We Partner with Organisations in their strategic Digital Transformation initiatives, driving a Data and AI First approach across business functions.
Sponge Global serves as the consulting and training arm of the DigiMinds Alliance. The alliance includes partners such as eLearning.lk , Maya Hive and Code94. Together, we support over 200 clients locally and globally.
Since our founding in 2011, Sponge Global has built a reputation as a leader in consulting and talent transformation. Our diverse team brings practical wisdom that extends far beyond conventional business advice.
Satisfied Clients
Founded
Consulting & Training Solutions
LMS & Learning Content Creation
Digital Marketing Solutions
Engineering Solutions
OpenAI - ChatGPT
Google - Gemini
Anthropic - ChatGPT
Perplexity
Write a 100-word summary of this leadership article for a LinkedIn post.
The audience is senior managers.
Use a confident and insightful tone.
Focus on how to lead transformation and build a future-ready team.
Here’s a sample style: ‘In today’s fast-changing world, leaders must do more than manage—they must inspire agility and action.
import pandas as pd
df = pd.read_csv ("ds.csv")
df.head()
Generate Mean, stanadard deviation, range, quartiles and percentiles of "columnName"
Generate Histogram with 5 bins of "columnName"
Generate a Box Plot of "columName"
Generate a Bar Chart of "columnName"
Generate a Pie Chart of "columnName"
Generate a Line Chart of "columnName"
Generate Scatter Plots
Generate Corelations
Generate Regression
Generate Contingency Tables
Generate Chi-Square Test of Independence
Generate Clustered Bar Charts or Mosaic Plots
Generate Relative Risk / Odds Ratios (in binary categories)
Generate Association Rules (if >2 categories or more variables)
Using dataframe df: assign "columnName" to a variable "y"
Assign "columnName" and "columnName" to variable "x"
Fit "x" and "y" into a linear regression model
Print ""coefficients" and "intercept" of the linear model
Generate a visualization of the model
Generate R2 accuracy of the model
Explain 0.4 R2 number
Generate a predictive model using linear regression with a 30% testing and 70% training. Use cross validation.
Generate R2 accuracy of the model
Explain 0.4 R2 number
make a prediction using the model
Using dataframe df: assign "columnName" to a variable "y"
Assign "columnName" and "columnName" to variable "x"
Generate a predictive model using random forest with a 30% testing and 70% training. Use cross validation.
Generate Confusion Matrix
Generate Accuracy of the Model
make a prediction using the model
import pandas as pd
df = pd.read_csv ("ds.csv")
df.head()
Generate Mean, stanadard deviation, range, quartiles and percentiles of "columnName"
Generate Histogram with 5 bins of "columnName"
Generate a Box Plot of "columName"
cmd
cd folder
mkdir newfolder
python -m pip install virtualenv
python -m venv venv
.\venv\Scripts\activate
pip install libraryName
echo.> NewFile.py
code .
run app.py
streamlit run app.py