Data Science & Machine Learning Course designed to build strong foundations in Python, SQL, Analytics, Machine Learning, MLOps, and Cloud Deployment, with real-world projects, placement assistance, interview preparation.
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This course is suitable for students, graduates, working professionals, and career switchers. Basic computer knowledge is required. Prior programming experience is helpful but not mandatory, as Python and fundamentals are covered from scratch
You will learn Python, SQL, Excel, Tableau/Power BI, Machine Learning, Time Series, and MLOps tools such as Docker, Kubernetes, Git/GitHub, MLflow, FastAPI, along with cloud deployment (AWS/GCP/Azure). The course includes live, industry-relevant projects like churn prediction, LTV calculation, data pipelines, forecasting systems, and analytics dashboards.
Yes. We provide placement assistance, including resume and LinkedIn optimization, mock interviews, career counseling, and interview scheduling support. Students receive a course completion certificate, internship opportunities, 2 months of spoken English training, and training for Global Jobs Applying Toolkit for international job applications.
Our Data Science and Machine Learning Course is a comprehensive, industry-aligned program designed to prepare students and professionals for high-demand roles in data-driven domains. The course begins with fundamental data analysis using Excel, Python, and Tableau/Power BI, and progressively advances into analytics, machine learning, deep learning, and MLOps.
You will develop strong programming expertise in Python, including data handling, object-oriented programming, and widely used libraries such as NumPy, Pandas, Matplotlib, and Seaborn. The curriculum also builds a solid foundation in statistics, probability, linear algebra, and calculus for machine learning, along with SQL, product analytics, KPIs, and business intelligence concepts essential for analyst and data science roles.
Advanced modules focus on supervised and unsupervised machine learning, time series forecasting, and practical algorithms such as Linear & Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, Clustering, PCA, ARIMA, and Prophet. You will also gain hands-on experience in MLOps and deployment, covering CI/CD pipelines, Docker, Kubernetes, Git/GitHub, MLflow, FastAPI, and deployment on AWS, GCP, and Azure.
The course includes live, industry-relevant projects such as customer churn prediction, LTV modeling, data lakes, streaming analytics dashboards, and forecasting systems, ensuring strong practical exposure. We also provide placement assistance, including resume and LinkedIn optimization, mock interviews, career guidance, and spoken English training at no extra cost, along with support for global job applications.