Best Data Science Course in Delhi
Master Data Science, Machine Learning, Generative AI, AI Tools, Python, and real-time projects through hands-on, industry-focused training. Learn from working IT professionals with 16+ years of experience.
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Certified Data Science Course in Delhi | AI, Machine Learning & Job-Oriented Training
Looking for the best Data Science course in Delhi, Data Science institute in Delhi, or a Data Science course near me? Technogeeks Software Training Institute (AI Academy) provides a job-ready certified Data Science with AI course in Delhi NCR with practical, industry-focused training in Python, SQL, Statistics, Machine Learning, Artificial Intelligence, Generative AI, Power BI, Data Analytics, and real-time projects.
Our Data Science offline course in Delhi is Best for beginners, freshers, working professionals, career switchers, and students. Also this Data Science training in Delhi is suitable if you are from an educational background in B.Com, M.Com, BBA, MBA, Economics, Statistics, Finance, and other non-technical backgrounds.
Get hands-on experience through real-time Data Science projects, practical assignments, industry case studies, resume building, AI Tools, mock interviews,certification and interview preparation. If you are searching for a Data Science course in Rohini or the best institute for Data Science, Technogeeks provides a practical learning path designed to make you industry-ready.
After completing Technogeeks Online Data Science course Training in Delhi NCR, you can apply for following job roles like :
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- AI Engineer
- Data Engineer
- Business Analyst
Key areas: Data Science | Python | Machine Learning | AI | Generative AI | SQL | Power BI | Business Analytics | Real-Time Projects | Interview Preparation
Data Science course fees and duration depend on the selected program and training format. Contact Technogeeks for the latest Data Science fees, course duration, offline/online batches, and placement support.
Course Curriculum
- What is Python and brief history
- Why Python and who use Python
- Discussion on Python 2 and 3 Unique features of Python
- Discussion on various IDE's
- Demonstration of practical use cases
- Python use cases using data analysis
- Installing python Setting up Python environment for development
- Installation of Jupyter Notebook
- Setting up Python environment for development
- How to access python course material using Jupyter
- Write your first program in python
- Python built-in functions
- Number objects and operations Variable assignment and keywords String objects and operations Print formatting with strings
- List objects and operations
- Tuple objects and operations
- Dictionary objects and operations
- Sets and Boolean
- Object and data structures assessment test
- Introduction to Python statements
- If, elif and else statements
- Comparison operators
- Chained comparison operators
- What are loops
- While loops
- Useful operators
- List comprehensions
- Statement assessment test
- Game challenge
- Methods What are various types of functions
- Creating and calling user defined functions
- Function practice exercises
- Lambda Expressions
- Map and filter
- Nested statements and scope
- Args and kwargs
- Functions and methods assignment
- Milestone Project (Making tic-tac-toe in python)
- Process files using python
- Read/write and append file object
- File functions
- File pointer and operations
- Introduction to error handling
- Try, except and finally
- Python standard exceptions
- User defined exceptions
- Unit testing
- File and exceptions assignment
- Python inbuilt modules
- Creating UDM-User defined modules
- Passing command line arguments
- Writing packages
- Define PYTHONPATH
- Name and Main
- Object oriented features Implement
- Object oriented with Python
- Creating classes and objects
- Creating class attributes
- Creating methods in a class
- Inheritance
- Polymorphism
- Special methods for class
- Assignment - Creating a python script to replicate deposits and withdrawals in a bank with appropriate classes and UDFs.
- Collections module
- Datetime module
- Python debugger
- Timing your code
- Regular Expressions
- StringIO
- Python decorators
- Python generators
- Python inbuilt modules
- Install packages on python
- Introduction to pip, easy install
- Multithreading
- Multiprocessing
- SQL integration with Python
- Table operations in SQL using Python
- CRUD operations in SQL
- Working on multiple tables using Python and SQL
- What is SQL?
- Why we need SQL Integration with Python
- Data types in SQL
- DDL, DML, TCL sub languages in SQL
- Significance and type of Joins in SQL
- Where clause in SQL
- Group by clause in SQL
- Create command in SQL
- Insert command in SQL
- Select command in SQL
- Select command variants in SQL
- Update command in SQL
- Delete command in SQL
- Drop command in SQL
- Truncate command in SQL
- REST principles
- Creating application endpoints
- Implementing endpoints
- Using Postman for API testing
- Python , Database and Front end integration concept, implementation
- Commit and rollback concept in SQL
- Introduction in Excel
- Data Cleaning & Preparation
- Formatting & Conditional Formatting
- Lookup Function
- Analyzing data with Pivot Tables
- Charts
- Data Visualization/Dashboarding using excel
- Data Analysis using statistics
- Lookup Function
- Introduction to data analysis
- Data analysis and Artificial Intelligence Bridge and connecting it to database.
- Introduction to Data Analysis libraries
- Data analysis introduction assignment challenge
- Why Data analysis?
- Introduction to Numpy arrays
- Creating and applying functions
- Numpy Indexing and selection
- Numpy Operations
- Exercise and assignment challenge
- Introduction to Series
- Introduction to DataFrames
- Data manipulation with pandas
- Missing data
- Groupby
- Operations
- Data Input and Output
- Pandas in depth coding exercises
- Text data mining and processing Data mining applications in Data engineering
- File system integration with Pandas
- Excel integration with Pandas
- Operations on Excel using dataframe
- Data aggregation on Excel Data
- Data visualization using Excel data
- Milestone Project – 2
- Plotting using Matplotlib Plotting
- Numpy arrays
- Plotting using object-oriented approach
- Subplots using Matplotlib
- Exercise and assignment challenge
- Matplotlib attributes and functions
- Matplotlib exercises
- Comparison Between Power BI & Programming Based Data Visualization
- Need of Power BI
- Types of Data Sources Supported by Power BI for Report Development
- How to Build Report & Dashboard in Power BI
- How to Build Charts in Power BI
- Data Visualization Using Power BI Features
- Types of Graphs
- Multiple Graphs Combinations
- Multiple File Formats Supported in Power BI
- Data Analysis Without Visualization
- Data Analysis With Visualization
- Need of Mathematics for Data Science
- Exploratory Data Analysis (EDA)
- Numeric Variables
- Qualitative and Quantitative Analysis
- Types of Data Formats
- Measuring the Central Tendency – The Model
- Measuring Spread – Variance and Standard Deviation
- Euclidean Distance
- Understanding Parametric Tests
- Confidence Coefficient
- Understanding Machine Learning
- Scope of ML
- Supervised and Unsupervised learning
- Introduction to Artificial Intelligence
- Introduction to Machine Learning
- Need of Machine learning in forecasting
- Demand of forecasting analytics in current industrial trends
- Introduction to Machine Learning Algorithms Categories
- Introduction to Regression
- Exercise on Linear Regression using sci-kit learn Library
- Project on Linear regression using USA_HOUSING data
- Evaluation of Linear regression using python visualizations
- Practice project for Linear regression using advertisement data set to predict appropriate advertisements for users.
- Introduction to Regression
- Project on Logistic regression using Dogs and horses' dataset
- Getting the correct number of clusters
- Standard scaling problem
- Practice project on KNN algorithm
- Intuition behind Decision trees
- Implementation of decision tree using a real time dataset
- Ensemble learning
- Decision tree and random forest for regression
- Decision tree and random forest for classification
- Evaluation of the decision tree and random forest using different methods
- Practice project on decision tree and random forest using social network
- Data to predict if someone will purchase an item or not
- Linearly separable data Non-linearly separable data
- SVM project with telecom dataset to predict the users portability
- Introduction to PCA Need for PCA
- Implementation to select a model on breast-cancer dataset
- Model evaluation
- Bias variance trade-off
- Accuracy paradox
- CAP curve analysis
- K-means Clustering Intuition
- Implementation of K-means with Python Using Mall Customers Data to Implement Clusters on the Basis of Spending and Income
- Hierarchical Clustering Intuition
- Implementation of Hierarchical Clustering with Python
- A priori theory and explanation
- Market basket analysis
- Implementation of Apriori
- Evaluation of association learning
- POC - To make a model to predict the relationship between frequently bought products together on the given dataset from a supermarket.
- Introduction to Natural Language processing
- NLTK Python library
- Data stemming technique
- Data Vectorization
- Exercise on NLTK
- POC - Apply NLP techniques to understand reviews given by customers in a dataset and predict if a review is good/bad without human intervention
- Neural Network and Deep Learning
- What is TensorFlow?
- TensorFlow Installation
- TensorFlow basics
- TensorFlow with Contrib Learn
- TensorFlow Exercise
- Keras Basics
- Pipeline implementation using Keras
- MNIST implementation with Keras
- Cloud integration with AWS cloud computing
- Hadoop
- HDFS
- Hive
- ETL Development with Python Scripting in Pandas
- Introduction to Generative AI
- Evolution of Generative AI in Industry
- Discriminative Models
- Generative Models
- Difference between Discriminative and Generative Models
- Overview of Foundation Models
- Overview of Large Language Models (LLMs)
- How Foundation Models and LLMs Power Today's AI Assistants and Tools
- Popular Generative AI Tools
- ChatGPT
- Gemini
- Copilot
- Neural networks foundations
- Understanding the Transformer architecture and attention
- Tokenization, embeddings and vector representations
- Calling LLM APIs with Python
- Retrieval-Augmented Generation (RAG) with custom data
- Vector databases and semantic search
- Building apps with LangChain
- Mini project - a document Q&A bot
- Zero-shot and few-shot prompting
- Chain-of-thought prompting, prompt patterns and
- Responsible AI use
- Project use cases Introduction
- Project Scenarios
- Project life cycle
- What is version controlling in project management
- What is GitHub
- Significance of GitHub in project management
- Code submission for testing and deployment
- Predictive analytics tools and techniques
- Project best practices
Our trainers are experts in their fields. They simplify complex concepts for the students and make them easy to understand. They solve each and every type of student's query. Their teaching method is more focused on real-time examples, preparing the students for industry interviews. Students will have one-on-one coaching sessions with them so that they will be able to ask questions at any time.
Key Highlights of Our Trainers:
- Certified Professionals with Over 8 Years of In-Depth Experience
- Imparted Knowledge to Over 2,000 Students Annually
- Demonstrated Strong Theoretical and Practical Expertise in Their Respective Domains
- Possess Expert-Level Subject Knowledge and Stay Current with Real-World Industry Applications
Why Choose Technogeeks?
Everything you need to launch your career in tech
Tools & Technologies You'll Master
23+ industry-standard tools covered in this program
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Start your journey with just ₹5,000. Watch a free demo first — pay the rest only after you're confident. No-cost installments available.
Training to Placement Journey
From your first session to landing your dream job — every step mapped out.
Frequently Asked Questions
Data Science is all about taking raw data and turning it into something useful that actually helps us to make better decisions and solve real time problems. It's a mix of collecting data and analyzing the data.
At Technogeeks, we don't teach only theory. You will work on real projects,to get hands-on training.
A person who works on data to find out beneficial information and solve real problems.
They collect the data from sources, study it, and use tools like python, sql, statistics, and machine learning to understand the data patterns and help companies to make better decisions.
Start by learning the basics, one step at a time:
Python → Statistics → SQL → Data Analysis → Machine Learning → Build Projects → Create a Portfolio
At TechnoGeeks, you'll learn through practical training, work on real projects, and follow a step-by-step learning path to become job-ready.
A Data Scientist typically:
- Collects and cleans data
- Finds patterns and trends
- Builds predictive models
- Uses machine learning algorithms
- Creates data-driven solutions
- Communicates insights to businesses
Simply put: They use the data to answer “What happened?”, “Why did it happen?” and “What could happen next?”
Begin with the fundamentals of Python, SQL, statistics, data visualization, and machine learning. Then build projects that demonstrate your skills.
Your roadmap can be:
Learn the skills → Build projects → Create your portfolio → Network → Apply for opportunities
TechnoGeeks can be your starting point for learning Data Science with a practical, career-focused approach.
These are highly valuable skills:-
- Python
- SQL & Databases
- Statistics & Mathematics
- Data Visualization
- Machine Learning
- Problem-Solving
- Business Understanding
- Real-World Projects
A degree can help you for graduation, but strong skills, projects, and practical experience can make a major difference.
Ready to start your Data Science journey? Learn, practice, build, and grow with TechnoGeeks.
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Data Science Certification Training locations in Delhi:
Connaught Place [110001], Karol Bagh [110005], Chandni Chowk [110006], Daryaganj [110002], Paharganj [110055], Rajinder Nagar [110060], Patel Nagar [110008], Punjabi Bagh [110026], Rajouri Garden [110027], Tilak Nagar [110018], Janakpuri [110058], Dwarka [110075], Vasant Kunj [110070], Saket [110017], Hauz Khas [110016], Greater Kailash [110048], Lajpat Nagar [110024], Kalkaji [110019], Malviya Nagar [110017], Vasant Vihar [110057], R K Puram [110066], Sarita Vihar [110076], Okhla [110020], Mayur Vihar [110091], Patparganj [110091], Laxmi Nagar [110092], Shahdara [110032], Krishna Nagar [110051], Ashok Vihar [110052], Rohini [110085], Pitampura [110034], Shalimar Bagh [110088], Model Town [110009], Civil Lines [110054], Nangloi [110041], Najafgarh [110043], Narela [110040], Sangam Vihar [110080], Paschim Vihar [110063], Hari Nagar [110064]
Locations Offered:
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