The second module explores key programming concepts, beginning with built-in and user-defined functions to enhance code reusability and efficiency. It covers string methods, including splitting strings for text manipulation. Learners will also delve into list methods such as slicing, using the in operator for membership testing, and joining lists. Iterations, including loops, are introduced to automate repetitive tasks, followed by combining loops and conditionals to create dynamic and logical programs. The module concludes with practice exercises to reinforce these concepts and improve problem-solving skills.

The third module focuses on the concepts of iterations, while loop and for loop in greater detail. We will specifically learn how to update variables, how to write while loops, execute infinite while loops and finishing iterations using “continue” statement. We will also look at writing definite loops using for statements. We will learn counting and summing iteratively going through loops. We will learn how to find out maximum and minimum elements, typically in a list, using loops. We will further go through iterating through lists and learn how to do debugging which is important as you do more advance programming.

The fourth module focuses on handling and analyzing data efficiently. It begins with understanding relative file structures for accessing and organizing files. Learners will explore Pandas DataFrames, a powerful data structure for managing datasets, along with slicing techniques to extract specific data. The module covers summary statistics to describe datasets and methods for comparing differences between means. Visualization techniques using Matplotlib and Seaborn will be introduced, including histograms, scatterplots, and barplots for effective data representation. Finally, practice exercises will reinforce these concepts, enabling learners to apply data analysis and visualization techniques effectively.

The fifth module delves into essential data structures and text processing techniques. It begins with tuples and dictionaries, exploring their properties and use cases. Learners will then cover list and dictionary comprehension, which provide efficient ways to create and manipulate data structures. The module introduces fundamental text analysis concepts, including counting words, calculating the type-token ratio, and analyzing word frequencies. Next, it covers tokenizing text and preprocessing, essential steps for cleaning and structuring textual data. Additionally, learners will practice reading text files to extract and analyze information. The module concludes with practice exercises to reinforce these concepts through hands-on experience.

The sixth module delves into essential data structures and text processing techniques. It begins with tuples and dictionaries, exploring their properties and use cases. Learners will then cover list and dictionary comprehension, which provide efficient ways to create and manipulate data structures. The module introduces fundamental text analysis concepts, including counting words, calculating the type-token ratio, and analyzing word frequencies. Next, it covers tokenizing text and preprocessing, essential steps for cleaning and structuring textual data. Additionally, learners will practice reading text files to extract and analyze information. The module concludes with practice exercises to reinforce these concepts through hands-on experience.

The seventh and final module introduces accessing and extracting data from the web. It begins with accessing databases via Web APIs, followed by constructing API GET requests to retrieve data. Learners will then explore parsing response texts and JSON files to extract meaningful information, such as counting the number of articles. The module also covers web scraping using BeautifulSoup, enabling automated data extraction from websites.