What is a data science course?

A data science course covers skills and tools like Python, SQL, and machine learning to analyze and interpret data.

Why should I choose this data science course in Bangalore?

Our course offers practical training, industry-relevant curriculum, and placement assistance to help you excel in data science careers.

What are the current trends in data science?

Emerging trends include AI integration, machine learning, cloud-based platforms, and advanced data visualization techniques.

Data Science, ML & AI Course in Bangalore

4.9 (22,514 reviews)

Comprehensive Data Science and AI training including Machine Learning, Deep Learning, and Data Analysis

Hands-on labs and real-world projects using AI tools

7-months of Intensive Training and LIVE Project mentoring

Unlimited access to Data Science Cloud Lab for practice

Key Features

Access to over 20 AI and Data Science tools and services.

Highly engaging sessions led by Data Science and AI certified experts.

Multiple industry-aligned capstone projects.

Speed Building Techniques and Mock tests.

Job + Personality oriented comprehensive programs.

Tips & tricks on how to use AI tools for efficient and optimized Data Science tasks.

Skills we will be Infusing!

Technical Skills

Interpersonal Skills

Quantitative Aptitude

Logical Reasoning

Articulateness

Interview Training

GD Training

Confidence building

Personality Development

Syllabus For Data Science , ML & AI Course in Bangalore

1. Introduction To Analytics
2. Analytics Real-Time Use Case
3. Tools & Framework Used

1. Introduction of Python
- Overview of Python
- Why Python
- When to Use Python
- Python Types
2. Python Installation
- Installing Python
- Setup in Windows, Unix, Linux
- Hands-On with Basic Python Commands
3. Basic Syntax / Construct of Python
- Identifiers
- Reserved Key Words
- Line/Indentation
- Multi-Line Statements
- Accessing/Parsing Command-Line Arguments
4. Python Variables & Types
- Variables and Naming rules
- Built-in Data Types in Python (Numeric, Int, Float, Complex)
- Sequence Types (List, Tuple, Range)
- Text Sequence (String)
- Set Types (Set, Frozensets)
- Mapping Types (Dictionary)
- Data Type Conversions (Built-in Types)
- Constants
5. Python Basic Operators
- Arithmetic
- Comparison
- Assignment
- Identity
- Logical
- Bitwise
6. Input & Output
- raw_input()
- input()
- Reading Dynamic Data from User
- Command Line Arguments
- Output Statement
7. Decision Making
- IF
- IF-ELSE
- Nested IF
- IF-ELIF-ELSE
- Single Statement Suites
8. Loops
- FOR LOOP
- WHILE LOOP
- INFINITE LOOP
- NESTED LOOP
- BREAK
- CONTINUE
- PASS
- Loop Control
9. Functions
- Built-in Functions
- User Defined Functions
- Functions Parameters
- Return Statement
- Returning Multiple Variables
- Types of Arguments (Positional, Keyword, Default, Variable Length)
- Variable Types (Global & Local)
- Recursive Function
- Anonymous Function
- Lambda Function
- Filter Function
- MAP Functions
- Reduce Function
- Function Alias
10. Function Decorators
- What Are Decorators?
- With & Without Decorators
- Decorator Chaining
11. Generators
- Introduction To Generators
- Advantage
- Performance Testing
12. Strings
- raw_input()
- input()
- Reading Dynamic Data from User
- Command Line Arguments
- Output Statement
13. List
- Accessing Values in Lists
- List Operations
- Built-in List Functions & Methods
- List Slicing
- List Comprehension
- Sorting & Reversing
14. Sets
- What is Set?
- Set Creation
- Accessing Set Values
- Adding Elements into Set
- Removing Elements
- Set Union Operation
- Set Intersection Operation
- Set Symmetric Difference
15. Tuples
- Tuples Introduction
- Accessing Tuple Values
- Tuple Operation
- Tuple Functions
- Deleting Tuples
16. Modules
- Modules Introduction
- Module - Aliasing
- Accessing Modules - Methods
- Module Import Possibilities
- Module Member - Aliasing
- Reloading Module
- Python In-Built Modules (Math, Random, etc.)
17. Python Packages
- What is a Package?
- Advantages of Package
- Package Creation
- Accessing Package Members
18. Errors & Exceptions
- Error Types
- What is Exception?
- Significance of Exception Handling
- Basic Exception handling
- Default Exception Handling
- Try With Multiple Except Block
- Control Flow (try-except) Block
- Types of Exceptions
- Defining and Using Custom Exceptions
19. Python Logging
- Logging Of Exceptions
- Logging Levels
- Logging Implementations
- Formatting Log Messages
- Adding Timestamp to Log Messages
- Changing Date & Time
- Root Logger
- Advanced Logging Features
- Logger With Configuration File
- Creating Custom Logger
- Advantages of Customized Logger
20. Debugging Using Assertions
- Debugging Using Assert
- Types of Assert Statements
- Exception Handling VS Assertions
21. Classes/Objects (OOPs)
- What is a Class?
- What is an Object?
- Reference Variable
- Self Variable
- Constructor
- Methods VS Constructors
- Types of Variables
- Accessing Instance Variable
- Deleting Instance Variable from Object
- Static Variable Handling
- Local Variables
- Types of Methods
- Setter & Getter Methods
- Enabling/Disabling Garbage Collector
- Destructors
- Finding Object References
22. Standard Libraries
- Operating System (OS) Interface
- Command Line Arguments
- Regular Expression (String Pattern Matching)
- Date and Time, Mathematics
23. Python File Handling
- Types of Files
- Opening File
- Closing File
- File Object - Properties
- Writing Data into Text Files
- Reading Data from Text Files
- WITH Statement
- seek() & tell() Methods
- Checking File Existence
- Handling Binary Files
- Handling CSV Files
- Zipping & Unzipping Files
- Directory Handling
- listdir() and walk()
- Pickling And Unpickling

1. Regular Expressions
- Introduction to Regular Expressions
- Area of Application
- re Module
- Character Class
- Pre-Defined Class
- Quantifiers
- Functions of re
- Special Symbols
- re.IGNORECASE
- Application Programs
2. NumPy
- Introduction
- Creating and Generating NumPy Array
- Random Modules
- NumPy Operations
- Indexing and Selection
- Broadcasting with NumPy Array
- Multi-Dimensional Array
- NumPy Functions
3. Pandas
- Introduction to Pandas
- Introduction to DataFrames
- Introduction to Series
- Creating DataFrames
- Row/Column Operations
- Conditional Selection
- Data Filtration
- Index Handling
- Multi-Level Indexing
- Handling Missing Values
- Grouping And Other Grouping Functionalities
- Data Aggregation
- Concatenate
- Merge
- Suffix
- Indicator
- Validate
4. MatPlotLib
- Introduction
- Different Methods of Creating Plots
- Functional Method of Plotting
- Single Plots
- Multiple Plots
- Sub-Plots
- Object Oriented Methods
- Figure Size, Aspect Ratio, DPI
- Figure Saving
- Legends, Colors, and Aesthetic Settings
- Types of Plots (Area Chart, Bar Chart, Line Chart, Box Plots, Hex Plots, KDE Plots, Time Series Visualization, And Many More)
5. SeaBorn
- SEABORN - Introduction
- Visualising Data Distribution
- displot()
- jointplot()
- pairplot()
- barplot()
- countplot()
- boxplot()
- violinplot()
- stripplot()
- swarmplot()
- Combining Plots
- heatmap()
- clustermap()
- FacetGrid()
- JointGrid()
- lmplot()
- Style & Color
- Scale and Context

1. Introduction
- Overview of Databases
- Installation of PostgreSQL
- Setting Up PgAdmin
- Creating Databases
2. SQL Fundamentals
- Select Fundamentals
- DISTINCT
- WHERE
- COUNT
- LIMIT
- ORDER BY
- BETWEEN
- IN
- LIKE
- WHERE VS HAVING
3. GROUP BY
- MIN MAX SUM & AVG
- GROUP BY
- HAVING
4. JOINS
- Overview of Joins
- AS Statement
- Join Types
- INNER JOIN
- OUTER JOIN
- LEFT OUTER JOIN
- RIGHT OUTER JOIN
- UNION
5. Advanced SQL Commands
- Timestamp and Extract
- Mathematical Functions
- String Functions
- Operators
- Subquery
- Self-Join
6. Database & Table Creation
- Data Types
- Primary Key
- Foreign Key
- Constraints
- Real-Time Table Creation Challenges
- INSERT
- UPDATE
- DELETE
- ALTER
- DROP
- CHECK Constraint
- NOT NULL Constraint
- UNIQUE Constraint
- VIEWS
7. PostgreSQL with Python
- Overview
- Psycopg2
8. Advanced Functions for Data Analytics
- Window Function
- Row_Number
- RANK
- DENSE_RANK
- LEAD
- LAG
- NTILE
- NTH_VALUE
- PERCENT_RANK
- FIRST_VALUE
- LAST_VALUE
9. CASE Statement
- CASE Expression
- CASE With Multiple Conditions
10. Common Table Expressions (CTE)
- What is CTE?
- Where to use CTE?
- Joining CTE with Table
- Using CTE with Window Function
- Advantages of CTE
11. SQL for Data Science & Business Intelligence
- Analyzing Customer Behavior
- Customer Value Analysis - Case Study
- LAG Function
- Order Status Analysis
- Predict Lifetime Value
- Identifying Cross-Selling Pattern
- Profit & Loss Analysis Project

1. Getting Started
- Introduction to Tableau
- Installation
- Basic Exercise
2. Tableau Basics - Your First Chart
- Business Challenge
- Connecting Tableau to Data Files
- Navigating Tableau
- Creating Calculated Fields
- Adding Colors
- Adding Labels & Formatting
- Exporting Worksheet
- Get the VIZ
3. Time Series Aggregation & Filters
- Introduction
- Data Extracts in Tableau
- Time Series
- Aggregation Granularity & Level of Details
- Area Chart - Highlighting
- Adding Filter & Quick Filters
4. Maps, Scatterplot & Dashboards
- Introduction
- Joining Data In Tableau
- Creating Map with Hierarchies
- Scatter Plot - Filter Application to Multiple Worksheets
- Creating Dashboard
- Interactive Action - Filter
- Interactive Action - Highlighting
5. Joining & Blending - Dual Axis Chart
- Introduction
- Joins
- Joins with Duplicates
- Joining on Multiple Fields
- Joining Data VS Blending Data
- Data Blending in Tableau
- Dual Axis Chart
- Creating Calculated Fields in Blend
6. Advanced Dashboards & Storytelling
- Fundamentals
- Dataset Connection with Tableau
- Mapping to Set Geographical Roles
- Creating Table Calculations
- Leveraging the Power of Parameters
- Tree Map Chart
- Customer Segmentation Dashboard
- Advanced Dashboard Interactivity
- Customer Segmentation Analysis
- Creating Storyline
7. Advanced Data Preparation
- Section Introduction
- Right Data Formats
- Data Interpreter
- Pivot
- Splitting Columns
- MetaData Grid
- Fixing Geographical Data Errors
8. Tableau New Features
- Introduction
- Challenge Startup Expansion Analytics
- Custom Territories Via Groups
- Custom Territories Via Geographical Roles
- Adding Highlighter
- Clustering in Tableau
- Cross-Database Joins
- Modeling With Clusters
- New Design Features
- New Mobile Features

1. Introduction to Statistics
- History of Statistics
- Basic Statistical Concepts
- Variables & Data
- Data Measurement Level
- Comparison of Four Levels
2. Grouping & Visualising Data
- What is Raw Data?
- Arranging & Grouping Data
- Frequency Distribution
- Class Mid Points
- Relative Frequency
- Cumulative Frequency
- Quantitative Data Graphs
- Histograms
- Frequency Polygons
- Qualitative Data Graphs
- Cross Tabulation
3. Describing Data - Statistics
- Measure of Central Tendency (Mean, Median, Mode, Percentiles, Quartiles)
- Measure of Variability
- Measure of Central Tendency & Variability - Grouped Data
- Measure of Shape (Skewness, Skewness & Relationship of Mean, Median, Mode, Coefficient of Skewness, Kurtosis)
4. Basics of Probability
- Introduction to Probability
- Basic Terminology in Probability
- Types of Probability
- Probability Rule
- Probabilities Under Conditions of Statistical Independence
- Probabilities Under Conditions of Statistical Dependence
- Bayes’ Theorem
5. Probability Distribution
- Discrete Probability Distribution
- Binomial Distribution
- Poisson Distribution
- Hypergeometric Distribution
- Continuous Probability Distribution
- Uniform Distribution
- Normal Distribution
- Exponential Distribution
6. Sampling Distribution Techniques
- Introduction to Sampling
- Random Sampling
- Non-Random Sampling
- Design Of Experiments
- Introduction to Sampling Distribution
- Relationship between Sample Size and Standard Error
7. Estimation
- Introduction to Estimation
- Point Estimates
- Interval Estimates
- Estimating Population Mean Using z Statistics
- Estimating Population Mean Using t Statistics
- Estimating Population & Proportion
- Estimating Sample Size
8. Testing Hypothesis
- Introduction to Hypothesis
- Types of Hypothesis
- Testing Hypothesis Using z Statistics
- Testing Hypothesis Using t Statistics
- Testing Hypothesis about a Proportion
- Testing Hypothesis About a Variance
- Measuring the Power of Hypothesis Test

1. Introduction - Machine Learning
- Introduction to Machine Learning
- Algorithm Classification: Supervised, Unsupervised, Reinforcement Learning
- Importing Important Libraries for Machine Learning
- Handling Missing Data
- Categorical Data
- Splitting Data Sets
- Feature Scaling
- Data Pre-processing
2. Simple Linear Regression
- Business Problem Statement
- Linear Regression Intuition
- Building Regression Model
- Testing the Model
- Use Case
3. Multiple Linear Regression
- Introduction to Linear Regression
- Business Problem Statement
- Model Building
- Dummy Variable Trap
- Testing Model
4. Basics of Probability
- Introduction to Probability
- Basic Terminology in Probability
- Types of Probability
- Probability Rule
- Probabilities Under Conditions of Statistical Independence
- Probabilities Under Conditions of Statistical Dependence
- Bayes’ Theorem
5. Probability Distribution
- Discrete Probability Distribution
- Binomial Distribution
- Poisson Distribution
- Hypergeometric Distribution
- Continuous Probability Distribution
- Uniform Distribution
- Normal Distribution
- Exponential Distribution
6. Sampling Distribution Techniques
- Introduction to Sampling
- Random Sampling
- Non-Random Sampling
- Design Of Experiments
- Introduction to Sampling Distribution
- Relationship between Sample Size and Standard Error
7. Estimation
- Introduction to Estimation
- Point Estimates
- Interval Estimates
- Estimating Population Mean Using z Statistics
- Estimating Population Mean Using t Statistics
- Estimating Population & Proportion
- Estimating Sample Size
8. Testing Hypothesis
- Introduction to Hypothesis
- Types of Hypothesis
- Testing Hypothesis Using z Statistics
- Testing Hypothesis Using t Statistics
- Testing Hypothesis about a Proportion
- Testing Hypothesis About a Variance
- Measuring the Power of Hypothesis Test

Overview of InfoDrafters

At InfoDrafters, located in BTM Layout, Bangalore, we specialize in delivering high-quality, industry-relevant training in Data Science, Machine Learning, AI, Data Analytics, AWS, and DevOps. Our programs are meticulously designed to equip students with both technical expertise and industry-ready skills, ensuring they are fully prepared to excel in their chosen careers.

InfoDrafters offers a dynamic learning environment focused on hands-on training through real-world projects. Our curriculum is continuously updated to reflect the latest trends and advancements in technology, ensuring students stay ahead of the curve. Here’s what makes InfoDrafters unique:

Industry-Experienced Trainers: All our courses are taught by professionals with real-world industry experience. They bring the latest practices and insights to ensure students receive the most relevant knowledge.
Comprehensive Curriculum: We provide a balanced curriculum that covers both theory and practical applications. Students work on live projects and case studies, building a strong portfolio to present to employers.
Flexible Learning Options: Choose from online live classes, in-person sessions, or hybrid learning formats. We offer weekend and evening batches for working professionals.
State-of-the-Art Infrastructure: Our BTM Layout facility is equipped with modern labs and the latest technologies. Online learners also benefit from a seamless virtual learning platform with recorded sessions available for revision.
Hands-on Projects: We emphasize practical learning through hands-on projects that simulate real-world industry challenges. Students use in-demand tools and technologies, gaining experience that directly translates to the workplace.
Personalized Mentoring: We cater to students at all levels, providing personalized support and feedback. Trainers offer one-on-one mentoring to guide students through their learning journey.
Career-Focused Training: InfoDrafters prioritizes job readiness, offering career guidance, resume-building workshops, mock interviews, and placement assistance. We are dedicated to helping students land their dream jobs.
Global Certification: Our courses come with globally recognized certifications, making students more competitive in the job market. InfoDrafters' certifications are highly regarded by employers.
Vibrant Community: At InfoDrafters, students join a thriving community of learners and professionals. Networking opportunities, peer-to-peer learning, and alumni connections enhance the learning experience.

With a proven track record of success, InfoDrafters continues to empower individuals and transform careers. Our commitment to quality education, career-focused training, and practical expertise makes us a leading institute for tech training in Bangalore.

Data Science Course Training in Bangalore

At InfoDrafters, located in BTM Layout, Bangalore, we are dedicated to providing top-tier training in Data Science, aimed at equipping students with both technical proficiency and industry-ready skills. Our Data Science program is meticulously structured to help students excel in this rapidly evolving field and to prepare them for successful careers in the world of data-driven decision-making.

Why Choose InfoDrafters for Data Science Training in Bangalore?

Industry-Experienced Trainers: Our Data Science course is taught by seasoned professionals with extensive industry experience. They bring the latest trends, practices, and insights from the field, ensuring you receive the most up-to-date and relevant knowledge.
Comprehensive Curriculum: The curriculum at InfoDrafters is balanced to cover both theoretical concepts and hands-on applications. Students will work on live projects and case studies, building a strong portfolio to present to employers.
Flexible Learning Options: We offer flexible learning modes, including online live classes, in-person sessions, and hybrid formats. Weekend and evening batches are available, making it easier for working professionals to participate.
State-of-the-Art Infrastructure: Our BTM Layout facility is equipped with modern labs and the latest technologies. For online learners, we provide a seamless virtual learning platform, with recorded sessions available for revision.
Hands-on Projects: InfoDrafters emphasizes practical learning through hands-on projects that simulate real-world industry challenges. Students get to use in-demand tools and technologies, providing valuable experience that can be directly applied in the workplace.
Personalized Mentoring: Our trainers offer one-on-one mentoring, catering to students at all levels of expertise. Personalized feedback and support ensure that every student progresses confidently through their learning journey.
Career-Focused Training: InfoDrafters is committed to making students job-ready by providing career guidance, resume-building workshops, mock interviews, and placement assistance. We aim to help students secure roles that match their career aspirations.
Globally Recognized Certification: Upon completion of our Data Science course, students receive an industry-recognized certification, boosting their credibility and employability.
Vibrant Community: By joining InfoDrafters, students become part of a thriving community of learners and professionals. Networking opportunities, peer-to-peer learning, and alumni connections enrich the overall learning experience.

Curriculum at InfoDrafters

Data Preparation and Manipulation
Data Visualization and Analytics
Data Modeling using Statistical and Machine Learning Techniques

Who Should Enroll at InfoDrafters?

This course is ideal for professionals looking to advance their skills in data science, AI, DevOps, and cloud technologies, including:

Engineers: Software engineers, mechanical engineers, and civil engineers looking to transition into data science, machine learning, AI, or cloud computing roles can benefit greatly from InfoDrafters’ hands-on training.
Data Analysts: Professionals currently working in data analysis roles who wish to deepen their knowledge in machine learning, data modeling, and AI applications will find InfoDrafters' advanced curriculum highly beneficial.
IT Professionals: Those already working in IT roles such as systems administrators, developers, or network engineers can upgrade their skills in areas like DevOps, AWS, and cloud computing to remain competitive in the evolving tech landscape.
Software Developers: Programmers and developers who want to move into the field of data science, AI, or DevOps and gain expertise in Python, automation, and machine learning will find InfoDrafters' courses to be a perfect fit.
Recent Graduates: Fresh graduates from computer science, IT, or engineering backgrounds looking to kickstart their careers in high-demand fields like data science, cloud architecture, and AI should enroll to build job-ready skills.
Career Changers: Professionals from non-technical backgrounds such as marketing, finance, or business who wish to shift into tech roles like data analytics, AI, or cloud-based services will find the learning path at InfoDrafters to be structured and accessible.
Project Managers & Team Leads: Managers who want to understand the technical landscape of their teams, particularly in fields like data science, cloud computing, and DevOps, can gain valuable insights to manage projects more effectively.
Entrepreneurs & Business Owners: Business professionals looking to leverage AI, data analytics, and cloud computing to improve business operations and make data-driven decisions can greatly benefit from InfoDrafters' courses.
Researchers & Academicians: Individuals involved in research or academic roles, especially those working on AI, machine learning, or big data projects, will find our in-depth curriculum helpful in staying updated with the latest industry trends and technologies.
Consultants: Freelancers or consultants who want to provide specialized services in data science, machine learning, cloud solutions, or DevOps can enhance their skill set to meet client demands.

InfoDrafters' courses are designed for anyone with a passion for learning and the desire to advance their technical skills, regardless of their current expertise level. Whether you're looking for career advancement, a complete career change, or simply expanding your knowledge, InfoDrafters has the right program for you.

Job Roles & Skills at InfoDrafters

Graduates of InfoDrafters can expect to explore a wide range of exciting career opportunities in the tech industry. Upon completing courses in Data Science, Machine Learning, AI, Data Analytics, AWS, and DevOps, you will be prepared to apply for various in-demand job roles, including:

Data Scientist: Analyze complex data sets, build predictive models, and help businesses make data-driven decisions. Skills in Python, R, SQL, and machine learning algorithms are crucial for this role.
Machine Learning Engineer: Develop, train, and deploy machine learning models to automate tasks and create intelligent applications. Skills in TensorFlow, PyTorch, and data pipeline management are essential.
AI Specialist: Work on building artificial intelligence applications, focusing on neural networks, deep learning, and natural language processing (NLP). AI specialists are crucial in fields like automation, robotics, and AI-driven products.
Data Analyst: Collect, process, and perform statistical analysis on data to provide actionable insights. Proficiency in Excel, SQL, data visualization tools (Tableau, Power BI), and Python is important.
DevOps Engineer: Bridge the gap between software development and operations teams by implementing continuous integration and continuous deployment (CI/CD) pipelines. Skills in cloud platforms (AWS, Azure), automation tools, and infrastructure management are essential.
Cloud Architect (AWS): Design and manage cloud-based infrastructure solutions, ensuring they are scalable, secure, and efficient. Mastery of AWS services like EC2, S3, Lambda, and security protocols is vital.
Big Data Engineer: Handle large-scale data storage and processing systems using Hadoop, Spark, and NoSQL databases. Expertise in distributed computing and ETL (Extract, Transform, Load) processes is required.
Business Intelligence Analyst: Use data analysis tools and techniques to provide business leaders with insights to drive strategy and decision-making. Familiarity with SQL, data modeling, and BI tools (e.g., Tableau, Looker) is important.
AI Research Scientist: Focus on cutting-edge research in artificial intelligence, developing innovative algorithms and models for real-world applications in areas like healthcare, finance, and automation.
Automation Engineer: Create automated solutions for repetitive tasks, testing, and deployment processes. Expertise in scripting, Python, and automation frameworks like Selenium is highly valuable.
AWS Solutions Architect: Design cloud solutions on AWS, aligning them with business goals while ensuring they are secure, scalable, and cost-effective. Strong knowledge of AWS architecture and security best practices is key.
DevOps Consultant: Help organizations implement DevOps methodologies to improve software delivery processes, reduce downtime, and increase efficiency. Mastery of tools like Docker, Kubernetes, Jenkins, and cloud platforms is essential.

By mastering these job roles, InfoDrafters students gain skills in Python, SQL, cloud technologies (AWS, Azure), machine learning frameworks, data analysis, DevOps tools (Docker, Kubernetes), automation, and much more. These roles are in high demand across industries like finance, healthcare, technology, and e-commerce, making InfoDrafters graduates highly sought after by employers.

Capstone Projects at InfoDrafters

Our capstone projects at InfoDrafters offer practical exposure by solving real-world industry problems. These projects help you gain confidence and enhance your portfolio.

Delivery Format at InfoDrafters

InfoDrafters offers flexible delivery formats: online live classes, weekend batches, and in-person sessions at our BTM Layout campus. Our courses are designed to suit your schedule and learning style.

Certification from InfoDrafters

Upon completion of any course, InfoDrafters awards industry-recognized certifications, which will boost your credibility and open up new job opportunities.

Fee Details at InfoDrafters

We offer competitive and affordable pricing for all our courses. At InfoDrafters, we believe in delivering quality education at reasonable fees. Scholarships and installment payment options are also available.

Career Support at InfoDrafters

InfoDrafters provides end-to-end career support, including resume-building workshops, interview preparation, and placement assistance. We have a dedicated placement team to guide you through the hiring process.

Success Stories from InfoDrafters

Our alumni have successfully transitioned to top companies in various industries. At InfoDrafters, we pride ourselves on our students’ success and continuously work to support their professional journeys.

Upcoming Batches at InfoDrafters

New batches at InfoDrafters start every month. Visit our website to check the upcoming schedule and book your seat in advance.

Demo & Brochure from InfoDrafters

Get a free demo of any course at InfoDrafters and download our course brochure for detailed information. We offer personalized counseling sessions to help you choose the right path.

FAQs & Blogs from InfoDrafters

Visit our FAQ section at InfoDrafters to get answers to common questions about our courses, fee structures, and placement support. Our blog section provides insights into industry trends and learning tips to help you stay ahead.

Q1. What courses does InfoDrafters offer?

At InfoDrafters, we offer specialized courses in Data Science, Machine Learning, Artificial Intelligence, Data Analytics, AWS, and DevOps. Each course is designed to provide hands-on learning with industry-standard tools.

Q2. Are there any prerequisites for the courses at InfoDrafters?

For most courses at InfoDrafters, no prior experience is required. However, having basic programming knowledge can be helpful. We also offer foundational classes to ensure everyone can follow along.

Q3. How is InfoDrafters different from other institutes?

InfoDrafters stands out by providing a unique blend of theory and hands-on practical learning through real-world projects, industry-recognized certifications, and personalized career support. Our instructors are experienced professionals working in top tech companies.

Q4. How can I enroll in a course at InfoDrafters?

You can enroll in a course by visiting our official website or contacting our admissions team. We also offer free demo sessions, so you can experience the learning environment before committing.

Q5. What is the duration of courses at InfoDrafters?

The duration varies by course. Most of our Data Science and AI courses run for 3-6 months, while our AWS and DevOps training programs are typically 2-3 months. Flexible learning options are available.

Q6. Does InfoDrafters offer any placement support?

Yes, InfoDrafters provides extensive placement support including resume-building workshops, interview preparation, mock interviews, and access to job opportunities. Our dedicated placement team helps guide you through the job search process.

Q7. Can I get a certificate after completing the course?

Yes, all students who successfully complete a course at InfoDrafters receive an industry-recognized certificate, which can boost your resume and job prospects.

Q8. Does InfoDrafters provide online classes?

Yes, InfoDrafters offers both online and offline classes to accommodate different learning preferences. You can choose the format that suits your schedule.

Q9. What is the fee structure for courses at InfoDrafters?

The fee structure varies depending on the course. At InfoDrafters, we offer affordable pricing, and installment payment options are available. You can contact our admissions team for detailed fee information.

Q10. Are there any scholarships available at InfoDrafters?

Yes, InfoDrafters offers merit-based scholarships to deserving students. Please contact our admissions team for more information on eligibility and application procedures.

For more in-depth insights, tips, and resources, visit our Blog Section.