The Data Scientist Masterclass is your all-in-one path to becoming a job-ready machine learning engineer or data scientist. This hands-on course covers the entire data science lifecycle: data preparation, statistical analysis, machine learning, and deployment.
About Course
You will work with real datasets to build, test, and evaluate machine learning models. Python, Pandas, Scikit-learn, Matplotlib, and deployment tools like Flask will be used. Projects will cover industries such as retail, finance, and healthcare.
No, the course starts with Python basics, so beginners are welcome.
You'll cover supervised, unsupervised, and deep learning techniques.
Yes, you'll build, train, and deploy models using real-world tools and platforms.
Absolutely—core concepts are explained with hands-on coding in Python.
Yes, you'll work with diverse datasets from finance, healthcare, marketing, and more.
What Will You Learn?
- Python for Data Science – Learn to manipulate data using Pandas and Numpy for real-time analysis.
- Data Cleaning & Feature Engineering – Master handling of missing values, encoding, scaling, and transformation of features.
- Machine Learning Models – Implement regression, classification, clustering, and recommendation algorithms using Scikit-learn.
- Model Evaluation – Use confusion matrix, AUC-ROC, cross-validation, and accuracy metrics to assess model performance.
- Data Visualization – Create impactful charts using Matplotlib and Seaborn to support storytelling with data.
- Model Deployment – Deploy models using Flask or Streamlit for production-ready APIs and apps.
Audience
Ideal for data analysts, computer science graduates, or career switchers interested in AI/ML.
Course Content:
Overview of data analytics and its importance in industries
Role of a data analyst
Understanding the data analytics process
Types of data: structured, unstructured, and semi-structured
Data quality, preprocessing, and cleaning
Data exploration and visualization
