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SLA Consultants Delhi: Courses & Fees
Python programming for Data Science 1 Month Course Regular
Fee Structure
Fee Detail
Details
One-Time Fee
₹10,000
Installment Option
₹11,500 (2 Weekly Installments)
Course Details Table
Particulars
Details
Course Name
Python for Data Science
Duration
1 Month
Training Approach
80% Practical, 20% Theoretical
Core Topics Covered
- Python Basics: Data Types, Operations, Iterators, Functions
- Data Structures: List, Dictionary, Tuples, Set
- Data Manipulation: Pandas, NumPy, Slicing, Aggregation
- Data Exploration: Univariate/Bivariate Statistics, Matplotlib, Seaborn
- Dimensionality Reduction: PCA, LDA
- Supervised Learning: Logistic Regression, SVM, Decision Trees, Random Forest, Boosting Techniques
- Machine Learning: Bias-Variance Tradeoff, Regularization, Hyperparameter Tuning
- Advanced Unsupervised Learning: K-means, Agglomerative Clustering, t-SNE
- Natural Language Processing (NLP): Tokenization, N-grams, TF-IDF, Topic Modeling
- Deep Learning: Neural Networks, CNN, RNN (GRU, LSTM), Optimization Techniques, Dropout
- Frameworks: PyTorch
- Hands-on with real-world datasets like Titanic and XYZ Company
Hands-On Experience
10+ Assignments and 2 Live Projects
Certification
Course Completion Certificate
Job Assistance
Not Included
Admission Mode
Direct Through Center
Training Mode
Offline
Demo Class
Demo Class Available
Why Join This Course?
Comprehensive Python Curriculum
| Fee Detail | Details |
|---|---|
| One-Time Fee | ₹10,000 |
| Installment Option | ₹11,500 (2 Weekly Installments) |
| Particulars | Details |
|---|---|
| Course Name | Python for Data Science |
| Duration | 1 Month |
| Training Approach | 80% Practical, 20% Theoretical |
| Core Topics Covered | - Python Basics: Data Types, Operations, Iterators, Functions |
| - Data Structures: List, Dictionary, Tuples, Set | |
| - Data Manipulation: Pandas, NumPy, Slicing, Aggregation | |
| - Data Exploration: Univariate/Bivariate Statistics, Matplotlib, Seaborn | |
| - Dimensionality Reduction: PCA, LDA | |
| - Supervised Learning: Logistic Regression, SVM, Decision Trees, Random Forest, Boosting Techniques | |
| - Machine Learning: Bias-Variance Tradeoff, Regularization, Hyperparameter Tuning | |
| - Advanced Unsupervised Learning: K-means, Agglomerative Clustering, t-SNE | |
| - Natural Language Processing (NLP): Tokenization, N-grams, TF-IDF, Topic Modeling | |
| - Deep Learning: Neural Networks, CNN, RNN (GRU, LSTM), Optimization Techniques, Dropout | |
| - Frameworks: PyTorch | |
| - Hands-on with real-world datasets like Titanic and XYZ Company | |
| Hands-On Experience | 10+ Assignments and 2 Live Projects |
| Certification | Course Completion Certificate |
| Job Assistance | Not Included |
| Admission Mode | Direct Through Center |
| Training Mode | Offline |
| Demo Class | Demo Class Available |
Master Python programming for Data Science, covering foundational concepts, data manipulation, visualization, machine learning, and deep learning.
Practical Learning Approach
Get hands-on experience with real-world datasets and projects, ensuring the application of concepts in practical scenarios.
Industry-Relevant Machine Learning Skills
Gain expertise in supervised and unsupervised learning techniques, dimensionality reduction, and natural language processing.
Introduction to Deep Learning
Understand neural networks and their advanced applications in image and sequence data through CNN, RNN, and modern optimization techniques.
Advanced Tools and Libraries
Learn to work with leading frameworks like PyTorch, Scikit-learn, Pandas, and Matplotlib to solve complex data challenges.
Structured and Time-Efficient Learning
Complete an in-depth, structured course in one month, balancing theoretical understanding and practical application.
Certification
Earn a certificate validating your skills in Python programming and data science to boost your career opportunities.
Suitable for All Levels
Whether you're a beginner or looking to specialize in Data Science, this course is tailored to accommodate learners of various expertise levels.
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