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Data Science Training in Hyderabad

This course structure provides a step-by-step introduction to data analysis, Python, machine learning, deep learning and AI without overwhelming students with technical details. It focuses on practical skills and real-world applications to make the content accessible to those without a technical background.

Course Duration

120 Days

Training Options

Classroom / Online



Why Should You Take Ai Data Science Course?

Ai Data Science is in demand.

Data Science Ai & ML is rated as one of the happiest professions.

Data Science Engineer has a great career path.

Instructor-led Ai Data Science Live Classes

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Course Price at

INR 20,000/-


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About Course

In this course you will get an introduction to the main tools and ideas which are required for Data Scientist/Business Analyst/Data Analyst/Analytics Manager/Actuarial Scientist/Business Analytic Practitioners. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. The course is a combination of various data science concepts such as machine learning, visualization, data mining, programming, data mugging, etc. There are three components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is manual calculations will be shown on how formulae’s are used behind the logics. The third is a practical introduction to the tools that will be used in the program like R/Python Programming and EXCEL.

About Trainer

  • 10+ years of Real-time Experience in Data Science Engineer
  • Expert in Ai & Ml Language etc.,
  • Experience in blend of Real-time Projects & Training’s

Training Options


INR 20,000/-


INR 20,000/-

Video Based Learning

INR 20,000/-

Skills Covered

Introduction to Programming and Python Basics

  • What is programming? Introduction to Python.
  • Installing Python and an Integrated Development Environment (IDE).

Python Fundamentals

  • Writing and running your first Python program (Hello World!).
  • Understanding variables and data types. integers, floating-point numbers, strings.

Input and Output

  • Basic functions. type(), str(), int(), float(), and round()
  • Basic input and output using `input()` and `print()`.

Decision Making and Loops

  • Conditional statements. `if`, `elif`, and `else`.
  • Introduction to Boolean logic.
  • Looping concepts. `for` and `while` loops.

More on Loops and Pattern Printing

  • Using loops for repetitive tasks.
  • Advanced looping patterns
  • Pattern printing exercises

Lists and Functions

  • Working with lists. creating, indexing, slicing, and modifying.
  • Introduction to functions. defining and calling functions.
  • Passing arguments and returning values from functions.

Advanced Data Types

  • More about strings. string methods and formatting.
  • Introducing tuples and dictionaries.
  • Sets and their applications.

File Handling and Exceptions

  • Reading and writing files in Python.
  • Handling exceptions using `try` and `except` blocks.
  • Using `with` statements for better file management.

Libraries and Modules

  • Introduction to Python libraries and modules.
  • Using built-in modules (e.g., `math`, `random`, `datetime`).
  • Exploring external libraries using `pip`.

Getting Started with Data Analysis

  • Introduction to Data and Its Importance
  • Overview of Data Analysis and Its Applications
  • Elements of Python for Data Analysis
  • Understanding Data Sources and Types
  • Data Cleaning and Data Preparation

Understanding Data Tools and Libraries

  • Introduction to Data Analysis Tools (Excel, Google Sheets)
  • Introduction to Data Visualization
  • Overview of Python Libraries for Data Analysis
  • Data Analysis Using Excel
  • Visualizing Data with Charts and Graphs

Exploring Data with Pandas

  • Introduction to Pandas and Its Importance
  • Reading and Interpreting Data Tables
  • Basic Data Cleaning Techniques
  • Data Filtering and Sorting
  • Creating Simple Data Visualizations



Introduction to Descriptive Statistics

  • Overview of Descriptive Statistics
  • Understanding Measures of Central Tendency
  • Exploring Measures of Dispersion
  • Visualizing Data Distributions
  • Practical Data Analysis Tips and Tricks

Introduction to Python for Data Analysis

  • Introduction to Python for Data Analysis
  • Setting Up Your Python Environment
  • Basic Data Structures in Python (Lists, Dictionaries)
  • Reading and Writing Data Files (CSV, Excel) with Python
  • Practical Data Cleaning with Python

Data Analysis with Pandas

  • Introduction to Pandas and Its Role in Data Analysis
  • Reading and Displaying Data with Pandas
  • Data Cleaning and Preparation with Pandas
  • Data Filtering and Sorting with Pandas
  • Visualizing Data Using Pandas

Introduction to Data Visualization

  • Understanding Data Visualization Principles
  • Basic Data Visualization with Matplotlib
  • Creating Charts and Graphs in Python
  • Customizing Visualizations and Adding Labels
  • Practical Data Visualization Projects

Exploring Descriptive and Inferential Statistics

  • Descriptive Statistics with Python
  • Measures of Central Tendency and Variability
  • Introduction to Probability and Inferential Statistics
  • Hypothesis Testing Concepts
  • Real-World Data Analysis Case Studies

Understanding Machine Learning

  • Introduction to Machine Learning and Its Applications
  • Types of Machine Learning (Supervised, Unsupervised)
  • Introduction to Scikit-Learn
  • Building a Simple Machine Learning Model
  • Real-World Machine Learning Use Cases

Machine Learning Fundamentals

  • Introduction to Data and Data Preprocessing
  • Data Exploration and Visualization
  • Data Cleaning and Handling Missing Values
  • Feature Engineering and Selection
  • Model Evaluation Metrics


  • Introduction to Classification Problems
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
  • k-Nearest Neighbors (k-NN)


  • Introduction to Regression Problems
  • Linear Regression
  • Polynomial Regression
  • Ridge and Lasso Regression
  • Building Regression Models


  • Introduction to Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN and Other Clustering Methods
  • Evaluating Clustering Performance

Dimensionality Reduction

  • Dimensionality Reduction Overview
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Implementing Dimensionality Reduction

NLP Basics

  • Introduction to Natural Language Processing
  • Text Preprocessing
  • Text Classification
  • Named Entity Recognition (NER)
  • Sentiment Analysis

Advanced NLP Topics

  • Word Embeddings (Word2Vec, GloVe)
  • Sequence-to-Sequence Models
  • Chatbots and Conversational AI
  • Text Generation with Recurrent Neural Networks (RNNs)
  • NLP Applications and Case Studies

Image Basics

  • Introduction to Computer Vision
  • Image Preprocessing and Enhancement
  • Image Segmentation
  • Object Detection
  • Image Classification

Advanced Topics

  • Convolutional Neural Networks (CNNs)
  • Transfer Learning in Computer Vision
  • Face Recognition
  • Image Generation with Generative Adversarial Networks (GANs)
  • Computer Vision Applications

Reinforcement Learning Basics

  • Introduction to Reinforcement Learning
  • Markov Decision Processes (MDPs)
  • Q-Learning
  • Deep Q-Networks (DQNs)
  • Policy Gradient Methods

AI Applications

  • Robotics and Autonomous Systems
  • Game Playing and AlphaZero
  • Autonomous Vehicles
  • AI in Healthcare
  • Ethical Considerations in AI

Deep Learning Fundamentals

  • Introduction to Deep Learning
  • Neural Networks and Backpropagation
  • Activation Functions and Optimization
  • Regularization Techniques
  • Implementing Neural Networks

Advanced Deep Learning Topics

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Models (GANs and VAEs)
  • Transfer Learning and Fine-Tuning
  • Deep Learning Applications

Ethical Considerations

  • AI Bias and Fairness
  • Privacy and Data Security
  • AI and Human Labor
  • AI Regulations and Policies
  • Responsible AI Development

Future Trends

  • Explainable AI (XAI)
  • Quantum Computing and AI
  • AI in Space Exploration
  • AI for Climate Change and Sustainability
  • Future Frontiers of AI
  • Exercises: Weekly practical coding exercises to reinforce learning.
  • Activities: Interactive activities and discussions to engage with course material.
  • Practice Programs: Weekly practice programming tasks to apply knowledge.
  • Assignments: Regular assignments to assess understanding and skills.
  • Mini-Projects: 4 Mini-projects to apply concepts and demonstrate proficiency.

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Data Science (Ai & ML)

QEdge Tech Data Science Professional Certificate Holders work at 100s of companies like


Sena Reddy
Sena Reddy
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This is the best institute for Data Science, They give placement assistance also, if you join in this institute your job struggles will end. Classrooms are very good. Recently I got placed through this institute. Let’s suggest joining people who are want to learn Data science with AI
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One of the best hub to prove yourself in data science engineer with supporting of friendly staff and faculty. Thank you qedge for giving me confidence, placement in good reputed company (caliber technologies) and good future.
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One of the best institute to learn Ai & ML course, It is the best opportunity to learn under exp faculty. they teaches very well. I got placement in middle of the course. I suggest to all learning data science in q- edge.

QEdge Training Features


Project Work



Resume Preparation

Interview Preparation

Resume Marketing

Placement Support

Frequently Asked Questions

Our Data Science Training is 120 Days duration.
Daily 1.5 hour theory + 1.5 hour practical session.
Monday to Saturday Sessions.
Any graduate BSC, BCom, B.Tech, MSC, MCA etc. with good communication skills can do this course and start your career as Data Science Engineer. And also working professionals (BPO / Banking / Sales / Customer Care etc.,) who want to change their domain
Our training will be a blend of theoretical and practical work on each topic. We also provide live exposure on projects and give assignments to improve your skill set.

Yes, you are going to work on real-time projects during learning to gain real-time projects experience.

After completion of training, you will receive a certificate of completion, which you can share with your friends, relatives, co-workers and potential employers.

Yes, you’ll be able to access your enrolled course materials through our Learning Management System. Practical Assignments, Day-to-Day Class videos and readings you can access through our LMS App.

Be Future Ready. Enroll Now

Structure your learning and get a certificate to prove it.

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