TNCS COMPUTER EDUCATION Pvt. Ltd.,

BASIC INFORMATION

Course Fees : 2700.00 5000.00/-
Course Duration : 3 MONTH
Minimum Amount To Pay : Rs.1000.00

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S0412 DATA SCIENCE LTE

Course Title: Data Science
Course Description:
This course covers the fundamentals of data science, including data wrangling, visualization, machine learning, and statistical modeling.

Course Objectives:
1. Understand the basics of data science and its applications.
2. Learn data wrangling and preprocessing techniques.
3. Understand data visualization principles and tools.
4. Learn machine learning algorithms and modeling techniques.
5. Apply statistical modeling and data analysis techniques.

Course Outline:
Week 1-2: Introduction to Data Science
1. Definition and history of data science
2. Data science process and methodology
3. Data types and sources

Week 3-4: Data Wrangling and Preprocessing
1. Data cleaning and preprocessing
2. Data transformation and feature engineering
3. Handling missing data and outliers

Week 5-6: Data Visualization
1. Data visualization principles and best practices
2. Data visualization tools (Matplotlib, Seaborn, Plotly)
3. Creating interactive and dynamic visualizations

Week 7-8: Machine Learning
1. Introduction to machine learning
2. Supervised learning algorithms (regression, classification)
3. Unsupervised learning algorithms (clustering, dimensionality reduction)

Week 9-10: Statistical Modeling
1. Introduction to statistical modeling
2. Linear regression and modeling
3. Time series analysis and forecasting

Week 11-12: Advanced Topics in Data Science
1. Deep learning and neural networks
2. Natural language processing and text analysis
3. Big data and data engineering

Recommended Books:
1. "Data Science Handbook" by Jake VanderPlas: A comprehensive guide to data science, covering topics from data wrangling to machine learning.
2. "Python Data Science Handbook" by Jake VanderPlas: A practical guide to using Python for data science, covering topics from data cleaning to visualization.
3. "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber: A comprehensive guide to data mining, covering topics from data preprocessing to pattern discovery.
4. "Machine Learning" by Andrew Ng and Michael I. Jordan: A comprehensive guide to machine learning, covering topics from supervised learning to deep learning.
5. "Statistical Learning with Sparsity" by Trevor Hastie, Robert Tibshirani, and Martin Wainwright: A comprehensive guide to statistical learning, covering topics from linear regression to sparse modeling.

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