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$20 USD / godz.
Flaga KENYA
nairobi, kenya
$20 USD / godz.
Obecnie jest 9:14 PM tutaj
Dołączył listopada 6, 2019
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Jackson M.

@jackwaraga

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$20 USD / godz.
Flaga KENYA
nairobi, kenya
$20 USD / godz.
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Powtórna Stawka Zatrudnienia

Data Analyst| Data Entry | Virtual Assistant

My Expertise: ● Data Exploration & Visualization in PowerBi, Excel, Rstudio, Google Collab, Shiny App ● Data Entry ● Dynamic Dashboard and Browser Automations ● Data Searching ● Microsoft Excel: Dashboard, Advance formulas, Web-Scraping, Data Analysis, and Data Entry. ● Google Sheets: Custom Scripts, Dashboard, Advance formulas ● Adobe Acrobat: Editing, Scripting, and Fillable forms ● Transcribing: English ● Photoshop Banner Design, Business Cards, Logos ● Data Analysis in R, MS Excel, Google Sheets ● QuickBooks Financial Software ● Linear & Logistic Regression in R ● Classification Models in R ● Decision Trees, Naive Bayes, Random Forest, Lasso Regression in R ● Data Manipulation & Visualization in R using the Tidyverse packages
Freelancer Data Processing Executives Kenya
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Skontaktuj się z Jackson M. w sprawie projektu

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Portfolio

11888849
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
11822207
Comprehensive Data Analysis of Power Bi
Power BI Dashboard Power BI Analysis
Comprehensive Data Analysis of Power Bi
Power BI Dashboard Power BI Analysis
Comprehensive Data Analysis of Power Bi
Power BI Dashboard Power BI Analysis
11822449
Google Sheets Data Entry Data Processing Data Formatting Copy Pasting
Google Sheets Data Entry & Data Processing
Google Sheets Data Entry Data Processing Data Formatting Copy Pasting
Google Sheets Data Entry & Data Processing
11822239
All types of Data Entry in Excel or Google Sheets. I offer error free data entry services - Converting hand writte tables to excel - Mining business information from Google Maps - Extracting information from websites - Pivot Tables - Simple and Advance Excel Formulas - Join one or more spreadsheets - Filtering & advance sorting
Data Entry | Data Preprocessing in Excel
All types of Data Entry in Excel or Google Sheets. I offer error free data entry services - Converting hand writte tables to excel - Mining business information from Google Maps - Extracting information from websites - Pivot Tables - Simple and Advance Excel Formulas - Join one or more spreadsheets - Filtering & advance sorting
Data Entry | Data Preprocessing in Excel
All types of Data Entry in Excel or Google Sheets. I offer error free data entry services - Converting hand writte tables to excel - Mining business information from Google Maps - Extracting information from websites - Pivot Tables - Simple and Advance Excel Formulas - Join one or more spreadsheets - Filtering & advance sorting
Data Entry | Data Preprocessing in Excel
11555151
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
11539363
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
11888849
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
Data Analysis in R Data Visualization Exploratory Data Visualization Statistical Modeling Machine Learning Linear & Logistic Regression Classification Analysis
Data Analysis in R
11822207
Comprehensive Data Analysis of Power Bi
Power BI Dashboard Power BI Analysis
Comprehensive Data Analysis of Power Bi
Power BI Dashboard Power BI Analysis
Comprehensive Data Analysis of Power Bi
Power BI Dashboard Power BI Analysis
11822449
Google Sheets Data Entry Data Processing Data Formatting Copy Pasting
Google Sheets Data Entry & Data Processing
Google Sheets Data Entry Data Processing Data Formatting Copy Pasting
Google Sheets Data Entry & Data Processing
11822239
All types of Data Entry in Excel or Google Sheets. I offer error free data entry services - Converting hand writte tables to excel - Mining business information from Google Maps - Extracting information from websites - Pivot Tables - Simple and Advance Excel Formulas - Join one or more spreadsheets - Filtering & advance sorting
Data Entry | Data Preprocessing in Excel
All types of Data Entry in Excel or Google Sheets. I offer error free data entry services - Converting hand writte tables to excel - Mining business information from Google Maps - Extracting information from websites - Pivot Tables - Simple and Advance Excel Formulas - Join one or more spreadsheets - Filtering & advance sorting
Data Entry | Data Preprocessing in Excel
All types of Data Entry in Excel or Google Sheets. I offer error free data entry services - Converting hand writte tables to excel - Mining business information from Google Maps - Extracting information from websites - Pivot Tables - Simple and Advance Excel Formulas - Join one or more spreadsheets - Filtering & advance sorting
Data Entry | Data Preprocessing in Excel
11555151
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
Exploratory Data Analysis The yesno variable tells us whether the email is spam or not, and we have information on the total length of UNINTERRUPTED CAPITAL LETTERS as well as occurrences of $, !, the word “money”, the string “000”, and the word “make” (all as proportions of characters or words). Notice that a lot of feature engineering. has gone into preparing the dataset; we don’t have the raw email contents but instead a set of features. engineered from that text. This means that we don’t need to think much about feature engineering for us. example here.
Spam eMail Classification in R
11539363
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R
Conducting Exploratory Data Analysis (EDA) on Ames Housing Data to comprehend variable distributions and their impact on the response variable, Sale Price. EDA aids in identifying missing values and outliers, crucial for informed decision-making. Careful handling of outliers and missing values is imperative to prevent data loss and retain key characteristics. EDA also helps you determine which observations are important to your response variable, more importantly, if your goal for analysis is prediction. It is important to throw the right data to your model for predictability and better performance of your machine learning model.
Exploratory Data Analysis on Ames Housing Data in R

Oceny

Zmiany zapisane
Brak ocen do zobaczenia!

Doświadczenie

Accountant

Cyntiac Digital Solutions
mar 2019 - gru 2021 (2 lata, 9 miesięcy)
• Filing and paying monthly statutory returns including VAT, PAYE, NSSF, AHL, NITA • Generating employees P9A forms to assist in end year return filing • Posting transactions on Quickbooks. • Keep up to date records of office expenses and revenue • Ensuring all payables are paid on time to avoid fines and penalties • Assist external auditors with the necessary documents and statements • Creating interactive dashboards to visualize our company performance. • Creating monthly financial reports.

Edukacja

Economics & Statistics

University of Nairobi, Kenya 2019 - 2022
(3 lata)

Kwalifikacje

Professional Data Analyst

Datacamp
2021
Certified Data Analyst with a professional certificate from Datacamp. Proficient in analytical fundamentals, exploratory data analysis, data management, and data visualization. Skilled in R and SQL, I bring a robust analytical toolkit to extract insights and drive informed decision-making. Ready to apply my expertise to elevate your data-driven projects.

Skontaktuj się z Jackson M. w sprawie projektu

Zaloguj się, aby omówić szczegóły na czacie.

Weryfikacje

Ulubiony Freelancer
Tożsamość
Płatność Zweryfikowana
Numer telefonu zweryfikowany
E-mail zweryfikowany
Połączone z Facebookiem

Najlepsze Umiejętności

Data Processing
Data Entry
Accounting
Excel
SQL

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