CompTIA Data X

Price
$2,495.00 USD

Duration
5 Days

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

CompTIA DataX Certification Prep (Exam DY0-001) is designed for experienced professionals aiming to validate their expertise in the evolving field of data science. This course equips learners with the knowledge and skills needed to pass the CompTIA DataX certification exam, focusing on expert-level data science tools, concepts, and processes. The course covers mathematical and statistical methods, data modeling, machine learning applications, and specialized data science operations to ensure comprehensive preparation for certification.

Course Objectives

Understand and apply advanced mathematical and statistical methods for data processing and cleaning.
Perform data modeling using sophisticated techniques and tools to extract actionable insights.
Implement and evaluate machine learning models for various data science tasks.
Apply deep learning concepts to build and enhance predictive models.
Design and manage data pipelines and workflows for efficient data processing.
Automate data processes to streamline data science operations in production environments.
Monitor and maintain machine learning models deployed in real-world scenarios.
Demonstrate knowledge of industry-specific data science applications and emerging trends in the field.

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Learning Credits: Learning Credits can be purchased well in advance of your training date to avoid having to commit to specific courses or dates. Learning Credits allow you to secure your training budget for an entire year while eliminating the administrative headache of paying for individual classes. They can also be redeemed for a full year from the date of purchase. If you have previously purchased a Learning Credit agreement with New Horizons, you may use a portion of your agreement to pay for this class.

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Corporate Tech Pass: Our Corporate Tech Pass includes unlimited attendance for a single person, in the following Virtual Instructor Led course types: Microsoft Office, Microsoft Technical, CompTIA, Project Management, SharePoint, ITIL, Certified Ethical Hacker, Certified Hacking Forensics Investigator, Java, Professional Development Courses and more. The full list of eligible course titles can be found at https://www.newhorizons.com/eligible.

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Course Prerequisites

5+ years of experience in data science, computer science, or a related field
Strong foundational knowledge in statistics, mathematics, and machine learning.

Agenda

Illustrating the Data Science Lifecycle

Recognize Lifecycle Frameworks
Common Lifecycle Frameworks
Apply the CRISP Workflow
Identify Tools and Best Practices
Develop a Folder Structure
Software Libraries and Dependency Licenses
Software Composition Analysis
APIs and Data Access
Documentation and Code Quality
The Basics of Syntax: R and Python
Ensure Code Quality
Live Lab: Exploring the DataX Lab Environment
Module Quiz

Analyzing Business Problems

Select the Appropriate Solution
Business Needs and Solution Identification
Model Selection
Live Lab: Enhancing Efficiency with Cost-Benefit Analysis
Recognize the Importance of Data Privacy and Security
Privacy and Security in Data Use
Masking Sensitive Data
Challenge Live Lab: Using Data Science to Predict Costs
Module Quiz

Collecting Data

Recognize Considerations of Data
Structured and Unstructured Data
Generated, Synthetic, and Public Data
Ensure Data Quality and Consistency
Store and Manipulate Data
Data Processing Infrastructure
Data Formats and Compression
Automated Workflows and Data Persistence
Data Refresh Cycles and Archiving
Data Batching, Streaming, and Pipelines
Consider Data Lineage to Perform Merging Techniques
Data Operations and Error Management
Live Lab: Streamlining Data Ingestion
Module Quiz

Cleaning and Preparing Data

Wrangle and Prepare Data
Data Transformation in Preprocessing
Encoding Techniques in Data Transformation
Applied Live Lab: Navigating Expansion through Data Insights
Preparing Data for Feature Engineering
Geocoding in Data Preprocessing
Scaling and Standardization in Machine Learning
Data Augmentation and Synthetic Data Generation
Challenge Live Lab: Unraveling Anomalies with EDA
Module Quiz

Describing Data Features

Explain the Basics of Time Series
Non-Linearity in Data
Non-Stationarity
Identify Lagged Observations
Seasonality in Time Series Data
Difference Observations in Time Series Analysis
Live Lab: Interpreting Data Features for Predictive Analytics
Identify Common Issues in Data
Multicollinearity in Time Series
Solve Matrix and Vectorization Problems
Granularity Misalignment in Data
Impact of Insufficient Features
Multivariate Outliers in a Dataset
Challenge Live Lab: Discovering Business Insights through Data Features
Module Quiz

Exploring Data

Demonstrate Exploratory Data Analysis
Introduction to Exploratory Data Analysis
EDA Tasks
Common EDA Mistakes
Categorizing Data
Univariate and Multivariate Analysis
Visualization Techniques
Common Visualizations
Conduct Statistical Analysis
Introduction to Statistical Analysis
Comparative Analysis
Regression Tests
Introduction to Probability Distributions
Probability Functions
Sampling Techniques
Utilize Techniques in Unsupervised Methods
Introduction to Clustering
Dimensionality Reduction
Eigenvectors and Eigenvalues
Implement Clustering
Clustering Models
Distance Metrics
Why Heuristics?
Heuristics Techniques
Finding the Optimal Number of Clusters
Live Lab: Decoding User Behavior with Cluster Analysis
Semi-Supervised Methods
Challenge Live Lab: Using Cluster Analysis for Strategic Transformation
Module Quiz

Navigating the Model Selection Process

Optimize the Model Selection Process
Managing Model Design Constraints
Literature Review and Model Selection
Explore Mathematical Areas
Linear Algebra Concepts
Calculus Concepts
Use Temporal Models
Time Series and Prediction
Types of Time Series Models
Conduct Longitudinal Studies and Survival Analysis
Live Lab: Predictive Forecasting with ARIMA Models
Address Research Questions Requiring Causal Explanation
Causal Inference and Experimental Design
Analyze Ad Campaign Effectiveness Using Causal Inference
Challenge Live Lab: Using Comprehensive Time Series Analysis for Forecasting
Module Quiz

Employing Machine Learning Methods

Explain Machine Learning Methods
Introduction to Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
The Model Evaluation and Selection Process
Using Metrics to Evaluate Models
Selecting the Appropriate Model
Model Drift
Specialized Machine Learning Techniques
Utilize Techniques in Supervised Methods
Regression Analysis
Linear Regression
Other Regression Models
Live Lab: Tackling Business Challenges with Logistic Regression
Live Lab: Constructing Decision Trees for Predictive Analysis
Ensemble Learning
Ensemble Learning Techniques
Live Lab: Data Modeling Using Decision Trees and Random Forests
Challenge Live Lab: Using Semi-Supervised Machine Learning Methods
Module Quiz

Experimenting with Deep Learning

Use Neural Network Architecture
Neural Networks
Artificial Neural Networks
Neural Network Layers
Perform Neural Network Activation Functions
Neural Network Activation Functions
Sigmoid
ReLU
Leaky ReLU
TanH
Plotting Activation Functions
Train Neural Networks
Training and Tuning Neural Networks
Neural Network Hyperparameters
Layer Tuning
Data Considerations in Neural Networks
Live Lab: Using Neural Networks for Image Processing
Use Advanced Deep Learning Concepts
Perceptron Algorithm
Word Embeddings
Live Lab: Using Neural Networks for Information Extraction
Challenge Live Lab: Image Processing with Deep Learning Techniques
Module Quiz

Evaluating and Refining Data Models

Optimize Models and Resources
Introduction to Benchmarking and Analyzing Business Requirements
Optimization Techniques
Applying Optimization Techniques in Scheduling and Pricing
Resource Allocation and Bundling Strategies Using Optimization Techniques
Explain Optimization Problem Types
Linear and Non-Linear Solvers in Optimization
Handling Boundary Cases and Unconstrained Optimization Techniques
Advanced Topics in Optimization: Bandit Problems and Local Maxima/Minima
Tune Hyperparameters
Accuracy of Predictions
Live Lab: Hyperparameter Tuning & Optimization
Challenge Live Lab: Evaluating and Tuning Data Models
Module Quiz

Communicating for Business Impact

Prepare Data for Stakeholders
Stakeholders
The Data Analysis Process
Data Quality and Integrity
Deliver the Data Story
Communication Approaches
Data Documentation and Compliance
Effective Reporting
Deep Dive into Data Types and Visualization
Visualization for Diverse Dimensions
Challenge Live Lab: Communicating for Business Impact
Module Quiz

Deploying Data Models

Replicate Data
Data Replication Techniques
Identify Replication Techniques
Describe Deployment Methodologies
CI/CD Pipelines in Software Development
Deployment of Machine Learning Models
Decipher ML Ops
Virtualization in IT Infrastructure
Code Isolation Techniques
Monitoring and Validation of Machine Learning Models
A/B Testing
Containerization and Microservice-Based Applications
Docker Containers
Pros and Cons of Microservices
Using Microservices
Illustrate Deployment Methodologies
On-Premises Deployment
Hybrid Deployment Models
Edge Deployment
Live Lab: Deploy IaC Templates in AWS
Module Quiz

Discovering Specialized Data Science Applications

Use Natural Language Processing
Introduction to Natural Language Processing
Preparing Data for NLP
Live Lab: Visualizing the Power of Word Through NLP
Use Computer Vision
Optical Character Recognition
Introduction to Image Processing
Advanced Concepts in Image Processing
Image Alterations and Adjustments
Image Augmentation in Machine Learning
Keras and TensorFlow
Live Lab: Using Computer Vision Tools to Mine Data
Perform Graph Analytics
Graph Theory and Heuristics
Introduction to Graph Analytics
Graph Machine Learning
Evaluate Techniques for Unique Events
Greedy Algorithms and Reinforcement Learning
Event Detection and Anomaly Detection
Multimodal Machine Learning
Edge Computing
Signal Processing
Module Quiz

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