Data Science Course Outline With Generative AI: PW Skills

Get an extensive data science course outline offered by PW Skills and become a certified data scientist this season. The complete syllabus is based on the latest industry curriculum to help students grab the best offer in the market.

Utilise the power of Generative AI tools and technologies within this course. In this article, let us get complete details on the data science course outline of PW Skills Data Science with Generative AI Course.

Table of Contents

Become a Certified Data Scientist with PW Skills

Learn top-in-demand data scientist skills with PW Skills Data Science Course. Enrol in this job assistance Data Science Program and discover a wide range of career opportunities in the field of data science.

Name of the course	Data Science with Generative AI Course
Duration	6 Months
Program	Job Assistance Program
Delivery Mode	Live + Recorded
Language	English
Real World Project	4+ Capstone Projects
Interview Opportunities	Yes
Certification	Yes

Data Science Course Outline

Let us quickly get to the data science course outline and discover the curriculum of the course below.

Module 1: Python Basics

Introduction to Python
Python Objects, Numbers, Strings, and Booleans
Operators
Container Objects
Python Type Conversion
Condition Statement
Loops
Break and Continue Statement
Python Namespace

Module 2: Data Types & Functions

Data Structure in Python
Strings Object basics
Inbuilt Methods
Splitting Inbuilt Methods
List Methods
List Comprehension
Dictionary
Function Basics
Generator Functions
Lambda Functions
Map, Reduce, Filter Functions

Module 3: OOPs

OOPS Basic Concepts.
Creating Classes
Pillars Of OOPS
Inheritance
Polymorphism
Encapsulation
Abstraction
Decorator
Class Methods And Static Methods
Special (Magic/Dunder) Methods
Property Decorators – Getters, Setters, And Deletes

Files & Exceptional Handling

Working With Files
Reading And Writing Files
Buffered Read And Write
Other File Methods.
Logging, Debugger
Modules And Import Statements
Exceptions Handling With Try-Except
Custom Exception Handling
List Of General Use Exception
Best Practice Exception Handling

Database & Web API

Multithreading
Multiprocessing
Mysql
Mongo Db
What Is Web Api
Difference B/W Api And Web Api
Rest And Soap Architecture
Restful Services

1. Python Module

This module in the Data Science Course outline consists of three important subparts that will help you learn Python language from scratch

Python Basics

In this module, you will learn about Basic python programming, data structure, and OOP concepts.

Advance Python

Here you will learn concepts like handling errors and exceptions in Python, logging event details with python modules

Database and Web API

Learn how to create web API, and DBMS like MySQL, and MongoDB. Create API connections using Python. Get a basic understanding of web application architecture.

Statistics Basics

Introduction To Basic Statistics Terms
Types Of Statistics
Types Of Data
Levels Of Measurement
Measures Of Central Tendency
Measures Of Dispersion
Random Variables
Set
Skewness
Covariance And Correlation

Statistics Advance

Probability Density/Distribution Function
Types Of The Probability Distribution
Binomial Distribution
Poisson Distribution
Normal Distribution (Gaussian Distribution)
Probability Density Function And Mass Function
Cumulative Density Function
Examples Of Normal Distribution
Bernoulli Distribution
Uniform Distribution
Z Stats

2. Statistics

Get familiar with the statistics concepts in this module and know the impact of statistics behind any business to be implemented.

Basics in Statistics

In this module get familiar with statistical concepts.

Advance Statistics

Get familiar with the advanced concepts of statistics and its impact in any business.

3. Machine Learning

Get familiar with machine learning models and algorithms in this module. Learn about feature engineering and Data analysis with important key parameters. The machine learning module is divided into eight distinct parts.

Module 1: Introduction to ML
Module 2: Feature Engineering
Module 3: Exploratory Data Analysis
Module 4: Regression
Module 5: Decision Trees & SVM
Module 6: Naive Bayes & Ensemble Techniques
Module 7: Boosting, KNN & Dimension Reduction
Module 8: Anomaly Detection & Time Series

Introduction

Get familiar with fundamental terminologies in the Data Science course outline.

Feature Engineering

Get familiar with how statistics make an impact behind any business idea to be implemented.

Exploratory Data Analysis

Learn to use statistics to explore data and find important insights from the data to monitor key parameters.

Machine Learning Algorithms

Regression Models
SVM
Clustering Algorithms
Decision Trees
Bagging and Boosting Techniques

Projects

There are advanced projects related to the course using ML Models and deploying it on the cloud platforms.

Spam detection
Climate Visibility

4. Deep Learning

Learn the concept of building neural networks in the data science domain. Learn how to use deep learning libraries like TensorFlow and PyTorch.

Neural Network
Tensorflow
PyTorch & CNN
Image Classifications
RCNN, YOLO, Detectron2 & TFOD2
Image Segmentation & Mask Rcnn
Object Detection
GAN

Artificial Neural Network (ANN)

Learn how to build neural networks and their impact on the data science domain. Learn demanding libraries of deep learning like Pytorch and Tensorflow within this module.

Convolutional Neural Networking (CNN)

This part provides CNN Models from scratch where you can learn how to create CNN Models and use it in image classification, object classification, and many more applications in the various industries.

Real World Projects

There are two important projects covered in the data science course outline which is Mini project using Tensorflow and Face detection project.

5. Natural Language Processing (NLP)

Learn how to prepare textual data output from NLP and the basics of RNN and other advanced models using NLP. With NLP, devices can easily recognize, understand, and generate text-like responses based on a rule-based machine language inspired by NLP. PW Skills Data Science course covers text processing, NLP libraries, models, and more.

Module 1: Introduction of NLP
Module 2: Text Processing using NLP
Module 3: NLP Libraries
Module 4: NLP Models
Module 5: Transfer Learning

6. Generative AI Module

The most in-demand module in the data science course outline is the utilization of generative AI. Learn current in demand AI tools and techniques used in the industry to analyze and synthesize data.

Hugging Face
LLMs
BERT
GPT
Image generator

Project

Cover innovative projects on how to build a chatbot from scratch using Langchain and Chainlit AI tools. It not only responds to textual queries but can also generate a visual representation of the output.

7. Power BI

Master Data Analytic Tools Power BI, which comes optional with the course. This tool is very helpful in making productive data visualization from raw data into interactive insights.

Data Science Course Outline: Real-World Projects

Build innovative real world projects within the data science course offered by PW Skills. Check the projects in the data science course outline below.

GenAi: An Alexa Like Assistant (Generative AI Project)

Build an AI assistant with chat and voice command which is useful for executing daily tasks such as web searching, knowledge extractions or providing personalized recommendations.

Customized Chat Bot ( Generative AI Project)

Build a Chatbot using the Long Chain and Chainlit framework which is capable of generating a question-answer system or RAG system that can extract important information from various sources of documents.

Named Entity Recognition ( Generative AI)

Utilise advanced AI transformer models to build a Name entity recognition project that can currently identify and extract any entities from a text document.