Best AI driven Data Science Course in Bangalore
Industry-based, job-oriented, hands-on interactive Data Science and AI course in Bangalore with Machine Learning, AI tools, and real-time projects. Learn from working IT professionals with 16+ years of experience.
No-cost EMI available
Best Data Science Course in Bangalore with Placement | Certification & Job-Oriented Training
Are you looking for the best Data Science Course, AI Data Science program, or a Data Scientist Course with practical training? At Technogeeks, we help students and working professionals to gain the skills that you need to build your successful career in Data Science, Artificial Intelligence, and Data Analytics.
Whether you are searching for a Data Science Course Near Me, Data Analytics Courses Near Me, Data Analyst Course Near Me, Technogeeks offers industry-focused training with live projects, expert mentors, and placement assistance.
Our AI and Data Science Course is designed according to the latest industry requirements. You will learn how to collect, clean, analyze, and visualize data while building Machine Learning and AI models used by top companies.
Who Can Join?
Our Data Science and AI Course is comfortable for:
- Freshers
- College Students
- Working Professionals
- Software Engineers
- Data Analysts
- Business Analysts
- IT Professionals
- Career Switchers
- Non-Technical Background
No prior programming experience is required. The course starts from the basics and it will cover all advanced concepts.
Career Opportunities After This Data Science Course
After completing the Data Science and Artificial Intelligence Course, you can easily apply for these roles such as:
- Data Scientist
- Data Analyst
- AI Engineer
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Engineer
- Python Developer
- AI Research Associate
These are the fastest-growing technology careers, with increasing demand and industries like finance, healthcare, retail, e-commerce, manufacturing, and IT services.
Data Science Certification
After completion of the training, learners receive a Data Science Certification from Technogeeks. The certification validates your practical skills and can strengthen your resume when you will apply for jobs.
Online & Classroom Data Science Training
If you're searching for a Data Science Course Online with Placement, AI Course in Bangalore, AI Courses in Bangalore, or Data Science and Analytics Courses, Technogeeks provides both online and classroom training with the same practical curriculum, mentor support and placement assistance until you do not get placed.
Start your Data Science Course journey with Technogeeks and gain the practical knowledge, confidence, and experience needed to build a successful career in Data Science and Artificial Intelligence.
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
Book Your Seat Today!
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
To become a data scientist with AI skills. You have to start with concepts like Python, statistics, SQL, machine learning, and AI. Then focus on working on real-world data science projects and also learn how to use AI tools for automation. Then make a strong portfolio. Our AI-integrated Data Science course from Technogeeks Bangalore helps you become job-ready.
Following are salary ranges for data scientist roles
| Data Scientist Level | Median Salary | Typical Salary Range |
|---|---|---|
| Data Scientist – India | ₹14 LPA | ₹9–22 LPA |
| Junior Data Scientist – India | ₹7 LPA | ₹4.5–11 LPA |
Source - Glassdoor's 2026 India salary data
Yes! Python & SQL are main skills for Data Science. But there is no need to have in-depth knowledge about coding. At Technogeeks data science institute in Bangalore, You will learn coding, analytics & machine learning from basic to advanced level. Start learning. Start building. Start your Data Science career!
Freshers can typically expect around ₹4–8 LPA, depending on skills, projects, company, and interview performance. TechnoGeeks Data Science Training helps you build job-ready skills in Python, SQL, Machine Learning, Data Analytics & real-world projects.
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Data Science Certification Training locations in Bangalore:
Indiranagar [560008], Koramangala [560034], BTM Layout [560068], Whitefield [560066], Electronic City [560100], Marathahalli [560037], HSR Layout [560102], Jayanagar [560041], JP Nagar [560078], Banashankari [560050], Rajajinagar [560010], Malleshwaram [560003], Yelahanka [560064], Hebbal [560024], RT Nagar [560032], Kalyan Nagar [560043], Kengeri [560060], Basavanagudi [560004], Vijayanagar [560040], Bellandur [560103], Sarjapur Road [560035], Mahadevapura [560048], KR Puram [560036], Hennur [560043], Nagawara [560045], Bannerghatta Road [560076], Bommanahalli [560068], Yeshwanthpur [560022], Peenya [560058], Ulsoor [560042]
Locations Offered:
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