Course Brief
In a world of ever increasing data and information overload, organizations cannot afford to bypass strong and legitimate computational solutions to boost quality decision-making. Big data, business analytics, data mining, cloud services, distributed processing, or business intelligence are just examples of current topics. The interface between technical computing and business acumen has long been object of study, with both extremes yielding sound payoffs. However, greater payoff tends to be achieved with a blended approach (usually as a result of well-formed teams). In order to perform at the optimal level, team members must step into the “other” area (out of comfort zone). This course is designed to help students with little or no prior knowledge in computer programming, who are interested in learning about logic and programming in the object-oriented paradigm, using Python (a current and relevant language) and C (a powerful and imperative computer language). 

Course Goals and Learning Objectives
In this course students will learn basic concepts in computer programming with emphasis on business. The goal is to help students experience a sound learning environment to develop programming skills in C and Python interacting with databases, to better perform in business. 
Upon successful conclusion of this course, students will be able to: 
1. Develop an attitude towards decision, information, data, systems, and computers, based on a critical view of the new business environment and intensified knowledge economy; 
2. Comprehend how problem-solving and data analytics evolved throughout time; 
3. Discuss programming in a stimulating fashion, beyond its mechanics, aiming at attracting even more talented people to this track; 
4. Analyze problems in light of object-oriented programming; 
5.Comprehend C and Python languages, including variables, expressions, files, flow statements, loop statements, functions, classes, methods, and modules; 
6. Comprehend database concepts and integration, SQL servers/clients, SQL statements; 
7. Apply solutions, mostly in Python, to solve statistical and graphical representation of datasets; 
8. Apply solutions, mostly in Python, to selected business problems.

1.	INTRODUCTION TO PROGRAMING IN BUSINESS 
2.	C, PYTHON AND THE OPERATIONAL ENVIRONMENT 
3.	VARIABLES, DATA TYPES, ARRAYS, OPERATORS, AND EXPRESSIONS 
4.	PROGRAMS AND FILES 
5.	FLOW CONTROL 
6.	LOOP CONTROL 
7.	FUNCTIONS, PARAMETERS AND RETURN VALUES 
8.	CLASSES, OBJECTS, ATTRIBUTES AND METHODS 
9.	LIBRARIES, MODULES AND PACKAGES 
10.	STATISTICS AND GRAPHICAL REPRESENTATIONS 
11.	DATABASES AND QUERIES 
12.	OPTIMIZATION WITH EXTENSIONS AND COMPILERS

1. INTRODUCTION TO PROGRAMING IN BUSINESS ORGANIZATIONS
1.1.	Knowledge economy, decision challenges, relevance of coding, job postings, future of business. 
1.2.	Problem-solving, structured/unstructured decisions, data, and analytics. 

2. C, PYTHON AND THE OPERATIONAL ENVIRONMENT 
2.1. Logic, computer languages, evolution, purpose, compilers, and interpreters. 
2.2. C, Python. 
2.3. PyCharm. 
2.4. Pytonista. 
2.5. Trinket.io. 
2.6. TutorialsPoint.com/CodingGround. 

3. VARIABLES, DATA TYPES, ARRAYS, OPERATORS, AND EXPRESSIONS 
3.1. Constants, variables, data types. 
3.2. Declarations. 
3.3. Create and manipulate. 
3.4. Operators and expressions. 
3.5. Arrays and operations. 
3.6. Protocol for variable names. 

4. PROGRAMS AND FILES 
4.1. Text editors and GUI programming environments. 
4.2. Input/output control. 
4.3. Persistent data. 
4.4. Managing files (OS). 

5. FLOW CONTROL 
5.1. If / Elif / Else statement. 
5.2. Switch. 
5.3. Examples. 

6. LOOP CONTROL 
6.1. For / Else statement. 
6.2. While / Else statement. 
6.3. Infinite Loop. 
6.4. Break/Continue. 
6.5. Examples. 

7. FUNCTIONS, PARAMETERS AND RETURN VALUES 
7.1. Logic, optimization, clarity, and performance. 
7.2. Reuse. 
7.3. Local / Global scope. 

8. CLASSES, OBJECTS, ATTRIBUTES AND METHODS 
8.1. Definition, uses, implementation, and examples. 

9. LIBRARIES, MODULES AND PACKAGES 
9.1. Concept. 
9.2. Flexible and extensible attributes of Python. 
9.3. Import Statement. 
9.4. Python Library Reference. 
9.5. Date / Time. 
9.6. Random. 
9.7. Financial examples (Numpy). 

10. STATISTICS AND GRAPHICAL REPRESENTATIONS 
10.1. Statistics examples (Scipy) 
10.2. Graphical examples (Matplotlib) 

11. DATABASES AND QUERIES
11.1. Databases, tables, views, procedures. 
11.2. Normalization. SQL statements. 
11.3. NoSQL paradigm, JSON. 
11.4. Multiple access control. 
11.5. Connections, Client/Server. 
11.6. Pandas, SQLite3, MYSQL. 

12. OPTIMIZATION WITH EXTENSIONS AND COMPILERS 
12.1. py2app, py2exe, pyInstaller. 
12.2. Cython. 
12.3. GCC.