Professional Certificate in Python for Data Science: First Steps

This course is designed for beginners with little to no programming experience who wish to transition into the field of data science. It introduces Python programming fundamentals essential for data analysis, including variables, data structures, and basic data manipulation techniques. Participants will gain hands-on experience using Python libraries like NumPy and Pandas.

Upon completion, learners will be able to write basic Python scripts for data manipulation, perform fundamental data analysis tasks, and understand the foundational skills needed to pursue advanced data science topics. The course also covers data visualization basics with Matplotlib, enabling learners to present data insights effectively.

1. 1. Introduction to Python for Data Science: Learners will be introduced to Python programming basics and its applications in data science. They will gain proficiency in setting up Python environments, basic syntax, and essential libraries like NumPy and Pandas.
2. 2. Data Manipulation and Cleaning: This module covers techniques for importing, cleaning, and manipulating data using Pandas. Learners will learn to handle missing data, perform data transformations, and prepare data for analysis.
3. 3. Data Visualization with Matplotlib and Seaborn: Learners will explore data visualization techniques using Matplotlib and Seaborn. They will create various types of plots, customize visual elements, and interpret visual data effectively.
4. 4. Data Analysis Fundamentals: This module provides an overview of data analysis processes, including descriptive and inferential statistics. Learners will perform data aggregation, analysis, and understand statistical concepts crucial for data science.
5. 5. Introduction to Machine Learning: An introduction to machine learning concepts and algorithms. Learners will learn about different types of machine learning (supervised, unsupervised, and reinforcement learning) and apply basic models using Scikit-learn.
6. 6. Predictive Modeling with Scikit-learn: Learners will delve into building predictive models using Scikit-learn. They will cover model selection, evaluation, and tuning techniques to improve model performance.
7. 7. Exploratory Data Analysis (EDA): This module teaches learners how to perform comprehensive exploratory data analysis. They will apply statistical tests, visualize data relationships, and uncover patterns and insights from data.
8. 8. Data Wrangling with Python: Advanced data manipulation techniques using Python and its libraries. Learners will work with complex data structures, perform complex data transformations, and optimize data processing pipelines.
9. 9. Text Data Processing: Introduction to text data manipulation and analysis. Learners will learn about text preprocessing, tokenization, stemming, and other techniques for handling unstructured textual data.
10. 10. Project: Building a Data Science Pipeline: Learners will work on a comprehensive project that integrates all the skills learned in the course. They will design, implement, and present a data science project, showcasing their ability to apply Python and data science techniques in real-world scenarios.