
Excel for Analytics – Functions, pivot tables, dashboards, forecasting.
Python Programming – Loops, functions, OOP, NumPy, Pandas.
R Programming Basics – Syntax, data structures, exploratory data analysis.
Statistics & Probability – Hypothesis testing, distributions, confidence intervals. Visualization Basics – Matplotlib, Seaborn, Tableau, Power BI. es (DDL, DML, DQL).
Build a strong programming & analytical foundation.
Apply statistics to real-world datasets.
Create visual dashboards & communicate insights effectively.

Data cleaning (handling missing values, outliers, duplicates).
Feature scaling & encoding categorical variables.
Data transformation & feature engineering.
Exploratory Data Analysis (EDA) – trend detection & anomaly spotting.
Convert raw, unstructured data into high-quality datasets.
Identify hidden patterns and anomalies.
Build data pipelines that improve accuracy of ML models.

Supervised Learning – Linear/Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost.
Unsupervised Learning – Clustering (K-Means, DBSCAN), Dimensionality Reduction (PCA, LDA).
Model Evaluation – Cross-validation, confusion matrix, ROC-AUC, F1 Score.
Build regression & classification models.
Understand when to use supervised vs unsupervised learning.
Evaluate models to avoid overfitting & underfitting.

Neural Networks – Backpropagation, activation functions.
CNNs – Image recognition, object detection.
RNNs & LSTMs – Sequence modeling, sentiment analysis.
NLP – Transformers, BERT, GPT.
Generative AI – GANs & autoencoders.
Build AI models for vision, text & sequential data.
Understand the power of transformers & LLMs.
Gain skills directly applicable to cutting-edge AI roles.

Time Series – ARIMA, SARIMA, Prophet.
Recommender Systems – Collaborative filtering, content-based.
Databases & SQL – Joins, CTEs, stored procedures.
Big Data – Spark, SparkSQL, PySpark.
Predict future trends with forecasting models.
Build recommender engines like Netflix/Amazon.
Work with massive datasets efficiently.

Cloud basics – AWS/GCP/Azure.
Docker & containerization.
Flask APIs for ML models.
CI/CD pipelines for ML.
Model monitoring & retraining.
Deploy models as real-world applications.
Automate ML workflows with CI/CD.
Manage models post-deployment for reliability.

Domain Specialisations – Finance, E-Commerce, Healthcare, HR.
Capstone Projects – End-to-end AI projects.
Career Launchpad – Resume building, mock interviews, recruiter access.
Solve real-world business problems.
Showcase AI projects in your portfolio.
Enter the job market as a confident, industry-ready professional.