Data Science

What is Data Science?

• Data science is an interdisciplinary field that uses domain experience, statistics, mathematics, computer science, and other knowledge from multiple fields to extract knowledge and insights from data.
• It is combination of tools, algorithms, and techniques to analyze large volumes of structured and unstructured data, uncover patterns, make predictions, and drive decision-making processes.

Why learn Data Science?

• Graduates of data science courses may pursue careers as data analysts, data scientists, machine learning engineers, business analysts, or other related roles, depending on their interests and specialization areas.
• Data science roles often offer lucrative salaries due to the high demand and specialized skill set required. Data scientists are among the highest-paid professionals in the tech industry.

What do we Provide?

• This course is to equip students with the skills and expertise needed to tackle real-world data problems across different industries, such as finance, healthcare, e-commerce, marketing, and more.
• Students learn how to collect, clean, analyze, and interpret data using tools and techniques such as Python programming, SQL databases, statistical methods, machine learning algorithms, and data visualization libraries.

Syllabus

Module 1: Introduction to Data Science +

• Overview of data science
• Role of data scientists
• Applications of data science in various industries

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Module 2: Mathematics and Statistics +

• Linear algebra
• Calculus
• Probability theory
• Descriptive and inferential statistics
• Hypothesis testing
• Regression analysis

Module 3: Programming Languages +

• Introduction to Python
• NumPy
• Pandas
• Matplotlib
• Introduction to R

Module 4: Introduction to SQL +

• Introduction to DBMS
• Set up and Installation
• SQL Commands
• Data Relationships
• Data Security
• Data backup and Utility

Module 5: Data Wrangling and Cleaning +

• Data acquisition
• Data cleaning and preprocessing
• Data transformation and normalization
• Handling missing data
• Data integration and merging

Module 7: Machine Learning Fundamentals +

• Supervised learning
• unsupervised learning
• semi-supervised learning
• validation techniques
• Cross-validation
• Bias-variance tradeoff
• Feature engineering and selection

Module 8: Machine Learning Algorithms Components +

• Linear regression
• Logistic regression
• Decision trees and random forests
• Support vector machines
• Clustering algorithms (K-means, hierarchical clustering)
• Dimensionality reduction techniques (PCA, t-SNE)

Module 9: Machine Learning Algorithms Components +

• Deploying Machine Learning Models
• Model APIs
• Monitoring and Scaling Models
• Containerization with Docker
• Web application development with Flask or Django
• Cloud platforms for deployment (e.g., AWS, Azure, Google Cloud)

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InfoStack Software Development, Training and Research Center, Solapur