Course Overview

Data science is a rapidly growing interdisciplinary field that combines various techniques and methods to extract valuable insights and knowledge from data. This course provides a comprehensive overview of the key concepts, tools, and techniques used in data science.

What You Will Learn

Introduction to Data Science
The course begins with an introduction to data science, covering its definition, importance, and applications across various industries. Students learn about the data science lifecycle, which includes data acquisition, data cleaning, exploratory data analysis, modeling, evaluation, and deployment.

Python Programming for Data Science
Python is a widely used programming language in data science due to its simplicity and versatility. Students are introduced to essential Python libraries such as NumPy, pandas, and Matplotlib for data manipulation, analysis, and visualization. They also learn how to use Jupyter Notebooks for interactive data exploration and analysis.

Data Wrangling and Preprocessing
Data wrangling involves cleaning, transforming, and preparing raw data for analysis. Students learn techniques for handling missing data, removing duplicates, and dealing with outliers. They also learn about data transformation techniques such as normalization and scaling.

Statistical Analysis
Statistical analysis is fundamental to data science for making inferences and drawing conclusions from data. Students learn about descriptive statistics, probability distributions, hypothesis testing, and correlation analysis. They also gain hands-on experience in using statistical methods to analyze real-world datasets.

Machine Learning Basics
Machine learning algorithms enable computers to learn from data and make predictions or decisions without being explicitly programmed. Students are introduced to supervised learning algorithms such as linear regression, logistic regression, and decision trees. They also learn about unsupervised learning algorithms such as k-means clustering and hierarchical clustering.

Advanced Machine Learning
Advanced machine learning topics covered in the course include ensemble methods like random forests and gradient boosting, support vector machines (SVM), and neural networks. Students learn how to apply these techniques to solve complex data science problems, such as image classification, natural language processing (NLP), and time series forecasting.

Big Data and Distributed Computing
With the exponential growth of data, big data technologies are essential for processing and analyzing large datasets efficiently. Students learn about distributed computing frameworks such as Hadoop and Apache Spark, which enable parallel processing of massive datasets across clusters of computers.

Data Science Ethics and Best Practices
Ethical considerations are crucial in data science to ensure the responsible and ethical use of data. Students explore topics such as privacy, security, bias, and fairness in data analysis. They also learn about best practices for data management, reproducibility, and collaboration in data science projects.

Capstone Project
The course culminates in a capstone project where students apply their knowledge and skills to solve a real-world data science problem. They identify a research question, collect and analyze data, develop and evaluate models, and present their findings and insights to their peers and instructors.

Prerequisites and Evaluation
Prerequisites for the course include a basic understanding of mathematics and statistics, familiarity with programming (preferably Python), and knowledge of databases and data manipulation techniques. Evaluation methods include assignments, quizzes, exams, and the assessment of the capstone project.

Study Options:

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