Do you dream of becoming a Data Scientist or starting a career in analytics? Our Data Science course in Thanjavur is designed for both beginners and professionals, making learning simple, practical, and career-focused. Whether you join online or attend classroom sessions, you’ll learn through real-world projects, guided exercises, and interactive case studies, gaining not just theoretical knowledge but also the practical skills to analyze data, build machine learning models, and tackle projects that reflect real industry challenges.
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Data Science & Python
Learn what data science is and use Python to solve practical, real-world data problems efficiently.
Data Analysis & Visualization
Explore, clean, and visualize data using Pandas, NumPy, Matplotlib, and Seaborn to gain actionable insights.
Statistics & Machine Learning
Build a solid math foundation and implement predictive and classification models with scikit-learn.
Data Cleaning & SQL
Get a clear idea of how IoT works, how devices are connected, and where IoT is used in daily life and industries.
Big Data & Cloud Deployment
Work with large-scale data using Hadoop and Spark, and deploy ML models on AWS, Azure, or Google Cloud.
Capstone Projects
Complete end-to-end projects in finance, healthcare, and e-commerce to showcase your data skills.
Career Opportunities in Data Science course
With data science skills, you open doors to some of the most in-demand careers today. After finishing this course, you’ll be prepared for roles such as:
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- AI/ML Engineer (entry-level)
- Data Engineer
- Big Data Specialist
- Quantitative Analyst
Tools You'll Learn In Our Data Science Course
- Overview of Data Science and its applications
- Data Science lifecycle
- Roles in Data Science
- Python basics (variables, data types, loops, functions)
- Working with libraries: NumPy, Pandas
- Data structures and file handling
- Data cleaning and preparation
- Exploratory Data Analysis (EDA)
- Visualization with Matplotlib & Seaborn
- Descriptive statistics
- Inferential statistics
- Hypothesis testing & probability distributions
- Supervised learning: regression & classification
- Unsupervised learning: clustering, dimensionality reduction
- Model evaluation & validation
- Ensemble methods (Random Forest, Gradient Boosting)
- Introduction to deep learning with TensorFlow & Keras
- Real-world case studies
- SQL queries & database management
- Data extraction with SQL & NoSQL databases
- Introduction to Hadoop & Spark
- Cloud platforms: AWS, Azure, Google Cloud
- Deploying ML models on the cloud
- Power BI & Tableau for visualization
- Building dashboards and reports
- End-to-end project in finance, healthcare, or e-commerce
- Presentation & evaluation