At a glance
- Location
- Bangalore · Mico Inc Bangalore
About the role
- As a Data Science Intern at Mico, you will work closely with the team to analyze datasets, identify meaningful patterns, apply statistical techniques, and support the development of data-driven solutions.
- You will gain hands-on experience in Python programming, exploratory data analysis, data preprocessing, statistical analysis, and machine learning fundamentals while working with real-world datasets.
Responsibilities
- Collect, clean, preprocess, and analyze structured datasets using Python.
- Perform exploratory data analysis (EDA) to identify patterns, trends, relationships, and anomalies in data.
- Apply statistical concepts and techniques, including hypothesis testing, p-values, correlation analysis, and regression.
- Build an understanding of linear regression, logistic regression, and other fundamental machine learning algorithms.
- Evaluate regression models using appropriate performance metrics and interpret model results.
- Identify and handle missing values, outliers, categorical variables, and imbalanced datasets.
- Perform feature engineering and prepare numerical, categorical, and Boolean variables for analysis and modelling.
- Write, test, and maintain Python code for data manipulation, analysis, and basic modelling.
- Document analytical findings, explain assumptions, and communicate results to team members.
Requirements
- Currently pursuing or recently completed a degree in Data Science, Statistics, Mathematics, Computer Science, or a related field.
- Basic to intermediate proficiency in Python programming.
- Familiarity with Python libraries such as Pandas, NumPy, and Scikit-learn.
- Understanding of fundamental statistical concepts, including mean, median, variance, correlation, hypothesis testing, and p-values.
- Knowledge of regression techniques, including simple and multiple linear regression and logistic regression.
- Understanding of model evaluation metrics, including R-squared, Adjusted R-squared, MAE, MSE, and RMSE.
- Familiarity with data cleaning, missing-value treatment, outlier detection, and feature engineering.
- Understanding of categorical and numerical data, Boolean variables, and appropriate data handling techniques.
- Basic knowledge of correlation analysis for numerical and categorical variables.
- Understanding of fundamental machine learning concepts, including overfitting, model validation, and class imbalance.
Benefits
- Gain hands-on experience working with real-world datasets and data science problems.
- Learn from senior professionals through guidance and mentorship.
- Apply statistical and machine learning concepts to practical business use cases.
- Develop your Python programming, analytical thinking, and problem-solving skills.
- Work in a collaborative and innovative environment.
- Be part of a flexible, inclusive, and growth-oriented culture.
Skills
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- SQL
- Jupyter Notebook
- Google Colab
