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

Additional course details:

Nexus Humans, CompTIA Data X training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward.

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the ITS Data Analytics course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

FAQ for the CompTIA Data X Course

Available Delivery Options for the CompTIA Data X training.

• Live Instructor Led Classroom Online (Live Online)
• Traditional Instructor Led Classroom (TILT/ILT)
• Delivery at your offices in London or anywhere in the UK
• Private dedicated course as works for your staff.

How many CPD hours does the CompTIA Data X training provide?

The 5 day. CompTIA Data X training course give you up to 30 CPD hours/structured learning hours. If you need a letter or certificate (Non PeopleCert programs only) in a particular format for your association, organisation or professional body please just ask.

Is the CompTIA Data X training appropriate for someone learning to use CompTIA Data X in a professional environment?

Yes the CompTIA Data X is appropriate for someone looking to use CompTIA Data X in a professional workspace or environment. But do make sure to note any prerequisites, read the course outline to ensure it is the right fit for you or your teams requirements and preferences.

Do you provide training for the CompTIA Data X.

Yes we provide corporate training, dedicated training and closed classes for the CompTIA Data X. This can take place anywhere in UK including, England, Scotland, Cymru (Wales) or Northern Ireland or live online allowing you to have your teams from across UK or further afield to attend a single training event saving travel and delivery expenses.

What is the duration of the CompTIA Data X program.

The CompTIA Data X training takes place over 5 day(s), with each day lasting approximately 8 hours including small and lunch breaks to ensure that the delegates get the most out of the day.

Why are Nexus Human the best provider for the CompTIA Data X?

Nexus Human are recognised as one of the best training companies as they and their trainers have won and hold many awards and titles including having previously won the Small Firms Best Trainer award, national training partner of the year for UK on multiple occasions, having trainers in the global top 30 instructor awards in 2012, 2019 and 2021. Nexus Human has also been nominated for the Tech Excellence awards multiple times. Learning Performance institute (LPI) external training provider sponsor 2024.

Is there a discount code for the CompTIA Data X training.

Yes, the discount code PENPAL5 is currently available for the CompTIA Data X training. Other discount codes may also be available but only one discount code or special offer can be used for each booking. This discount code is available for companies and individuals.