Best Data Science Training institute in Dehradun offering 100% Placement in 2023 | Data Science course in Dehradun

Data Science Training in Dehradun 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 one of the most sought-after courses in the field of technology. 4achievers in Dehradun is one of the leading training institutes offering Data Science courses. 4Achievers Data Science course is designed to help students gain the knowledge and skills required to become a successful Data Scientist.

Before taking the Data Science course, it is important to have a basic understanding of mathematics, statistics, and programming. Students should also have a basic knowledge of computer science and data structures. 4Achievers is also beneficial to have a basic understanding of machine learning and artificial intelligence.

4Achievers Data Science course tutorial at 4achievers in Dehradun covers topics such as data mining, data visualization, predictive analytics, and natural language processing. 4Achievers course also covers the use of tools such as Python, R, and SQL. Students will also learn about the fundamentals of data science and how to use data to solve real-world problems.

4Achievers Data Science course study material provided by 4achievers in Dehradun includes lectures, tutorials, and assignments. 4Achievers course also includes hands-on projects and case studies. 4Achievers course also includes a comprehensive set of resources such as books, videos, and online courses.

At 4achievers in Dehradun, students can take the Data Science course and gain the knowledge and skills required to become a successful Data Scientist. 4Achievers Data Science course in Dehradun provides students with the opportunity to learn the fundamentals of data science and how to use data to solve real-world problems. 4Achievers Data Science training institute in Dehradun provides students with the resources and support they need to succeed in their Data Science career.

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

Data Science is an interdisciplinary field of study that combines mathematics, computer science, and statistics to analyze and interpret data. 4Achievers is a rapidly growing field, and 4achievers in Dehradun offers an excellent Data Science course to help students gain the necessary skills and knowledge to become successful Data Scientists.

4Achievers pre-requisite for taking the Data Science course at 4achievers in Dehradun is a basic understanding of mathematics, computer science, and statistics. 4Achievers course tutorial covers topics such as data collection, data cleaning, data analysis, data visualization, machine learning, and deep learning. 4Achievers course also includes a comprehensive study material that covers the fundamentals of Data Science and provides hands-on experience with real-world datasets.

To gain a deeper understanding of Data Science, students can also look into research work, publications, and books related to the field. Some of the most popular research works in Data Science include “A Survey of Machine Learning Algorithms for Data Science” by Y. Chen et al., “Data Science: A Comprehensive Overview” by M. E. J. Newman, and “Data Science for Business” by F. Provost and T. Fawcett. Popular publications related to Data Science include “Data Science for Dummies” by Lillian Pierson and “Data Science for Business” by Foster Provost and Tom Fawcett. Popular books related to Data Science include “Data Science from Scratch” by Joel Grus, “Data Science for Beginners” by John D. Kelleher, and “Data Science for Business” by Foster Provost and Tom Fawcett.

By taking the Data Science course at 4achievers in Dehradun, students can gain the necessary skills and knowledge to become successful Data Scientists. With the help of the course tutorial, study material, research works, publications, and books, students can gain a comprehensive understanding of Data Science and its applications.

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

Data Science is a field of study that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. 4Achievers is a multi-disciplinary field that combines mathematics, statistics, computer science, and other domains to analyze data and derive insights from it. Data Science has been around for decades, but its popularity has grown exponentially in the last few years due to the availability of large amounts of data and the advancements in computing power and technology. 4Achievers evolution of Data Science can be traced back to the early days of computing when scientists and mathematicians used computers to analyze data. In the 1960s, the term “Data Science” was first used by John Tukey, a statistician and mathematician. He used the term to refer to the use of computers to analyze data and draw insights from it. Since then, Data Science has evolved and grown to become a multi-disciplinary field that combines mathematics, statistics, computer science, and other domains to analyze data and derive insights from it. Data Science is now used in a wide range of industries, from finance and healthcare to retail and marketing. 4Achievers fundamental of Data Science is to use data to solve problems and make decisions. This involves collecting, cleaning, organizing, analyzing, and interpreting data. Data Science also involves building models and algorithms to make predictions and draw insights from the data. Data Science is a rapidly growing field and is being used in many different industries to gain insights and make decisions. 4achievers in Dehradun provides a comprehensive Data Science course that covers the fundamentals of Data Science, including data collection, cleaning, organization, analysis, and interpretation. 4Achievers course also covers the use of data to build models and algorithms to make predictions and draw insights from the data. 4Achievers course also covers the history of Data Science, its evolution, and the major changes that have taken place in the field. 4Achievers course also covers the use of data to solve problems and make decisions. 4Achievers course also covers the use of data to build models and algorithms to make predictions and draw insights from the data. Popular Data Science research work includes topics such as machine learning, deep learning, natural language processing, and predictive analytics. Popular Data Science publications include journals such as the Journal of Machine Learning Research, the Journal of Artificial Intelligence Research, and the International Journal of Data Science. Popular Data Science books include titles such as “Data Science for Business” by Foster Provost and Tom Fawcett, “Data Science for Dummies” by Lillian Pierson, and “Data Science for Beginners” by John D. Cook. 4achievers in Dehradun provides a comprehensive Data Science course that covers the fundamentals of Data Science, its history, evolution, and major changes, as well as the use of data to solve problems and make decisions. 4Achievers course also covers the use of data to build models and algorithms to make predictions and draw insights from the data. 4Achievers course also covers popular Data Science research work, publications, and books. With the help of 4achievers in Dehradun, students can gain a comprehensive understanding of Data Science and its applications.