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CompTIA DataX (DY0-001) | CompTIA DataX Certification Prep
Installations
Installing Anaconda Distribution for Windows (10:35)
Installing Anaconda Distribution for MacOs (6:17)
Project Files
Installing Anaconda Distribution for Linux (14:43)
Reviewing The Jupyter Notebook (12:54)
Reviewing The Jupyter Lab (11:37)
Overview of Jupyter Notebook and Google Colab (5:31)
The appropriate statistical method or concept
CompTIA DataX Course Introduction (7:12)
Mathematics and Statistics (5:22)
T-Tests (4:52)
P-value (6:53)
Let's Practice- Paired Samples t-Test in Python (6:56)
Hypothesis Testing (5:17)
Chi squared (2:49)
Analysis of Variance (ANOVA) (7:25)
Let's Practice- ANOVA Test in Python (5:01)
Let's Practice- Chi-Square Test in Python (4:58)
Confidence Intervals (2:54)
Classification vs Regression (3:23)
Regression Error Metrics (9:48)
Let's Practice Regression Perfomance Metrics (12:03)
Classification Error Metrics (18:27)
Let's Practice Classification Perfomance Metrics (4:36)
Gini Index (4:46)
Entropy & Information Gain (4:10)
Let's Practice- Gini Index, Entropy, Information Gain (5:08)
ROC AUC (8:52)
AIC BIC (5:54)
Correlation Coefficients (6:28)
Let’s Practice- Correlation Coefficients (17:53)
Central Limit Theorem (5:10)
Law of Large Number (2:52)
Probability and synthetic modeling concepts
Skewness (5:31)
Distributions (7:54)
Kurtosis (5:02)
Let’s Practice- Distributions, Skewness, Kurtosis Lesson 1 (19:07)
Let’s Practice- Distributions, Skewness, Kurtosis Lesson 2 (3:46)
Let’s Practice- Distributions, Skewness, Kurtosis Lesson 3 (3:16)
Heteroskedasticity vs. Homoskedasticity (5:53)
Probability Density Function(PDF) (4:12)
Let’s Practice- Probability Density Function(PDF) (4:17)
Probability Mass Function(PMF) (3:47)
Cumulative Distribution Function(CDF) (4:03)
Let’s Practice- Cumulative Distribution Function(CMF) (6:31)
Probability (6:40)
Types of Missingness (6:53)
Let’s Practice- Types of Missingness (18:45)
Oversampling (4:59)
Stratification (3:29)
The importance of linear algebra and basic calculus concepts.
Linear Algebra Lesson 1 (8:19)
Linear Algebra Lesson 2 (4:31)
Linear Algebra Lesson 3 (5:11)
Calculus (5:55)
Compare and contrast various types of temporal models
Time Series Lesson 1 (5:02)
Time Series Lesson 2 (4:35)
Longitudinal Studies (3:41)
Survival Analysis (6:02)
Causal Inference (7:57)
(EDA) method or process
Univariate Analysis (9:16)
Exploratory Data Analysis(EDA) Method or Process (5:48)
Let’s Practice- Univariate Analysis (17:23)
Multivariate Analysis (7:49)
Let’s Practice- Multivariate Analysis Lesson 1 (8:26)
Let’s Practice- Multivariate Analysis Lesson 2 (13:05)
Let’s Practice- Multivariate Analysis Lesson 3 (7:00)
Let’s Practice- Multivariate Analysis Lesson 4 (11:23)
Let’s Practice- Multivariate Analysis Lesson 5 (8:03)
Let’s Practice- Multivariate Analysis Lesson 6 (12:37)
Identification of Object Behaviors and Attributes (7:10)
Visualization Type(Charts & Graphs) (2:59)
Box and Whisker Plot (7:47)
Violin Plot (4:56)
Scatter Plot & Bar Chart (3:41)
Line Chart (2:30)
Histogram – Waterfall (3:27)
Heatmap & Correlation Plot (4:54)
Sankey Diagram & Quartile-Quartile(Q-Q) Plot (5:44)
Density Plot & Scatter Plot Matrix (5:31)
Feature Type Identification (9:06)
Let’s Practice- Feature Type Identification (3:35)
Analyze common issues with data
Common Issues Lesson 1 (6:01)
Common Issues Lesson 2 (8:09)
Let’s Practice- Multicollinearity (5:21)
Data enrichment and augmentation techniques.
Data Transformation Lesson 1 (5:11)
Feature Engineering (5:21)
Let’s Practice- One-Hot Encoding (5:10)
Data Transformation Lesson 2 (5:27)
Let’s Practice- Pivoting (10:46)
Geocoding (5:47)
Scaling (7:24)
Let’s Practice- Scaling(Robust Scaler) (2:40)
Standardization (6:40)
Let’s Practice- Scaling(Standard Scaler) (7:09)
Additional Data Sources (8:03)
Let’s Practice- Data Sets (16:03)
Conduct a model design iteration process.
Design and Spesification (9:22)
Performance Evaluation (8:15)
Model Selection (5:56)
Let’s Practice- Hyperparameter Tuning (13:03)
Requirements Validation (6:13)
Analyze results of experiments
Performance Benchmarking (5:13)
Specification Testing Results (4:49)
Final Performance Measures (3:13)
Satisfy Business Requirements (3:49)
Translate results and communicate via appropriate methods
Effective Data Visualization and Reporting Techniques Lesson 1 (5:57)
Effective Data Visualization and Reporting Techniques Lesson 2 (4:41)
Data Visualization Best Practices and Pitfalls (5:18)
Chart Accessibility (6:47)
Data & Model Documantation (6:02)
Apply foundational machine-learning concepts
Loss Function (3:39)
Bias-Variance Trade-Off (10:47)
Variable Feature Selection (3:56)
Let’s Practice- Multicollineartiy & VIF (6:47)
Class Imbalance (3:50)
Regularization (4:16)
K-Fold Cross Validation (4:17)
Let’s Practice- K-Fold Cross Validation With Python (6:33)
The Curse of Dimensionality (7:20)
Occam's Razor Law of Parsimory (4:24)
In Sample vs. Out of Sample (4:20)
Interpolation vs. Extrapolation (4:18)
Ensemble Models (7:13)
Hyperparameter Tuning (6:24)
Let’s Practice- Hyperparameter Tuning Lesson 1 (10:15)
Let’s Practice- Hyperparameter Tuning Lesson 2 (13:07)
Classifiers (4:49)
Recommender Systems Lesson 1 (4:57)
Recommender Systems Lesson 2 (4:23)
Regressors (4:38)
Embeddings (4:48)
Post Hoc Model Explainability (5:21)
Interpretable Model (6:08)
Model Drift Causes (3:21)
Data Leakage (3:52)
Supervised machine-learning concepts
Linear Regression Theory Lesson - 2 (4:42)
Linear Regression Theory Lesson - 1 (7:56)
Let’s Practice- Linear Regression (20:00)
Logistic Regression Algorithm Theory (4:39)
Let’s Practice- Logistic Regression (7:07)
Linear Discriminant Analysis (LDA) (2:48)
Quadratic Discriminant Analysis (QDA) (4:00)
Association Rules (3:18)
Naive Bayes (3:43)
Decision Tree Algorithm Theory (9:31)
Let’s Practice- Decision Tree (6:28)
Random Forest Algorithm Theory (5:46)
Let’s Practice- Random Forest (8:21)
Boosting (3:57)
Bootstrap Aggregation (Bagging) (3:35)
Concepts related to deep learning
Artificial Neural Network Architecture Lesson 1 (4:40)
Artificial Neural Network Architecture Lesson 2 (4:38)
Artificial Neural Network Architecture Lesson 3 (5:44)
Dropout (4:45)
Batch Normalization (4:06)
Early Stopping (4:08)
Schedulers (4:11)
Back Propagation (4:28)
Shot-based Learning Techniques (3:51)
Deep Learning Frameworks (11:51)
Optimizers (5:21)
Model Types (9:06)
Concepts related to unsupervised machine learning
K-Means Clustering (4:10)
Let’s Practice- K-Means Clustering Lesson 1 (7:06)
Let’s Practice- K-Means Clustering Lesson 2 (6:50)
Let’s Practice- K-Means Clustering Lesson 3 (6:51)
Let’s Practice- K-Means Clustering Lesson 4 (7:08)
Hierarchical Clustering Algorithm Theory (4:39)
Let’s Practice- Hierarchical Clustering Lesson 1 (7:50)
Let’s Practice- Hierarchical Clustering Lesson 2 (5:54)
Density-Based Spatial Clustering of Applications with Noise (4:48)
Principal Component Analysis(PCA) Theory (8:47)
Let’s Practice- Principal Component Analysis(PCA) Lesson 1 (5:17)
Let’s Practice- Principal Component Analysis(PCA) Lesson 2 (1:56)
Let’s Practice- Principal Component Analysis(PCA) Lesson 3 (7:30)
t-Distributed Stochastic Neighbor Embedding (t-SNE) (5:10)
Uniform Manifold Approximation and Projection (5:39)
K-Nearest Neighbors(KNN) (6:33)
Let’s Practice- K-Nearest Neighbors(KNN) (8:52)
Singular Value Decomposition (2:48)
The role of data science in various business functions
Compliance, Security, and Privacy (8:55)
Measures, Metrics, and Key Performance Indicators (KPIs) (4:47)
Requirements Gathering (4:43)
The process of and purpose for obtaining different types of data
Generated Data (6:19)
Synthetic Data (7:36)
Commercial Public Data (6:51)
Data ingestion and storage concepts
Infrastructure Requirements (3:32)
Data Format- Lesson 1 (4:44)
Data Format- Lesson 2 (3:38)
Streaming (2:45)
Batching (3:34)
Pipeline Implementation (5:07)
Orchestration Automation (5:51)
Persistence (4:31)
Refresh Cycles (4:20)
Archiving (4:24)
Data Lineage (5:41)
Merging - Combining (5:27)
Let’s Practice- Merging - Combining Lesson 1 (12:38)
Let’s Practice- Merging - Combining Lesson 2 (10:45)
Let’s Practice- Merging - Combining Lesson 3 (5:37)
Let’s Practice- Merging - Combining Lesson 4 (9:44)
Let’s Practice- Merging - Combining Lesson 5 (7:34)
Let’s Practice- Merging - Combining Lesson 6 (11:41)
Cleaning (6:39)
Data Errors (4:59)
Outliers (5:16)
Let’s Practice- Outliers Lesson 1 (8:33)
Let’s Practice- Outliers Lesson 2 (9:58)
Let’s Practice- Outliers Lesson 3 (10:53)
Let’s Practice- Outliers Lesson 4 (8:21)
Let’s Practice- Outliers Lesson 5 (7:50)
Data Flattening (3:48)
Imputation Types (5:22)
Let’s Practice- Imputation Types (18:32)
Ground Truth Labeling (5:45)
Data ingestion and storage concepts
Version control (7:27)
Data science workflow models (7:22)
Integrated Development Enviroment(IDE) (5:55)
Dependency Licencing (4:08)
Access via Application Programming Interface(API) (4:33)
Process Documentation (4:18)
Clean Code Methods (4:00)
Unit Test Writing (3:52)
DevOps and MLOps principles in data science
Data Replication (3:58)
Continuous Integration - Continuous Deployment (CI - CD) (4:37)
Model Deployment (5:18)
Container Orchestration (4:16)
Virtualization (5:35)
Code Isolation (5:19)
Model Performance Monitoring (4:54)
Model Validation (4:48)
Compare and contrast various deployment environments
Cloud Deployment (4:59)
Containerization (3:27)
Cluster Deployment (4:53)
Hybrid Deployment (3:41)
Edge Deployment (4:29)
One-Premises Deployment (4:43)
Compare and contrast optimization concepts
Constrained Optimization (8:59)
Unconstrained Optimization (4:33)
Natural language processing (NLP) concepts
Word Embeddings (4:31)
Tokenization -Bag of Words (4:26)
Term Frequency-Inverse Document Frequency (TF-IDF) (2:43)
Document Term Matrix (4:13)
Edit Distance (4:29)
Large Langulage Model (6:19)
Text Preparation (5:46)
Topic Modeling (5:31)
Disambiguation (5:31)
NLP Applications Lesson 1 (5:36)
NLP Applications Lesson 2 (4:15)
NLP Applications Lesson 3 (8:25)
Computer vision concepts
Optical Character Recognition (3:13)
Object - Semantic Segmentation (5:35)
Tracking (5:42)
Sensor Fusion (6:20)
Data Augmentation (7:12)
Other specialized applications in data science
Heuristics (6:42)
Graphs Analysis - Graph Theory (6:47)
Greedy Algorithms (4:56)
Reinforcement Learning (7:21)
Event Detection (5:26)
Fraud Detection (5:44)
Anomaly Detection (5:41)
Multimodal Machine Learning (6:29)
Optimization for Edge Computing (6:11)
Signal Processing (6:28)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
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