Site Map - skillsoft.digitalbadges-eu.skillsoft.com
- User Authentication
- Riziki Assey's Credentials
- Riziki Assey's Wallet
- Analytics Literacy for Business Professionals
- Motivating Action with a Compelling and Data-driven Story
- Basic Analytical Methods
- Visualizing Data for Impact: Data Storytelling
- Working with Data for Effective Decision Making
- Business Strategy: Managing Organizational Value
- Leadercamp on-demand: Business Acumen Series - Planning
- Leadercamp on-demand: Business Acumen Series on Operations
- Leadercamp on-demand: Business Acumen Series on Strategy
- Leadercamp on-demand: Business Acumen Series on Finance
- Leadercamp on-demand: Business Acumen Series on People
- Generative AI Models: Generating Data Using Generative Adversarial Networks
- Using OpenAI APIs: Exploring APIs with the OpenAI Playground
- Using OpenAI APIs: Using Image & Audio APIs
- Using OpenAI APIs: Accessing OpenAI APIs from Python
- Generative AI Models: Generating Data Using Variational Autoencoders
- Final Exam: Generative AI Introduction and Overview
- Using OpenAI APIs: Fine-tuning Models, the Assistants API, & Embeddings
- Generative AI Introduction and Overview
- Track 1: Generative AI Overview
- Generative AI Models: Getting Started with Autoencoders
- An Introduction to Generative AI
- Creating Data APIs Using Node.js
- Machine & Deep Learning Algorithms: Introduction
- R for Data Science: Data Visualization
- Advanced Visualizations & Dashboards: Visualization Using Python
- Data Science Statistics: Applied Inferential Statistics
- Machine & Deep Learning Algorithms: Regression & Clustering
- Data Insights, Anomalies, & Verification: Handling Anomalies
- Data Recommendation Engines
- Machine & Deep Learning Algorithms: Imbalanced Datasets Using Pandas ML
- Machine & Deep Learning Algorithms: Data Preparation in Pandas ML
- Data Research Techniques
- Advanced Visualizations & Dashboards: Visualization Using R
- Data Insights, Anomalies, & Verification: Machine Learning & Visualization Tools
- Data Research Exploration Techniques
- Data Research Statistical Approaches
- Data Scientist
- Storytelling with Data: Introduction
- Data Science Statistics: Using Python to Compute & Visualize Statistics
- Raw Data to Insights: Data Ingestion & Statistical Analysis
- Data Analysis using Spark SQL
- Tableau Desktop: Real Time Dashboards
- Storytelling with Data: Tableau & Power BI
- Data Lake Framework & Design Implementation
- Data Architecture Deep Dive - Design & Implementation
- Data Lake Architectures & Data Management Principles
- Data Driven Organizations
- Final Exam: Data Wrangler
- Data Architecture Deep Dive - Microservices & Serverless Computing
- Raw Data to Insights: Data Management & Decision Making
- Data Wrangler
- Data Analysis Using the Spark DataFrame API
- Optimizing Query Executions with Hive
- Using Hive to Optimize Query Executions with Partitioning
- Bucketing & Window Functions with Hive
- Filtering Data Using Hadoop MapReduce
- Hadoop MapReduce Applications With Combiners
- Advanced Operations Using Hadoop MapReduce
- Loading & Querying Data with Hive
- Viewing & Querying Complex Data with Hive
- Technology Landscape & Tools for Data Management
- Machine Learning & Deep Learning Tools in the Cloud
- MongoDB Querying
- MongoDB Aggregation
- Getting Started with Hive
- Data Wrangling with Trifacta
- Final Exam: Business Analyst to Data Analyst
- Analyzing Data Using Python: Data Analytics Using Pandas
- Cleaning Data in R
- Python - Using Pandas to Work with Series & DataFrames
- Using BigML: Getting Hands-on with BigML
- Analyzing Data Using Python: Filtering Data in Pandas
- Analyzing Data Using Python: Importing, Exporting, & Analyzing Data With Pandas
- Analyzing Data Using Python: Cleaning & Analyzing Data in Pandas
- Using BigML: Building Supervised Learning Models
- Using BigML: Unsupervised Learning
- VBA: Leveraging VBA to Work with Charts, Stocks, & MS Access
- Using BigML: An Introduction to Machine Learning & BigML
- Final Exam: Business Analyst
- VBA: Getting Started with VBA in Excel
- VBA: Building User Interfaces with Forms in VBA & Excel
- Complete Guide to Excel 365: Working With Charts & Sparklines
- Complete Guide to Excel 365: Using Formatting, Styles, & Themes
- Complete Guide to Excel 365: Linking, Printing, & Protecting Workbooks
- Complete Guide to Excel 365: What-If Analysis, Solver, & Analysis ToolPak
- Complete Guide to Excel 365: Pivot, PowerPivot, & Financial Modeling
- Python - Pandas Advanced Features
- Python - Advanced Operations with NumPy Arrays
- Python - Using Pandas for Visualizations and Time-Series Data
- Python - Manipulating & Analyzing Data in Pandas DataFrames
- Python - Introduction to Pandas and DataFrames
- Python for Data Science: Basic Data Visualization Using Seaborn
- Complete Guide to Excel 365: Validating, Cleaning, & Performing Lookups on Data
- Python for Data Science: Advanced Data Visualization Using Seaborn
- Statistical & Hypothesis Tests: Performing Two-sample T-tests & Paired T-tests
- Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis
- Track 2: Statistics and Probability
- Probability Distributions: Understanding Normal Distributions
- Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing
- Probability Distributions: Getting Started with Probability Distributions
- Probability Theory: Understanding Joint, Marginal, & Conditional Probability
- Probability Theory: Creating Bayesian Models
- Probability Distributions: Uniform, Binomial, & Poisson Distributions
- Statistical & Hypothesis Tests: Using the One-sample T-test
- Streaming Data Architectures: Processing Streaming Data with Spark
- Probability Theory: Getting Started with Probability
- Final Exam: Statistics and Probability
- Core Statistical Concepts: Statistics & Sampling with Python
- Complete Guide to Excel 365: Getting Started
- Core Statistical Concepts: An Overview of Statistics & Sampling
- Data Science Tools
- The Four Vs of Data
- Python - Introduction to NumPy for Multi-dimensional Data
- Final Exam: Data Scientist
- Streaming Data Architectures: An Introduction to Streaming Data in Spark
- Data Integration
- Estimates & Measures
- Clustering, Errors, & Validation
- Data Communication & Visualization
- Data Transformation
- Data Filtering
- Data Gathering
- Organizational IT Trends, Analytics, and Application Development
- Spark for High-speed Big Data Analytics
- Techniques for Big Data Analytics
- Big Data Concepts: Big Data Essentials
- Big Data Concepts: Getting to Know Big Data
- Developing Personal Accountability
- Taking Responsibility for Your Accountability
- Riziki Assey's Transcript
- Riziki Assey's Wallet
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