Core Data Science Skills
Advanced Data Science Tools and Technologies
This amalgarmation of skills empowers you with the capability to:
  • Extract valuable insights from diverse data sources.
  • Build predictive models and forecast future trends.
  • Analyze and interpret complex data sets.
  • Communicate your findings effectively
    to a wider audience.
  • Utilize cutting-edge technologies
    like LLMs and cloud computing.
Master these skills and technologies to unlock
the potential of data science, driving positive change across diverse fields. Enroll now to unleash the power of AI Simpliefie & IBM's Data Science mastery.
What Is Data Science?

Extracting valuable insights from data to drive better decision-making.

  • Unearthing hidden gems:
  • From insights to impact:
  • A multi-faceted approach:
Gain deeper insihts
Who Is a Data Scientist?

Decode data secrets, shape the future with solutions.

  • The data whisperers:
  • Problem-solving rockstars:
  • The future-proof profession:
Gain deeper insihts
Why you should take a Data Science Course

Embark on a data-driven career with the promise of high demand, lucrative salaries, and endless opportunities.

High demand and lucrative salaries

Data Scientists are among the highest-paid professionals in the world, with a median annual salary of over $100,000.

Endless job opportunities

Data Scientist demand soaring across industries, from healthcare to finance, manufacturing to retail.

Solving real-world problems

Data Scientists make a real-world impact, enhancing decisions, efficiency, and innovation for organizations.

Continuous growth and learning

The field of Data Science is constantly evolving, so you'll always have new opportunities to learn and grow.

Who Should do the Data Science Course
  • Domain experts with quantitative skills: Analysts, marketers, engineers, even healthcare professionals seeking deeper insights.
  • Data wranglers: BI, data warehousing, reporting specialists ready to level up.
  • Quant minds: Statisticians, economists, mathematicians craving a practical data toolkit.
  • Tech-savvy problem solvers: Software programmers yearning to unlock data's potential.
  • Business brains: Analysts and Six Sigma consultants looking to optimize strategies with data.
  • Analytical go-getters: Fresh graduates with a hunger to learn and a knack for numbers.
Statistics to support the demand for Data Scientists
  • India has positioned itself as a frontrunner in global Artificial Intelligence (AI) skill adoption and talent concentration, achieving a notable
    score of 3.09.
  • With a current talent pool of 4.16 lakh AI professionals as of August 2023, the nation is strategically poised to fulfill the existing demand of around 6.29 lakh, a number projected to escalate to one million by 2026.
  • A Study conducted by McKinsey Global Institute, the United States is anticipated to face a deficit of approximately 190,000 professionals possessing expertise in data science and deep analytical skills by 2028.
  • Harvard Business Review named "Data Scientist" the "Sexiest Job of the 21st Century."

Core Data Science Skills:

Python: It reigns supreme as the primary proramming language for data science. Its extensive libraries, like NumPy, Pandas, Scikit-learn and TensorFlow, provide powerful functionalities that empower data enthusiasts with a versatile tool for every step of the data science journey.

Predictive Analytics using Reression: Learn how to build models to predict future trends and outcomes based on historical data.

Text Analytics and NLP: Analyze and extract valuable insihts from textual content usin techniques like sentiment analysis and topic modeling.

Data Mining (Unsupervised): Discover hidden patterns and relationships within lare datasets without any predefined assumptions.

Shiny App & Python Flask for Develoyment: Shere your insihts with others by building interactive web applications using Shiny for R and Flask for Python.

Statistical Analysis: Understand the fundamental principles of statistics for data analysis and hypothesis testing.

Data Visualization using Tableau: Communicate your findings effectively through compelling and informative data visualizations.

Advanced Data Science Tools and Technologies:

Apache Spark: Leverae a powerful distributed computing framework for processing lare scale data efficiently.

Microsoft Azure: Utilize Azure cloud services for data storage, analytics and AI development.

Large Language Models (LLM) & ChatGPT: Explore the capabilites of advanced LLMs like ChatGPT for tasks like text generation and translation.

Machine Learning (Surprised): Build intelligent models that learn from data and make predictions without being explicitly programmed.

R Tools: Master the R programming language for statistical analysis and data visualization.

Forecasting Analystics: Gain expertise in forecasting future trends and events using time-series data.

My SQL: Manage and Analyze data using this popular relational database management system.

Additional Learning:

Big Data Hadoop & SAS Base: Deepen your knowlede of big data technologies and advanced statistical analysis tools through self-paced learning.

What is Data Science?

Extracting valuable insights from data to drive better decision-making.

Unearthing hidden gems:
Data science goes beyond just collecting and storing data. It's about using sophisticated techniques and tools to extract valuable insihts from that data. These insights can reveal hidden patterns, trends and behaviors that can inform better decision-making across various industries.

From insights to impact:
Data Science isn't just about cool visualizations and fancy algorithms. It's about translating those insiht into tangible actions that drive real business results. Whether it's optimizing marketing campaingns, predicting customer churn, or developing new products, data science is the bridge between information and impact.

A multi-faceted approach:
Data science isn't just for mathematicians and statisticians anymore. It's a blend of various skills and disciplines, including programming, domain expertise, communication and critical thinking. This allows data scientists to tackle complex problems from different angles and extract the most meaningful information.

What is a Data Scientist?

Unearths hidden secrets in data, translates them into solutions and shapes the future.

The data whisperers:Data scientists are the modern-day detectives, unearthing secrets hidden within vast amounts of data. They use their analytical prowess and technical expertise to ask the right questions, design effective experiments and interpret the results to tell compellin stroies with data.

Problem-solving rockstars: Data scientists aren't just number crunchers; they're crative problem solvers. They combine their technical skills with business acumen to trackle real-world challenges, from fraud detection to healthcare optimization. Their ability to think outside the box and translate complex findings into actionable solutions is what makes them invaluable.

The future-proof profession: In today's data-driven world, data scientists are in high demand across all industries. Their skills are so critical that Harvard named it the "sexiest job of the 21st century." With the even-growin volume and complexity of data, the demand for skilled data scientists is only going to skyrocket, making it a future-proof career path.

Data Science is open to a wide range of professionals and can be a powerful career booster. Explore Now

Module 1 - Introduction To Machine Learning And AI

Basic Concept
ML and AI introduction
Applications of ML and AI

Module 2 -Programming

Basic Programming
NLP Libraries - Spacy & Gensim
OpenCV & Tensorflow, Keras

Module 3 - Math Foundation

Basic Statistics
Sampling & Sampling Statistics
Inferential Stats : Hypothesis Testing
Linear Algebra
Vector Operation

Module 4 - Machine Learning And Ensemble Methods

Unsupervised K-Means & Hierarichal Clustering
Linear Regression
Logistic Regression
Evaluation Metrics
Train,Test & Validation Distribution
Gradient Descent
Decision Tree & KNN
Random Forest | Bagging & Boosting

Module 5 - Intro To Neural Network & Deep Learning

Deep Learning Importance [Strength & Limiltation]
Feed Forward & Backward Propagation
Neural Network Overview
Neural Network Representation
Activation Function
Loss Function
Importance of Non-linear Activation Function
Gradient Descent for Neural Network

Module 6 - Parameter & Hyperparameter

Practical Aspect
Train, Test & Validation Set
Vanishing & Exploding Gradient
Bias Correction
RMS Prop
Learning Rate

Module 7 - Computer Vision

Image Preprocessing
Introduction to Computer Vision ,Image, image transformation, filters, noise removal, edge detetction, non-max suppression , hysterisis
Advanced CNN concepts -1
"Object detection concepts, Bounding box, object detection models, landmark detection, RCNN, fast RCNN, faster RCNN, mask RCNN, YOLO pre-trained models, transfer learning , segmentation concepts"
Advanced CNN concepts -2
Advanced CNN models applications, face detection and recognition, different techniques in face recognition, style transfer

Module 8 -Speech Analytics

Speech Processing
"Introduction, Automated Speech Recognition (ASR) "
Speech Synthesis
Text to speech conversion, voice assistant devices, building alexa slkills

Module 9 - Generative

Autoencoders & Decoders
Basics of autoencoders, different types of autoencoders, applications with examples , variational autoencoders, intro to Gen AI
Generative Adverserial Networks (GAN's)
GAN basics and foundations, upsampling , GAN models, evaluate GAN Models, inception score, frechet inception distance, GAN loss functions
GAN's different types
Conditional GAN, Info GAN, Auxillary GAN etc, applications
GAN use cases
Image translation applications, cycle GAN concepts and implemenations

Module 10 -Reinforcement Learning

Reinforcement Learning
Intro to RL, Q learning, Exploration , exploitation
Reinforcement learning applications
Work with deep RL libraries, openai gym library, policy gradient concepts, Actor-critic methods, Proximal policy Optimization (PPO) and related concepts

Module 11 -RNN And LSTM

Forecasting deep learning
ARIMA, Deep learning models for forecasting (RNN, LSTM , Transformer applications)

Module 12 - NLP

Basic NLP concepts & models
"Introduction to Text Mining,VSM, word embeddings applications, RNN , GRU, LSTM models, Intro to Transformers, Attention (Elmo, BERT , T5)"
"Text Mining & NLP applications, Web Scraping"
"Word clouds and Doucument Similarity using cosine similarity, Named Entity Recognition, machine translation using hugging face libraries, Emotion Mining using different libraries, web scraping"
Naive Bayes
"Text classification using Naïve Bayes, frequqentists vs bayesian , apriori, posteriori distributions Bayesian estimators: posterior mean, posterior median"
"Advanced NLP models , Generative AI using LLM's"
"Intro to Transformers & Attention (Single Head,Multi Head) , pretrained models (GPT, BERT ,BART, T5) models with applications , examples using python Intro to Different types of Transformer encoder models- Basic BERT, RoBERTa, DistilBERT etc. Intro to Different types of Transformer decoder models-GPT, GPT2, other variants of GPT etc, GPT progress, calling OPENAI api's , LLM playgrounds Intro to Different types of Transformer sequence to sequence models-BART, T5"

Value Add Courses

Basics Of Python

Python Introduction - Programing Cycle of Python
Python IDE and Jupyter notebook
Data type
Code Practice Platform
create , insert , update and delete operation , Handling erros
Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
Decision making - Loops
While loop, for loop and nested loop
Number type conversion - int(), long(). Float ()
Mathametical functions , Random function , Trigonometric function
Strings- Escape char, String special Operator , String formatting Operator
>Build in string methods - center(), count()decode(), encode()
Python List - Accessing values in list, Delete list elements , Indexing slicing & Matrices
Built in Function - cmp(), len(), min(), max(), list comprehension
Tuples - Accessing values in Tuples, Delete Tuples elements , Indexing slicing & Matrices
Built in tuples functions - cmp(), len ()
Dictionary - Accessing values from dictionary, Deleting and updating elements in Dict.
Properties of Dist. , Built in Dist functions & Methods, Dict comprehension
Date & time -Time Tuple , calendor module and time module
Function - Define function , Calling function
pass by refernece as value , Function arguments , Anonymous functions , return statements
Scope of variables - local & global , Decorators and recursion
Map reduce and filter
Operator -Arthmatic ,comparison , Assignment ,Logical , Bitwise opeartor
Decision making - Loops
Import statemnts , Locating modules - current directory , Pythonpath
Dir() function , global and location functions and reload () functions , Sys module and subprocess module
Packages in Python
Files in Python- Reading keyboard input , input function
Opening and closing files . Syntax and list of modes
Files object attribute- open , close . Reading and writing files , file Position.
Renaming and deleting files
Pickle and Json
mkdir methid, chdir () method , getcwd method , rm dir
Exception Handling
Exception handling - List of exceptions - Try and exception
Try- finally clause and user defined exceptions
OOP concepts , class , objects , Inheritance
Overriding methods like _init_, Overloading operators , Data hiding
Regular Expressions
match function , search function , matching vs searching
Regular exp modifiers and patterns
SQLite and My SQL
Data base connectivity
Methods- MySQL , oracle , how to install MYSQL , DB connection
create , insert , update and delete operation , Handling erros
Introduction to Django framwork , overview , environment
Apps life cycle , creating views
Application, Rest API


Introduction to ChatGPT and AI
What is ChatGPT?
The history of ChatGPT
Applications of ChatGPT
ChatGPT vs other chatbot platforms
Industries using ChatGPT
The benefits and limitations of ChatGPT
Future developments in ChatGPT technology
Ethical considerations related to ChatGPT and AI
Types of AI and Chatgpt Architecture
Narrow AI
Strong AI
Chatgpt architecture
ChatGPT Functionalities and Applications
How does ChatGPT work?
ChatGPT Functionalities
Drafting emails and professional communication
Automating content creation
Resume and Cover letter creation
Research and information gathering
Brainstorming ideas and creative problem solving
Best Practices for Using ChatGPT
ChatGPT Prompt Engineering
What is Prompt Engineering?
Types of Prompts
Crafting Effective Prompts
Using ChatGPT to generate prompt

Basics Of Python

Foundations of Generative AI
Understanding AI: Descriptive vs Generative AI
The nature of AI, comparison of descriptive and generative AI
Introduction to Natural Language Processing
Core concepts in NLP, basics of language understanding
Understanding Large Language Models (LLMs)
Overview of LLMs, their scope, capabilities, and use cases
Introduction to GPT & Chat GPT
What is GPT, its evolution, and generational changes
Introduction to Prompt Engineering
The Fundamentals of Prompt Engineering
What is prompt engineering, its importance, types of prompts
Content Generation with Prompts
Strategies for generating text, video scripts, and music using prompts
Tokens and Parameters in AI
The role and understanding of tokens, introduction to prompt parameters
Advanced Prompt Techniques
Zero-Shot to Few-Shot Learning
Deep dive into zero-shot, one-shot, and few-shot learning
Fine-Tuning AI Model Parameters
Introduction to model parameter adjustments
Hallucinations and Bias in AI
Strategies for managing AI hallucinations and biases
Advanced Prompt Engineering Techniques
Methods for crafting complex prompts, incorporating creativity and context
Refining and Optimizing Prompts
Techniques for prompt refinement and iterative improvement
Evaluating and Testing Prompts
Metrics for Evaluating Prompts
How to assess prompt quality and performance
Human Evaluation of Prompts
Techniques for collecting and analyzing human feedback on prompts
Testing Prompts on Different Models and Tasks
How to assess prompt performance across different AI models and tasks
Generative AI
Understand the working of LLMs
LLM, Use Cases, Text Generation, Chatbot Creation, Foundations of Generative Models & LLM, Generative Adversarial Networks (GANs), Autoencoders in Generative AI, Significance of Transformers in AI, "Attention is All You Need" - Transformer Architecture, Reinforcement Learning, RLHF
Real World Applications and Case Studies
Real-world applications and case studies of LLMs
Fine Tuning and Evaluating LLMs
Instruction fine-tuning, Fine-tuning on a single task, Multi-task instruction fine-tuning, Model evaluation, Benchmarks, Parameter efficient fine-tuning (PEFT), PEFT techniques 1: LoRA, PEFT techniques 2: Soft prompts, Lab 2 walkthrough
Evaluation Matrix
Rouge1, BLEU, Meteor, CIDEr
MLOps Concepts
Intro to MLOps
What is MLOps, Different stages in MLOps, ML project lifecycle, Job Roles in MLOps
Design and Development
What is Development stage of an ML workflow , Pipelines and steps, Artifacts, Materializers, Parameters & Settings
Stacks & components, Orchestrators, Artifact stores, Flavors etc.
ML Server infrastructure, Server deployment , Metadata tracking, Collaborations, Dashboards

AI Simplifie isn't just about acquiring knowledge; it's about empowering you to succeed.


Live projects: Dive deep into real-world challanges from diverse industries, building a strong portfolio and gaining invaluable experience.
50+ labs and 30+ assignments: Put your theoretical knowlede to the test with practical exercises and assignments, solidifying your skills.
1500+ interview preparation questions: Conquer your anzieties and master interview techniques with our comprehensive interview prep program.


Industry-best trainers: Learn from exerienced professionals who are passionate about their fields and share their insights and practical knowledge.
Tailored curriculum: Choose from various curriculums designed to meet your specific goals and interests, ensuring you gain the most relevant skills.
Lifetime access: Stay ahead of the curve with our e-learning platform, where you can revisit course materials and access new resources anytime.


Dedicated placement cell: We work tirelessly to connect you with career opportunities and help you land your dream job.
Personalized guidance: Receive ongoing support throughout your training and beyound, with access to our dedicated team through WhatsApp, calls and emails.
Peer support: Connect with fellow learners, build a strong network, and benefit from mutual support and shared experiences.

AI Simplifie is more than just a training institute it's a community that invests in your success. Join us and unlock your full potential.


At Ai Simplifie, we believe that exceptional learning requires exceptional instructors. That's why our faculty is comprised of passionate trainers possessing 12+ years of industry experience. They are not just teachers; they are mentors, guides and industry veterans who share their knowledge and insights to empower you for success. Here's what sets our faculty apart:
Real-world expertise
Passionate about teaching
Dedicated to your success
Industry connections
Excellent communication skills


At AI Simplifie, we believe that a strong foundation in both theory and practical application is crucial for success. That's why we offer an exhaustive course curriculum designed to equip you with the necessary knowledge and hands-on experience to thrive in your chosen field. Our curriculum features:
Comprehensive coverage of all relevant topics
Industry-aligned content
Hands-on learning
Expert-led sessions
Flexible learning options


At AI Simplifie, we understand that theoretical knowledge alone is not enough. We believe in learning by doing, which is why we offer real-life projects and bootcamps as an integral part of our curriculum.


Exposure to real-world data challenges:
Application of theoretical knowledge:
Skill development
Collaboration and teamwork
Confidence building


Intensive training on specific skills
Expert guidance and mentorship
Networking opportunities
Rapid skill acquisition
Career advancement

Let's embark on this transformative journey together! Join us...

Learning Path: Your Roadmap to Success with Internship Program

This conprehensive path equips you with the knowledge, skills and confidence to land your dream job and thrive in your career.


Master the fundamentals through expert-led training and intership experience.
Apply you learning to real-world scenarios through case studies, projects and intershipp tasks.
Deepen your knowledge with specialized dives, customized learning paths and intership-specific learning modules.


Gain practical experience through real-world data projects, teamwork and internship responsibilities.
Build your resume and portfolio with industry-relevant skills acquired through your internship.
Refine your communication, interview and negotiation skills through workshops, coaching and internship interactions.


Connect with industry leaders through networking events, guest speakers, and internship partnerships.
Receive personalized career coaching and launch a successful job search with internship guidance.
Transition from student to professional with confidence and adaptability, thanks to your internship preparation.


Cultivate a lifelong learning mindset and embrace continuous up skilling opportunities available through your internship and beyond.
Gain industry recognition and leverage your knowledge for impact through your internship projects.
Build a strong professional network through internship connections and fellow learners and connect with mentors for ongoing guidance.

Let's embark on this transformative journey together! Join us...