Why Python?
Python derives its power and popularity on its strength of its simplicity and being open source. Python is an ideal language to learn for beginners as it is simple and easy to learn. Even for those who do not have any programming background. Python is an open source widely used general purpose high level programming language that has been used for over two decades. Python has simple programming construct, relatively less number of lines of code, easy readability and powerful features make it a language of choice for millions of applications. It is supported on many diverse platforms and hence is widely deployed on diverse applications from Finance, Analytics, machine learning, web development, gaming, Automation, Big Data etc.What is Python?
Python is a popular general purpose programming language used for both large and small-scale applications. Python’s wide-spread adoption is due in part to its large standard library, easy readability and support of multiple paradigms including functional, procedural and object-oriented programming styles. Python modules exist for interacting with a variety of databases making it an excellent choice for large-scale data analysis and the Python programming language is often the choice for introductory courses in data science and machine learning. Python is a key language that companies are adopting as a platform of choice for multiple applications.Who Should Attend the Python Training?
- Beginners who want to acquire Python scripting skills
- Advanced Python users, who want to take their skills to the next level
- System Administrators who want to automate their day to day tasks using Python
- Network Administrators who want to use Python to automate the task of managing large networks
- Database Admin, database programmers
- Web Developers
- Testers who want to move into Python based automation
- Fresh Graduates looking for their first job
- Mobile Testers
- Automation Engineers
What is the Python Programming Language used for?
- Data Analysis
- Web development
- DevOp/ system administration/ writing automation scripts
- Programming of web parsers/ scrapers
- Machine learning
- Educational purpose
- Software testing/ writing automated tests
About Data Science and Artificial Intelligence Training
Python is the most commonly used programming language in data science—with almost 70% of data scientists reporting that they use it. It has surpassed R for the number one spot and has maintained this position due to its ease of use, powerful libraries and packages, clear and user-friendly documentation, and abundant community support. Python is easier to read and write than most other general-purpose languages, especially for analytical computing and quantitative data analysis. Data scientists are already handling complex analysis of data, so they don’t need their programming language to be complicated, too. Python is known for its simple syntax and ease of use—even for beginners. Python is open sourced and has numerous libraries and packages available for data science. While some other languages (like Ruby) have clean and simple syntaxes, they don’t offer the same variety of scientific computing and machine learning libraries as Python.What you’ll learn?
In this course you will learn about data mining algorithms and their applications. Further, you will also be guided on how to use the data mining algorithms in KNIME and Python. This course will cover data sets from multiple domains and how to apply Data Mining algorithms on the available data, how to get value out of data mining algorithms, and how to present the output of those algorithms. By the end of the course, you will have enough knowledge and hands-on expertise in Python to use and apply them in the real world around you.Click on Semester for more detail.
Python
Python Course Overview
| Module | Topics |
|---|---|
| 1. An Introduction to Python | A Brief History of Python Versions |
| 2. Python Fundamentals | Installing Python, Environment Variables, Executing Python from the Command Line, IDLE, Editing Python Files, Python Documentation, Getting Help, Dynamic Types, Python Reserved Words, Naming Conventions |
| 3. Language Components | Indenting Requirements, The if Statement, Relational Operators, Logical Operators, Bit Wise Operators, The while Loop, break and continue, The for Loop |
| 4. Collections | Lists, Tuples, Sets, Dictionaries, Sorting Dictionaries, Copying Collections |
| 5. Functions | Defining Your Own Functions, Parameters, Function Documentation, Keyword and Optional Parameters, Passing Collections to a Function, Variable Number of Arguments, Scope, Functions – “First Class Citizens”, Passing Functions to a Function, Mapping Functions in a Dictionary, Inner Functions, Closures |
| 6. Modules | Modules, Standard Modules – sys, Standard Modules – math, Standard Modules – time, The dir Function |
| 7. Exceptions | Errors, Run Time Errors, The Exception Model, Exception Hierarchy, Handling Multiple Exceptions, raise, Assert, Writing Your Own Exception Classes, The pickle Module |
| 8. Classes in Python | Classes in Python, Principles of Object Orientation, Creating Classes, Instance Methods, File Organization, Special Methods, Class Variables, Inheritance, Polymorphism, Type Identification, Custom Exception Classes |
| 9. GUI | Tk GUI toolkit, Creating Components Button, Canvas, Creating Checkbutton, Label, Listbox, Message, Text etc, Creating Container Frame |
| 10. Databases | Creating databases with MySQL, Creating, retrieving, updating and deleting records, Creating a database object |
Data Science
Data Science Course Overview
| Module |
|---|
| 1. Basics of Data Science Flow |
| 2. Anaconda Installation |
| 3. Intro to Python |
| 4. Python Objects & Data Structure |
| 5. Subsetting (Strings, Lists, Dictionaries) |
| 6. Python Comparison Operators |
| 7. Python Statements |
| 8. Methods & Functions |
| 9. Importing Data in Python |
| 10. NumPy & Pandas Basics in Python |
| 11. Subsetting Dataframes in Pandas |
| 12. Hands-On Data Wrangling on Python |
| 13. Data Cleaning in Python |
| 14. String Operations in Data Wrangling |
| 15. Object Types Conversion in Data Wrangling |
| 16. Data Aggregation using Group By, Pivot and Melt |
| 17. Dealing with Multi-indexing in Data Wrangling |
| 18. iloc vs loc for Subsetting Dataframe |
| 19. Row vs Column Concatenation |
| 20. Multi-Indexing and Index Slicing |
| 21. Iteration through Dataframe |
| 22. Types of Variables |
| 23. Data Visualizations |
Artificial Intelligence
Artificial Intelligence Course Overview
| Module | Topics |
|---|---|
| 1. Introduction to Artificial Intelligence | |
| 2. Classification and Regression using Supervised Learning | |
| 3. Predictive Analytics with Ensemble Learning | |
| 4. Detecting Patterns with Unsupervised Learning | |
| 5. Logic Programming | |
| 6. Heuristic Search Techniques | |
| 7. Genetic Algorithms | |
| 8. Building Games with Artificial Intelligence | |
| 9. Natural Language Processing | |
| 10. Probabilistic Reasoning for Sequential Data |
Fees and Durations
- Duration: Three Months
- Timing: 11:00am to 2:00pm
- Days: Weekend (Sundays Only)
- Course Fee: Rs.15,000/-
- Student Benefits: eKit, Participation Certificate
