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Data Science is data-driven science that endeavors to provide content through analysis and explanation of large quantities of complex data. Data science combining logical thinking from the outlet data, development of appropriate algorithms and promote the technology to solve complex problems. In this course you will get a professional qualification that shows the ability of the candidate to achieve complete subject knowledge and acquire all the basic tools and algorithmic rules used in Data Science. This course will make the student get the leading job posts in the MNC.

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- Python for Data Science
- Introduction to Statistics
- Descriptive Statistics
- Probability Theory and Distributions
- Picturing your Data
- Inferential Statistics
- Hypothesis Testing
- Analysis of variance(ANOVA)
- Regression
- Model post fitting for Inference
- Categorical Data Analysis
- Model Building and scoring for Prediction
- Introduction to Machine Learning
- Linear Regression with one variable
- Linear Regression with Multiple Variable
- Logistic Regression
- Multiclass Classification
- Regularization
- Model Evaluation and Selection
- Support Vector Machine
- Decision Tree, Random Forest
- Unsupervised Learning
- Dimensionality Reduction
- Introduction to text analytics
- Introduction to Neural Network

- Introduction to Machine Learning
- Python fundamentals and advanced
- Regression
- Categorical Data Analysis
- Model Building and scoring for Prediction
- Multiclass / Multi - Label Classification
- Imbalanced Dataset
- Model Evaluation and Selection
- Support Vector Machine
- K - Nearest Neighbours(K - NN)
- Decision Tree, Random Forest
- Unsupervised Learning
- Dimensionality Reduction
- Principal Component Analysis and applications
- Introduction to text analytics / Natural Language Processing
- Bag of Words
- TF - IDF•
- LDA(Latent Discriminant Analysis)
- Model Selection, Ensemble models
- XG - Boost
- Introduction to Neural Network
- Recommender Systems

- Introduction to AI
- Agents and Search
- A * Search and Heuristics
- Constraint Satisfaction Problems
- CSPs II
- Game Trees: Minimax
- Game Trees: Expectimax;Utilities
- Markov Decision Processes
- Markov Decision Processes II
- Reinforcement Learning
- Reinforcement Learning II
- Probability
- Bayes ' Nets: Representation
- Bayes ' Nets: Independence
- Bayes ' Nets: Inference
- Bayes ' Nets: Sampling
- Decision Diagrams / VPI
- HMMs: Filtering
- HMMs: Wrap - up / Speech
- ML: Naive Bayes
- ML: Perceptron
- ML: Kernels and Clustering
- ML: Neural Networks and Decision Trees
- Robotics / Language / Vision
- Miscellaneous Topics

- Deep Learning Introduction
- Image Classification
- Loss Functions and Optimization
- Introduction to Neural Networks
- Convolutional Neural Networks
- Training Neural Networks, part I
- Training Neural Networks, part II
- Deep Learning Software
- CNN Architectures
- Recurrent Neural Networks
- Detection and Segmentation
- Visualizing and Understanding
- Generative Models
- Deep Reinforcement Learning

- Introduction to Tableau Desktop
- Tableau Desktop Interface
- Connecting Data Sources
- Organizing Data
- Formatting Data
- Calculations
- Visualizations
- Analysis using Desktop
- Mapping
- Fields in Tableau
- Parameters
- Create Dashboards and Stories
- Tableau Online
- Tableau Project

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