Best Data Science Course in Hyderabad

Fundamentals of Python:

• Data Types
• Control Flow and Conditional Statements
• Functions
• File Handling
• Error and Exception Handling
• 00PS

Data Manipulation:

• NumPy
• Pandas

Data Visualization using Python:

• Matplotlib
• Seaborn

Exploratory Data Analysis using Python:

• Missing Values Treatment
• Outliers Treatment

Introduction to Statistics:

• Population and Sample
• Sampling Techniques

Descriptive Statistics:

• Measures of Central Tendencies
• Measures of Dispersion
• Measures of Shape
• Box Plots and IQR Method

Probability Distributions:

• Bernoulli Distribution
• Binomial Distribution
• Poison Distribution
• Normal Distribution
• Standard Normal Distribution

Inferential Statistics:

• Central Limit Theorem
• Null and Alternate Hypothesis
• Type-I and Type -II Errors
• T-Test and Z-Test
• ANOVA and Chi-Square Test
• A/B Testing

• Descriptive Analysis
• Correlation and covariance analysis
• Data Visualizations
• Missing Value Treatment
• Outliers Treatment

Getting Started with SQL:

• SQL Queries
• CRUD and ACID

Data Manipulation:

• INSERT, UPDATE, DELETE and ALTER
• Working with Multiple Tables
• Sub Queries and Nested Queries
• Aggregating Data with GROUP BY

Advanced SQL Techniques:

• Window Functions and Analytics
• Common Table Expressions
• Stored Procedures and Functions

Data Visualization with SQL:

• Integrating SQL with BI Tools (e.g., Tableau, Power BI)
• Generating Charts and Reports

Linear Algebra:

• Linear Algebra and Eigen Vectors
• Essentials of Coordinate Geometry

Calculus:

• Derivatives
• Applications of Derivatives
• Optimization Techniques

Fundamentals of Machine Learning

Linear Regression:

• OLS Method, R^2, Adjusted R12
• Feature Extraction and Cross Validation
• Overfitting and Underfitting
• Bias -Variance Trade-off
• Hyper Parameter Tuning and Grid Search
• Gradient Descent

• Introduction to AWS SageMaker
• Data Preparation and Model Training
• Model Serialization and Storage
• Model Deployment with SageMaker
• Monitoring and Debugging
• Model Updates and A/B Testing
• Security and Access Control
• Cost Optimization and Best Practices

• Introduction to Power BI
• Connecting to Data
• Data Modelling Visualization Basics
• Advanced Visualizations
• Dashboard Creation
• Publishing and Sharing
• Data Refresh and Scheduled Refresh

Fundamentals of Natural Language Processing:

• Text Preprocessing
• NLP Libraries and Tools

Text Analysis and Information Retrieval:

• Text Classification
• Named Entity Recognition (NER)
• Text Summarization
• Text Similarity and Clustering

Applied NLP and Sentiment Modelling:

• Sentiment Analysis
• Language Models

• Introduction to Generative Al
• Probability and Information Theory
• Introduction to Generative Models
• Working with VAEs
• Working with GANs
• Transfer Learning with Pretrained Models
• Ethics and Bias in Generative Al
• Practical Applications
• Challenges and Future Trends

Foundations of Deep Learning &Neural Networks:

• Neural Networks Basics
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Computer Vision Fundamentals

Advanced Computer Vision and Deep Learning:

• Deep Learning Frameworks
• Transfer Learning and Pre-trained Models
• Generative Adversarial Networks (GANs)
• Combining Deep Learning and Computer Vision
• Ethics and Bias in Deep Learning

• Introduction to Snowflake
• Data Loading and SQL Basics
• Data Transformation
• Data Warehousing and Security
• Query Performance Optimization