Data Science CourseLive online across India · Classroom in Rasipuram, near Namakkal & Salem
Live data science course — Python, statistics, machine learning and real projects — online from anywhere in India or in our Rasipuram classroom near Namakkal & Salem. Placement support.
Course overview
Unlocking Data Science: From Python Foundations to Cutting-Edge Machine Learning Techniques
Course Description:
Embark on a comprehensive Data Science journey with GeekBase Technology. This course covers everything from Python basics to advanced machine learning, including data analysis, visualization, and predictive modeling. Gain hands-on experience with tools like Pandas, build models using regression and classification, and apply your skills through real-world projects. Join GeekBase Technology’s Data Science course and start your path to a successful career.
Key Highlights:
Target Audience:
Ambitious data enthusiasts eager to build a strong foundation in Data Science.
Students aiming to excel in data analysis, machine learning, or AI-driven careers.
Professionals looking to elevate their expertise with advanced Data Science tools and methodologies.
Mentor Support:
Learners will have access to an experienced instructor who will provide support through one on one meeting, live Q&A sessions, and email to answer questions and provide guidance throughout the course.
Learn online from anywhere in India — or in our classroom
🌐 Live online — pan India
Join live, trainer-led Data Science classes from any city or town in India. Ask questions in class, get your code reviewed and practise on GoCode between sessions.
🏫 Classroom — Rasipuram
Prefer learning in person? Our centre in Rasipuram (Namakkal district) is a short trip from Salem, Namakkal, Erode and Tiruchengode.
Curriculum
13 modulesModule 1: Python Programming Foundations
- Introduction to Python: history, variables, data types.
- Control flow: loops and conditionals.
- Data structures: lists, tuples, dictionaries.
- Functions, error handling, and memory management.
- File handling: reading and writing files.
Module 2: Numerical Computing with NumPy
- Introduction to NumPy: arrays, creation, and operations.
- Array indexing, slicing, and reshaping.
- Mathematical operations and broadcasting.
- Aggregation, statistical functions, and advanced array operations.
Module 3: Data Handling with Pandas
- Introduction to Pandas: DataFrames and Series.
- DataFrame operations: indexing, selection, filtering.
- Data manipulation: sorting, grouping, and aggregating.
- Handling missing data and imbalanced datasets.
- Merging, joining, and concatenating DataFrames.
Module 4: Data Visualization Techniques
- Introduction to Matplotlib: basic plots and customization.
- Advanced plotting with Seaborn: statistical and categorical plots.
- Interactive visualizations with Plotly: 3D plots and time series.
- Data storytelling and effective visualization techniques.
Module 5: Databases and Exploratory Data Analysis
- Introduction to MongoDB: setup, CRUD operations, and querying.
- Introduction to MySQL: setup, SQL queries, and advanced techniques.
- Exploratory Data Analysis (EDA): objectives, techniques, and descriptive statistics.
Module 6: Probability and Statistical Analysis
- Basics of probability: conditional probability and distributions.
- Bayes theorem and applications.
- Probability distributions: normal, binomial, Poisson.
- Hypothesis testing: p-values, confidence intervals, and significance.
Module 7: Feature Engineering and Data Preparation
- Handling missing data and outliers.
- Feature extraction and creation: transformations and scaling.
- Data encoding: one-hot encoding and label encoding.
- Techniques for handling imbalanced datasets.
- Understanding covariance and correlation.
Module 8: Supervised Learning – Regression
- Introduction to supervised learning: regression and classification.
- Linear regression: concepts, implementation, and regularization.
- Evaluation metrics: MSE, RMSE, R-squared.
- Real-world regression problems and case studies.
Module 9: Supervised Learning – Classification
- Logistic regression: concepts and implementation.
- Evaluation metrics: accuracy, precision, recall, F1-score.
- K-Nearest Neighbors (KNN) and Decision Trees.
- Advanced techniques: random forests, gradient boosting.
- Classification case studies and real-world applications.
Module 10: Unsupervised Learning and Dimensionality Reduction
- Introduction to unsupervised learning: K-means clustering.
- Clustering applications and visualizations.
- Dimensionality reduction with PCA.
- Practical applications and handling high-dimensional data.
Module 11: Image and Text Data Analysis
- Basics of image data: formats, loading, and processing.
- Feature extraction from images.
- Text data analysis: tokenization, stop word removal, lemmatization, and stemming.
- Syntactic and semantic text analysis.
Module 12: Advanced Natural Language Processing (NLP)
- Introduction to NLP: tokenization and syntactic analysis.
- Part-of-Speech (PoS) tagging and Named Entity Recognition (NER).
- Text normalization: lemmatization and stemming.
- Semantic analysis: word embeddings and similarity.
- Sentiment analysis and text classification projects.
Module 13: Data Visualization Tools and Career Development
- Introduction to Power BI: data transformation and visualization.
- Advanced Power BI features: DAX, data modeling, and filtering.
- Introduction to Tableau: visualization techniques and interactivity.
- Exercise using Power BI or Tableau
Certification
Course Certification:
Upon successful completion of the course, there will be cumulative test conducted and students who scored above 60% marks will receive a certificate of completion from GeekBase Technology, which can be used to showcase their newly acquired Data Science skills.
Note: Test will be a MCQ pattern and maximum two attempts allowed.
Why certified Data Scientist ?
Frequently asked questions
Can I join the Data Science course from outside Tamil Nadu?
Yes. The live online batch is open to learners anywhere in India — Chennai, Bengaluru, Hyderabad, Pune, Kochi or a small town. You attend the same live classes, with the same trainers and curriculum, as the classroom batch. Classes are taught in Tamil, English.
Where is the classroom for the Data Science course?
Our classroom centre is on Bharathidhasan Street, Rasipuram (Namakkal district) — between Salem and Namakkal, and within reach of Erode and Tiruchengode.
What is the fee and duration of the Data Science course?
The course runs for 15 Weeks and the fee is ₹30,000 (regular price ₹35,000). EMI options are available — message us on WhatsApp for the current batch dates and offers.
Do I get a certificate and placement support?
Yes. You receive a course completion certificate, and placement support includes resume reviews, mock interviews and practice on GoCode, our coding and interview-prep platform.
