You will get Data Cleaning and Transformation:
Project details
Objective: Clean and transform raw data into a usable format, ensuring accuracy, consistency, and readiness for analysis or reporting.
Steps Taken:
Data Loading and Inspection: Import the dataset and inspect its structure, identifying any issues such as missing values, duplicates, or inconsistent data types.
Data Cleaning:
Handle missing values (e.g., drop, fill with mean/median/mode).
Remove duplicate rows.
Address outliers using methods like IQR or Z-scores.
Data Transformation:
Standardize text (e.g., convert to lowercase).
Convert date formats.
Split or merge columns as needed.
Create new calculated columns based on existing data.
Verification: Double-check that the cleaned data meets the specified requirements and runs properly.
Delivery: Provide the cleaned dataset in the requested format (CSV, Excel), along with any necessary Python scripts or documentation for future use.
Outcome: A well-organized, clean dataset ready for analysis, with clear documentation and tools for reuse.
Steps Taken:
Data Loading and Inspection: Import the dataset and inspect its structure, identifying any issues such as missing values, duplicates, or inconsistent data types.
Data Cleaning:
Handle missing values (e.g., drop, fill with mean/median/mode).
Remove duplicate rows.
Address outliers using methods like IQR or Z-scores.
Data Transformation:
Standardize text (e.g., convert to lowercase).
Convert date formats.
Split or merge columns as needed.
Create new calculated columns based on existing data.
Verification: Double-check that the cleaned data meets the specified requirements and runs properly.
Delivery: Provide the cleaned dataset in the requested format (CSV, Excel), along with any necessary Python scripts or documentation for future use.
Outcome: A well-organized, clean dataset ready for analysis, with clear documentation and tools for reuse.
Programming Languages
HTML & CSS, PythonCoding Expertise
PSD to HTML, Performance Optimization, DesignWhat's included
| Service Tiers |
Starter
$50
|
Standard
$150
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
Install Script | |||
Test Script | - | ||
Task Automation | - | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100
Additional Revision
+$75About Caroline
Freelance Data Scientist | Python, MATLAB, Chemistry Applications
Quantico, United States - 2:16 pm local time
I am a dedicated Data Analyst and Computational Chemistry Specialist with a strong background in Python, MATLAB, Excel, and data entry. My expertise extends to scientific computing, machine learning (Scikit-learn, PyTorch), and chemistry-related data analysis.
With hands-on experience in creating analytical models, processing complex datasets, and developing computational tools, I bring a meticulous approach to problem-solving. Whether you're looking for custom Python scripts, MATLAB analysis, Excel automation, or chemistry-focused data visualization, I deliver reliable and efficient solutions tailored to your needs.
What I Offer
Python scripting for data analysis, automation, and visualization.
Machine learning solutions using Scikit-learn and PyTorch.
Excel automation (formulas, macros, and data cleanup).
MATLAB programming for engineering and chemistry-related tasks.
Data entry and database organization with attention to detail.
Chemistry-focused computational projects.
Why Choose Me?
Proven experience combining technical skills with real-world applications.
Strong organizational and time management skills to meet deadlines.
Clear communication to ensure project success and client satisfaction.
Let’s work together to turn your ideas into actionable solutions!
Steps for completing your project
After purchasing the project, send requirements so Caroline can start the project.
Delivery time starts when Caroline receives requirements from you.
Caroline works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Assessment & Planning
Understand the Client’s Requirements: Review the dataset to understand its structure, size, and quality. Clarify any ambiguities regarding the client's needs. Confirm the preferred output format and any specific transformations or calculations.
Load & Inspect the Data