Study Course Plan

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Course Title:	Introduction to Data Science
Course Code:	DS101
Instructor:	[YOUR NAME]
Institution:	[YOUR COMPANY NAME]
Semester:	Fall 2055

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Course Description

This course provides an introduction to the fundamental concepts and techniques in data science. It covers the entire data science pipeline, from data collection and cleaning to analysis, visualization, and modeling. Students will learn how to apply statistical techniques, machine learning algorithms, and data visualization tools to solve real-world problems. No prior experience in programming or statistics is required.

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Course Objectives

By the end of the course, students will be able to:

1. Understand the basics of data science, including data collection, cleaning, and pre-processing.

2. Use programming languages like Python to analyze data.

3. Apply fundamental statistical techniques for data analysis.

4. Understand machine learning models and how to implement them.

5. Present data findings using visualization tools such as Matplotlib and Tableau.

6. Solve real-world problems using data-driven insights.

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Prerequisites

• Basic knowledge of mathematics (high school level)

• Familiarity with using computers and the internet

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Required Materials

• Textbook: “Data Science from Scratch” by Joel Grus (3rd Edition, 2055)

• Software: Python (version 4.0), Jupyter Notebooks, Tableau

• Laptop with at least 8GB of RAM for running data analysis

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Course Schedule

Week	Topic	Assignment/Activity	Due Date
1	Introduction to Data Science	Reading: Chapter 1, Hands-on activity	Week 2
2	Data Collection and Cleaning	Homework: Data wrangling exercise	Week 3
3	Introduction to Python	Python assignment #1	Week 4
4	Exploratory Data Analysis (EDA)	EDA Project	Week 5
5	Data Visualization Techniques	Visualization project	Week 6
6	Introduction to Machine Learning	Machine Learning assignment	Week 7
7	Supervised Learning Models	Midterm Exam	Week 8
8	Unsupervised Learning Models	Project work	Week 9
9	Model Evaluation and Tuning	Final project preparation	Week 10
10	Final Project Presentations	Submission of final project	Week 11

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Grading Criteria

Category	Percentage
Assignments (5)	30%
Midterm Exam	20%
Final Project	30%
Class Participation	10%
Attendance	10%

Grading Scale:

• A: 90-100%

• B: 80-89%

• C: 70-79%

• D: 60-69%

• F: Below 60%

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Course Policies

• Attendance: Regular attendance is mandatory. Absences must be communicated in advance.

• Late Assignments: Assignments submitted after the due date will incur a 10% deduction per day.

• Academic Integrity: All students are expected to uphold the highest standards of academic honesty. Plagiarism and cheating will result in disciplinary action.

• Office Hours: Available by appointment on weekdays from 2:00 PM to 4:00 PM via Zoom.

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Additional Resources

• Online Platform: All course materials, announcements, and assignments will be available on the [YOUR COMPANY NAME] learning management system.

• Tutoring: Additional help sessions will be available upon request.

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Course Outline

1. Introduction to Data Science and the Data Pipeline
   Overview of data science and its applications in various industries. Understanding the data science workflow.

2. Data Collection and Preprocessing
   Techniques for collecting raw data and cleaning it for analysis.

3. Python for Data Science
   Hands-on programming sessions with Python, covering basic syntax, data types, and libraries such as NumPy and Pandas.

4. Exploratory Data Analysis (EDA)
   Learning how to explore and summarize data. Introduction to statistical summaries and visualizations.

5. Data Visualization
   Creating informative and aesthetically pleasing visualizations using tools like Matplotlib, Seaborn, and Tableau.

6. Introduction to Machine Learning
   Introduction to machine learning concepts, focusing on supervised and unsupervised learning.

7. Supervised Learning
   In-depth study of classification and regression models, such as decision trees and linear regression.

8. Unsupervised Learning
   Exploring clustering algorithms such as K-Means and dimensionality reduction techniques like PCA.

9. Model Evaluation
   Understanding techniques for evaluating model performance, including cross-validation and confusion matrices.

10. Final Project
    Students will apply all the skills learned in the course to a real-world data problem and present their findings.

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