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MODULES COVERED:
Here's a brief overview of what you might expect to learn in a data analytics course with
Python:
Python Basics: The course usually starts with an introduction to Python programming
language, covering basic syntax, data types, control structures, functions, and libraries.
1.
Data Manipulation with Pandas: Pandas is a powerful library for data manipulation and
analysis in Python. You'll learn how to import, clean, transform, and analyze data using
Pandas DataFrame.
2.
Data Visualization with Matplotlib and Seaborn: Visualization is a crucial aspect of data
analysis. You'll learn how to create various types of plots and visualizations to explore
and communicate insights from data.
3.
Statistical Analysis with Python: You'll learn basic statistical concepts and how to
perform statistical analysis using Python libraries such as NumPy and SciPy.
4.
Introduction to Machine Learning: Many data analytics courses also include an
introduction to machine learning concepts and algorithms using libraries like Scikit-learn.
You'll learn about supervised and unsupervised learning, model evaluation, and basic
machine learning techniques.
5.
Data Cleaning and Preprocessing: Real-world datasets are often messy and require
cleaning and preprocessing before analysis. You'll learn techniques for handling missing
data, outliers, and other data cleaning tasks.
6.
Data Analysis Projects: Hands-on projects are an essential part of data analytics courses.
You'll work on real-world datasets and projects to apply the skills you've learned and gain
practical experience.
7.
Advanced Topics: Depending on the course, you may also cover advanced topics such as
time series analysis, text mining, web scraping, and more.
8.