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Data Science Training in Mumbai Syllabus - prepared by 4Achievers Trainers

Introduction to Data Science

Introduction to Data Science
- Introduction to Data Science
- Data Exploration and Visualization
- Data Cleaning and Pre-processing
- Machine Learning Algorithms
- Predictive Modeling and Analysis
Data Collection and Preprocessing
- Data Acquisition
- Data Cleaning
- Data Transformation
- Data Integration
- Data Reduction
Exploratory Data Analysis
- Data Wrangling
- Data Visualization
- Descriptive Statistics
- Inferential Statistics
- Model Building
Data Visualization
- Types of Data Visualization
- Data Visualization Tools
- Data Visualization Techniques
- Data Visualization Best Practices
- Data Visualization Applications
Machine Learning
- Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning
Big Data Tools and Technologies
- Hadoop
- Apache Spark
- Apache Kafka
- Apache Flink
- Apache Storm
Natural Language Processing
- Text Pre-processing
- Tokenization
- Part-of-Speech Tagging
- Syntax Analysis
- Semantic Analysis
Deep Learning
- Introduction to Deep Learning
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Autoencoders
Model Evaluation and Interpretation
- Model Selection
- Model Performance Evaluation
- Model Interpretation
- Model Validation
- Model Diagnostics

Data Collection and Preprocessing

Introduction to Data Collection
- Types of Data Collection
- Data Collection Tools
- Data Collection Strategies
- Data Preprocessing
- Data Cleaning
Types of Data Sources
- Structured Data Sources
- Unstructured Data Sources
- Semi-Structured Data Sources
- Internal Data Sources
- External Data Sources
Data Collection Techniques
- Survey
- Experiment
- Observation
- Text Mining
- Web Scraping
Data Preprocessing
- Data Acquisition
- Data Cleaning
- Data Transformation
- Data Reduction
- Data Discretization
Data Cleaning
- Identifying Missing Values
- Handling Outliers
- Formatting Data
- Data Transformation
- Data Normalization
Data Transformation
- Data Extraction
- Data Cleaning
- Data Transformation
- Data Normalization
- Data Aggregation
Data Integration
- Data Extraction
- Data Transformation
- Data Cleansing
- Data Normalization
- Data Aggregation
Data Visualization
- Data Acquisition
- Data Cleaning
- Data Transformation
- Data Reduction
- Data Integration

Exploratory Data Analysis

Introduction to Exploratory Data Analysis
- Types of Exploratory Data Analysis
- Descriptive Statistics
- Data Visualization
- Data Transformation
- Model Building and Evaluation
Data Visualization
- Introduction to Data Visualization
- Types of Graphs
- Data Visualization Tools
- Exploratory Data Analysis
- Data Visualization Best Practices
Descriptive Statistics
- Data Exploration
- Data Visualization
- Data Summarization
- Descriptive Statistics
- Inferential Statistics
Data Pre-processing
- Data Cleaning
- Data Transformation
- Data Reduction
- Data Discretization
- Data Visualization
Inferential Statistics
- Descriptive Statistics
- Univariate Analysis
- Bivariate Analysis
- Multivariate Analysis
- Correlation and Regression
Model Building and Evaluation
- Data Exploration
- Data Visualization
- Data Cleaning
- Feature Engineering
- Model Selection and Evaluation

Data Visualization

Data Visualization Basics
- Introduction to Data Visualization
- Types of Data Visualization
- Data Visualization Tools
- Data Visualization Techniques
- Best Practices in Data Visualization
Data Visualization Tools and Techniques
- Introduction to Data Visualization
- Types of Data Visualization
- Data Visualization Tools
- Data Visualization Techniques
- Best Practices in Data Visualization
Data Visualization with Python
- Introduction to Data Visualization
- Matplotlib Library
- Seaborn Library
- Plotly Library
- Dashboard Creation
Data Visualization with R
- Introduction to Data Visualization
- Exploring Data with Visualization
- Data Visualization with ggplot2
- Customizing Plots with ggplot2
- Interactive Visualization with Shiny
Data Visualization with Tableau
- Introduction to Tableau
- Data Connections in Tableau
- Tableau Visualization Techniques
- Tableau Dashboards
- Tableau Storytelling
Advanced Data Visualization
- Exploratory Data Analysis
- Data Visualization Libraries
- Data Visualization Techniques
- Data Visualization Tools
- Data Visualization Best Practices
Data Storytelling
- Types of Visualizations
- Data Exploration
- Data Representation
- Data Visualization Tools
- Data Storytelling Techniques

Machine Learning Algorithms

Linear Regression
- Simple Linear Regression
- Multiple Linear Regression
- Assumptions of Linear Regression
- Model Evaluation
- Regularization Techniques
Logistic Regression
- Introduction to Logistic Regression
- Assumptions of Logistic Regression
- Logistic Regression Model Building
- Logistic Regression Evaluation
- Logistic Regression Applications
Decision Trees
- Introduction to Decision Trees
- Types of Decision Trees
- Building a Decision Tree
- Pruning a Decision Tree
- Applications of Decision Trees
Random Forests
- Introduction to Random Forests
- Decision Trees
- Bagging and Random Forests
- Feature Importance
- Hyperparameter Tuning
Support Vector Machines
- Introduction to Support Vector Machines
- Kernel Functions
- Support Vector Classifier
- Support Vector Regression
- Applications of SVM
Naive Bayes
- Bayes Theorem
- Naive Bayes Classifier
- Gaussian Naive Bayes
- Multinomial Naive Bayes
- Bernoulli Naive Bayes
K-Nearest Neighbors
- Introduction to K-Nearest Neighbors
- Distance Metrics
- Hyperparameter Tuning
- Pros and Cons of K-Nearest Neighbors
- Applications of K-Nearest Neighbors
K-Means Clustering
- Introduction to K-Means Clustering
- K-Means Algorithm
- Choosing the Number of Clusters
- K-Means Applications
- Limitations of K-Means Clustering
Ensemble Learning
- Bagging
- Boosting
- Stacking
- Random Forest
- AdaBoost
Dimensionality Reduction Algorithms
- Principal Component Analysis
- Factor Analysis
- Independent Component Analysis
- Non-Negative Matrix Factorization
- t-Distributed Stochastic Neighbor Embedding

Model Evaluation and Optimization

Model Evaluation
- Cross Validation
- Holdout Method
- K-Fold Cross Validation
- Learning Curves
- Grid Search
Model Optimization
- Feature Selection
- Hyperparameter Tuning
- Regularization
- Model Selection
- Bias-Variance Tradeoff
Model Selection
- Cross Validation
- Hyperparameter Tuning
- Model Selection Metrics
- Grid Search
- Ensemble Learning
Hyperparameter Tuning
- What is Hyperparameter Tuning?
- Types of Hyperparameter Tuning
- Hyperparameter Tuning Algorithms
- Hyperparameter Tuning Strategies
- Automated Hyperparameter Tuning
Cross-Validation
- K-Fold Cross-Validation
- Leave-One-Out Cross-Validation
- Stratified K-Fold Cross-Validation
- Repeated Random Sub-Sampling
- Validation Curves
Bias-Variance Tradeoff
- Definition of Bias-Variance Tradeoff
- Bias-Variance Decomposition
- Regularization and Bias-Variance Tradeoff
- Model Selection and Bias-Variance Tradeoff
- Cross-Validation and Bias-Variance Tradeoff
Ensemble Methods
- Bagging
- Boosting
- Stacking
- Random Forest
- AdaBoost
Model Performance Metrics
- Accuracy
- Precision
- Recall
- F1 Score
- ROC Curve

Applied Machine Learning

Introduction to Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Feature Engineering
- Model Evaluation
Supervised Learning
- Regression
- Classification
- Decision Trees
- Support Vector Machines
- Naive Bayes
Unsupervised Learning
- Clustering
- Association Rule Mining
- Dimensionality Reduction
- Anomaly Detection
- Generative Models
Feature Engineering
- Data Pre-processing
- Feature Selection
- Feature Extraction
- Feature Scaling
- Feature Transformation
Model Evaluation and Selection
- Cross Validation
- Bias-Variance Tradeoff
- Model Selection
- Hyperparameter Tuning
- Performance Metrics
Hyperparameter Tuning
- Introduction to Hyperparameter Tuning
- Grid Search
- Random Search
- Bayesian Optimization
- Automated Hyperparameter Tuning
Deep Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Neural Networks
- Deep Learning Algorithms
Computer Vision
- Image Classification
- Object Detection
- Image Segmentation
- Image Processing
- Neural Networks
Natural Language Processing
- Text Preprocessing
- Text Representation
- Feature Extraction
- Text Classification
- Text Clustering
Reinforcement Learning
- Markov Decision Process
- Temporal Difference Learning
- Q-Learning
- Monte Carlo Methods
- Deep Reinforcement Learning

Deep Learning

Introduction to Deep Learning
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Autoencoders
- Reinforcement Learning
Neural Networks & Deep Learning
- Introduction to Deep Learning
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Autoencoders
Convolutional Neural Networks
- Introduction to Convolutional Neural Networks
- Convolutional Layer
- Pooling Layer
- Fully Connected Layer
- Training and Optimization of CNNs
Recurrent Neural Networks
- Introduction to Recurrent Neural Networks
- Long Short-Term Memory Networks
- Gated Recurrent Unit
- Bidirectional Recurrent Neural Networks
- Applications of Recurrent Neural Networks
Autoencoders
- Introduction to Autoencoders
- Types of Autoencoders
- Autoencoders for Dimensionality Reduction
- Autoencoders for Image Reconstruction
- Autoencoder Applications
Generative Adversarial Networks
- Introduction to GANs
- Generative Models
- Discriminative Models
- Adversarial Training
- Applications of GANs
Reinforcement Learning
- Markov Decision Process
- Temporal Difference Learning
- Q-Learning
- Monte Carlo Methods
- Policy Gradient Methods
Applications of Deep Learning in Data Science
- Introduction to Deep Learning
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Autoencoders

Big Data Analysis

Introduction to Big Data
- What is Big Data?
- Characteristics of Big Data
- Big Data Analysis Tools
- Big Data Analytics
- Big Data Applications
Understanding Data Structures
- Introduction to Big Data Analysis
- Types of Data Structures
- Algorithms for Big Data Analysis
- Data Storage and Processing
- Visualizing Big Data
Data Acquisition and Pre-Processing
- Data Acquisition
- Data Cleaning
- Data Transformation
- Data Integration
- Data Reduction
Exploratory Data Analysis
- Data Wrangling
- Descriptive Statistics
- Visualization Techniques
- Inferential Statistics
- Model Building
Data Visualization
- Types of Visualizations
- Data Exploration
- Data Visualization Tools
- Data Visualization Techniques
- Data Visualization Best Practices
Statistical Modeling and Machine Learning
- Introduction to Big Data Analysis
- Data Pre-processing
- Statistical Modeling
- Machine Learning Algorithms
- Big Data Visualization
Big Data Analysis in the Cloud
- Introduction to Big Data
- Cloud Computing
- Data Storage and Processing
- Data Visualization
- Machine Learning
Big Data Analytics Tools and Applications
- Hadoop
- Apache Spark
- NoSQL Databases
- Data Mining
- Machine Learning

Data Science in Practice

Introduction to Data Science
- Data Science Workflow
- Data Exploration and Visualization
- Data Preprocessing
- Model Building and Evaluation
- Deployment of Models
Data Acquisition and Preprocessing
- Data Acquisition
- Data Cleaning
- Data Transformation
- Data Integration
- Data Visualization
Exploratory Data Analysis
- Data Wrangling
- Data Visualization
- Descriptive Statistics
- Inferential Statistics
- Model Building
Statistical Modeling
- Introduction to Statistical Modeling
- Types of Statistical Modeling
- Model Building and Evaluation
- Model Validation
- Model Interpretation
Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Natural Language Processing
- Deep Learning
Data Visualization
- Introduction to Data Visualization
- Types of Data Visualization
- Data Visualization Tools
- Data Visualization Techniques
- Best Practices for Data Visualization
Big Data Analytics
- Data Pre-processing
- Exploratory Data Analysis
- Data Visualization
- Machine Learning
- Model Evaluation
Data Science in Practice
- Data Exploration
- Data Visualization
- Data Pre-processing
- Machine Learning
- Model Evaluation

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Data Science Prerequisites, Tutorials, and Study Materials - Learn the Fundamentals of Data Science

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Data Science Research, Publications, and Books - Discover the Latest in Data Science Research

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Data Science History and Fundamentals - Explore the Evolution and Building Blocks of Data Science

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Data Science has come a long way since its inception in the 1950s. 4Achievers has evolved to include a wide range of tools and techniques used to analyze data and uncover insights. Data Science courses in Mumbai offered by 4achievers provide students with the opportunity to gain hands-on experience with the tools and techniques used in Data Science. Data Science research work, publications, and books are available to help students gain a better understanding of the field.