Data Analysis Services

Python Analysis for research that needs to scale beyond a spreadsheet.

When your dataset is too large, too messy, or your model too complex for Excel or SPSS — our analysts build clean, reproducible Python scripts and Jupyter notebooks for PhD, MTech and MBA research, fully documented and ready to defend.

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lotus_research_analysis — Python 3.12
import pandas as pd df = pd.read_csv(“survey_raw.csv”) df = df.drop_duplicates() df[‘income’] = df[‘income’].fillna(df[‘income’].median()) # reverse-code Likert items df[‘q4’] = 6 – df[‘q4’]

Data Cleaning & Wrangling

Pandas & NumPy pipelines that turn raw, multi-source exports into one clean, analysis-ready dataset.

  • Missing value treatment & outlier detection
  • Merging multi-sheet / multi-survey datasets
  • Reverse-coding & variable recoding
from scipy import stats import statsmodels.api as sm t_stat, p_val = stats.ttest_ind(grp_a, grp_b) model = sm.OLS(y, sm.add_constant(X)).fit() print(model.summary())

Statistical Modelling

SciPy & statsmodels for hypothesis testing, regression and every test your committee expects.

  • t-Test, ANOVA, Chi-Square, correlation
  • Linear & logistic regression with full summary output
  • Time-series & panel data modelling
from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) model = RandomForestClassifier().fit(X_train, y_train) print(model.score(X_test, y_test))

Machine Learning Models

scikit-learn pipelines for predictive research — classification, clustering & forecasting.

  • Regression, classification & clustering models
  • Feature engineering & cross-validation
  • Model evaluation: accuracy, F1, ROC-AUC
import seaborn as sns import matplotlib.pyplot as plt sns.set_theme(style=“whitegrid”) sns.boxplot(data=df, x=“group”, y=“score”) plt.savefig(“figure_3.png”, dpi=300)

Publication-Ready Visuals

Matplotlib, Seaborn & Plotly charts styled for journal submission and thesis appendices.

  • High-resolution figures at journal-standard DPI
  • Interactive dashboards for viva presentations
  • APA / IEEE-formatted labelling & captions
from nltk.sentiment import SentimentIntensityAnalyzer sia = SentimentIntensityAnalyzer() df[‘sentiment’] = df[‘response’].apply( lambda x: sia.polarity_scores(x)[‘compound’] )

Text & Sentiment Analysis

NLTK & spaCy pipelines for open-ended survey responses, interview transcripts and social data.

  • Sentiment scoring & theme extraction
  • Word frequency & co-occurrence analysis
  • Qualitative-to-quantitative coding support
What’s Included

One notebook. Every method your committee will ask for.

We don’t just “run a script” — we hand over a documented, reproducible Python notebook with commented code, so you can explain and re-run every step during your viva.

01

Data Cleaning with Pandas

Missing values, duplicates, outliers and multi-source merges handled in a clean, reproducible pipeline.

02

Statistical Testing

t-Test, ANOVA, Chi-Square, correlation and regression using SciPy and statsmodels, with full output summaries.

03

Machine Learning Models

Classification, regression and clustering with scikit-learn — feature selection through to evaluation metrics.

04

Time Series & Forecasting

ARIMA, exponential smoothing and trend decomposition for longitudinal or panel research data.

05

Data Visualization

Journal-ready charts with Matplotlib, Seaborn and Plotly, exported at publication resolution.

06

Text & Sentiment Analysis

NLP pipelines for open-ended survey responses, interview transcripts and social-media datasets.

07

Large-Scale Data Handling

Efficient processing of large CSV, Excel or API-sourced datasets that spreadsheets can’t handle smoothly.

08

Jupyter Notebook Delivery

Fully commented, cell-by-cell notebooks — reproducible, re-runnable and easy to walk through in a viva.

09

Results Interpretation

Plain-English write-up of every output, ready to paste into your Findings or Results chapter.

How It Works

From raw dataset to a reproducible notebook, in four steps.

Share Your Data

Send your dataset, API source or scraped data over WhatsApp or email, in any common format.

Scope the Analysis

We map your research questions & hypotheses to the exact statistical or ML methods required.

Build the Notebook

Our analysts write clean, commented Python code — every output traceable to a reproducible cell.

Review & Handover

You get the notebook, figures and a written interpretation, plus revisions until your guide signs off.

Toolkit

Built on the same libraries used in published research.

Pandas NumPy SciPy statsmodels scikit-learn Matplotlib Seaborn Plotly NLTK / spaCy Jupyter Notebook
“

My dataset had over 40,000 survey rows — Excel kept crashing. The Python notebook they built ran the same models in seconds, and I could re-run every cell myself.

— PhD Scholar, Data Analysis Client
Good to Know

Frequently asked questions

No. We write and comment every line of code so you can walk through it in your viva, but you don’t need prior Python experience to receive or use the notebook.

Python is the better fit for very large datasets, machine learning models, text/sentiment analysis, or when your methodology needs custom statistical models that Excel or SPSS don’t support out of the box.

A fully commented Jupyter Notebook (or .py script), exported charts/figures, and a written, plain-English interpretation of every result.

Standard turnaround is 3–5 working days depending on dataset size and complexity. A 24-hour express option is available for urgent submissions.

Yes. All datasets are handled under strict confidentiality and are never shared or reused, in line with our standard research-data protocols.

Ready When You Are

Send your dataset — get a reproducible Python analysis back.

No fixed packages, no guesswork. Tell us your objectives and hypotheses, and we’ll scope exactly what your chapter needs.

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