Data Science & Machine Learning

Week 1: Python for Data Science Foundations

Establishes a strong programming and analytical base for data science workflows.

Python core & environment

• Python syntax, control structures, functions, virtual environments (Anaconda/VS Code/Jupyter)

• Working with notebooks, scripts, and project structuring

Numerical & data handling

• NumPy arrays, broadcasting, vectorized operations

• Pandas Series/DataFrames: indexing, filtering, merging, reshaping

• Handling missing values, duplicates, outliers

Exploratory Data Analysis (EDA)

• Descriptive statistics, distribution analysis

• Data visualization with Matplotlib & Seaborn

• Correlation analysis, feature understanding

Data preprocessing

• Encoding categorical variables

• Feature scaling (StandardScaler, MinMaxScaler)

• Train-test split and data leakage prevention

Week 2: Statistics & Data Analysis for Machine Learning

Builds mathematical intuition and statistical rigor essential for ML models.

Descriptive & inferential statistics

• Mean, median, variance, skewness, kurtosis

• Probability distributions (Normal, Binomial, Poisson)

• Sampling techniques and central limit theorem

Statistical inference

• Hypothesis testing (z-test, t-test, chi-square)

• Confidence intervals and p-values

• A/B testing fundamentals

Correlation & regression analysis

• Pearson vs Spearman correlation

• Simple & multiple linear regression assumptions

• Multicollinearity and variance inflation factor (VIF)

Data preparation for ML

• Feature selection techniques

• Handling imbalanced datasets

• Introduction to feature engineering

Week 3: Machine Learning – Supervised Learning

Introduces core predictive modeling techniques with practical implementation.

ML foundations

• Types of ML: supervised, unsupervised, semi-supervised

• Model training pipeline and evaluation metrics

Regression models

• Linear, Ridge, Lasso, ElasticNet

• Evaluation: RMSE, MAE, R²

Classification models

• Logistic Regression

• k-Nearest Neighbors (kNN)

• Decision Trees

• Naïve Bayes

Model evaluation

• Confusion matrix, precision, recall, F1-score

• ROC-AUC curve

• Cross-validation techniques

Hands-on

• End-to-end supervised ML project using Scikit-learn

Week 4: Machine Learning – Advanced & Unsupervised Learning

Elevates to optimization, pattern discovery, and real-world ML challenges.

Ensemble learning

• Random Forest

• Gradient Boosting, XGBoost (conceptual + hands-on)

• Bias-variance tradeoff

Unsupervised learning

• K-Means clustering

• Hierarchical clustering

• DBSCAN

• Dimensionality reduction: PCA

Model optimization

• Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

• Feature importance and model interpretability

• Handling overfitting and underfitting

ML pipelines

• Building reusable ML pipelines

• End-to-end workflow automation

Week 5: Applied Data Science, Deployment & Capstone

Concludes with real-world application, deployment basics, and portfolio readiness.

Applied domains

• Business analytics

• Finance & FinTech

• Healthcare & IoT

• Social media & text analytics (intro to NLP)

Model deployment basics

• Saving/loading models (pickle, joblib)

• Building APIs using Flask/FastAPI

• Intro to cloud & ML lifecycle concepts (MLOps overview)

Ethics & governance

• Responsible AI

• Bias, fairness, and explainability

• Data privacy and compliance basics

Capstone project

• Problem framing & dataset selection

• EDA → Feature Engineering → Modeling → Evaluation

• Final presentation & documentation (GitHub + report)

Tools & Technologies Covered

• Python, NumPy, Pandas

• Matplotlib, Seaborn

• Scikit-learn

• Jupyter Notebook

• MySQL (optional for data sourcing)

• PowerBI/Tableau (optional visualization layer)

• Git & GitHub

Learning Outcomes

• Build and evaluate ML models end-to-end

• Apply statistical reasoning to real datasets

• Develop industry-grade data science projects

• Gain deployment-ready and portfolio-ready skills

NB: Time and slot will vary according to availability of mentor, minimum mentee count, and company policies.

Program Title: Data Science & Machine Learning

Duration: 3 Months Training + Project | Total Hours: 50(2 Hours/Day, 5 Days/Week)
Mode of Delivery: Virtual [Live Interactive Sessions & Recording support until batch completion]
Schedule: February 16, 2026 to March 31, 2026(Training) & April 1, 2026 to April 30, 2026(Project)
Time:  19:00-21:00

Eligibility:

• IT and Non-IT Graduates
• College Students looking for internship
• Aspirants looking for a career opening and reopening or career-switching

Get 20.26% off using promocode : TransEduverse2026
Offer valid till February 14, 2025 23:59

THREE MONTH INTERNSHIP CERTIFICATION WILL BE PROVIDED WITH 1 YEAR OF PLACEMENT ASSISTANCE AND SUPPORT

NB: Laptop/Desktop with stable internet connectivity is essential along with Active LinkedIn account. 

Enrollment & Contact
Website: www.transeduverse.tech
Email: mail@transeduverse.tech
Phone: +91 854 7152 888