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Data Science Certification Course in Bangalore with AI & ChatGPT
- Earn Industry-recognized certification
- Build real-time projects with industry-aligned tools
- Live Interactive sessions from industry veterans
- Updated curriculum designed for AI-era
- Dedicated Job Assistance and Resume Building
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Success Stories – They Believed, Learned & Achieved!
Success Isn’t Luck – It’s Learning. They Chose to Upskill – Now They’re in the World’s Best Companies!
“Transitioning into analytics felt challenging at first, but steady learning and hands-on work made the shift smooth and achievable.”
Veeresh Kottargi 
“Focusing on real-world tasks made learning clearer for me — that practical approach shaped my career direction.”
Aman Bind
“I stopped doubting my potential the day I started building projects — that’s when everything began to change for me.”
Avina Garg
“I spent time understanding data workflows in depth, and that clarity helped me grow steadily in my career.”
Aniket Singh
“Every phase of my journey taught me one thing — when you keep upgrading yourself, new leadership opportunities naturally open up.”
Aditya Agrawal
“I focused on building practical skills and kept improving a little every day — that consistency made all the difference in my journey.”
Gopi Kiran
Our learners are working in leading organizations
Gain industry-ready skills and earn an official certificate from DataFlair.
Online Data Science Course in Bangalore Curriculum
Python
Stats
Data Science and ML with AI
1. Getting Started with Python Programming
- Introduction to Python and its role in modern technology
- Why Python is preferred for Data Science, AI, and automation
- Python Key features
- Difference between Platform-dependent & Independent Languages
- History of Python
2. Executing your first Python program
- Installing Python
- Setting up the development environment
- Writing and executing your first Python program
- Understanding how Python code runs internally
3. Python Data Types and Core Objects
- Python statements and indentation rules
- Rules for naming variables and identifiers
- Taking input from the user
- Python Data Types
- Python Variables
- Type conversion and type casting
4. Python Input and Output
- Taking input from the user
- Input functions in Python
- Output functions in Python
- Python Replacement Operator
- Printing with the format() Method in Python
5. Operators and Expressions in Python
- Arithmetic and assignment operators
- Comparison and logical operators
- Membership and identity operators
- Bitwise operators and number system basics
- Operator precedence and associativity
- Practice problems using operators
6. Conditional Programming in Python
- Decision-making using if statements
- if-else and if-elif-else conditions
- Nested conditional statements
- Loops and Iterative Programming
- while loop and for loop
- Loop control using break and continue
- Nested loops
7. Strings and Text Handling in Python
- Creating and accessing strings
- String slicing and indexing
- Important string methods
- String formatting using f-strings and format()
- Searching, replacing, and splitting text
8. Python Collections
- Lists and list operations
- Tuples and tuple usage
- Sets and unique data handling
- Frozen sets and immutability
- Dictionaries and key-value data
- Bytes and Bytearray Collections in Python
- Practical programs using collections
9. Functions and Modular Programming
- Need for functions in Python
- Creating and calling user-defined functions
- Function parameters and return values
- Positional, keyword, default, and variable-length arguments
- Local and global variables
- Function scope
- Practical function-based programming examples
10. Recursion in Python
- Concept of recursion
- Recursive function flow
- Base condition and recursive call
- Factorial using recursion
- Fibonacci series using recursion
- Number reversal using recursion
11. Lambda Functions
- Anonymous functions using lambda
- map(), and filter() in Python
- Using the reduce() function
12. Arrays, NumPy, and Numerical Data Handling
- Introduction to arrays in Python
- Difference between list and array
- Introduction to NumPy arrays
- Creating 1D, 2D, and multi-dimensional arrays
- Array indexing and slicing
- Array attributes: shape, size, ndim, dtype
- Object Referneces in Python
- Copy vs view in NumPy
- Matrices using NumPy
13. Searching and Sorting Basics
- Binary search logic
- Bubble sort implementation
- Practical searching and sorting examples
14. Object-Oriented Programming in Python
- Procedural vs object-oriented programming
- Classes and objects
- Instance variables and methods
- Constructor and init() method
- self keyword
- Encapsulation and data hiding
- Getters and setters
- Class variables and static methods
15. Advanced OOP Concepts in Python
- Inheritance and its types
- super() method
- Method overriding
- Polymorphism
- Operator overloading
- Abstract classes
- Interfaces in Python
- Composition and aggregation
- Is-A and Has-A relationships
- Practical OOP-based programs
16. Exception Handling in Python
- Errors vs exceptions
- Need for exception handling
- try, except, else, and finally blocks
- Handling multiple exceptions
- Raising exceptions manually
- Creating custom exception
17. File Handling and Data Storage
- Introduction to file operations
- Reading and writing text files
- Appending data to files
- Working with file modes
- Using the with statement for safe file handling
- Reading files line by line
- Counting words, lines, and characters
- Binary file handling
- Working with pickle files
- Reading and writing CSV files
- Using seek() and tell()
18. Python Libraries for Data Science
- NumPy for numerical computing
- Pandas for data analysis
- Matplotlib for plotting graphs
- Seaborn for statistical visualization
- Scikit-learn for Machine Learning
- Working with DataFrames
- Creating charts and plots
- Preparing data for analysis and ML models
1. Introduction to Statistics for Data Science
- Meaning of statistics in simple terms
- Why statistics is important for Data Science
- Descriptive and inferential statistics
- Qualitative and quantitative data
- Examples of statistics in real-world data projects
2. Fundamentals of Statistics
- Measures of Central Tendency: Mean, Median, Mode
- Dispersion Statistics
- Range, Variance and Standard deviation
- Coefficient of variation
3. Distribution and Data Shape
- Skewed distribution
- Normal distribution
- Kurtosis
- Visual understanding of distribution
4. Statistical Graphs and Data Visualization
- Histogram
- Bar chart
- Pie chart
- Box plot
- Scatter plot
- Line chart
- Correlation heatmap
5. Probability Fundamentals
- Understanding Probability
- Probability basics
- Axioms of Probability
- Conditional probability
- Bayes’ theorem and its Application
6. Probability Distributions
- Random variables
- Discrete and continuous probability distributions
- Probability mass function
- Probability density function
- Cumulative distribution function
- Binomial distribution
- Poisson distribution
- Normal distribution
- Uniform distribution
- Exponential distribution
- Log-normal distribution
7. Sampling and Statistical Inference
- What is Sampling?
- Sampling distribution
- What is hypothesis testing?
- Null hypothesis and alternative hypothesis
- Type I and Type II errors
- Significance level
- p-value interpretation
- Confidence interval
- Margin of error
8. Regression
- Regression and Its Types
- Root Mean Square Error
- K Nearest Neighbor Algorithm
- Support Vector Machine
- Random Forest Algorithm
- Clustering
1. Data Science Foundations
- What is Data Science?
- Why companies use Data Science
- Data Science vs Data Analytics
- Data Science vs Artificial Intelligence
- Data Science vs Machine Learning
- Role of Python in Data Science
- Data Science project lifecycle
- Real-world examples from different industries
2. Career Roadmap in Data Science
- Skills required to become a Data Scientist
- Demand for Data Science
- Career paths in Data Science
- Roadmap for AI & Data Science
- Career Growth in Data Science
- Salary Trends in Data Science
- Data Science Future
3. Data Science Project Workflow
- Understanding the business problem
- Defining project objectives
- Collecting relevant data
- Understanding dataset structure
- Cleaning and preprocessing data
- Performing Exploratory Data Analysis
- Roles for Data Science: Data Engineer, Data Scientist, ML Engineer
4. Analytics
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
5. Data Science Industry Applications
- Data Science in banking and finance
- Data Science in healthcare
- Data Science in retail and e-commerce
- Data Science in education
- Data Science in logistics and supply chain
- Data Science in manufacturing
- Data Science in agriculture
- Data Science in marketing and advertising
6. Exploratory Data Analysis
- Meaning of Exploratory Data Analysis
- Importance of EDA
- Numerical and categorical feature analysis
- Univariate analysis
- Bivariate analysis
- Multivariate analysis
- Outlier detection and treatment
- Correlation analysis
- Feature engineering basics
- Extracting business insights from data
7. Introduction to Machine Learning
- What is Machine Learning?
- How Machine Learning is different from traditional programming
- AI, ML, Deep Learning, and Data Science relationship
- Features and labels
- Training data and testing data
- Model training and prediction
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Overfitting and underfitting
- Bias and variance
- Machine Learning workflow
- Project Architecture of Data Science
8. Supervised Learning – Regression
- What is regression?
- Simple linear regression
- Polynomial regression
- Ridge regression
- Lasso regression
- Regression evaluation metrics
- MAE, MSE, RMSE, and R² score
- Regression business use cases
9. Supervised Learning – Classification
- What is classification?
- Types of Classification
- Data Preparation
- Difference Between Label Encoding & One-Hot Encoding
- Feature Scaling
- Train-Test Split
- Classification vs regression
- Data preparation for classification
- Handling imbalanced datasets
- Model Evaluation Metrics
- Accuracy, precision, recall, and F1-score
- Confusion matrix
- ROC curve and AUC score
- Overfitting & Underfitting
- Classification use cases in industry
- Regularization Techniques
10. K-Nearest Neighbors
- Introduction to KNN
- How KNN makes predictions
- KNN for classification
- KNN for regression
- Distance metrics in KNN
- Choosing the value of K
- Categorical Features Handling
- Advantage & Limitations of KNN
- Evaluation Metrics
- Model Tuning in KNN
- Cross-Validation in KNN
11. Decision Tree
- Introduction to Decision Tree
- How a Decision Tree makes decisions
- Decision Tree Components
- Gini impurity
- Entropy
- Information gain
- Overfitting in Decision Trees
- Pre-pruning and post-pruning
- Hyperparameter tuning
- Decision Tree advantages and limitations
- Evaluation Metrics
- Hyperparameter Tuning
- Cross-Validation
12. Random Forest
- Introduction to Random Forest
- Random Forest over a Single Tree
- Concept of Random Forest
- Working of of Random Forest
- Important Random Forest hyperparameters
- Random Forest advantages and limitations
- Random Forest use cases in finance, healthcare, and marketing
13. Unsupervised Learning
- What is unsupervised learning?
- Difference between supervised and unsupervised learning
- When to use Unsupervised learning
- Unsupervised Learning Application
- Important Algorithms in Unsupervised Learning
- Real-world uses of unsupervised learning
- Evaluation metrics in unsupervised learning
14. Clustering Algorithms
- What is clustering?
- Classification vs clustering
- K-Means clustering
- Elbow method
- Hierarchical clustering
- Agglomerative clustering
- Divisive clustering
- Dimensionality reduction in ML
- Clustering real-world use cases
15. Machine Learning in Business
- Predictive modeling for business problems
- Customer segmentation
- Personalization systems
- Time series forecasting overview
- Anomaly detection
16. Scikit-Learn for Machine Learning
- Introduction to Scikit-learn
- Features of Scikit-learn
- Installing and importing Scikit-learn
- Building end-to-end ML workflows with scikit-learn
17. Natural Language Processing
- What is Natural Language Processing?
- Why NLP is important in AI
- NLP applications in real life
- Text cleaning and preprocessing
- Sentiment analysis
18. Artificial Intelligence
- Introduction to Artificial Intelligence
- Introduction to Generative AI
19. Deep Learning Foundations
- What is Deep Learning?
- Importance of AI
- Deep Learning vs Machine Learning
- Why Deep Learning is useful for complex data
- Deep Learning Applications
- Case studies of Deep Learning
- Key Terminologies of Deep Learning
- Layers in neural networks
- Role of neuron layers
- Weights and biases
- Activation functions
- Forward propagation
- Backpropagation
- Loss function
- Gradient descent
- Optimizers
- ResNet50
- Vanishing gradients
- Transfer Learning
- DenseNet121
- RNN in ML
- ANN vs CNN vs RNN
- LSTM in ML
- RNN vs LSTM
20. Frameworks of Deep Learning
- Practical implementation with TensorFlow
- Practical implementation with Keras
- Practical implementation with PyTorch
- TensorFlow vs PyTorch
- PyTorch Features
- Real-world use cases of PyTorch
21. Neural Networks Types
- Fully connected neural networks
- CNN for images
- RNN for sequential data
22. Prompt Engineering
Module 1: Introduction of Prompt Engineering
- Prompt Engineering
- Importance of working with AI models like GPT and DALL·E.
- Applications pf prompt engineering across industries
Module 2: Prompt Design Fundamentals
- Types of prompts: Instruction-based prompts, Conversational prompts, and Contextual prompts
- Strategy for effective prompts: Clarity, specificity, and relevance.
Module 3: Challenges of Prompt Engineering
- AI model Limitations
- Ethical considerations while designing and using pro
23. Generative AI
Module 1: Generative AI Introduction
- What is Generative AI?
- Traditional AI Vs Generative AI.
- Real-world applications of Generative AI:
- Chatbots and conversational AI.
- Image generation.
- Video synthesis and editing.
Module 2: Foundations of Generative AI
- Understanding Generative Models:
- How the models create new data.
- Types of Generative AI:
- GANs (Generative Adversarial Networks).
- VAEs (Variational Autoencoders).
- Transformers (foundation of GPT, DALL·E, and other tools).
Module 3: Popular Tools and Frameworks for GenAI
- Deep Learning frameworks
- APIs and tools of GenAi
- Working with pre-trained models for task simplification
24. Large Language Models
- Introduction to Large Language Models
- Features of LLMs
- Importance of LLMs
- Role of LLMs in modern AI applications.
- Real-world use cases of LLMs
- LLMs Fundamentals
- Understanding the work of LLMs
- Transformer Architecture.
- Attention Mechanism.
- Tokenization process.
- Data preprocessing
- Training Datasets overview
- Natural Language Processing
- Use Cases of LLMs
- LLMs Application
25. Agentic AI & AI Agents
- Fundamentals of Agentic AI
- Evolution of Agentic AI: From reactive systems to proactive agents
- Working of Agentic AI
- Agentic AI Applications
- Distinction of Agent vs. Model vs. Tool
- Agentic AI and AI Agents difference
- System of Multi-Agent
- Characteristics of AI agents: autonomy, proactivity, adaptability
- Use Cases of AI Agents
- Architecture of Agent
- Open-source agent framework
26. Data Engineering
- Basics of Data Engineering
- Big Data Introduction
- Distinction between Big Data and Data Science
- Key features of Big Data
- Five Vs
- Importance of Big Data
- Big Data Evolution and Its Role
- Use Cases of Big Data in Industries
27. Hadoop
- Hadoop basics
- Installing Hadoop
- Hadoop architecture
- Understanding Hadoop Ecosystem: Hive, Pig, HBase, and Sqoop
- Working of HDFS and MapReduce
- HDFS Setting and Navigation
- HDFS Commands for File Management
- How MapReduce Works
28. PySpark
- Spark Introduction
- RDDs
- Data Processing using PySpark
- Understanding Spark SQL
Course Objectives
Why
What
Prerequisites
Who
Course Benefits
Jobs
Companies
Objectives of the Data Scientist Course in Bangalore
- Data Collection, Analysis, and Interpretation: Learners can collect, process, and analyze large, complex datasets to find useful insights.
- Statistical Methods and Data Visualization: Learn statistical techniques and visualization tools required for interpreting data and effectively communicating findings.
- Programming Languages: Develop proficiency in programming languages such as Python and R, which are important for data manipulation and analysis.
- Machine Learning and Predictive Modelling: Combine knowledge of machine learning algorithms and predictive modelling to generate models for classification, regression, clustering, and recommendation systems.
- Model Development and Evaluation: Learners should be equipped to develop, tune, and validate machine learning models to ensure optimal performance.
- Ethical Data: Practices and understanding the importance of ethical data management, including data privacy, security, and fairness in data science practices.
- Career readiness: Prepares students for meaningful employment by providing them with the skills and knowledge to handle innovation and solve real-world problems using data.
The ultimate goal of the data science classes in Bangalore is to empower students to use data effectively to drive innovation, gain competitive advantages, and address complex challenges across various industries through informed data-driven strategies.
Why should you learn Data Science?
Learning Data Science is highly valuable due to the massive demand for skills that convert raw data into actionable insights, leading to high-paying, versatile career opportunities across industries like technology, healthcare, and finance. It drives innovation, enables better decision-making, and allows for solving complex, real-world problems.
What is Data Science?
Data science is the field of study that uses scientific methods, algorithms, and programming to gain knowledge and insights from data. It’s the process of collecting, analysing, and interpreting large amounts of data to uncover hidden patterns and trends. Data scientists construct questions around specific data sets and then use tools such as data analytics, machine learning, and artificial intelligence (AI) to find patterns, create predictive models, and develop insights that guide business decision-making.
What to do before you begin a Data Scientist course in Bangalore?
While the program is comprehensive, it is designed for professionals who have a foundational understanding in certain areas to ensure they can keep pace with the curriculum. The prerequisites are not intended to be a barrier but to establish a baseline that allows the course to delve into complex data science topics effectively.
- A basic knowledge of mathematics and statistics is required, as these concepts are fundamental to machine learning and data analysis.
- Applicants should have a basic understanding of any programming language, which will help them quickly grasp Python.
- A curious mind and a desire to learn about data science will help make this course interesting.
Who should choose this Data Science course in Bangalore with placement?
These online Data science classes in Bangalore are beneficial for a wide range of individuals who are willing to work with data and get meaningful insights.
- IT Graduates
- Aspiring Analysts
- Statistical Research Enthusiasts
- Data Engineers
- Aspiring Economists
- Business Owners and Entrepreneurs
- Marketers
You can get the following benefits on enrolling in the Best Data Science courses in Bangalore:
- Unlock doors of immense career opportunities with industries and companies looking for data experts.
- Get along with real-world projects that bring practical experience and build a professional portfolio.
- Increase your efficiency by becoming capable of handling data and interpreting it.
- Gain expertise in widely used software and programming languages like Python.
- Learn to solve business-related problems by implementing data techniques.
- Boost your earnings and increase your value in the job market with Data Science skills.
Job opportunities after Data Science training in Bangalore
Gain the skills to acquire various job roles, some of which are mentioned here:
- Data Consultant
- AI/ML Researcher
- Data Product Manager
- Healthcare Data Analyst
- Machine Learning Engineer
- Business Intelligence Analyst
- Data Engineer
- Quantitative Analyst (Quant)
Our students are working in leading organizations
Data Science Online Training in Bangalore FAQs
Why is data science important?
Data science is the field of study that uses scientific methods, algorithms, and programming to gain knowledge and insights from data. It is important because it allows businesses to make informed decisions based on data, solve complex problems, and gain a competitive advantage.
Who should consider enrolling in the online Data Science Classes in Bangalore?
This is the Best Data Science course in Bangalore with placement that can benefit anyone interested in working with data, including students, professionals from various industries like IT, finance, and healthcare, and those willing to switch careers.
What fundamental skills are covered in this Data Science training in Bangalore?
The Data Science course with placement in Bangalore covers data analysis, visualization, statistical analysis, machine learning, programming (primarily in Python), and domain knowledge. It also focuses on critical thinking and problem-solving skills.
How long does an average Data Science course last?
The online Data Science training in Bangalore is designed to be self-paced. You can complete the course according to your schedule because of the flexibility feature of this course.
Do I need programming or math experience to enroll?
Even though it is not compulsory to have programming or math experience, having these skills will help you in smooth learning.
What types of employment opportunities can I expect after completing this Data Science course?
You can choose from several jobs, such as becoming a data scientist, machine learning engineer, business intelligence analyst, and many more. This Data Science course in Bangalore with placement unlocks various fields and exciting job roles.
Does the course include real-world projects?
Yes, this best data science courses in bangalore includes real-world projects with hands-on training and practical exercises to make you ready for jobs. These projects will help build a strong portfolio and attract potential employers.
What prerequisites are needed before enrolling?
This Data Science course with placement in Bangalore is designed for beginners, so that you can learn from the basics and build foundational skills. But having some knowledge about programming and basic math, like algebra and statistics, will be helpful.
Is the schedule suitable for working professionals?
This Data Science course is scheduled to meet the requirements of working professionals. As this is a self-paced course, you can access this course at any time.
Will I learn how to analyze data and build predictive models?
This course will cover theoretical as well as practical implementation, in which you will learn about predictive modelling, analyzing and processing data, prediction analysis, and many more.
