CompTIA DataX (DY0-001)
This CompTIA DataX training is your all-access pass to the data analytics skills that get you hired.
Dive into data collection, exploration, and machine learning. Then, level up with neural networks, NLP, and real-world applications. From probability distributions to model deployment, get ready to pass the CompTIA DY0-001 exam.
What You`ll Learn on This Course
- Data Science Fundamentals: Master core concepts, best practices, and real-world applications of data science.
- Statistical & Mathematical Mastery: Understand probability, inferential stats, linear algebra, and calculus for data analysis.
- Data Wrangling & Cleaning: Learn to collect, store, explore, and fix messy data.
- Machine Learning Modeling: Build, evaluate, and deploy supervised & unsupervised models (regression, clustering, neural networks, NLP).
- MLOps & Model Deployment: Validate models, communicate insights, and manage ML workflows in production.
This course comes with:
Curriculum
- 2 Sections
- 0 Lessons
- 26 Weeks
Expand all sectionsCollapse all sections
- Lesson Plan
Introduction
- About the DataX Certification
- How This Course Is Organized
2 What Is Data Science?
- Data Science
- Data Science Best Practices
- Summary
- Exam Essentials
3 Mathematics and Statistical Methods
- Calculus
- Probability Distributions
- Inferential Statistics
- Linear Algebra
- Summary
- Exam Essentials
4 Data Collection and Storage
- Common Data Sources
- Data Ingestion
- Data Storage
- Managing the Data Lifecycle
- Summary
- Exam Essentials
5 Data Exploration and Analysis
- Exploratory Data Analysis
- Common Data Quality Issues
- Summary
- Exam Essentials
6 Data Processing and Preparation
- Data Transformation
- Data Enrichment and Augmentation
- Data Cleaning
- Handling Class Imbalance
- Summary
- Exam Essentials
7 Modeling and Evaluation
- Types of Models
- Model Design Concepts
- Model Evaluation
- Summary
- Exam Essentials
8 Model Validation and Deployment
- Model Validation
- Communicating Results
- Model Deployment
- Machine Learning Operations (MLOps)
- Summary
- Exam Essentials
9 Unsupervised Machine Learning
- Association Rules
- Clustering
- Dimensionality Reduction
- Recommender Systems
- Summary
- Exam Essentials
10 Supervised Machine Learning
- Linear Regression
- Logistic Regression
- Discriminant Analysis
- Naive Bayes
- Decision Trees
- Ensemble Methods
- Summary
- Exam Essentials
11 Neural Networks and Deep Learning
- Artificial Neural Networks
- Deep Neural Networks
- Summary
- Exam Essentials
12 Natural Language Processing
- Natural Language Processing
- Text Preparation
- Text Representation
- Summary
- Exam Essentials
13 Specialized Applications of Data Science
- Optimization
- Computer Vision
- Summary
- Exam Essentials
0 - Hands-on LAB Activities
Mathematics and Statistical Methods
- Calculating Probabilities Using a PDF
- Exploring Discrete Probability Distributions
- Calculating Skewness and Kurtosis in Data
- Simulating the CLT
- Performing a Chi-Squared Test of Independence
- Performing a t-test for Two Samples
- Performing a Two-Way ANOVA
- Working With Matrices
2 Data Collection and Storage
- Generating Synthetic Data
3 Data Exploration and Analysis
- Creating a Bar Chart and a Scatterplot
- Creating a Box and Whisker Plot
- Creating a Sankey Diagram
4 Data Processing and Preparation
- Transforming the Data
- Flattening Hierarchical Data in Python
- Dealing with Missing Values
- Performing Oversampling and Undersampling
5 Modeling and Evaluation
- Performing Survival Analysis Using the Kaplan-Meier Estimator
- Creating a Time-Series Model to Predict Future Values
- Performing the Holdout Method for Model Training and Evaluation
- Evaluating Model Performance Using K-Fold Cross-Validation
- Evaluating Model Performance Using Bootstrapping
- Evaluating a Regression Model
- Evaluating a Classifier Model
- Plotting the ROC Curve
6 Model Validation and Deployment
- Selecting the Optimal Model
7 Unsupervised Machine Learning
- Performing K-Means Clustering
8 Supervised Machine Learning
- Using Linear Regression for Cost Prediction
- Using Logistic Regression for Patient Classification
- Classifying Species Using Decision Trees
9 Neural Networks and Deep Learning
- Creating an ANN Model
- Using CNN for Image Classification
10 Natural Language Processing
- Using NLP for Analyzing Tweets
11 Specialized Applications of Data Science
- Optimizing an ML Model
- Implementing Computer Vision Techniques
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