Description
Python and Data Analytics
Brief Contents
- Operations using Data Types and Operators
- Evaluate expressions to identify the data types Python assigns to variables
• str, int, float, and bool
1.2 Perform and analyze data and data type operations
• Data type conversion, indexing, slicing, construct data structures, lists, list operations
1.3 Determine the sequence of execution based on operator precedence
• Assignment, comparison, logical, arithmetic, identity (is), containment (in)
1.4 Select operators to achieve the intended results
• Assignment, comparison, logical, arithmetic, identity (is), containment (in)
2. Flow Control with Decisions and Loops
2.1 Construct and analyze code segments that use branching statements
• if, elif, else, nested and compound conditional expressions
2.2 Construct and analyze code segments that perform iteration
• while, for, break, continue, pass, nested loops, loops that include compound conditional expressions 3. Input and Output Operations
3.1 Construct and analyze code segments that perform file input and output operations
• open, close, read, write, append, check existence, delete, with statement
3.2 Construct and analyze code segments that perform console input and output operations
• Read input from console, print formatted text (string.format() method, f-String method), use command-line arguments
4. Code Documentation and Structure
4.1 Document code segments
• Use indentation, white space, comments, and documentation strings; generate documentation by using pydoc
4.2 Construct and analyze code segments that include function definitions
• Call signatures, default values, return, def, pass
5. Troubleshooting and Error Handling
5.1 Analyze, detect, and fix code segments that have errors
• Syntax errors, logic errors, runtime errors
5.2 Analyze and construct code segments that handle exceptions
• try, except, else, finally, raise
5.3 Perform unit testing
• Unittest, functions, methods, and assert methods (assertIsInstance, assertEqual, assertTrue, assertIs, assertIn)
6. Operations using Modules and Tools
6.1 Perform basic file system and command-line operations by using built-in modules
• io, os, os.path, sys (importing modules, opening, reading and writing files, command-line arguments)
6.2 Solve complex computing problems by using built-in modules
• Math (fabs, ceil, floor, trunc, fmod, frexp, nan, isnan, sqrt, isqrt, pow, pi) datetime (now, strftime, weekday), random (randrange, randint, random, shuffle, choice, sample)
Training and Technical Support
Training by the experts working on latest tools in the industry
All tasks will be implemented in the Lab
Assignment of each class will be the extension of the lab task
A guided project is part of the training where the expert will visit as per schedule
Engage the candidates to learn freelancing activities
Appear in the international Certification if opted.
Preferred Audience for Specialist Track
Students with basic IT understanding and related degrees
Students who want to be part of Data Science Team
Training Duration & Schedule
03 Months
Classes Timings: 9:00am - 12:15pm OR 3:00pm - 6:15pm Break 15 minutes.
03 sessions / week (3 hrs each)
Total: 108 hours (Training: 72 hours, Guided Project: 36 hours)
•Technical Content - 06 weeks (54 hours)
•Free-lancing training - 02 weeks (18 hours)
•Guided Project Work - 04 weeks (36 hours)
International Certifications
Certificate of Training by NUML
IT Professional: Python (If opted)