Technical Skills & Projects
Click the dropdown arrow next to each subject to see the concepts for scripting, programming, and query languages, as well as software, applications, operating systems, and packages that I have experience with.
Embedded Files
Cybersecurity & IT Support
Cybersecurity & IT Support
Cybersecurity:
Automation, CLI, IDSs, Network Security, Network Protocol Analyzers, NIST CSF, regex, SIEM Tools
IT Support:
Hardware, Software, Networking, Subnetting, Routing, Managing Processes, Infrastructure Services, Windows Active Directory, IT Security, Encryption, Authentication, Configuration, Troubleshooting, Best Practices
Linux:
|, >, >>, -iname, -mmin, -mtime, -name, apropos, cat, cd, cp, chmod, chown, clear, dpkg -s, rwxrwxrwx, echo, expr, find, grep, head, ifconfig, less, ls, man, mkdir, mv, nano, pwd, rm, rmdir, sudo, sudo apt-get install - update - remove, tail, tcpdump - curl - port - -c -D -i -n -nn -r -v -w -x, touch, useradd, userdel, usermod, whatis, whoami
tcpdump
Wireshark
Data Analysis, Data Science, & Business Intelligence
Data Analysis, Data Science, & Business Intelligence
Data:Â
accessibility, aggregation, anomaly detection, automation, bias, categorical & numeric, cleaning, collection, databases, database partitions & indexes, database performance, ecosystem, ethics, ETL / ELT pipelines, filtering, formats & structures, integrity, keys, lists, management, merging, metadata, mining, models, optimization, organization, outlier detection, profiling, transformation, types & values, use, qualitative & quantitative, queries, schemas, security, sorting, sources, structuring, syntax, validation, vectors, writing clean code
Data Analysis:Â
analysis of variance, analytical thinking, case studies, communication, context, correlation & causation, creating a scope of work, data analysis process, dashboarding, decision making, EDA, file naming, interpretations, insights, limitations, mathematical thinking, objective thinking, PACE, phases, predictions, presentation, solutions, stakeholder roles, visualizations
Statistics / Inferential Statistics: Â
A/B testing, confidence intervals, descriptive / inferential, experimental design, hypothesis testing, measures of central tendency, measures of dispersion, measures of position, one-sample tests, P-value, sampling distributions, sampling methods, skewness, statistical significance, two-sample tests, type I & type II errors
Regression Analysis:Â
ANOVA / ANCOVA / MANOVA / MANCOVA, chi-squared goodness of fit, compare regression models, complex data relationships, evaluation metrics, hypothesis testing with chi-squared, linear regression, logistic regression / binomial logistic regression, measures of uncertainty, model assumptions, model interpretation, multicollinearity, multiple regression, multiple regression assumptions, overfitting, regression results, regularization: lasso / ridge / elastic net regression, uncertainty evaluation, variable selection methods
Probability:Â
Bayes's theorem, binomial distribution, conditional probability, continuous probability distributions, discrete probability distributions, Poisson distribution, probability and events, probability of multiple events, z-scores
Machine Learning:Â
bagging, bootstrap aggregation, categorical features and classification models, categorical vs. continuous data, class balancing, clustering, deep learning, ethics, feature engineering / extraction / selection / transformation, hyperparameter tuning, imbalanced dataset issues, K-means, K-means inertia and silhouette coefficient metrics, K-means model building, key evaluation metrics for classification models, machine learning solutions, Naive Bayes classifiers, one hot encoding, Python IDEs, random forest, random forest building, recommendation systems, supervised learning, tree-based models, tuning a decision tree, validation & cross validation, when features are infinite, XGBoost building
IDEs, Data Analysis Software & Technologies, Languages & More:
Power BI:
advanced editor, actions & triggers, appending & merging, applied steps, calculated columns, connectors, data flows, DAX, DirectQuery / import / dual mode, fact / dimension tables, helper queries, measures, Microsoft Power Query Editor, model relationships / cardinality / cross filter direction, normalized / denormalized data, query folding, query parameters, schema - flat / star / snowflake, time intelligence calculations
Additional:
BigQuery, Cognos, Dataflow, Jupyter Notebooks, Kaggle, Looker Studio, Python 3, PyCharm, R, R Markdown Notebooks, RStudio, spreadsheets, SQL, SQLite, Tableau, VS Code
Google Cloud Engineering
Google Cloud Engineering
Google Cloud Fundamentals: Core Infrastructure
cloud computing, IaaS & PaaS, Google cloud resource hierarchy, IAM, service accounts, cloud identity, interacting with Google Cloud, VPC networking, Compute Engine, scaling virtual machines, important VPC compatibilities, Cloud Load Balancing, Cloud DNS and Cloud CDN, connecting networks to Google VPC, Google Cloud storage options, cloud storage classes and data transfer, Cloud SQL, Cloud Spanner, Firestore, Cloud Bigtable, comparing storage options, containers, Google Kubernetes Engine, Cloud Run, development in the cloud
Essential Google Cloud Infrastructure: Foundation
Cloud Console and Cloud Shell, projects, build a deployment using Marketplace, use Marketplace to build a Jenkins Continuous Integration environment, administer VM host services through SSH, explore default VPC network, create auto mode network with firewall rules, convert an auto mode network to a custom mode network, create custom mode VPC networks with firewall rules, create VM instances using Compute Engine, explore connectivity for VM instances across VPC networks, configure a VM instance that doesn't have an external IP address, connect to a VM instance using an IAP tunnel, enable Private Google Access on a subnet, configure a Cloud NAT gateway, verify access to public IP addresses of Google APIs, create several standard VMs, create advanced VMs, customize an application server, install and configure necessary software, configure network access, schedule regular backups
Essential Google Cloud Infrastructure: Core Services
IAM, creating custom roles, members, service accounts, IAM best practices, exploring IAM, grant and revoke Cloud IAM roles, create and work with buckets and objects, apply Customer Supplied Encryption Keys, Access Control Lists, Life-Cycle Management, Object Versioning, Directory Synchronization, Cross-Project Resource Sharing, Cloud Storage features, choosing a storage class, Firestore, create a Cloud SQL database, Cloud Spanner, fine-grained access control, configure a VM to run a proxy, create a connection between an application and Cloud SQL, connect an application to Cloud SQL using Private IP address, cloud monitoring, dashboarding, creating alerts with multiple conditions, create resource groups, create uptime checks, launch a simple Google App Engine application, error reporting, Cloud Debugger, fix a bug and monitor in Cloud Operations
Elastic Google Cloud Infrastructure: Scaling and Automation
interconnecting networks, load balancing and autoscaling, infrastructure automation, managed services, create two VPC networks, subnets, and instances in Cloud Shell, configure HA VPN gateways, configure dynamic routing with VPN tunnels, configure global dynamic routing mode, verify and test HA VPN gateway configuration, create a custom firewall rule, managed instance groups, HTTP(S) load balancing, Cloud CDN, SSL proxy / TCP proxy load balancing, network load balancing, internal load balancing, choosing a load balancer, create an instance template, create HTTP and health check firewall rules, create a custom image for a web server, create an instance template based on the custom image, create two managed instance groups, configure an HTTP load balancer with IPv4 and IPv6, stress test an HTTP load balancer, automating the deployment of infrastructure using Terraform
Architecting with Google Kubernetes Engine: Foundations
Containers and Kubernetes, Kubernetes Architecture, deploy a Kubernetes cluster using GKE, deploy pods to a GKE cluster, view and manage Kubernetes objects, resource management, resource hierarchy, Google Cloud's shared security model, billing, budget alerts, quotas, Cloud Shell, Cloud Shell Editor, SSH, Container Registry, use Cloud Build to build and push containers, use Artifact Registry to store and deploy containers, control plane components, YAML files, Pods, Controller Objects, namespaces
Power BI
Power BI
Power BI Desktop & Power BI Service:
Get data from sources:
Identify and conntect to a data source.
Change data source settings, including credentials, privacy levels, and data source locations.
Select a shared dataset, create a local dataset.
Choose between DirectQuery, Import, and Dual mode.
Clean data:
Evaluate data, including data statistics and column properties.
Resolve inconsistencies, unexpected or null values, and data quality issues.
Resolve data import errors.
Transform and load data:
Select appropriate column data types.
Create and transform columns.
Transform a query.
Design a star schema that contains facts and dimensions.
Identify when to use a reference or duplicate queries and the resulting impact.
Merge and append queries.
Identify and create appropriate keys for relationships.
Configure data loading for queries.
Model data:
Design and implement a data model:
Configure table and column properties.
Implement role-playing dimensions.
Define a relationship's cardinality and cross-filter direction.
Create a common date table.
Implement row-level secuirty roles.
Create model calculations by using DAX:
Create single aggregation measures.
Identify implicit measures and replace them with explicit measures.
Use basic statistical functions.
Create semi-additive measures.
Create a measure by using quick measures.
Create calculated tables.
Optimize model performance:
Improve performance by identifying and removing unnecessary rows and columns.
Identify poorly performing measures, relationships, and visuals by using Performance Analyzer.
Improve performance by choosing optimal data types.
Improve performance by summarizing data.
Visualize and analyze data:
Create reports:
Identify and implement appropriate visualizations.
Format and configure visualizations.
Use a custom visual.
Apply and customize a theme.
Configure conditional formatting.
Apply slicing and filtering.
Configure the report page.
Use the Analyze in Excel feature.
Choose when to use a paginated report.
Enhance reports for usability and storytelling:
Configure bookmarks.
Create custom tooltips.
Edit and configure navigation for a report.
Apply sorting.
Configure sync slicers.
Group and layer visuals by using the Selection pane.
Drill down into data using interactive visuals.
Configure export of report content and perform an export.
Design reports for mobile devices.
Incorporate the Q&A feature in a report.
Identify patterns and trends:
Use the Analyze feature in Power BI.
Use grouping, binning, and clustering.
Use AI visuals.
Use reference lines, error bars, and forecasting.
Detect outliers and anomalies.
Create and share scorecards and metrics.
Deploy and maintain assets:
Create and manage workspaces and assets:
Create and configure a workspace.
Assign workspace roles.
Configure and update a workspace app.
Publish, import, and update assets in a workspace.
Create dashboards.
Choose a distribution method.
Apply sensitivity labels to workspace content.
Configure subscriptions and data alerts.
Promote or certify Power BI content.
Manage global options for files.
Manage datasets:
Identify when a gateway is required.
Configure a dataset scheduled refresh.
Configure row-level security group membership.
Provide access to datasets.
Project Management
Project Management
Project Management
Building a Team, Change Management, Continuous Improvement, Cost Benefit Analysis, Evaluation Questions, Executive Summaries, Impact Reports, Organizational Structures, Project Charters, Project Execution, Project Initiation, Risk Management, Time Estimation
5S, Agile, DevOps, Kanban, Lean, Retrospectives, ROAM Analysis, Scrum, Six Sigma, SMART Goals, OKRs, VUCA, Waterfall, XP
Software:
Asana
Python 3
Python 3
Python 3
algorithms, arrays, conditional statements, dictionaries, encoding, extracting, filtering, functions, grouping, joining, keys, lists, loops - for - while, merging, object oriented programming, Python Standard Library modules, regex, sets, slicing, sorting, strings, tuples, variables, vectors
Packages:
BeautifulSoup, Dash, Django, Matplotlib, NumPy, Pandas, Plotly, Pygame, PyTorch, Scikit-learn, SciPy, Seaborn, Statsmodels, TensorFlow, XGBoost
R
R
RÂ
Packages:
tidyverse, dplyr, forcats, ggplot2, lubridate, magrittr, readr, scales, skimr, stringr, tibble, tidyr
Spreadsheets
Spreadsheets
Excel / Sheets:
Pivot tables, sort, filter, visualizations, conditional formatting
Functions:
AND, AVERAGE, AVERAGEIF, CONCAT, COUNT, COUNTA, COUNTBLANK, COUNTIF, COUNTIFS, DATE, DATEDIF, DATEVALUE, DAY, DAYS, FIND, HLOOKUP, HOUR, IF, IFS, INDEX, INT, LARGE, LEFT, LEN, LOWER, MATCH, MAX, MEDIAN, MID, MIN, MINUTE, MODE, MONTH, NETWORKDAYS, NOW, OR, PROPER, RIGHT, SEARCH, SECOND, SMALL, SORT, SUM, SUMIF, SUMIFS, SUMPRODUCT, TEXTJOIN, TEXTSPLIT, TIME, TODAY, TRIM, UNIQUE, UPPER, VLOOKUP, WEEKDAY, WEEKNUM, XLOOKUP, YEAR
SQL & DBMS
SQL & DBMS
MySQL - SQLite - DB2:
_, %, =, >, <, <>, >=, <=, !, ALTER TABLE - ADD COLUMN, ALTER TABLE - ALTER COLUMN, ALTER TABLE - DROP COLUMN, ALTER TABLE - RENAME COLUMN, AND, AVG, BETWEEN, CASE WHEN, CONCAT, COUNT, CREATE TABLE, DELETE, DISTINCT, DROP TABLE, END AS, FROM, GROUP BY, HAVING, INSERT, JOIN, LCASE, LENGTH, LIKE, LIMIT, MAX, MIN, NOT, OR, ORDER BY, ROUND, SELECT, SET, SUM, THEN, TRUNCATE TABLE, UCASE, UPDATE, VALUES, WHERE
Additional
Additional
Additional Skills:
Java, JavaScript, xml, json, PHP, HTML, CSS, Adobe Illustrator, Microsoft - Office
IBM Data Science Professional Certificate Capstone Project
IBM Data Science Professional Certificate Capstone Project
SpaceX Falcon 9 Stage 1 Reuse Prediction
This project was created for the IBM Data Science Professional Certificate Capstone Project. SpaceX launch Falcon 9 launch data was put through the Data Science process to gather insights regarding booster reuse.
https://github.com/3XHX/IBM-DATA-SCIENCE-CAPSTONE-PROJECT---SPACEX
_IBM DATA SCIENCE certificate CAPSTONE project.pptxIBM Data Analyst Professional Certificate Capstone Project
IBM Data Analyst Professional Certificate Capstone Project
Stack Overflow Survey
This project was created for the IBM Data Analyst Professional Certificate Capstone Project. Current technology trends are compared with future reported trends based on participation of survey respondents.
IBM DATA ANALYST CAPSTONE PROJECT.pptxGoogle Business Intelligence Dashboard Projects with Tableau
Google Business Intelligence Dashboard Projects with Tableau
Google Fiber
This dashboard was created for the Google Business Intelligence Certificate capstone project. It shows the relationships between call volume, call purpose, market location, and more.
Metro Interstate Traffic Volume
This dashboard was created for the Google Business Intelligence Certificate. It shows a comparison between traffic volume throughout days of the year by hour, yearly volume by weather condition, and holiday traffic.
NOAA Lightning Strikes Dashboard
This dashboard was created for the Google Advanced Data Analytics Professional Certificate. It shows a map containing lightning strike data for a region in the United States, along with a heatmap, and a bar chart.
Athens Airbnb Data
This dashboard was created for the Google Business Intelligence Certificate. It shows a map of rental properties in Athens, Greece broken down by rental type and average price for each neighborhood.
Seoul Bicycle Rentals
This dashboard was created for the Google Advanced Data Analytics Professional Certificate. This sample data set shows the relationships between bicycle rentals and weather conditions.
Google Advanced Data Analytics Capstone Project with Python
Google Advanced Data Analytics Capstone Project with Python
Capstone Project - Providing Data Driven Suggestions for HR.pdfGoogle Data Analytics Capstone Project with R
Google Data Analytics Capstone Project with R
To view the complete code for this case study see the Kaggle R Markdown Document:
https://www.kaggle.com/code/coreymalowney/case-study-cyclistic-d-2204-d-2303
Case Study - Cyclistic - Sites.pptxAdditional Python Activities - Machine Learning
Additional Python Activities - Machine Learning
Activity - Machine Learning - Annotated follow-along guide_ Apply inertia and silhouette score with Python.pdfActivity - Machine Learning - Annotated follow-along guide_ Build an XGBoost model with Python.pdfActivity - Machine Learning - Annotated follow-along guide_ Construct a Naive Bayes model with Python.pdfActivity - Machine Learning - Annotated follow-along guide_ Use K-means for color compression with Python.pdfActivity - Machine Learning - Build a K-means model.pdfActivity - Machine Learning - Build a Naive Bayes model.pdfActivity - Machine Learning - Build an XGBoost model.pdfActivity - Machine Learning - Annotated follow-along guide_ Build a decision tree with Python.pdfActivity - Machine Learning - Annotated follow-along guide_ Build and cross-validate a random forest model with Python.pdfActivity - Machine Learning - Annotated follow-along guide_ Tune and validate decision trees with Python.pdfActivity - Machine Learning - Annotated follow-along guide_Feature engineering with Python.pdfActivity - Machine Learning - Build a decision tree.pdfActivity - Machine Learning - Build a random forest model.pdfActivity - Machine Learning - Perform feature engineering.pdfAdditional Python Activities - Regression Analysis
Additional Python Activities - Regression Analysis
Activity - Regression - Perform logistic regression.pdfActivity - Regression - Annotated follow-along guide_ Interpret multiple regression results with Python.pdfActivity - Regression - Annotated follow-along guide_ Explore linear regression with Python.pdfActivity - Regression - Perform multiple linear regression.pdfActivity - Regression - Annotated follow-along guide_ Use Python to conduct a hypothesis test.pdfActivity - Regression - Annotated follow-along guide_ Explore one-way versus two-way ANOVA tests with Python.pdfActivity - Regression - Annotated follow-along guide_ Construct a logistic regression model with Python.pdfPage updated
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