certification Training

Topic A: Initiate a Data Science Project

• Data Science
• Design Thinking
• Project Scope
• Scope Creep
• Project Specifications and Objectives
• Project Timeline
• Project Deliverables
• Project Stakeholders
• Stakeholder Requirements
• Proof of Concept (POC)
• Minimum Viable Product (MVP)
• Data Privacy and Security
• Example Security Policies
• Data Access
• Data Governance
• Guidelines for Initiating a Data Science Project
• Initiating a Data Science Project

Topic B: Formulate a Data Science Problem

• The Data Science Process
• The Data Science Skillset
• Shifting Skillsets
• Problem Formulation
• Common Issues Addressed by Data Science
• Modeling Data
• Data Science Outcomes
• Learning Modes
• Randomness and Uncertainty
• Guidelines for Formulating a Data Science Problem
• Formulating a Data Science Problem

Topic A: Extract Data

• Datasets
• Structure of Data
• Terms Describing Portions of Data
• Data Quantity
• Big Data
• Data Sources
• Third-Party Data
• Open Datasets
• Public APIs
• Public API Example
• Extract, Transform, and Load (ETL)
• Data Validation
• Comma-Separated Values (CSV) Files
• Data Read from CSV Files
• Guidelines for Reading Data from CSV Files
• Reading Data from CSV Files
• SQL Queries
• NoSQL Queries
• Guidelines for Extracting Data with Database Queries
• Extracting Data with Database Queries
• Data Read from Cloud Storage
• Data Consolidation
• Data Joins
• Inner Join Example
• Guidelines for Consolidating Data from Multiple Sources
• Consolidating Data from Multiple Sources

Topic B: Transform Data

• Preliminary Data Transformation
• Data Preparation and Cleaning
• Types of Data
• Operations You Can Perform on Different Types of Features
• Continuous vs. Discrete Variables
• Data Parsing
• Guidelines for Parsing Data
• Data Irregularities
• Identification of Corrupted or Unusable Data
• Guidelines for Handling Irregular and Unusable Data
• Handling Irregular and Unusable Data
• Correction of Data Formats
• Date Conversion
• Guidelines for Correcting Data Formats
• Correcting Data Formats
• Deduplication
• Deduplication Without a Key
• Deduplication of Columns
• Guidelines for Deduplicating Data
• Deduplicating Data
• Word Embedding
• Text Data Transformation Techniques
• Image Data Representation
• Guidelines for Transforming Data
• Handling Textual Data

Topic C: Load Data

• Data Loading Considerations
• Data Loading: Databases
• Guidelines for Loading Data into Databases
• Loading Data into a Database
• Data Loading: DataFrames
• Guidelines for Loading Data into DataFrames
• Loading Data into a DataFrame
• Data Loading: Text Files
• Guidelines for Loading Data into Text Files
• Exporting Data to a CSV File
• ETL Endpoints
• Guidelines for Configuring ETL Endpoints
• Data Loading: Visualization Tools
• Guidelines for Loading Data into Visualization Tools
• Exploring Data Visualization Tools

Topic A: Examine Data

• Exploratory Data Analysis
• Dataset Content and Format
• Analysis of Feature Types
• Target Features
• Feature Relevance
• Representative Data
• Additional Sampling Techniques
• Imbalanced Datasets
• Errors, Outliers, and Noise
• Correlations
• Correlation Coefficient
• Correlation Strength
• Guidelines for Examining Data
• Examining Data

Topic B: Explore the Underlying Distribution of Data

• Frequency Distributions
• Probability Distributions
• Normal Distribution
• Non-Normal Distributions
• Descriptive Statistical Analysis
• Central Tendency
• When to Use Different Measures of Central Tendency
• Variability
• Range Measures
• Variance and Standard Deviation
• Calculation of Variance
• Variance in a Sample Set
• Calculation of Standard Deviation
• Uses for Standard Deviation
• Skewness
• Calculation of Skewness
• Kurtosis
• Calculation of Kurtosis
• Statistical Moments
• Guidelines for Exploring the Underlying Distribution of Data
• Exploring the Underlying Distribution of Data

Topic C: Use Visualizations to Analyze Data

• Visualizations
• Histograms
• Guidelines for Analyzing Data Using Histograms
• Analyzing Data Using Histograms
• Box Plots
• Violin Plots
• Guidelines for Analyzing Data Using Box Plots and Violin Plots
• Analyzing Data Using Box Plots and Violin Plots
• Scatter Plots
• Line Plots
• Area Plots
• Guidelines for Analyzing Data Using Scatter Plots, Line Plots, and Area Plots
• Analyzing Data Using Scatter Plots and Line Plots
• Bar Charts
• Guidelines for Analyzing Data Using Bar Charts
• Analyzing Data Using Bar Charts
• Geographical Maps
• Heatmaps
• Guidelines for Analyzing Data Using Maps
• Analyzing Data Using Maps
• Plots in Combination (Bar Chart Grid)
• Plots in Combination (Pair Plot)
• Guidelines for Using Visualizations to Analyze Data
• Comparing Visual Analysis Methods

Topic D: Preprocess Data

• Data Analysis Prompts Further Transformation
• Data Preprocessing
• Identification of Missing Values
• Imputation of Missing Values
• Guidelines for Handling Missing Values
• Handling Missing Values

• Feature Scaling
• Normalization and Standardization
• Additional Transformation Functions
• Guidelines for Applying Transformation Functions to Datasets
• Applying Transformation Functions to a Dataset
• Feature Engineering
• Data Encoding
• Data Encoding Methods
• Guidelines for Encoding Data
• Encoding Data
• Continuous Variable Discretization
• Bin Determination
• Guidelines for Discretizing Variables
• Discretizing Variables
• Feature Splitting
• Guidelines for Splitting Features
• Splitting and Removing Features
• Dimensionality Reduction
• Dimensionality Reduction Methods
• Guidelines for Performing Dimensionality Reduction
• Performing Dimensionality Reduction
• Guidelines for Preprocessing Data

Comparing Data Preprocessing Techniques

Topic A: Identify Machine Learning Concepts

• Machine Learning
• Machine Learning Models
• Machine Learning Algorithms
• Algorithm Selection
• Iterative Tuning
• Compromises
• Bias and Variance
• Model Generalization
• The Bias–Variance Tradeoff
• Holdout Method
• Cross-Validation
• Parameters
• Guidelines for Training Machine Learning Models
• Identifying Machine Learning Concepts

Topic B: Test a Hypothesis

• Hypothesis
• Design of Experiments
• Hypothesis Testing
• A/B Tests
• A/B Tests and Machine Learning
• Additional Hypothesis Testing Methods
• p-value
• Confidence Interval
• Confidence Interval Visualization
• Calculation of Confidence Interval
• Guidelines for Testing a Hypothesis
• Testing a Hypothesis

Topic A: Train and Tune Classification Models

• Binary Classification
• Logistic Regression
• Decision Boundary
• Multi-Label Classification
• Multi-Class Classification
• Multinomial Logistic Regression
• Guidelines for Training Logistic Regression Models
• Training a Logistic Regression Model
• k-Nearest Neighbor (k-NN)
• k Determination
• Guidelines for Training k-NN Models
• Training a k-NN Model
• Support-Vector Machines (SVMs)
• SVMs for Linear Classification
• Hard-Margin Classification
• Soft-Margin Classification
• Guidelines for Training SVM Classification Models
• Training an SVM Classification Model
• Naïve Bayes
• Naïve Bayes Example
• Naïve Bayes Classification Characteristics
• Guidelines for Training Naïve Bayes Models
• Training a Naïve Bayes Model
• Decision Tree
• Classification and Regression Tree (CART)
• Gini Index Example
• Customer Retention Example Tree
• CART Hyperparameters
• Pruning
• Ensemble Learning
• Random Forest
• Gradient Boosting
• Gradient Boosting: Residuals
• Gradient Boosting: Estimations
• Guidelines for Training Classification Decision Trees and Ensemble Models
• Training Classification Decision Trees and Ensemble Models
• Hyperparameter Optimization
• Grid Search
• Randomized Search
• Bayesian Optimization
• Guidelines for Tuning Classification Models
• Tuning Classification Models

Topic B: Evaluate Classification Models

• Evaluation Metrics
• Classification Model Performance
• Considerations When Choosing Classification Metrics
• Confusion Matrix
• Accuracy
• Precision
• Recall
• Precision–Recall Tradeoff
• F1 Score
• Specificity
• Receiver Operating Characteristic (ROC) Curve
• Thresholds
• Area Under Curve (AUC)
• Learning Curve
• Guidelines for Evaluating Classification Models
• Evaluating Classification Models

Topic A: Train and Tune Regression Models

• Linear Regression
• Linear Equation
• Linear Equation Example Data
• Straight Line Fit to Example Data
• Linear Equation Shortcomings
• Linear Regression in Machine Learning
• Linear Regression in Machine Learning Example
• Matrices in Linear Regression
• Normal Equation
• Guidelines for Training Linear Regression Models
• Training a Linear Regression Model
• Regression Using Decision Trees and Ensemble Models
• Guidelines for Training Regression Trees and Ensemble Models
• Training Regression Trees and Ensemble Models
• Forecasting
• Autoregressive Integrated Moving Average (ARIMA)
• ARIMA Temperature Example
• Guidelines for Training Forecasting Models
• Cost Function
• Regularization
• Regularization Techniques
• Gradient Descent
• Learning Rate
• Grid/Randomized Search for Regression
• Guidelines for Tuning Regression Models
• Tuning Regression Models

Topic B: Evaluate Regression Models

• Cost Functions Used to Evaluate Regression Models
• Mean Squared Error (MSE)
• Mean Absolute Error (MAE)
• Coefficient of Determination
• Guidelines for Evaluating Regression Models
• Evaluating Regression Models

Topic A: Train and Tune Clustering Models

• k-Means Clustering
• Global vs. Local Optimization
• k Determination
• Guidelines for Training k-Means Clustering Models
• Training a k-Means Clustering Model
• k-Means Clustering Shortcomings
• Hierarchical Clustering
• Hierarchical Clustering Applied to a Spiral Dataset
• Guidelines for Training Hierarchical Clustering Models
• Training a Hierarchical Clustering Model
• Latent Class Analysis
• Clustering Hyperparameters and Tuning
• Guidelines for Tuning Clustering Models
• Tuning Clustering Models

Topic B: Evaluate Clustering Models

• Evaluation Metrics for Clustering
• Elbow Point
• Cluster Sum of Squares
• Silhouette Analysis
• When to Stop Hierarchical Clustering
• Dendrogram
• Guidelines for Evaluating Clustering Models
• Evaluating Clustering Models

Topic A: Communicate Results to Stakeholders

• Know Your Audience
• Derive Insights from Findings
• Explainability
• Global vs. Local Interpretability
• Additional Factors That Drive Outcomes
• Present Model Results
• Use Visuals in Presentations
• Dashboards
• Cumulative Gains Charts
• Lift Charts
• Guidelines for Communicating Results to Stakeholders
• Communicating Results to Stakeholders

Topic B: Demonstrate Models in a Web App

• Web App
• HTML
• CSS
• JavaScript
• Web Frameworks
• Common Web Frameworks
• Flask
• Django
• Guidelines for Demonstrating Models in a Web App
• Demonstrating Models in a Web App

Topic C: Implement and Test Production Pipelines

• Put a Model into Production
• Data Pipelines
• Model Drift
• Docker
• Kubernetes
• Amazon SageMaker
• Azure Machine Learning
• Monitor Models in Production
• Pipeline Monitoring Solutions
• Guidelines for Implementing and Testing Production Pipelines
• Building an ML Pipeline