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Udemy is a well-known online platform for learning various skills and subjects. One of the popular courses available on Udemy is “Python – Data Analytics – Real World Hands-on Projects”. This course is designed for those who want to learn how to use Python for data analysis and how to apply it to real-world projects. In this article, we will discuss this course in detail, its objectives, and what students can expect to learn from it.
Python – Data Analytics – Real World Hands-on Projects Course Overview:
The Python – Data Analytics – Real World Hands-on Projects course is an intermediate-level course that requires some prior knowledge of Python programming language. The course is divided into several sections, each covering different topics related to data analysis. It consists of ten projects, with each project focusing on a different aspect of data analysis. Each project is designed to give students hands-on experience in using Python for data analysis.
The course instructor, Ardit Sulce, is a software engineer with extensive experience in Python and data analysis. He has created several popular courses on Udemy, and his teaching style is highly praised by his students. In this course, Ardit Sulce uses his experience to create a learning environment that is easy to follow and engaging.
Python – Data Analytics – Real World Hands-on Projects Course Objectives:
The primary objective of the Python – Data Analytics – Real World Hands-on Projects course is to teach students how to use Python for data analysis. The course aims to provide students with hands-on experience in using Python libraries such as Pandas, Numpy, and Matplotlib for data analysis. By the end of the course, students will be able to:
- Use Python for data analysis
- Apply data analysis techniques to real-world projects
- Clean and prepare data for analysis
- Visualize data using Matplotlib
- Perform statistical analysis on data
- Create predictive models using Machine Learning algorithms
Python – Data Analytics – Real World Hands-on Projects Course Curriculum:
The Python – Data Analytics – Real World Hands-on Projects course consists of ten projects. Each project covers a different aspect of data analysis and is designed to provide students with hands-on experience in using Python for data analysis. Here is a brief overview of the ten projects covered in the course:
- Analyzing NYC High School Data: In this project, students will analyze data from NYC High Schools to determine if there is any correlation between SAT scores and demographic factors such as race, gender, and income.
- Analyzing CIA Factbook Data: In this project, students will analyze data from the CIA Factbook to determine if there is any correlation between a country’s population density and its level of development.
- Analyzing Employee Exit Surveys: In this project, students will analyze data from employee exit surveys to determine why employees are leaving a company and identify any patterns or trends in the data.
- Analyzing Movie Data: In this project, students will analyze data from movie ratings to determine what factors make a movie successful and what factors make a movie a failure.
- Analyzing Sales Data: In this project, students will analyze sales data to identify trends in sales and determine what factors contribute to increased sales.
- Analyzing Weather Data: In this project, students will analyze weather data to identify patterns and trends in weather and determine how weather affects various aspects of life.
- Analyzing US Census Data: In this project, students will analyze US Census data to determine demographic trends in the US population and how they have changed over time.
- Predicting Housing Prices: In this project, students will use Machine Learning algorithms to predict housing prices based on various factors such as location, size, and age of the house.
- Predicting Stock Prices: In this project, students will use Machine Learning algorithms to predict stock prices based on historical data.
- Detecting Fraud: In this project, students will use Machine Learning algorithms to detect fraud in credit card transactions.
