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

Data Science Training in Indore 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 a rapidly growing field that is transforming the way businesses and organizations operate. 4achievers in Indore offers a comprehensive Data Science course that provides students with the knowledge and skills they need to become successful data scientists.

4Achievers Data Science course pre-requisites include a basic understanding of statistics, programming, and databases. Students should also have a good understanding of mathematics and be able to use software such as Excel, R, and Python. Additionally, students should have a basic understanding of machine learning and artificial intelligence.

4Achievers Data Science course tutorial provides students with an in-depth understanding of the fundamentals of data science. 4Achievers tutorial covers topics such as data collection, data analysis, data visualization, machine learning, and artificial intelligence. 4Achievers tutorial also covers the application of data science in various fields such as finance, healthcare, and marketing.

4Achievers Data Science course study material includes textbooks, lecture slides, and assignments. 4Achievers textbooks cover topics such as data collection, data analysis, data visualization, machine learning, and artificial intelligence. 4Achievers lecture slides provide an overview of the topics covered in the course. 4Achievers assignments help students practice the concepts they have learned in the course.

At 4achievers in Indore, students can take the Data Science course to gain the knowledge and skills they need to become successful data scientists. 4Achievers course provides students with the necessary pre-requisites and study material to become successful in the field. 4Achievers Data Science course in Indore is designed to provide students with the skills and knowledge they need to become successful data scientists. 4Achievers Data Science training institute in Indore provides students with the resources and guidance they need to succeed in the field.

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

Data Science is an interdisciplinary field that combines computer science, mathematics, and statistics to analyze and interpret large datasets. 4Achievers is used to gain insights from data and make predictions about future trends. Data Science is becoming increasingly important in today's world, as businesses and organizations rely more and more on data-driven decisions. 4achievers in Indore provides a comprehensive Data Science course that covers the fundamentals of the field, including data exploration, data visualization, machine learning, and deep learning. 4Achievers course also covers topics such as data engineering, data wrangling, and data analysis. 4Achievers course also covers the use of popular tools and technologies such as Python, R, and Hadoop. 4Achievers course is designed to give students the skills and knowledge they need to become successful data scientists.

In addition to the course, 4achievers in Indore also provides tutorials and study materials to help students gain a deeper understanding of Data Science. 4Achievers tutorials and study materials cover topics such as data mining, data cleaning, data analysis, and data visualization. 4Achievers tutorials and study materials also provide an overview of popular Data Science research, publications, and books. Popular Data Science research includes topics such as natural language processing, computer vision, and reinforcement learning. Popular Data Science publications include journals such as the Journal of Machine Learning Research, the International Journal of Data Science and Analytics, and the IEEE Transactions on Knowledge and Data Engineering. Popular Data Science books include titles such as Introduction to Data Science by Bill Howe, Data Science for Business by Foster Provost and Tom Fawcett, and Data Science and Big Data Analytics by EMC Education Services.

By taking the Data Science course at 4achievers in Indore, students will gain the knowledge and skills needed to become successful data scientists. 4Achievers course provides an in-depth understanding of the fundamentals of Data Science, including data exploration, data visualization, machine learning, and deep learning. 4Achievers tutorials and study materials provide an overview of Data Science research, publications, and books, as well as the use of popular tools and technologies such as Python, R, and Hadoop. With the knowledge and skills gained from the Data Science course, students will be well-prepared to take on Data Science roles in the industry.

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

Data Science is a relatively new field of study that has emerged in the last few decades. 4Achievers is a combination of computer science, mathematics, and statistics that is used to analyze large datasets to gain insights and make predictions. Data Science has its roots in the field of statistics, which has been used for centuries to analyze data and draw conclusions. However, the advent of powerful computers and the development of sophisticated algorithms has enabled Data Science to become a much more powerful tool. 4Achievers fundamental of Data Science involves the use of data to answer questions, make predictions, and identify patterns. Data Science also involves the use of various techniques such as machine learning, natural language processing, and deep learning to extract meaningful insights from data. Data Science has been used in a variety of industries, including finance, healthcare, retail, and marketing. Data Science has also been used to improve the efficiency of businesses and to make better decisions. 4Achievers history of Data Science dates back to the 1950s when the first computers were developed and used for data analysis. Since then, the field of Data Science has grown exponentially, with the development of powerful algorithms and the availability of large datasets. Today, Data Science is used in almost every industry to gain insights and make better decisions. Data Science course in Indore is offered by 4achievers and it is designed to teach students the fundamentals of Data Science, including data analysis, machine learning, natural language processing, and deep learning. 4Achievers course also covers the history and evolution of Data Science, and provides students with the opportunity to gain hands-on experience in data analysis and predictive modeling. 4Achievers course also covers the fundamentals of data visualization and provides students with the opportunity to explore and analyze data. 4Achievers course also provides students with the opportunity to learn about the latest trends in Data Science, such as artificial intelligence and big data. Data Science training institute in Indore offers a comprehensive curriculum that covers the fundamentals of Data Science, including data analysis, machine learning, natural language processing, and deep learning. 4Achievers course also covers the history and evolution of Data Science, and provides students with the opportunity to gain hands-on experience in data analysis and predictive modeling. 4Achievers course also covers the fundamentals of data visualization and provides students with the opportunity to explore and analyze data. 4Achievers course also provides students with the opportunity to learn about the latest trends in Data Science, such as artificial intelligence and big data. 4Achieversre are a number of Data Science research works, publications, and books available for students to explore. Some of the popular Data Science research works include “Data Science for Business” by Foster Provost and Tom Fawcett, “Data Science for Dummies” by Lillian Pierson, “Data Science for Beginners” by John D. Cook, and “Data Science for Everyone” by John D. Cook. Some of the popular Data Science publications include “Data Science for Business” by Foster Provost and Tom Fawcett, “Data Science for Dummies” by Lillian Pierson, “Data Science for Beginners” by John D. Cook, and “Data Science for Everyone” by John D. Cook. Some of the popular Data Science books include “Data Science for Business” by Foster Provost and Tom Fawcett, “Data Science for Dummies” by Lillian Pierson, “Data Science for Beginners” by John D. Cook, and “Data Science for Everyone” by John D. Cook. Data Science course in Indore offered by 4achievers provides students with a comprehensive understanding of the fundamentals of Data Science, including data analysis, machine learning, natural language processing, and deep learning. 4Achievers course also covers the history and evolution of Data Science, and provides students with the opportunity to gain hands-on experience in data analysis and predictive modeling. 4Achievers course also covers the fundamentals of data visualization and provides students with the opportunity to explore and analyze data. 4Achievers course also provides students with the opportunity to learn about the latest trends in Data Science, such as artificial intelligence and big data. Data Science training institute in Indore offers a comprehensive curriculum that covers the fundamentals of Data Science, including data analysis, machine learning, natural language processing, and deep learning. 4Achievers course also covers the history and evolution of Data Science, and provides students with the opportunity to gain hands-on experience in data analysis and predictive modeling. 4Achievers course also covers the fundamentals of data visualization and provides students with the opportunity to explore and analyze data. 4Achievers course also provides students with the opportunity to learn about the latest trends in Data Science, such as artificial intelligence and big data. Data Science has evolved significantly over the years and has become an essential part of many businesses. 4Achievers history of Data Science dates back to the 1950s when the first computers were developed and used for data analysis. Since then, the field of Data Science has grown exponentially, with the development of powerful algorithms and the availability of large datasets. Today, Data Science is used in almost every industry to gain insights and make better decisions.