Curriculum of Data Science Course in Delhi
Introduction To Data Science
- Data Science vs. Data Analytics
- The data science workflow
- Tools and environments (Python, Jupyter)
- Concepts of Regression and Classification
Data Acquisition And Cleaning
- Data sources and collection
- Data cleaning and preprocessing
- Handling missing data
Predictive Analytics
- What is regression analysis?
- Types of regression (linear, multiple, polynomial, etc.)
- Use cases and applications of regression in data science
Linear Regression
- Simple linear regression
- Multiple linear regression
- Assumptions of linear regression
- Model interpretation and coefficients
- Model evaluation metrics (A-squared, MSE, MAE)
Logistic Regression
- Introduction to logistic regression
- Logistic regression vs. linear regression
- Binary and multinomial logistic regression
- Odds ratio and log-odds interpretation
- Model evaluation for classification ASSESSMENT FOR LINEAR AND LOGISTIC REGRESSION
Model Evaluation And Validation
- Cross-validation and train-test split
- Evaluation metrics (accuracy, precision, recall, F1-score)
- Bias-variance trade-off
Supervised Learning Algorithms
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Decision Tree
- What is a decision tree? and How decision trees work.
- Decision Tree Splitting Criteria
- Dealing with categorical features.
- Advantages and Disadvantages of Using a Decision tree Algorithm.
Random Forest
- What is a Random Forest?
- The concept of bagging and boosting
- Random Forest Features and Hyperparameters
- Handling Imbalanced Data
- Feature Importance and lnterpretability
Naive Bayes Algorithm
- Introduction to Naive Bayes
- Types of Naive Bayes
- Probability Distributions
- Training and Classification
Svm (Support Vector Machine)
- Introduction to Support Vector Machines
- Support Vector Classification (SVC)
- Support Vector Machines for Regression (SVR)
- Kernel Trick and Non-Linear SVM ASSESSMENT FOR EACH ALGORITHM OF MACHINE LEARNING
Unsupervised Learning Algorithms
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UNSUPERVISED LEARNING ALGORITHMS K-MEANS ALGORITHM
- Introduction to Clustering
- K-Means Algorithm
- Objective Function and Optimization
- Challenges and Limitations
K-nearest Neighbors (K-nn)
- Introduction to K-NN
- Distance Metrics
- Hyperparameter K
- Decision Boundary and Majority Voting ASSESSMENT FOR EACH ALGORITHM OF UNSUPERVISED LEARNING
Deep Learning
- Introduction to Deep Learning and TensorFlow.
- Convolutional Neural Networks (CNNs) with TensorFlow.
- Recurrent Neural Networks (RNNs) and Sequence Models.
- Advanced Deep Learning with TensorFlow and Keras.
Introduction To Nlp
- Tokenization
- Stop words removal
- Stemming and Lemmatization
- Text cleaning and normalization
Text Preprocessing
- Tokenization
- Stop words removal
- Stemming and Lemmatization
- Text cleaning and normalization
Text Representation
- Bag of Words (BoW)
- Term Frequency-Inverse Document Frequency (TF-IDF)
- Word embeddings (Word2Vec, GloVe)
- Document-term matrices
Sentiment Analysis And Text Classification
- Sentiment analysis techniques
- Binary and multi-class classification
- Building a sentiment analysis model
Understanding Data Science Course
Embarking on a data science course involves delving into a comprehensive curriculum that covers fundamental theories, advanced techniques, and practical applications. It's not just about learning the theories; practical application plays a pivotal role in mastering this field. The course encompasses statistical analysis, machine learning, data visualization, and much more.
Why Choose HTS for a Data Science Course
High Technologies Solutions stands out as a leading training provider in Delhi, offering specialized data science courses. Their approach involves a blend of theoretical knowledge and practical exposure, ensuring students are job-ready upon completion of the course. The institute is renowned for its commitment to quality education and excellence in training.
Course Structure and Modules
The data science course at High Technologies Solutions is structured meticulously, covering various modules. Students undergo intensive training in data analysis, machine learning algorithms, data modeling, and more. The course structure is designed to impart in-depth knowledge and hands-on experience.
Experiential Learning and Practical Exposure
At High Technologies Solutions, emphasis is placed on experiential learning and providing practical exposure to real-world scenarios. Students have the opportunity to work on live projects, enabling them to apply their knowledge in a practical setting. This hands-on experience is invaluable in developing critical skills required in the industry.
Flexible Learning Options
High Technologies Solutions caters to different learning preferences by offering various modes of learning for Best Data Science Course in Delhi With Placement. Students can opt for online courses, in-person training, or a hybrid model. This flexibility ensures that learners can choose a format that aligns with their schedules and learning styles.
Data Science Course Fees in delhi
Affordability is a key factor in choosing a training program. High Technologies Solutions provides competitive pricing for their Data Science Traing Course Fees in Delhi, ensuring value for money. They offer flexible payment plans, scholarships, and discounts, making quality education accessible to a wide range of individual.
Job Assistance and Career Opportunities
Upon completion, you'll receive career guidance and job assistance, including:
- Resume building workshops
- Mock interviews and interview preparation
- Placement support through industry connections
- Networking Opportunities
- Skill Enhancement Programs
- Portfolio Development Assistance
- Post-placement Support
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Why Choose Us?
Industry-Experienced Faculty
Learn from experts with years of hands-on experience in the field.
Practical Approach
Hands-on projects and real-case scenarios for a deeper understanding.
Certificate
Receive a recognized certificate upon successful completion.
Networking Opportunities
Connect with industry professionals and expand your network.
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