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we’ve designed online courses to meet your needs and prepare you for a data science career. You’ll learn MS SQL, Power BI and fundamentals of Python and machine learning.
Data Analytics with Python Syllabus
Rev 07.14.2020
Subject PTY 101
Subject Description: Python Fundamentals
Subject Hours: 150 contact hours (75 hours of lecture, 75 hours of lab)
Performance objectives: You will be introduced to what Python is and learn the fundamentals of Python programming.
After successful completion of this and the following Python course work, students would be qualified for Entry-level data science / data analyst positions that require Python coding as a requisite.
Prerequisites: Acceptance into the program
Required Tools: Any Modern laptop w 8 gb min of ram. No chrome books.
Instructional Methods:
1. Lecture
- Lab
Maximum Student: Instructor Ratio: 30:1
Content Outline Weekly:
Week 1: Overview of Python and demonstration of different Applications where Python is used.
Week 2: Discuss Python Scripts on UNIX/Windows. Values, Types, Variables
Week 3: Operands and Expressions Conditional Statements Loops
Week 4: Variables. Demonstrating Conditional Statements. Demonstrating Loops. Writing to the screen
Basis of Grades: Tests and/or Quizzes 25 percent
Final Exam 25 percent
Class and/or Homework assignments 25 percent
Attendance 25 percent
Subject PTY 102
Subject Description: Object Oriented Programming with Python.
Subject Hours: 150 contact hours (75 hours of lecture, 75 hours of lab)
Performance objectives: Students will have a firm grasp on designing and writing Python Scripts on UNIX/Windows to include values, types and variables.
After successful completion of this, the preceding and following Python course work, students would be qualified for Entry-level data science / data analyst positions that require Python coding as a requisite.
Prerequisites: Acceptance into the program
Required Tools: Any Modern laptop w 8 gb min of ram. No chrome books.
Instructional Methods:
1. Lecture
- Lab
Maximum Student: Instructor Ratio: 30:1
Content Outline Weekly:
Week 5: Functions – Syntax, Arguments, Keyword Arguments, Return Values
Week 6: Sorting – Sequences, Dictionaries, Limitations of Sorting
Week 7: Working with functions in Python
Week 8: Error and Exception management in Python
Basis of Grades: Tests and/or Quizzes 25 percent
Final Exam 25 percent
Class and/or Homework assignments 25 percent
Attendance 25 percent
Subject PTY 103
Subject Description: Scripting with Python
Subject Hours: 150 contact hours (75 hours of lecture, 75 hours of lab)
Performance objectives: Students will become familiar with Python Operands and Expressions Conditional Statements and Loops.
After successful completion of this, the preceding and following Python course work, students would be qualified for Entry-level data science / data analyst positions that require Python coding as a requisite.
Prerequisites: Acceptance into the program
Required Tools: Any Modern laptop w 8 gb min of ram. No chrome books.
Instructional Methods:
1. Lecture
- Lab
Maximum Student: Instructor Ratio: 30:1
Content Outline Weekly:
Week 9 File Operations using Python
Week 10: Working with data types of Python. Operations on arrays indexing slicing and iterating Reading and writing arrays on files
Week 11: Lambda Functions and Object-Oriented Concepts
Week 12: Python for Data Visualization
Basis of Grades: Tests and/or Quizzes 25 percent
Final Exam 25 percent
Class and/or Homework assignments 25 percent
Attendance 25 percent
Subject PTY 104
Subject Description: Python and statistics, different types of measures and probability distributions, and the supporting libraries used for data visualization
Subject Hours: 150 contact hours (75 hours of lecture, 75 hours of lab)
Performance objectives: This course helps students get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, students will learn in detail about data visualization.
After successful completion of this and the preceding Python course work, students would be qualified for Entry-level data science / data analyst positions that require Python coding as a requisite.
Prerequisites: Acceptance into the program
Required Tools: Any Modern laptop w 8 gb min of ram. No chrome books.
Instructional Methods: 1. Lecture
- Lab
Maximum Student: Instructor Ratio: 30:1
Content Outline Weekly:
Week 13: NumPy – arrays Operations on arrays Indexing slicing and iterating Reading and writing arrays on files
Week 14: Pandas – data structures & index operations Reading and Writing data from Excel/CSV formats into Pandas matplotlib library Grids, axes, plots Markers, colours, fonts and styling Types of plots – bar graphs, pie charts, histograms
Week 15: Pandas library- Creating series and dataframes, Importing and exporting data. Probability Distributions in Python
Basis of Grades: Tests and/or Quizzes 25 percent
Final Exam 25 percent
Class and/or Homework assignments 25 percent
Attendance 25 percent