Python for Data Science

Go to Course: https://www.coursera.org/learn/python-data-science

Introduction

Explain the significance of Python in data science and its real-world applications.

Apply Python to manipulate and analyze diverse data sources, using Pandas and relevant data types

Create informative data visualizations and draw insights from data distributions and feature relationships

Develop a comprehensive data preparation workflow for machine learning, including data rescaling and feature engineering

Syllabus

Introduction to Python for Data Science

In the first module of the Python for Data Science course, learners will be introduced to the fundamental concepts of Python programming. The module begins with the basics of Python, covering essential topics like introduction to Python.Next, the module delves into working with Jupyter notebooks, a popular interactive environment for data analysis and visualization. Learners will learn how to set up Jupyter notebooks, create, run, and manage code cells, and integrate text and visualizations using Markdown. Additionally, the module will showcase real-life applications of Python in solving data-related problems. Learners will explore various data science projects and case studies where Python plays a crucial role, such as data cleaning, data manipulation, statistical analysis, and machine learning.By the end of this module, learners will have a good understanding of Python, be proficient in using Jupyter notebooks for data analysis, and comprehend how Python is used to address real-world data science challenges.

By the end of this module, learners will acquire essential skills in working with various types of data. They will have a solid grasp of Python programming fundamentals, including data structures and libraries. They will be proficient in loading, cleaning, and transforming data, and will possess the ability to perform exploratory data analysis, employing data visualization techniques. They will also gain insights into basic statistical concepts, such as probability, distributions, and hypothesis testing.

By the end of this module, learners will gain a comprehensive understanding of statistical concepts, data exploration techniques, and visualization methods. Learners will develop the skills to identify patterns, outliers, and relationships in data, making informed decisions and formulating hypotheses. Ultimately, they will emerge with the ability to transform raw data into meaningful insights, effectively communicate their findings through data storytelling, and apply EDA across diverse real-world applications.

By the end of this module, learners will acquire the essential skills to effectively transform raw and often messy data into a structured and suitable format for advanced analysis. They will master the techniques for handling missing values, identifying and dealing with outliers, encoding categorical variables, scaling and normalizing numerical features, and handling textual or unstructured data. Learners will also be proficient in detecting and addressing data inconsistencies, such as duplicates and errors. Learners will be able to treat data to make it suitable for further analysis. Upon completion of this module, Upon completion

By the end of this module, learners will develop a profound understanding of how to craft and enhance features to optimize the performance of machine learning models. They will be adept at identifying relevant variables, creating new features through techniques such as one-hot encoding, binning, and polynomial expansion, and extracting valuable information from existing data, like dates or text, using methods like feature extraction and text vectorization. Learners will also grasp the concept of feature scaling and normalization to ensure the consistency and comparability of feature ranges. With these skills, they will possess the ability to shape data effectively, amplifying its predictive power and contributing to the construction of robust, high-performing machine learning pipelines.