Data Science
Syllabus Of Data Science
Syllabus
- Python Programming Introduction
- Syntax, Comments, Variable, Data Types
- Python String
- Python Operators
- List /Tuple /Sets /Dictionaries
- If else/For Loop
- Functions
- Python Numpy
- Python Pandas
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- Data Structures & Algorithms
- Exploratory Data Analysis
- Data Manipulation
- Data Wrangling
- Univariate Data Analysis
- Bivariate Data Analysis
- Multivariate Data Analysis
- Data Mining
- Applied Data Analytics
- Introduction to Basic Statistics
- Descriptive Statistics
- Probability and Probability Distribution
- Inferential Statistics
- Hypothesis Testing
- Introduction to Sampling
- Statistical Modeling
- Types of Distributions
- Categorical Data Analysis
- Statistical Quality Control
- Analytical Tools for Statistics
- Stochastic Processes and Models
- Linear Regression Models
- Nonparametric & Nonlinear Regression Models
- Introduction to Mathematical Foundations
- Numerical Analysis
- Calculus
- Differentiation
- Computational Mathematics
- Linear Algebra
- Vector and Matrices
- Programming language: Python, R
- Python for Data Science
- Python Packages and Libraries
- Query Language: SQL
- Database Management
- Types of Machine Learning
- Supervised Learning
- Regression Models
- Linear Regression
- Logistic Regression
- Classification Models
- Model Evaluation Metrics
- Decision Tree
- Random Forest
- Naive Bayes
- K-Nearest Neighbors
- Support Vector Machines
- Ensemble Techniques (Random Forest, Bagging, Boosting)
- Unsupervised Learning
- Segmentation using Clustering
- K-means Clustering
- Agglomerative Clustering
- Hierarchical Clustering
- Spectral Clustering (DBSCAN)
- Dimensionality Reduction
- Principal Component Analysis
- Singular Value Decomposition
- Market Basket Analysis
- Apriori algorithm
- Association Rule Mining
- Reinforcement Learning
- Forecasting
- Regularization
- Bias-Variance Tradeoff
- Feature Learning
- Techniques to improve the Machine Learning model
- Model Deployment
- Basics of Neural Networks
- Perceptrons
- Multi-Layer Perceptron
- Forward and Backward Propagation
- Gradient Descent Algorithm
- Loss Function
- Activation Functions
- Optimizers
- Supervised deep learning
- Artificial Neural Network (ANN)
- Perceptron (Single and Multi-Layer)
- Convolution Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Variations of RNN:
- Long-Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Unsupervised deep learning
- Autoencoders
- Deep Belief Networks
- Boltzmann Machine
- Restricted Boltzmann Machine
- Generative adversarial networks (GANs)
- Encoder-Decoder Model (Seq2Seq Models)
- Attention Models
- Transformers
- R-CNN and its variations:
- Fast R-CNN
- Faster R-CNN
- Masked R-CNN
- Graph Neural Networks
- Deep Learning in Natural Language Processing (NLP)
- Transfer Learning
- Techniques to improve the Deep Learning model
- Image, Text, and Audio Processing
- Deep Learning Frameworks such as Keras, TensorFlow, PyTorch
- Text Mining
- Natural Language Processing
- Natural Language Understanding
- Natural Language Generation
- Machine translation
- Language detection & Translation (Google translator)
- Text Recommendations
- Chatbots/VoiceBots/Personal Assistant systems
- Vectorization
- Countvectorizer
- TF-IDF Vectorizer
- N-Grams
- Word Embeddings
- Word2vec
- Glove
- Sentiment Analysis (Twitter feeds, reviews, feedback et al)
- Intent Analysis (Analyzing customer reviews)
- Email Classification (Google email classification – Primary-Social-Promotions-SPAM etc)
- Text Summarization (Google News)
- Fake news identification
- Social Network Analysis (community detection)
- Optical Character Recognition (OCR)
- Text recommendations/suggestions (Email replies, autofilling, message replies)
- Text Association Analysis
- Topic Modeling
- Latent Dirichlet Allocation (LDA)
- Latent Semantic Allocation (LSA)
- Non-negative Matrix-Factorization (NNMF)
- Speech to Text
- Automatic Text Generation
- Information Retrieval
- Information Extraction
- Data Visualization and Interpretation
- Power BI
- Tableau
- Introduction to ML Ops
- Deployment of ML Model in the Cloud
- Pipeline
- Based On ANN,CNN,NLP projects