FRENCH-I

Course Description

FRE101 is the initial beginner level French language course which aims to facilitate the recognition and production by students of basic written and oral expressions used for describing themselves and people in their immediate environment through these concepts : French sounds and alphabet, some objects in one’s bag and in a class-room, French for communicating in class, numbers, dates, days, months, greetings, countries, nationalities, languages, contact info, professions, places of work, family, clothing items, colors, some descriptive adjectives, some regular (to be named, stay, speak, work etc.) and irregular verbs (avoir , être etc.), subject and object pronouns, feminine/masculine and plural/singular markers ( of : indefinite /definite articles, demonstrative nouns, descriptive and possessive adjectives , question words) and basic question and affirmative/negative phrases.

FRENCH-I

Evaluation Tools (Active Term)

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Reference Books & Course Materials

1. 01 TEXTO A1 - METHODE DE FRANCAIS
2. 02 TEXTO A1 - CAHIER D'ACTIVITE

Learning Outcomes

1. L01 1-acquire necessary basic vocabulay for daily use.
2. L02 2-be able to catch the main point in short clear messages (written/spoken).
3. L03 3-be able to introduce themselves and exchange information.
4. L04 4-be able to write and narrate a paragraph about themselves and family members.

Program Outcomes

1. P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
2. P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
3. P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
4. P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
5. P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
6. P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
7. P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
8. P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
9. P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
10. P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.

Po-Lo Matrix