Data Science Training in Chennai

Data Science also called as data-driven science. Data science is order to solve analytically complex problems.  It is a multidisciplinary field of data inference, algorithm development, and technology improvement from various forms. Data Science is similar to Data mining. It is used by mathematics, statistics, computer science, information science, machine learning, data mining, data visualization, databases, classification, cluster analysis and data quantification. Greens Technologys helps to get up to date knowledge in Data science.

Data Science Training in Chennai Tambaram:

Training in Tambaram is one of the best Data Science Training Institutes in Chennai. We are focusing Placement based hands-on training for Data Science Course to students. Our Data Science Course Trainers are taking the classes from basic level to Advanced Level. Our Students can get the job easily in MNC companies in Chennai and Bangalore once complete their Data Science Classes. Our Data Science Course content and syllabus are revised monthly once by our experienced trainers. Our Data Science training syllabus is designed for fulfilled to our student’s needs. Students can get the up-to-date technical knowledge in Data Science field. Our 6+ years of experienced trainers are working hard to improve their student knowledge and helps to attend the interviews confidently.

Our Training center offering Data Science Training in various places around the Chennai. We are providing well infrastructure with fully equipped lab facilities to our students. We pay more attention to the needs of our students. Already we have trained more than 200+ Students in Chennai with placement. Data Science Training timing schedule on weekday classes, Weekend classes, Evening classes and Fast Track classes. Our Data Science Course fees is based on student’s requirement and value for money. After completing Data Science Classes, providing free resume building classes, Aptitude classes, Mockup Interview Sessions.

Our Data Science Course syllabus covering some important topics like Introduction to R Programming, R Data Structure, importing data, manipulating data, Using functions in R, R Programming, charts and plots, Machine Learning Algorithm, Statistics, Data science real-time project and Data science placement training in Chennai.

Data Science Training in Chennai syllabus:

Python for Data Science

Topics Covered:

• Python syntax and data structures (lists, tuples, dictionaries)
• Functions, loops, conditionals
• File handling and exceptions
• Object-oriented programming basics
• Working with libraries like math, datetime, and os

SQL for Data Analysis

Topics Covered:

• Basic SQL syntax and commands
• Filtering, sorting, and aggregating data
• Joins (INNER, LEFT, RIGHT, FULL)
• Subqueries and nested queries
• Window functions and CTEs
• Integration of SQL with Python using libraries like sqlite3 and SQLAlchemy

NumPy – Numerical Computing

Topics Covered:

• Arrays and vectorized operations
• Array indexing and slicing
• Mathematical functions and statistics
• Broadcasting and reshaping
• Random number generation

Pandas – Data Manipulation

Topics Covered:

• Series and DataFrame objects
• Data cleaning, filtering, and transformation
• Handling missing data
• Merging, joining, and concatenating datasets
• Groupby operations and aggregation
• Time series analysis

Data Visualization with Matplotlib

Topics Covered:

• Creating line, bar, pie, histogram plots
• Customizing axes, labels, legends, and annotations
• Multi-plot layouts using subplots
• Saving plots in different formats

Advanced Visualization with Seaborn

Topics Covered:

• Distribution plots (histograms, KDE)
• Categorical plots (box, violin, swarm)
• Heatmaps and correlation matrices
• Pair plots and joint plots
• Custom color palettes and themes

Supervised Learning

Topics Covered:

• Regression techniques (Linear, Polynomial, Ridge, Lasso)
• Classification algorithms (Logistic Regression, KNN, Decision Trees, Random Forest, SVM)
• Model evaluation metrics: accuracy, precision, recall, F1-score
• Confusion matrix and ROC-AUC
• Cross-validation and hyperparameter tuning

Unsupervised Learning

Topics Covered:

• Clustering (K-Means, Hierarchical, DBSCAN)
• Dimensionality reduction (PCA, t-SNE)
• Association rule learning (Apriori, Eclat)
• Anomaly detection techniques

Deep Learning

Topics Covered:

• Basics of neural networks and perceptron
• Activation functions and loss functions
• Feedforward and backpropagation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) and LSTMs
• Using TensorFlow and Keras for model building

Natural Language Processing (NLP)

Topics Covered:

• Text preprocessing (tokenization, stemming, lemmatization)
• Bag of Words and TF-IDF
• Named Entity Recognition (NER)
• Sentiment Analysis
• Text classification using machine learning
• Transformer-based models (BERT, GPT overview)

Computer Vision

Topics Covered:

• Image processing with OpenCV
• Image classification and object detection
• Feature extraction and edge detection
• Face recognition and segmentation
• Building and training CNNs for vision tasks