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Data Sciences Course Overview

This 12-week data science course is tailored for beginners and early professionals who want to gain a solid foundation in data science principles and techniques. Covering essential topics such as data wrangling, analysis, machine learning, and visualization, this course offers a hands-on, practical approach to learning. Students will work with real-world datasets, using Python and key data science libraries to build their analytical skills. By the end of the course, students will have developed a working knowledge of data science, along with a portfolio project to showcase their skills.

Course Outline

Week 1-2: Introduction to Data Science & Python Basics

Overview of Data Science: Key concepts, workflow, and applications in various fields.
Python Fundamentals: Variables, data types, operators, and control flow.
Data Science Libraries: Overview of libraries like NumPy, Pandas, and Matplotlib.
Project: Write a Python script for basic data manipulation tasks.

Week 3-4: Data Wrangling & Cleaning

Data Collection and Import: Loading data from different sources.
Data Cleaning Techniques: Handling missing values, data types, and duplicates.
Exploratory Data Analysis (EDA): Basic statistics, data visualization.
Project: Clean and explore a real-world dataset using Pandas.

Week 5-6: Data Visualization

Data Visualization Principles: Best practices for effective data presentation.
Using Matplotlib & Seaborn: Line plots, bar charts, histograms, and heatmaps.
Advanced Visualizations: Pair plots, box plots, and customized visualizations.
Project: Create visualizations to summarize insights from a dataset.

Week 7-8: Introduction to Statistics and Probability

Descriptive Statistics: Mean, median, mode, variance, and standard deviation.
Probability Basics: Probability distributions and random variables.
Statistical Testing: Hypothesis testing, p-values, and confidence intervals.
Project: Analyze dataset characteristics and conduct statistical tests.

Week 9-10: Introduction to Machine Learning

Supervised Learning Basics: Introduction to regression and classification.
Data Preprocessing for Machine Learning: Scaling, encoding, and feature selection.
Building a Model: Fit and evaluate a simple machine learning model.
Project: Develop a basic predictive model using Scikit-Learn.

Week 11: Capstone Project Development

Project Planning: Choose a dataset and define a data science problem to solve.
Model Selection and Validation: Select appropriate model(s) and evaluate results.
Feedback & Adjustments: Receive personalized feedback on your project.

Week 12: Final Project Presentation & Review

Project Finalization: Complete and polish the capstone project.
Presentation: Present findings and demonstrate data science skills.
Next Steps: Resources for further learning, portfolio building, and career guidance.

Outcome: By the end of the course, students will have developed a solid foundation in data science tools and techniques, along with a capstone project to showcase their abilities. This course prepares students for further study or entry-level roles in data analytics or data science.