Data Science with Deep Learning

This Data Science with Python course gives you a complete overview of Python’s data analytics tools and techniques. Learning python is a crucial skill for many data science roles, and you can develop it with this Python data science course. With a blended learning approach, you can learn Python for data science along with concepts like data wrangling, mathematical computing, and more. Unlock your career as a data scientist with Learning Center Data Science with Python training.

Data Science with Python Certification Key Highlights

Eligibility Criteria

You can join data science after 12th. Students from science background (preferrably engineering / maths / technology) can apply to any data science course at the graduation, certification, or diploma level. You just need to have a minimum of a UG degree in any specialization to become a Data Scientist.
The applicant should have a Bachelor's degree in Science / Engineering / Business Administration / Commerce / Mathematics / Computer Applications or a Masters degree in Mathematics / Statistics / Commerce with 50% or equivalent passing marks.

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.



Live Interactive Training from Industry Experts
Real Time Doubt Solving
Assistance with Practical


Python Codes
Lecture Recordings
Interview Questions
Practice Examples


Beginner to Advanced Level Projects
Assistance in Creating Projects
Helps Enhance Your Resume

  • What is Deep Learning?
  • Applications of Deep Learning
  • Installation of Tensorflow and Keras
  • Model Class
  • Sequential Class
  • Core Layers
  • Convolutional Layer
  • Pooling Layer
  • Locally Connected Layers
  • Recurrent Layers
  • Embedding Layers
  • Merge Layers
  • Artificial Neural Network
  • Convolutional Neural Network
  • Recurrent Neural Network
  • Restricted Boltzmann Machine