Best Data Science Courses in Hyderabad

Data Cleaning
1. Missing Data Handling
2. Noise & Dublicate Removal
Data Manipulation
1. Add/Remove columns
2. Data types Synchronization
Data Transformation
1. Normalization
2. Standardization
Data Corelation
1. Pearson Correlation Coefficient
2. Spearman Correlation Coefficient
Data Conclusions
1. Hypothesis Testing
2. Decision Making
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Project 2 : Data Pipeline Project 
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Introduction to Python
1. Basic Syntax and Data Types
2. Control Structures and Functions
Data Structures and Manipulation
1. Lists, Tuples, Sets and Dictionaries
2. List Comprehensions and Built-in Functions
File Handling and Input/Output Operations
1. Reading and Writing Files
2. Working with CSV and JSON Files
Object-Oriented Programming (OOP)
1. Classes and Objects
2. Inheritance and Polymorphism
Advanced Python Concepts
1. Exception Handling
2. Types of Exceptions  

NumPy (Numerical Python)
1. Array creation and manipulation.
2. Mathematical operations and statistical functions.
3. Broadcasting and vectorized operations.
4. Random number generation.
5. Fourier transforms and signal processing.
Pandas (Python Data Analysis Library)
1. Data manipulation with DataFrame.
2. Data cleaning and preprocessing.
3. Handling missing data and outliers.
4. Grouping, Merging and joining datasets.
5. Time series analysis and datetime manipulation.
Plotly (Interactive Visualization Library)
1. Interactive plotting for exploratory data analysis.
2. Building Basic & Advance Charts.
3. Customizing plot layouts and styles.
4. Animating plots and charts.
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Project 3 : Data Analysis Project (EDA) 
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Introduction to Machine Learning
1. Overview of Machine Learning Concepts
2. Types of Machine Learning Algorithms
Supervised Learning
1. Regression
2. Classification
3. Metrics
ML Algorithms
1. Linear Regression
2. Logistic Regression
3. Gradient Descent
4. K-nearest neighbors
5. Decision Trees
Unsupervised Learning
1. K-means Clustering
2. Principal Component Analysis (PCA)
Model Evaluation and Validation
1. Cross-Validation Techniques
2. Evaluation Metrics for Regression and Classification
Feature Selection and Engineering
1. Importance of Feature Selection
2. Techniques for Feature Engineering
Ensemble Learning
1. Bagging and Random Forests
2. Boosting Methods: AdaBoost and Gradient Boosting
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Project 4 : Machine Learning Project 

Introduction to Deep Learning
1. Fundamentals of Neural Networks
2. Activation Functions and Network Architectures
3. Forward propagation & Back propagation
Convolutional Neural Networks (CNNs)
1. Understanding Convolutional Layers
2. Pooling Layers and Feature Extraction
Recurrent Neural Networks (RNNs)
1. Sequential Data Processing with RNNs
2. Long Short-Term Memory (LSTM) Networks
Deep Learning Frameworks
1. Introduction to Pytorch
2. Neural Networks training
3. NN, CNN, RNN & LSTM with Pytorch
Model Optimization and Deployment
1. Techniques for Model Optimization
2. Deploying Deep Learning Models in Production Environments
Ethical Considerations in Deep Learning
1. Bias and Fairness in Deep Learning Models
2. Responsible AI and Privacy Concerns
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Project 5 : Deep Learning Project 

Introduction to Computer Vision
1. Fundamentals of Image Processing
2. Overview of Computer Vision Applications
Image Processing Techniques
1. Image Filtering and Convolution
2. Edge Detection and Feature Extraction
Image Classification
1. Basics of Image Classification
2. Convolutional Neural Networks (CNNs) for Image Classification
Semantic Segmentation
1. Principles of Semantic Segmentation
2. Deep Learning Approaches: Fully Convolutional Networks (FCNs), U-Net
Object Detection
1. Understanding Object Detection Algorithms
2. Popular Object Detection Architectures: YOLO, SSD, Faster R-CNN 

Introduction to NLP
1. Overview of Natural Language Processing
2. Applications and Importance of NLP
Text Preprocessing
1. Tokenization and Text Normalization
2. Stopword Removal and Stemming/Lemmatization
Statistical NLP Techniques
1. Bag-of-Words Model and TF-IDF Representation
2. N-gram Language Models and Probabilistic Methods
Word Embeddings
1. Word2Vec and GloVe Embeddings
2. Contextual Embeddings: ELMo and BERT
Text Classification
1. Sentiment Analysis and Text Classification Techniques
2. Building Text Classifiers with Machine Learning and Deep Learning
Named Entity Recognition (NER)
1. Overview of Named Entity Recognition
2. NER Approaches: Rule-based and Machine Learning-based Methods
Sequence-to-Sequence Models
1. Introduction to Sequence-to-Sequence Models
2. Applications: Machine Translation, Text Summarization
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Project 6 : CV or NLP Project 
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Language Models (LMs)
1. Understanding Language Models and their Importance
2. Pre-trained Language Models: BERT, GPT, XLNet
Fine-tuning Pre-trained LMs
1. Transfer Learning for NLP Tasks
2. Fine-tuning Strategies and Techniques
BERT (Bidirectional Encoder Representations from Transformers)
1. Architecture and Components of BERT
2. BERT-based Applications: Text Classification, Named Entity Recognition
GPT (Generative Pre-trained Transformer)
1. Architecture and Components of GPT
2. GPT-based Applications: Text Generation, Question Answering
Recent Advances in NLP
1. State-of-the-art NLP Models: GPT-4, BERT Large, T5
2. Current Trends and Future Directions in NLP Research
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Project 7 : Real Company Project 
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