Data science course in coimbatore

• Introduction to Python
• Operators
• String, List and Tuple
• Set and Dictionary
• Conditional statements and loops
• Functions in Python
• OOPs
• Polymorphism and Encapsulation
• Exception Handling, File Handling and Debugging
• Modules & Packages
• Regular Expressions
• Decorators and Generators
• Comprehension in Python

1 – Introduction to Data Science

• Fundamentals of data science
• Data science vs. data analysis vs. data engineering
• Descriptive and predictive analytics
• The data science life cycle
• Working with different data types: structured, semi-structured, and unstructured
• Applications of data science across various industries

2 – Data Acquisition:

• Beautiful Soup
• Scrapy

3 – Data Manipulation:

• Numpy
• Pandas

4 – Data Visualization:

• Matplotlib
• Seaborn

• Probability concepts
• Statistical measures
• Joint probability
• Conditional probability and Bayes theorem
• Measures of location and variability
• Probability distributions
• Sampling methods
• Key statistical concepts
• Statistical hypothesis testing

1- Machine learning – Regression

• Machine learning basics and types
• Deep learning and recommender systems
• Regression concepts
• Univariate and multivariate linear regression
• Feature scaling
• Linear regression with scikit-learn
• Regularization (Lasso, Ridge, ElasticNet)
• Support vector regression
• Nearest neighbour regression
• Decision tree regression
• Feature engineering and categorical variables encoding
• Numerical variables transformation
• Feature selection (wrapper and intrinsic methods)
• Model evaluation metrics
• Dummy regressors
• Cross-validation

2 – Machine learning – Classification

• Types of classification problems
• Logistic regression
• Support vector machines
• Decision trees
• Naive Bayes
• K-nearest neighbors
• Ensemble learning
• XGBoost, LightGBM, and CatBoost
• Learning curves
• Model evaluation
• Dummy estimators
• Handling imbalanced class problems
• Hyperparameter optimization

3 – Machine learning – Clustering

• Unsupervised learning
• K-means clustering
• Hierarchical clustering
• DBSCAN clustering and customer segmentation
• Apriori algorithm and association rules
• Principal component analysis for dimensionality reduction
• Semi-supervised learning techniques

4 –  Machine Learning Tools and Libraries

• Automated machine learning with Pandas Profiling and PyCaret
• RAPIDS (Using GPU for Fast Computations

5 – Big Data Tools and Technologies

• What is big data?
• Hadoop ecosystem
• MapReduce framework
• Apache Spark and its components

• Data preprocessing with scikit-learn
• Data modeling with scikit-learn
• Clustering with scikit-learn
• Gradient boosting with XGBoost

• Introduction to deep learning
• Neural networks
• Feedforward neural networks
• Backpropagation
• Convolutional neural networks
• Recurrent neural networks
• LSTM networks

• Introduction to TensorFlow and Keras
• Building and training deep learning models
• Evaluating and optimizing deep learning models

• Introduction to building scalable model pipelines
• Applied data science
• Python for scalable compute
• Cloud environments and coding environments
• Introduction to datasets
• Prototype models
• Models as web endpoints
• Deploying a web endpoint
• Models as serverless functions
• Containers for reproducible models
• Workflow tools for model pipelines
• PySpark for batch pipelines
• Cloud Dataflow for batch modeling
• Streaming model workflows