Data Science with Python Certification Key Highlights
- 50+ Live sessions across two months
- 63 Hrs Self-paced Videos
- 200 Hrs Project & Exercises
- Learn from Professional
- 1:1 with Industry Mentors
- Resume Preparation & LinkedIn Profile Review
- 24*7 Support
- No Cost EMI Option
Eligibility Criteria
Course Curriculum
1.1 Introduction to Python Language
1.2 Features, the advantages of Python over other programming languages
1.3 Python installation – Windows, Mac & Linux distribution for Anaconda Python
1.4 Deploying Python IDE
1.5 Basic Python commands, data types, variables, keywords and more
Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac.
2.1 Built-in data types in Python
2.2 Learn classes, modules, Str(String), Ellipsis Object, Null Object, Ellipsis, Debug
2.3 Basic operators, comparison, arithmetic, slicing and slice operator, logical, bitwise
Hands-on Exercise –
1. Write your first Python program
2. Write a Python Function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. create an object
3.1 Learning Objective
3.2 Expressions
3.3 Conditional Statement
3.4 Demo - Conditional Statement
3.5 Loops - for loop
3.6 Loops - while loop
3.7 Demo - Loops
3.8 Functions
4.1 How to write OOP concepts program in Python
4.2 Connecting to a database
4.3 Classes and objects in Python
4.4 OOPs paradigm, important concepts in OOP like polymorphism, inheritance, encapsulation
4.5 Python functions, return types and parameters
4.6 Lambda expressions
Hands-on Exercise –
1. Creating an application which helps to check balance, deposit money and withdraw the money using the concepts of OOPS.
6.1 Python files I/O Functions
6.2 Lists and related operations
6.3 Tuples and related operations
6.4 Strings and related operations
6.5 Sets and related operations
6.6 Dictionaries and related operations
Hands-on Exercise
1 Tuple - properties, related operations, compared with list
2 List - properties, related operations
3 Dictionary - properties, related operations
4 Set - properties, related operations
7.1 Introduction to Exception Handling
7.2 Scenarios in Exception Handling with its execution
7.3 Arithmetic exception
7.4 RAISE of Exception
7.5 What is Random List, running a Random list on Jupyter Notebook
7.6 Value Error in Exception Handling.
Hands-on Exercise –
1. Demo on Exception Handling with an Industry-based Use Case.
8.1 Introduction to web scraping in Python
8.2 Installing of beautiful soup
8.3 Installing Python parser lxml
8.4 Various web scraping libraries, beautifulsoup, Scrapy Python packages
8.5 Creating soup object with input HTML
8.6 Searching of tree, full or partial parsing, output print
Hands-on Exercise –
1. Installation of Beautiful soup and lxml Python parser
2. Making a soup object with input HTML file
3. Navigating using Py objects in soup tree.
Python Projects
Project 1
Products rating prediction for Amazon
Help Amazon, a US-based e-commerce company, improve its recommendation engine by predicting ratings for the non-rated products and adding them to recommendations accordingly.
Project 2
Demand Forecasting for Walmart
Predict accurate sales for 45 Walmart stores, considering the impact of promotional markdown events. Check if macroeconomic factors have an impact on sales.
Project 3
Improving customer experience for Comcast
Provide Comcast, a US-based global telecom company, key recommendations to improve customer experience by identifying and improving problem areas that lower customer satisfaction.
Tools Covered
Certification & Faq
Once you successfully complete the Data Science with Python training, provide you with an industry-recognized course completion certificate which will have a lifelong validity
Online Classroom:
- Attend one complete batch of Data Science with Python training.
- Submit at least one completed project.
Online Self-Learning:
- Complete 85% of the course
- Submit at least one completed project.