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

Data Science Training in Greater Noida 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

4Achievers understand how important it is to have an in-depth knowledge of this Data Science field, especially if you want to get a Data Science job in a big company.join us today!

First name

Last name

Phone number

We care about your data in our privacy policy.

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

Data Science is an ever-evolving field that is gaining immense popularity in the modern world. 4Achievers is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. 4achievers is one of the leading Data Science training institutes in Greater Noida that offers comprehensive Data Science courses to help students gain in-depth knowledge and expertise in this field.

To take the Data Science course at 4achievers, the pre-requisite is a basic understanding of mathematics, statistics, and programming. Students should have a basic knowledge of Python, R, and SQL. 4Achieversy should also have a basic understanding of probability, linear algebra, and calculus. Furthermore, they should have an understanding of machine learning, data mining, and data visualization.

4Achievers Data Science course tutorial at 4achievers includes lectures, practical sessions, and hands-on projects. 4Achievers lectures cover the fundamentals of Data Science, such as data wrangling, data pre-processing, data analysis, and data visualization. 4Achievers practical sessions involve working on real-world datasets and applying the concepts learned in the lectures. 4Achievers hands-on projects involve building a data science project from scratch and deploying it in the real world.

4Achievers Data Science course study material at 4achievers includes books, online resources, and videos. 4Achievers books cover topics such as data wrangling, data pre-processing, data analysis, and data visualization. 4Achievers online resources include tutorials, blogs, and articles on Data Science. 4Achievers videos provide an in-depth understanding of the concepts and tools used in Data Science.

At 4achievers, the Data Science course in Greater Noida provides students with the necessary skills and knowledge to become a successful Data Scientist. 4Achievers course covers topics such as data wrangling, data pre-processing, data analysis, and data visualization. 4Achievers course also provides students with the opportunity to work on real-world datasets and build data science projects from scratch. With the help of the Data Science training institute in Greater Noida, students can gain the necessary skills and expertise to become a successful Data Scientist.

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

Data Science is a rapidly growing field that is becoming increasingly popular in the tech industry. 4Achievers is a combination of mathematics, computer science, and business analytics that helps to analyze data and make decisions based on the data. 4Achievers is a powerful tool to uncover hidden trends and patterns in data, which can be used to make better business decisions. 4achievers in Greater Noida offers a comprehensive course in Data Science that covers all the essential topics related to the field. 4Achievers course is designed to help students gain a thorough understanding of the fundamentals of Data Science and its applications. 4Achievers course is divided into modules that cover topics such as data collection and analysis, machine learning, data visualization, and data mining. 4Achievers course also covers topics such as data security, data governance, and data engineering. In addition, the course also provides hands-on experience with the tools and techniques used in Data Science. 4Achievers course is suitable for both beginners and experienced professionals. To be eligible for the Data Science course at 4achievers in Greater Noida, students must have a basic knowledge of mathematics, computer science, and business analytics. 4Achievers course also requires students to have a basic understanding of programming languages such as Python and R. 4Achievers course is also suitable for those who are interested in pursuing a career in Data Science. Some of the popular research works in Data Science include ‘Data Mining: Concepts and Techniques’ by Jiawei Han and Micheline Kamber, ‘Data Science for Business’ by Foster Provost and Tom Fawcett, and ‘4Achievers Elements of Statistical Learning’ by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Popular publications in the field of Data Science include ‘Data Science for Dummies’ by Lillian Pierson, ‘Data Science from Scratch’ by Joel Grus, and ‘Data Science for Business’ by Foster Provost and Tom Fawcett. Popular books related to Data Science include ‘Data Science for Business’ by Foster Provost and Tom Fawcett, ‘Data Science from Scratch’ by Joel Grus, and ‘Data Science for Dummies’ by Lillian Pierson. Data Science is a rapidly growing field and the demand for skilled professionals in the field is increasing. 4achievers in Greater Noida offers a comprehensive course in Data Science that covers all the essential topics related to the field. 4Achievers course is designed to help students gain a thorough understanding of the fundamentals of Data Science and its applications. 4Achievers course is suitable for both beginners and experienced professionals. With the help of the Data Science course at 4achievers in Greater Noida, students can gain the skills and knowledge necessary to pursue a successful career in the field of Data Science.

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

Data Science is a rapidly growing field that has its roots in the early days of computing. 4Achievers is a combination of mathematics, computer science, and domain knowledge. Data Science is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. 4Achievers is used to analyze data and make predictions about the future. Data Science has become an integral part of many industries, including healthcare, finance, retail, and technology. 4Achievers history of Data Science can be traced back to the 1950s, when computers were first used to store and analyze data. Since then, the field has seen tremendous growth and development, leading to the emergence of new technologies and techniques. In the early days, Data Science was mainly used for data analysis and statistical modeling. However, with the development of computing power and the availability of large datasets, Data Science has evolved to encompass a wide range of topics, including machine learning, artificial intelligence, natural language processing, and deep learning. Data Science is a complex field and requires a strong foundation in mathematics, computer science, and domain knowledge. 4Achievers fundamental concepts of Data Science include data collection, data cleaning, data analysis, and data visualization. Data Science also involves the use of algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, to make predictions and solve problems. Data Science also requires an understanding of the underlying data structures, such as databases, and the ability to interpret the results of data analysis. Data Science is a rapidly growing field and is becoming increasingly important in many industries. 4achievers in Greater Noida offers a comprehensive Data Science course that covers the fundamentals of Data Science, including data collection, data cleaning, data analysis, data visualization, and machine learning. 4Achievers course also covers the history of Data Science, the major changes in the field, and the current trends in the industry. 4Achievers course is designed to provide students with the skills and knowledge needed to become successful Data Scientists. 4Achievers course includes lectures, hands-on exercises, and projects to help students gain a better understanding of the field. 4Achievers course also provides students with the opportunity to research and publish Data Science research work, publications, and books.