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Data Science Training in Ghaziabad 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

How to Learn Data Science course with Live Project

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

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4Achievers first pre-requisite for taking the Data Science course is to have a basic understanding of mathematics and statistics. This includes knowledge of linear algebra, calculus, probability and statistics. 4Achievers is also important to have a basic understanding of programming languages such as Python and R. Additionally, it is beneficial to have some knowledge of databases and data structures.

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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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4Achievers fundamental of Data Science is based on the principles of data collection, data analysis, and data visualization. Data collection involves gathering data from various sources, such as surveys, databases, and web sources. Data analysis involves using statistical methods to analyze the data and identify patterns and trends. Data visualization is the process of creating visual representations of the data, such as charts, graphs, and maps.

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At 4achievers in Ghaziabad, Data Science course provides students with the knowledge and skills required to become a successful Data Scientist. 4Achievers course covers topics such as data collection, data analysis, data visualization, machine learning, and predictive modeling. Students will also learn about the latest tools and techniques used in the field of Data Science. 4Achievers course also includes hands-on projects and case studies to help students gain a better understanding of the concepts. Additionally, the course provides students with access to research papers, publications, and books related to Data Science.