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Data Science Training in Delhi 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

Data Science is an emerging field of study that has become increasingly popular in recent years. 4Achievers is a field of study that combines mathematics, statistics, computer science, and other disciplines to analyze and interpret large amounts of data. 4achievers is a leading Data Science training institute in Delhi that offers comprehensive and comprehensive Data Science course.

To take the Data Science course at 4achievers, the pre-requisite is a basic understanding of mathematics, statistics, and computer science. Students must also have a basic understanding of programming languages such as Python, R, and SQL. Additionally, knowledge of data analysis and machine learning techniques is also beneficial.

4Achievers Data Science course at 4achievers includes tutorials on data analysis, machine learning, and data visualization. Students will learn how to use data to make decisions and solve problems. 4Achieversy will also learn how to use data to create predictive models and develop data-driven solutions. Additionally, the course covers topics such as data mining, natural language processing, and big data.

4Achievers Data Science course at 4achievers also includes study material such as lecture notes, assignments, and practice tests. 4Achievers lecture notes provide an overview of the topics covered in the course. 4Achievers assignments help students apply the concepts they have learned in the course. 4Achievers practice tests help students assess their understanding of the material.

At 4achievers, the Data Science course is designed to equip students with the skills and knowledge they need to succeed in the field of Data Science. 4Achievers course provides students with the tools and techniques they need to analyze and interpret large amounts of data. 4Achievers also provides them with the skills to develop data-driven solutions and create predictive models. With the help of the Data Science course at 4achievers, students can gain the skills and knowledge they need to become successful Data Scientists.

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

Data Science is a rapidly growing field that combines scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. 4Achievers is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. 4Achievers is a field of study that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science is a field of study that is used to analyze and interpret large amounts of data to uncover meaningful patterns, trends, and relationships. To become a successful Data Scientist, one needs to have a good understanding of mathematics, statistics, computer science, and programming. Additionally, one should have knowledge of data mining, machine learning, and artificial intelligence.

In order to take the Data Science course at 4achievers in Delhi, one must have a basic understanding of mathematics, statistics, computer science, and programming. Additionally, one should have knowledge of data mining, machine learning, and artificial intelligence. 4Achievers course will provide an in-depth tutorial on the fundamentals of Data Science, including data analysis, data visualization, and machine learning. 4Achievers course will also provide an introduction to the various tools and techniques used in Data Science, such as Python, R, and Tableau.

4Achievers course will also provide study material such as books, research papers, and publications related to Data Science. Some of the popular books related to Data Science are “Data Science for Business” by Foster Provost and Tom Fawcett, “Data Science for Dummies” by Lillian Pierson, and “Data Science from Scratch” by Joel Grus. Popular research papers related to Data Science include “A Survey of Machine Learning Algorithms for Data Mining” by Xindong Wu, “Data Mining: Concepts and Techniques” by Jiawei Han and Micheline Kamber, and “Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten and Eibe Frank. Popular publications related to Data Science include “Data Science Central”, “Data Science 4Achieversekly”, and “Data Science Magazine”.

Data Science is a rapidly growing field and there are many opportunities for those who are interested in pursuing a career in this field. Taking the Data Science course at 4achievers in Delhi will provide the necessary knowledge and skills to become a successful Data Scientist. 4Achievers course will provide an in-depth tutorial on the fundamentals of Data Science, including data analysis, data visualization, and machine learning. Additionally, the course will provide study material such as books, research papers, and publications related to Data Science. With the right knowledge and skills, one can become a successful Data Scientist and make a successful career in this field.

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

Data Science is a rapidly evolving field that has been around for decades, but has seen a surge in popularity in recent years. 4Achievers is a combination of several disciplines, including mathematics, computer science, statistics, and machine learning. 4Achievers is used to analyze large datasets and uncover insights and patterns that can be used to make decisions and predictions. Data Science has been used in a variety of industries, from healthcare to finance, to help organizations make better decisions and drive innovation. 4Achievers history of Data Science can be traced back to the 1950s, when the first computers were developed. At this time, computers were used to store and analyze data, and the first statistical models were developed. In the 1970s, the focus shifted to using computers to analyze large datasets, and the first machine learning algorithms were developed. In the 1980s, the focus shifted to using computers to analyze large datasets and uncover patterns and insights. In the 1990s, the focus shifted to using computers to analyze large datasets and uncover patterns and insights, and the first data mining algorithms were developed. In the 2000s, the focus shifted to using computers to analyze large datasets and uncover patterns and insights, and the first predictive models were developed. Today, Data Science is used to analyze large datasets and uncover insights and patterns that can be used to make decisions and predictions. 4Achievers fundamental concepts of Data Science include data collection, data cleaning, data analysis, data visualization, and machine learning. Data collection involves gathering data from various sources, such as databases, websites, and surveys. Data cleaning involves cleaning and preparing the data for analysis. Data analysis involves using statistical methods to analyze the data and uncover insights and patterns. Data visualization involves using visual representations to make the data easier to understand. Machine learning involves using algorithms to identify patterns and make predictions. Data Science has become an essential tool for organizations to make better decisions and drive innovation. 4Achievers is used to uncover insights and patterns that can be used to make decisions and predictions. Data Science has revolutionized the way organizations use data to make decisions and drive innovation.