Most popular
-90%
Self-Paced Data Science Internship (Recorded + Practical | 1 Month)
What you'll learn
Build a strong foundation in Python programming for Data Science
Understand essential statistical concepts including distributions, hypothesis testing, and correlation
Work with real-world datasets using NumPy for numerical computing and Pandas for data manipulation
Perform data cleaning, preprocessing, and feature preparation for data science workflows
Create meaningful visualisations using Matplotlib and Seaborn to extract actionable insights
Conduct structured Exploratory Data Analysis (EDA) across univariate, bivariate, and multivariate levels
Implement Regression algorithms including Linear, Ridge, Lasso, and Polynomial Regression
Implement Classification algorithms including Logistic Regression, Decision Trees, and Random Forest
Evaluate models using industry-standard metrics — RMSE, R², Accuracy, Precision, Recall, F1, and ROC-AUC
Tune and optimise models using Cross-Validation, GridSearchCV, and sklearn Pipelines
Develop an end-to-end Data Science Capstone Project on a real-world dataset
Prepare professional project documentation and a structured Jupyter Notebook
Present technical findings clearly with problem statement, methodology, and results
Gain practical, job-ready Data Science skills across the full pipeline — from raw data to trained model
Receive Internship Completion Certificate, Training Certificate, and Letter of Recommendation within 24 hours of successful completion.
Trending
-90%
Self-Paced Data Science Internship (Recorded + Practical | 1 Month)
What you'll learn
Build a strong foundation in Python programming for Data Science
Understand essential statistical concepts including distributions, hypothesis testing, and correlation
Work with real-world datasets using NumPy for numerical computing and Pandas for data manipulation
Perform data cleaning, preprocessing, and feature preparation for data science workflows
Create meaningful visualisations using Matplotlib and Seaborn to extract actionable insights
Conduct structured Exploratory Data Analysis (EDA) across univariate, bivariate, and multivariate levels
Implement Regression algorithms including Linear, Ridge, Lasso, and Polynomial Regression
Implement Classification algorithms including Logistic Regression, Decision Trees, and Random Forest
Evaluate models using industry-standard metrics — RMSE, R², Accuracy, Precision, Recall, F1, and ROC-AUC
Tune and optimise models using Cross-Validation, GridSearchCV, and sklearn Pipelines
Develop an end-to-end Data Science Capstone Project on a real-world dataset
Prepare professional project documentation and a structured Jupyter Notebook
Present technical findings clearly with problem statement, methodology, and results
Gain practical, job-ready Data Science skills across the full pipeline — from raw data to trained model
Receive Internship Completion Certificate, Training Certificate, and Letter of Recommendation within 24 hours of successful completion.
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All Data Science Internship (1 Month) Courses
-90%
Self-Paced Data Science Internship (Recorded + Practical | 1 Month)
💻 Best experienced on laptop or desktop. Interactive video lessons with …
What you'll learn
Build a strong foundation in Python programming for Data Science
Understand essential statistical concepts including distributions, hypothesis testing, and correlation
Work with real-world datasets using NumPy for numerical computing and Pandas for data manipulation
Perform data cleaning, preprocessing, and feature preparation for data science workflows
Create meaningful visualisations using Matplotlib and Seaborn to extract actionable insights
Conduct structured Exploratory Data Analysis (EDA) across univariate, bivariate, and multivariate levels
Implement Regression algorithms including Linear, Ridge, Lasso, and Polynomial Regression
Implement Classification algorithms including Logistic Regression, Decision Trees, and Random Forest
Evaluate models using industry-standard metrics — RMSE, R², Accuracy, Precision, Recall, F1, and ROC-AUC
Tune and optimise models using Cross-Validation, GridSearchCV, and sklearn Pipelines
Develop an end-to-end Data Science Capstone Project on a real-world dataset
Prepare professional project documentation and a structured Jupyter Notebook
Present technical findings clearly with problem statement, methodology, and results
Gain practical, job-ready Data Science skills across the full pipeline — from raw data to trained model
Receive Internship Completion Certificate, Training Certificate, and Letter of Recommendation within 24 hours of successful completion.
