This course prepares you for the CompTIA DataX (DY0-001) certification, teaching essential data science skills such as data collection, cleaning, analysis, mach...
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About This Course
CompTIA is committed to helping you achieve the tech career you deserve through leading certifications, courses, and expert guidance. In today’s competitive job market, demonstrable skills are essential. This course provides the knowledge and hands-on activities needed to confidently perform in any data science-related role.
Designed to prepare you for the CompTIA DataX (DY0-001) Certification Exam, this course also broadens your expertise, opening doors to various career opportunities in the fast-growing data science field. Career options include data scientist, quantitative analyst, machine learning engineer, predictive analyst, and AI engineer.
Upon completing this course, you will be able to:
Course Design
This course uses a learning progression model to maximize knowledge retention and skill development. It incorporates contextual learning, practical exercises, personalized feedback, and real-world application to demonstrate skill mastery.
Throughout the course, you’ll engage in various activities to practice skills and assess your understanding. The course is organized into modules and lessons, each ending with a quiz to verify comprehension. Many modules also feature live lab challenges to test your practical abilities.
Prerequisites
To succeed in this course, a minimum of five years of hands-on experience as a data scientist or equivalent knowledge is highly recommended.
Please note that prerequisites for this course may differ from those required for the CompTIA certification exam. For the latest exam requirements, visit: www.comptia.org/training/resources/exam-objectives.
© 2024 The Computing Technology Industry Association, Inc. (CompTIA). © 2024 TestOut Corporation. All rights reserved. References to any product, service, or method are for educational purposes only and do not imply endorsement. Neither CompTIA nor TestOut is affiliated with any mentioned companies or endorses their products or services.
This course prepares you for the CompTIA DataX (DY0-001) certification, teaching essential data science skills such as data collection, cleaning, analysis, mach...
Flexible online learning tailored to your lifestyle and goals.
One-on-one mentorship from industry professionals to accelerate your growth.
Create portfolio projects that showcase your expertise to potential employers.
1.0 Intoduction
1.1 Understanding Lifecycle Frameworks
1.1.1 Commonly Used Lifecycle Frameworks
1.2 Discover Tools and Best Practices
1.2.1 Software Libraries and Their Dependency Licenses
1.2.2 Software Composition Analysis
1.2.3 API Integration and Data Access
1.2.4 Documentation & Code Standards
1.2.5 Syntax Fundamentals in R and Python
1.3 Lesson Recap
0:20:03.0 Introduction
3.1 Understand Key Data Considerations
3.1.1 Structured and Unstructured
3.1.2 Types of Data: Generated, Synthetic, and Public
3.2 Store and Manipulate Data
3.2.1 Infrastructure for Data Processing
3.2.2 Data Encoding and Compression
3.2.3 Workflow Automation and Data Persistence
3.2.4 Data Refresh and Archiving Strategies
3.2.5 Data Processing: Batching vs. Streaming
3.2.6 Managing Data Operations and Errors
3.3 Lesson Recap
0:25:01.0 Introduction
4.1 Data Wrangling and Preparation
4.1.1 Transforming Data During Preprocessing
4.1.2 Data Transformation Using Encoding Techniques
4.1.3 Data Preparation for Feature Engineering
4.1.4 Data Preprocessing with Geocoding
4.1.5 Scaling and Standardization in Machine Learning
4.1.6 Synthetic Data Generation and Data Augmentation
4.2 Lesson Recap
0:25:05.0 Introduction
5.1 Understanding the Basics of Time Series
5.1.1 Data Non-Linearity
5.1.2 Data Non-Stationary
5.1.3 Time Series Data Seasonality
5.1.4 Understanding Difference Observations in Time Series Analysis
5.2 Recognizing Data Quality Issues
5.2.1 Multicollinearity Issues in Time Series
5.2.2 Misaligned Granularity in Data
5.2.3 Impact of Insufficient Features
5.2.4 Multivariate Outliers
5.3 Lesson Recap
0:20:06.0 Introduction
6.1 Perform Exploratory Data Analysis
6.1.1 Understanding Exploratory Data Analysis
6.1.2 Exploratory Data Analysis Tasks
6.1.3 Frequent Errors in Exploratory Data Analysis
6.1.4 Classifying Data
6.1.5 Exploratory Data Analysis Types
6.1.6 Techniques for Visualizing Data
6.1.7 Common Visualizations
6.2 Perform Data Statistical Analysis
6.2.1 Understanding Statistical Analysis
6.2.2 Comparison-Based Analysis
6.2.3 Regression Testing
6.2.4 Understanding Probability Distributions
6.2.5 Understanding Probability Functions
6.2.6 Sampling Techniques
6.3 Using Methods in Unsupervised Analysis
6.3.1 Basics of Clustering
6.3.2 Dimensionality Reduction
6.3.3 Eigenvectors and Eigenvalues
6.4 Apply Clustering Techniques
6.4.1 Types of Clustering Models
6.4.2 Distance Metrics
6.4.3 The Importance of Heuristics
6.4.4 Heuristics Techniques
6.4.5 Determining the Best Number of Clusters
6.4.6 Semi-Supervised Methods Part 1
6.4.7 Semi-Supervised Methods Part 2
6.5 Lesson Recap
0:45:07.0 Introduction
7.1 Enhance the Model Selection Process
7.1.1 Best Practices for Managing Model Design Constraints
7.1.2 Literature Review and Model Selection
7.2 Examine Key Mathematical Topics
7.2.1 Key Concepts in Linear Algebra
7.2.2 Core Concepts in Calculus
7.3 Apply Temporal Models
7.3.1 Time Series and Prediction
7.3.2 Classifications of Time Series Models
7.4 Respond to Research Questions that Demand Causal Insight
7.4.1 Causal Inference and Experimental Design
7.5 Lesson Recap
0:45:08.0 Introduction
8.1 Describe Machine Learning Techniques
8.1.1 Introduction to Machine Learning
8.1.2 Supervised Learning
8.1.3 Unsupervised Learning
8.1.4 Reinforcement Learning
8.1.5 The Process of Evaluating and Selecting Models
8.1.6 Applying Metrics to Assess Models
8.1.7 Model Selection Criteria
8.1.8 Understanding Model Drift
8.1.9 Specialized Machine Learning Techniques
8.2 Applying Techniques in Supervised Learning
8.2.1 Understanding Regression Analysis
8.2.2 Introduction to Linear Regression
8.2.3 Advanced Regression Models
8.2.4 Ensemble Learning
8.2.5 Ensemble Learning Techniques in Machine Learning
8.3 Lesson Recap
0:20:09.0 Introduction
9.1 Implement Neural Network Architecture
9.1.1 Neural Networks
9.1.2 ANN (Artificial Neural Networks)
9.1.3 Neural Network Layers
9.2 Apply Activation Functions in Neural Networks
9.2.1 Neural Network Activation Functions
9.2.1 Neural Network Activation Functionsmp4
00:03:189.2.2 Understanding the Sigmoid Function
9.2.3 ReLU Activation Function
9.2.4 Leaky ReLU
9.2.5 TanH
9.2.6 Plotting Activation Functions
9.2.6 Plotting Activation Functions
00:04:559.3 Training Neural Networks
9.3 Training Neural Networks
9.3.1 Training and Tuning Neural Networks
9.3.2 Hyperparameters in Neural Networks
9.3.3 Tuning Neural Network Layers
9.3.4 The Importance of Data in Neural Networks
9.4 Integrate Advanced Deep Learning Strategies
9.4.1 The Perceptron Learning Algorithm
9.4.2 Word Embeddings
9.5 Lesson Recap
0:45:010.0 Introduction
10.1 Enhancing Models and Resource Efficiency
10.1.1 Fundamentals of Benchmarking and Business Requirement Analysis
10.1.2 Optimization Strategies
10.1.3 Optimization Techniques for Effective Scheduling and Pricing
10.1.4 Optimization-Based Strategies for Resource Allocation and Bundling
10.2 Explain Different Types of Optimization Problems
10.2.1 Linear Solvers and Non-linear Solvers: A Comparison Linear Solvers
10.2.2 Techniques for Handling Boundary Cases and Unconstrained Optimization
10.2.3 Advanced Optimization Concepts: Bandit Problems and Local Maxima/Minima
10.3 Tune Hyperparameters
10.3.1 Prediction Accuracy
10.4 Lesson Recap
0:25:011.0 Introduction
11.1 Ready Data for Stakeholder
11.1.1 Stakeholders
11.1.2 The Data Analysis Process
11.1.3 Data Quality and Integrity
11.2 Present the Data Narrative
11.2.1 Communication Best Practices
11.2.2 The Importance of Data Documentation and Compliance
11.2.3 Effective Reporting in Data Analytics
11.2.4 In-Depth Exploration of Data Types and Visualization
11.2.5 Data Visualization Across Multiple Dimensions
11.3 Lesson Recap
0:20:012.0 Introduction
12.1 Data Replication
12.1.1 Data Replication Techniques
12.2 Deployment Methodologies
12.2.1 Implementing a CI/CD Pipeline for Your Python Application
12.2.2 Machine Learning Model Deployment
12.3 Understanding MLOps
12.3.1 IT Infrastructure Virtualization
12.3.2 Techniques for Isolating Code
12.3.3 Machine Learning Model Monitoring and Validation
12.3.4 A/B Testing
12.3.5 Microservices and Containerization Application
12.3.6 Docker Container Life Cycle
12.3.7 Advantages and Disadvantages of Microservices
12.3.8 Working with Microservice Architecture
12.4 Illustrate Deployment Methodologies
12.4.1 On-Premises Deployment
12.4.3 Edge Computing Deployment
12.5 Lesson Recap
0:20:013.0 Introduction
13.1 Apply Natural Language Processing
13.1.1 Introduction to Natural Language Processing
13.1.2 Data Preprocessing for NLP
13.2 Apply Computer Vision Techniques
13.2.1 OCR (Optical Character Recognition)
13.2.2 Image Processing: An Overview
13.2.3 Advanced Topics in Image Processing
13.2.4 Modifying and Enhancing Images
13.2.5 Enhancing Machine Learning with Image Augmentation
13.2.6 Keras and TensorFlow
13.3 Perform Network and Graph Analytics
13.3.1 Exploring Graph Theory and Heuristic Methods
13.3.3 Machine Learning on Graphs
13.4 Assess Methods for Handling Unique Events
13.4.1 Fundamentals of Greedy Algorithms and Reinforcement Learning
13.4.2 Identifying Events and Detecting Anomalies
13.4.3 Understanding Multimodal Learning
13.4.4 Edge Network Computing
13.4.5 Signal Processing Techniques
13.5 Lesson Recap
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