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$35 USD/uro
Zastava UNITED STATES
indianapolis, united states
$35 USD/uro
Trenutno je 6:49 pop. tukaj
Datum pridružitve julij 25, 2023
0 Priporočila

Lawrence B.

@lawbuk

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$35 USD/uro
Zastava UNITED STATES
indianapolis, united states
$35 USD/uro
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Stopnja ponovnega najema

Electr. Eng | Data Analyst | Python | Azure | BI

Senior. System Engineer, a holder of a Ba in Computer Information Systems, Bs in Electrical Engineering, and a master's degree in electrical engineering from the esteemed University of St. Thomas, St. Paul, Minnesota, USA, and a master's degree in data Analytics from the University of Indianapolis, Indiana, USA With over 8 years of electrical engineering experience and more than 3 years of specialized experience in advanced data analytics and data science. My proficiency in this field enables me to unravel complex business challenges through data-driven insights, providing innovative solutions that yield tangible and impactful results. As a highly skilled data analyst, I am genuinely passionate about leveraging big data to solve real-world problems and make a positive impact in the field of analytics and beyond. Further more, I am committed to using my financial, technical, and data analytics expertise to support critical decision-making processes for your businesses/projects. I am devoted to understanding your unique I am devoted to understanding your unique business needs and fostering collaborative partnerships to develop tailored solutions that precisely align with your specific requirements I am excited to embark on this journey with you, utilizing my skills and experience to support your endeavors and drive remarkable success. Looking forward to working together and achieving great heights!
Freelancer Python Developers United States
Glej vse Python Developers v United States

Stopite v stik s/z Lawrence B. glede vašega dela

Prijavite se, da se o podrobnostih pogovorite v klepetu.

Portfelj

11210968
Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio, GitHub • Libraries: Pandas (for analysis), Seaborn, NumPy, Matplotlib, Plotly (for visual), Scikit-learn (for Machine learning) Analytics work: • Requirements Gatherings • Data collection procedures • Data integration: Combine multiple data sources. • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Statistical Modeling, Classification, and regression problems • Consultaation
Analytics to support academic research
11210962
Key activities performed: • Data cleaning, exploratory analysis, linear programming with Excel Solver, analytic methods with sensitivity analysis • Continuous evaluation with recommendations to maximize sponsorships and educational opportunities for the beneficiaries. • Quantitative data analysis. • Data storytelling Reports: charts, and tables. • Consultation Skills and Training: • Visualization Tools: Tableau, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project
Quantitative Analysis to Support Grant Application
11210925
Work Performed: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Data storytelling BI Reports: Interactive KPI dashboards, charts, and tables to summarize key findings in a visually appealing way tailored to project needs. • Statistical Modeling, Classification, and regression problems • Consultation: Help clients understand their data and identify areas for improvement • Quantitative Analysis • Azure Machine Learning Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio,
Time Series Analysis and Interactive BI Reporting
Work Performed: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Data storytelling BI Reports: Interactive KPI dashboards, charts, and tables to summarize key findings in a visually appealing way tailored to project needs. • Statistical Modeling, Classification, and regression problems • Consultation: Help clients understand their data and identify areas for improvement • Quantitative Analysis • Azure Machine Learning Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio,
Time Series Analysis and Interactive BI Reporting
Work Performed: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Data storytelling BI Reports: Interactive KPI dashboards, charts, and tables to summarize key findings in a visually appealing way tailored to project needs. • Statistical Modeling, Classification, and regression problems • Consultation: Help clients understand their data and identify areas for improvement • Quantitative Analysis • Azure Machine Learning Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio,
Time Series Analysis and Interactive BI Reporting
11210946
Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: RStudio, GitHub Analytics work: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Statistical Modeling, Classification, and regression problems
Predictive Modeling -R, RStudio, RShiny App
Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: RStudio, GitHub Analytics work: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Statistical Modeling, Classification, and regression problems
Predictive Modeling -R, RStudio, RShiny App
11210919
Data Analytics
11210968
Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio, GitHub • Libraries: Pandas (for analysis), Seaborn, NumPy, Matplotlib, Plotly (for visual), Scikit-learn (for Machine learning) Analytics work: • Requirements Gatherings • Data collection procedures • Data integration: Combine multiple data sources. • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Statistical Modeling, Classification, and regression problems • Consultaation
Analytics to support academic research
11210962
Key activities performed: • Data cleaning, exploratory analysis, linear programming with Excel Solver, analytic methods with sensitivity analysis • Continuous evaluation with recommendations to maximize sponsorships and educational opportunities for the beneficiaries. • Quantitative data analysis. • Data storytelling Reports: charts, and tables. • Consultation Skills and Training: • Visualization Tools: Tableau, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project
Quantitative Analysis to Support Grant Application
11210925
Work Performed: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Data storytelling BI Reports: Interactive KPI dashboards, charts, and tables to summarize key findings in a visually appealing way tailored to project needs. • Statistical Modeling, Classification, and regression problems • Consultation: Help clients understand their data and identify areas for improvement • Quantitative Analysis • Azure Machine Learning Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio,
Time Series Analysis and Interactive BI Reporting
Work Performed: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Data storytelling BI Reports: Interactive KPI dashboards, charts, and tables to summarize key findings in a visually appealing way tailored to project needs. • Statistical Modeling, Classification, and regression problems • Consultation: Help clients understand their data and identify areas for improvement • Quantitative Analysis • Azure Machine Learning Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio,
Time Series Analysis and Interactive BI Reporting
Work Performed: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Data storytelling BI Reports: Interactive KPI dashboards, charts, and tables to summarize key findings in a visually appealing way tailored to project needs. • Statistical Modeling, Classification, and regression problems • Consultation: Help clients understand their data and identify areas for improvement • Quantitative Analysis • Azure Machine Learning Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Data Analysis Tools: Databricks, Minitab, • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: Jupyter Notebook, Azure Databricks Platform, Spyder IDE, RStudio,
Time Series Analysis and Interactive BI Reporting
11210946
Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: RStudio, GitHub Analytics work: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Statistical Modeling, Classification, and regression problems
Predictive Modeling -R, RStudio, RShiny App
Skills and Training: • Programming Languages: SQL, Python, Apache Spark, R, MATLAB • Visualization Tools: Tableau, Microsoft PowerBI, Minitab, Microsoft Excel • Microsoft Suite: Word, PowerPoint, Excel, Project • Environment: RStudio, GitHub Analytics work: • Data collection procedures • Data integration: Combine multiple data sources. • Data ETL: - Extract, Transform, and Load) • Data preprocessing and manipulation • Data cleansing and preparation • Exploratory data analysis (EDA: (identify patterns and trends). • Statistical Modeling, Classification, and regression problems
Predictive Modeling -R, RStudio, RShiny App
11210919
Data Analytics

Ocene

Spremembe shranjene
Tukaj ni nobenih ocen!

Izkušnje

Senior System Engineer | Data Analyst

Cummins Inc
jan. 2020 - Trenutno
• Oversee data acquisition at PSBU, providing technical advice and ensuring data consistency. • Utilize analytics tools for engine performance analysis and problem-solving. • Create user-friendly scripts for automated weekly engine health reports. • Lead analytics for various projects, coaching stakeholders in using analytics tools through my weekly open analytics hours. • Identify and resolve data quality issues to enhance PSBU’s digital capabilities.

Senior System Engineer

Cummins Inc
jan. 2018 - dec. 2019 (1 leto, 11 mesecev)
• Assisted in data logger setup and processed time-based duty cycle data for efficient data collection and usability. • Used analytics tools for complex data analysis and weekly fault code report generation. • Conducted duty cycle analysis, fuel analysis and Exhaust System Cleaning (ESC) assessments to ensure engine performance and EPA compliance. • Led cross-functional teams in translating customer feedback into clear performance requirements.

Project Coordinator Engineer

Cummins Inc
jun. 2015 - dec. 2017 (2 leti, 6 mesecev)
• Assist with budget planning and tracking, research, and analytical support to provide input into the design and development of project plans and timelines. • Assist with managing projects and use quality tools to identify areas of risk. • Document and share team learnings with other teams drawing on other experiences to enhance the success of the project as well as identifying, tracking, and working with project teams to resolve project issues.

Izobrazba

Masters in Data Analytics

University of Indianapolis, United States 2019 - 2021
(2 leti)

Ms in Electrical Engineering

University of Saint Thomas, United States 2013 - 2015
(2 leti)

BA in Computer Information Systems

University of St. Thomas, United States 2008 - 2013
(5 let)

Kvalifikacije

Academy Accreditation - Databricks Lakehouse Fundamentals

Databricks
2023
Earners of the Lakehouse Fundamentals accreditation have demonstrated the understanding of fundamental concepts related to Databricks Lakehouse Platform. Skills / Knowledge Databricks Lakehouse Databricks machine learning Databricks SQL Delta lake Security

Data Analyst with Python Track

DataCamp
2020
Python Programing

Business Leadership

McKinsey & Company
2023
Business Leadership program, which provides leaders with a foundational understanding of business acumen and key functional areas including strategy, operations, marketing and sales, and more

Publikacije

PERFORMANCE COMPARISON OF DIGITAL MODULATION TECHNIQUES FOR ENERGY EFFICIENT SENSOR NETWORK DESIGN

International Journal of Arts & Sciences; Cumberland Vol. 4, Iss. 19, (2011): 61-65.
Digital modulation techniques are crucial in many applications, but resource constraints on devices require simple, cost-effective solutions. This study suggests a heterogeneous modulation scheme where sensors choose a modulation based on their data rate to save energy. Compared to traditional methods, this scheme improves network lifetime.

American Baseball Fans Do Not Influence Game Outcomes

AccessEcon, vol. 41(2), pages 741-750.
The paper examines U. S. Major League Baseball (MLB) data to evaluate home-field advantage and the relationship between fan attendance and game outcomes. Due to the COVID-19 pandemic, fans were not allowed in stadiums during the 2020 MLB regular season. The results indicate the absence of fans did not statistically alter home-field advantage when controlling for quality of play.

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