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

Data Science Training in Chandigarh 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

4achievers in Chandigarh offers a comprehensive Data Science course for those interested in learning the fundamentals of this field. 4Achievers course is designed to provide a comprehensive overview of the data science concepts and techniques. To enroll in the Data Science course, the pre-requisite is a basic knowledge of mathematics, statistics, and computer science.

4Achievers Data Science course tutorial at 4achievers in Chandigarh covers topics such as data exploration, data cleaning, data manipulation, data visualization, machine learning, and deep learning. 4Achievers course also covers topics such as data mining, natural language processing, and predictive analytics. 4Achievers course also includes hands-on experience with tools and techniques used in data science.

4Achievers Data Science course study material at 4achievers in Chandigarh includes books, videos, and tutorials. 4Achievers course also includes practice exercises, case studies, and projects. 4Achievers course also includes a comprehensive assessment of the student's knowledge and skills. 4Achievers course also includes a comprehensive review of the student's performance.

4Achievers Data Science training institute in Chandigarh at 4achievers provides students with the opportunity to learn from experienced professionals. 4Achievers institute also provides students with the opportunity to network with other students and professionals in the field. 4Achievers institute also provides students with the opportunity to gain hands-on experience with the tools and techniques used in data science.

4Achievers Data Science course in Chandigarh at 4achievers is designed to provide students with the knowledge and skills required to become a successful data scientist. 4Achievers course is designed to help students understand the fundamentals of data science and develop the skills to apply the concepts to real-world problems. 4Achievers course also provides students with the opportunity to gain hands-on experience with the tools and techniques used in data science. 4Achievers Data Science training institute in Chandigarh at 4achievers provides students with the opportunity to learn from experienced professionals. 4Achievers institute also provides students with the opportunity to network with other students and professionals in the field. 4Achievers institute also provides students with the opportunity to gain hands-on experience with the tools and techniques used in data science.

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

Data Science is a rapidly growing field of study that has become increasingly popular in recent years. 4Achievers is a multidisciplinary field that combines elements of mathematics, computer science, and statistics to analyze and interpret data. 4achievers in Chandigarh offers a comprehensive Data Science course that covers the fundamentals of the subject. 4Achievers course is designed to provide students with the knowledge and skills necessary to become successful Data Scientists.

4Achievers prerequisites for taking the Data Science course at 4achievers in Chandigarh include a basic understanding of mathematics, computer science, and statistics. Students should also have a basic understanding of data analysis and data visualization. Additionally, it is recommended that students have some experience with programming languages such as Python and R.

4Achievers Data Science course tutorial at 4achievers in Chandigarh covers topics such as data mining, machine learning, natural language processing, and deep learning. 4Achievers tutorial also covers topics such as data pre-processing, feature engineering, and model evaluation. Additionally, the tutorial provides students with hands-on experience in working with real-world datasets.

4Achievers Data Science course study material at 4achievers in Chandigarh includes textbooks, lecture notes, and online resources. 4Achievers textbooks cover topics such as data mining, machine learning, natural language processing, and deep learning. 4Achievers lecture notes provide students with an overview of the topics covered in the course. Additionally, the online resources provide students with additional resources such as tutorials, sample code, and datasets.

Data Science research work names include topics such as predictive analytics, anomaly detection, and recommender systems. Popular Data Science publications include journals such as the Journal of Machine Learning Research, the IEEE Transactions on Neural Networks and Learning Systems, and the ACM Transactions on Knowledge Discovery from Data. Popular Data Science books include titles such as Introduction to Machine Learning with Python by Andreas C. Muller and Sarah Guido, Deep Learning with Python by Francois Chollet, and Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron.

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

Data Science is a rapidly growing field that has emerged from the convergence of computer science, mathematics, and statistics. 4Achievers is a multidisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science has been around for decades, but it has only recently gained mainstream attention. 4Achievers history of Data Science can be traced back to the 1950s, when the first computers were developed and used for data analysis. In the 1960s, the field of statistics began to be used for data analysis, and in the 1970s, the first computer-based statistical software packages were developed. In the 1980s, the field of machine learning was developed, which allowed computers to learn from data and make predictions. In the 1990s, the internet revolutionized the way data was collected and analyzed, and the field of Data Science was born. 4Achievers fundamental of Data Science include the ability to collect, store, and analyze large amounts of data, as well as the ability to use data to make predictions and decisions. Data Science requires a deep understanding of mathematics, statistics, computer science, and software engineering. 4Achievers also requires an understanding of the business context in which the data is used. Data Science professionals must be able to identify patterns in data and use them to create models that can be used to make decisions. Data Science professionals must also be able to interpret the results of their models and make recommendations based on the data. Data Science is an important field of study for anyone who wants to understand the world around them and make informed decisions. 4achievers provides an excellent Data Science course in Chandigarh that covers all the fundamentals of Data Science. 4Achievers course covers topics such as data collection, storage, and analysis, as well as machine learning and predictive analytics. 4Achievers course also covers the history of Data Science, from the 1950s to the present day, and provides an overview of the major changes that have occurred in the field. In addition to the fundamentals, the course also covers research work names, publication names, and book names related to Data Science. 4Achievers course provides a comprehensive overview of the field and is suitable for anyone who wants to gain a deeper understanding of Data Science and its applications.