Best Data Science Training in T. Nagar With Placement | Updated 2025

Data Science Training for All Graduates, NON-IT, Diploma & Career Gaps — ₹18,500/- only.

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Data Science Training in T. Nagar

  • Enroll in Our Best Data Science Training Institute in T. Nagar to Build In-Demand Data Skills.
  • Comprehensive Data Science Course in T. Nagar Covers Excel, SQL, Python and Power BI.
  • Gain Practical Knowledge Through Live Projects and Real-time Data Scenarios.
  • Choose Flexible Learning Schedules Weekday, Weekend and Fast-Track Batches Available.
  • Get Industry-Recognized Data Science Certification Course in T. Nagar With Placement Support.
  • Receive Assistance for Resume Building, Interview Preparation and Career Guidance.

WANT IT JOB

Become a Data Scientist in 3 Months

Freshers Salary

3 LPA

To

8 LPA

Quality Training With Affordable Fees in T. Nagar!
INR ₹28000
INR ₹18500

10580+

(Placed)
Freshers To IT

5845+

(Placed)
NON-IT to IT

8955+

(Placed)
Career Gap

4876+

(Placed)
Less Then 60%

Our Hiring Partners

Overview of Data Science Course

Our Data Science Training in T. Nagar is designed for fresh graduates wanting to begin a career in data analytics. You will learn basics of data handling, Python programming, statistics and machine learning in an easy and practical way. This Data Science Course includes hands-on projects to help you understand real-world applications. You'll also get guidance on building your resume and preparing for interviews. After completing the course, you’ll receive a recognized Data Science Certification. We also offer strong Data Science Placement support to help you get hired in top companies.

What You'll Learn From Data Science Training

Master core concepts in Data Science including data preprocessing, feature engineering, statistical modeling and data storytelling.

Learn key tools such as Python, Pandas, NumPy and Matplotlib to analyze, visualize and interpret large datasets effectively.

Understand machine learning algorithms like regression, classification and clustering for solving real-world problems.

Build hands-on experience through real-time data science projects and case studies aligned with industry needs.

Enhance your ability to draw actionable insights and make data-driven decisions using advanced analytics techniques.

Receive industry-recognized certification and personalized career support through our Data Science Training in T. Nagar to launch your journey as a professional data scientist.

Additional Info

Course Highlights

  • Start Your Data Science Journey: Master Python, R, Tools for Data Visualization, Machine Learning and SQL – All in One Course.
  • Get Strong Job Support With Opportunities From Top Companies Hiring for Data Science Roles.
  • Join Over 11,000 Students Got Trained and Placed Through Our 350+ Hiring Partners.
  • Train With Experienced Instructors Bring Over 10 Years of Real Industry Knowledge.
  • Enjoy Easy-to-understand Lessons, Real-time Projects and Full Career Support Throughout the Course.
  • Affordable Fees, Flexible Batch Timings and 100% Placement Help – Perfect for Beginners and Freshers.
  • Build Real Skills and Gain Hands-on Experience to Start Your Career in Data Science With Confidence.

Exploring the Benefits of Data Science Course in T. Nagar

  • High Demand Jobs – Data Science is one of most in-demand career options today. Many companies are looking for skilled data professionals to help them make smart decisions. As businesses generate more data, they need experts to handle and study it. This creates a huge number of job opportunities in various industries. With the right skills, you can get a good job even as a fresher. The demand is growing, and so are the chances for career growth.
  • Great Salary Packages – Data Scientists are among the highest paid professionals in tech industry. Employers are ready to provide attractive salary packages to qualified candidates. As your skills and experience grow, your earnings also increase. Even beginners can earn a decent salary after completing proper training. That’s why companies pay more for data experts.
  • Career Flexibility – Data Science allows you to work in different industries like healthcare, finance, marketing, retail and more. It gives you the freedom to choose a field that interests you. Whether it's predicting sales or analyzing patient data, data science plays a role everywhere. You can switch industries without starting from scratch. The skills you learn are useful in many areas. This makes your career more flexible and exciting.
  • Real-World Impact – Data Science helps in solving real life problems by turning raw data into useful insights. It can help companies save money, improve customer experience or even detect diseases early. Your work as a data scientist can make a big difference. You help leaders make better decisions that affect people’s lives. Knowing that your skills bring positive change gives more meaning to your job. Its a career where your work truly matters.
  • Continuous Learning – Data Science is a field where you keep learning new things all the time. With new tools, technologies and methods coming in, it keeps your job exciting. You will never get bored because there is always something new to explore. This helps you grow your knowledge and stay updated in your career. Learning doesn’t stop after training it becomes part of your daily work. This makes journey more rewarding and interesting.

Essential Tools for Data Science Training

  • Python – Python is a beginner friendly programming language widely used in data science. It helps you clean, analyze and visualize data easily. With powerful libraries like Pandas, NumPy and Matplotlib, Python makes data handling simple. It is flexible and works well for both small and large data projects. Many companies prefer Python, making it an important tool to learn.
  • SQL – Databases are communicated with using SQL (Structured Query Language). It helps you store, retrieve and manage data from large data systems. Learning SQL allows you to pull out specific data from huge datasets quickly. Its easy to learn and very useful when working with structured data. Most data science jobs expect you to know basic SQL.
  • Excel – Excel is one of the most commonly used tools for basic data analysis. It helps in organizing data, creating charts and performing calculations. You can use Excel functions to summarize and clean data easily. Its perfect for small datasets and quick analysis. Many companies still use Excel, so its a useful tool to know.
  • Tableau – Tableau is data visualization tool that helps you create clear and interactive charts and dashboards. It allows you to turn raw data into easy to understand visuals. This helps people make better business decisions based on data. Even without coding, you can use Tableau to create powerful reports. Its widely used in industries for visual storytelling.
  • Jupyter Notebooks – Jupyter Notebook is an open-source tool used to write and run Python code. Its perfect for testing code, creating charts and explaining steps using text and visuals. Its often used in learning, sharing and presenting data science projects. You can mix code with explanations, which makes learning more interactive. Its a favorite tool for students and professionals alike.

Top Frameworks Every Data Science Should Know

  • TensorFlow – TensorFlow is a Google open-source framework for developing deep learning and machine learning models. It helps data scientists create smart systems like image recognition, chatbots and recommendation engines. TensorFlow is powerful, flexible and used by companies worldwide. It supports both small and large-scale projects. Even beginners can start learning it with the help of its user-friendly tools.
  • PyTorch – PyTorch is another popular deep learning framework, developed by Facebook. It is known for its simple structure and easy-to-understand code, making it a great choice for beginners. PyTorch allows you to build models quickly and test them easily. Its widely used in research as well as real-time applications. Many data scientists use PyTorch for natural language processing and computer vision projects.
  • Scikit-learn – Scikit-learn is a Python-based framework used for building simple and effective machine learning models. It includes many ready-to-use algorithms for classification, regression, clustering and more. Its ideal for beginners because of its simple syntax and excellent documentation. Scikit-learn is perfect for small to medium sized projects and helps you learn the core of machine learning. It works well with other tools like Pandas and NumPy.
  • Keras – Keras is high level deep learning framework that runs on top of TensorFlow. With only few lines of code, it enables you to construct complicated neural networks. Keras is known for being easy to use, making it perfect for beginners in deep learning. It supports fast testing and experimenting with different models. Many people use Keras to build applications such image recognition and text analysis.
  • Apache Spark – Apache Spark is powerful data processing framework used for handling large-scale data. It helps in analyzing big datasets much faster than traditional tools. Spark supports multiple languages like Python, Scala and Java and it also includes machine learning features. Its widely used in industries where huge amounts of data need to be processed quickly. Learning Spark is useful for data scientists working with big data.

Must-Have Skills You’ll Gain in a Data Science Training

  • Data Analysis – Data analysis is skill of studying data to find patterns, trends and useful insights. You will learn collect, clean and organize data using tools like Excel, Python or SQL. This skill helps you understand what the data is telling you. It’s a key part of solving real-world business problems. With strong data analysis skills, you can support better decision-making.
  • Programming with Python – Python is most widely used programming language in data science. Writing code to handle, analyze and visualize data will be presented to you. Python is beginner-friendly and has many libraries like Pandas and Matplotlib that make tasks easier. Learning Python helps you automate work and build machine learning models. Its an essential skill for every data scientist.
  • Machine Learning Techniques – The main goal of the machine learning is teach computers to make decisions and learn from data. In the course, you’ll explore common algorithms like regression, classification and clustering. You will also learn to train models and test their accuracy. These skills help you build intelligent systems that solve problems without manual coding. Its a major part of data science today.
  • Data Visualization – Data visualization is ability to turn numbers and patterns into easy-to-understand charts and graphs. You’ll learn to use tools like Tableau, Power BI, or Python libraries to create visuals. This skill helps communicate complex data in a simple way to others. Good visuals help teams understand insights quickly. Its especially useful when presenting your findings to clients or managers.
  • Statistical Thinking – Statistics helps you understand data deeper and make better predictions. You will learn to apply concepts like probability, averages and hypothesis testing. These methods are used to understand if patterns in data are real or just random. Strong statistical thinking builds the foundation for all advanced data science techniques. Its a must-have skill for analyzing data properly.

Exploring Roles and Responsibilities of Data Science Course

  • Data Architect – Data Architects design the blueprint for data management systems, ensuring data is organized, secure and easily accessible. They decide on database structures, data integration methods and compliance standards. Their role is critical in setting up robust data infrastructures that support analytics and business operations. To match technological solutions with business objectives, they work together with the data science and IT departments. Expertise in cloud data platforms and data governance is key for this role.
  • Statistician – Statisticians use mathematical models and statistical techniques to collect, analyze and interpret data. They focus on hypothesis testing, probability and data sampling to support research and decision-making. Their insights help organizations understand variability, risk and relationships in data. Surveys and experiments are frequently designed by statisticians. Strong skills in R or Python and a solid background in mathematics are essential.
  • AI Engineer – AI Engineers develop intelligent systems that simulate human reasoning, learning and problem-solving abilities. They build AI models for tasks such as natural language processing, image recognition or recommendation engines. Their work combines deep learning, reinforcement learning and robotics with real-world applications. They often work in areas like automation, virtual assistants and predictive technologies. A deep understanding of neural networks, data structures and algorithm optimization is required.
  • Big Data Engineer – Big Data Engineers manage and process massive datasets using distributed computing technologies. They work with tools like Apache Hadoop, Hive and Spark to build systems that can handle high-volume, high-velocity data. Their responsibilities include data ingestion, transformation and storage in real-time or batch processing systems. They ensure scalability, performance and reliability of data platforms. The role demands expertise in programming, data warehousing and cloud infrastructure.
  • Data Visualization Specialist – Data visualization specialists are in charge of transforming complicated data into easily understood visual narratives. They design charts, graphs, dashboards and interactive visuals using tools like Tableau, D3.js or Power BI. Their work helps stakeholders quickly grasp insights and trends in the data. They combine data analysis skills with a strong sense of design and user experience. Clear communication and creative thinking are essential in this role.

Top Reasons Freshers Should Choose a Career in Data Science

  • No Need for Coding Background – You don’t need to be an expert in coding to start learning data science. Many training programs teach Python and other tools from the basics. This makes it easier for freshers from any background to get started. With interest and practice, anyone can learn and succeed.
  • Growing Job Market – Data Science is one of the fastest-growing fields in the world today. Companies in every industry are hiring data professionals to handle and study data. This creates more job opportunities, even for beginners. Training helps freshers get ready for these roles.
  • High Starting Salary – Even entry-level data science jobs offer good salary packages. With the right skills and training, freshers can start their careers with a strong income. Companies are willing to pay well for skilled data professionals. This makes it financially rewarding career from the start.
  • Career Growth Opportunities – Data Science Course in Offline offers many chances to grow into roles like Data Analyst, Machine Learning Engineer or Data Scientist. With experience and continued learning, freshers can quickly move up in their careers. The field encourages learning and upgrading skills. This makes it a long-term career path.
  • Real-World Impact – Working in data science lets you solve real problems and help companies make smart decisions. You can work on interesting projects in health, business, or tech industries. This makes the job meaningful and exciting. Freshers can feel proud of making a difference through data.

How Data Science Skills Open Doors to Remote Jobs

  • High Demand Across Industries – Data Science skills are high demand across many industries like healthcare, finance, e-commerce and marketing. Since companies worldwide need data experts, many offer remote job options. With the right skills, you can work for global clients without leaving home. The demand ensures more job opportunities for remote roles. This makes it easier to start or grow your career from anywhere.
  • Work Can Be Done Online – Automation tools and machine learning models reduce manual tasks and increase efficiency. As a Remote ML Engineer or AI Consultant, you can build smart systems that run independently, allowing companies to scale globally. These roles don't require physical presence, making them ideal for remote work. Your ability to automate adds real value in distributed teams.
  • Cloud-Based Data Tools Mastery – Most data science tasks like coding, analyzing data and creating reports can be done with just a laptop and internet. You don’t need to be in an office to complete your work. Tools like Jupyter Notebook, Python and cloud platforms allow you to work remotely with ease. You can collaborate with teams online using video calls and shared documents. This makes data science perfect for remote jobs.
  • Freelance and Contract Opportunities – With data science skills, you can take freelance or contract projects on platforms like Upwork, Fiverr or Toptal. These short-term remote jobs help you earn money while building experience. Many businesses need data insights but don’t hire full-time roles, so they seek freelancers. This gives you the freedom to choose your clients and schedule. Its a great way to grow your portfolio from home.
  • Global Job Market Access – Once you learn data science, you are not limited to jobs in your local area. You can apply to companies in the USA, UK, Canada or anywhere else that hires remote talent. This increases your chances of finding a job with better pay and growth. Many businesses are willing to hire remote data scientists and your abilities can meet demands around the world. You just need a strong resume and internet access.
  • Project-Based Work Suits Remote Setup – Many data science roles are project based, where you are given a task and expected to deliver results. This configuration is ideal for working remotely since it eliminates the need for continual supervision. You can work at your own pace and report progress regularly. As long as you meet the deadlines and deliver quality results, location doesn’t matter. This makes remote data science roles both flexible and rewarding.

What to Expect in Your First Data Science Job

  • Learning Never Stops – In your first data science job, you’ll quickly realize that learning continues on the job. You’ll face new tools, projects and real-world challenges that aren’t always in textbooks. Don’t worry if you don’t know everything right away your team will support you. Be open to feedback and ask questions when needed. Every project will help you grow your skills.
  • Working with a Team – You won’t work alone instead, you’ll often collaborate with developers, analysts and business teams. You’ll need to explain your findings clearly to both technical and non-technical people. Good communication is equally important as technical proficiency. Teamwork helps projects move smoothly and improves your learning. Be prepared to learn from others and exchange ideas.
  • Cleaning Data is Common – A big part of your job will be cleaning and organizing messy data before analyzing it. Real-world data often comes with errors, missing values or confusing formats. It may seem repetitive at first, but its a critical part of any data project. Clean data leads to better results and accurate insights. This skill is highly valued in the industry.
  • Solving Business Problems – Your main goal will be solve real problems and help your company make better decisions using data. You’ll be expected to turn raw numbers into useful information. This could mean predicting sales, improving customer experience or reducing costs. Your work will often directly impact the business. That’s what makes a data science job meaningful.
  • Using Tools and Writing Code – You’ll regularly use tools like Python, SQL and data visualization software to complete your tasks. Writing code to analyze and present data will become part of your daily work. You don’t need to be a coding expert from day one, but basic skills are important. Over time, your coding confidence will grow. Practice and real-world use will make you better.

Leading Companies Hiring for Data Science Professionals

  • Google – Google is one of biggest tech companies that hires data scientists for search, advertising, YouTube and AI projects. Data professionals at Google work with huge datasets and cutting-edge tools. They help improve user experience and develop smart solutions. Its a dream company for many data science professionals.
  • Amazon – Amazon uses data science to manage everything from product recommendations to delivery systems. Data scientists here work on customer behavior analysis, inventory forecasting and pricing models. Its a great place to learn and grow in real-time data environments. The company values innovation and data-driven decisions.
  • Microsoft – Microsoft hires data scientists to work on products like Azure, Office and LinkedIn. They use data to improve software performance, security and user satisfaction. Employees get to work with advanced tools and AI models. Microsoft offers strong career development and global opportunities.
  • IBM – IBM has been a leader in technology and AI for years and continues to invest in data science. They hire professionals for roles in machine learning, cloud computing, and data analytics. IBM works on projects across industries like healthcare, finance and retail. Its known for research, innovation and professional growth.
  • Accenture – Accenture is global consulting firm that hires data scientists to help businesses make smarter decisions. They offer services in analytics, AI and cloud solutions. Data scientists at Accenture solve real-world problems for clients worldwide. Its a great choice for those who want to work on varied projects across industries.
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Upcoming Batches For Classroom and Online

Weekdays
25 - Aug - 2025
08:00 AM & 10:00 AM
Weekdays
27 - Aug - 2025
08:00 AM & 10:00 AM
Weekends
30 - Aug - 2025
(10:00 AM - 01:30 PM)
Weekends
31 - Aug - 2025
(09:00 AM - 02:00 PM)
Can't find a batch you were looking for?
INR ₹18500
INR ₹28000

OFF Expires in

Who Should Take a Data Science Training

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Working Professionals

Diploma Holders

Professionals from Other Fields

Salary Hike

Graduates with Less Than 60%

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Job Roles For Data Science Course in T. Nagar

Data Scientist

Data Analyst

ML Engineer

Data Engineer

BI Analyst

Statistician

AI Engineer

Decision Scientist

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Tools Covered For Data Science Training

TensorFlow Tableau-2 Scikit-learn RStudio python-1 Jupyter-Notebook power-biv-2 Apache-Spark-2

What’s included ?

Convenient learning format

📊 Free Aptitude and Technical Skills Training

  • Learn basic maths and logical thinking to solve problems easily.
  • Understand simple coding and technical concepts step by step.
  • Get ready for exams and interviews with regular practice.
Dedicated career services

🛠️ Hands-On Projects

  • Work on real-time projects to apply what you learn.
  • Build mini apps and tools daily to enhance your coding skills.
  • Gain practical experience just like in real jobs.
Learn from the best

🧠 AI Powered Self Interview Practice Portal

  • Practice interview questions with instant AI feedback.
  • Improve your answers by speaking and reviewing them.
  • Build confidence with real-time mock interview sessions.
Learn from the best

🎯 Interview Preparation For Freshers

  • Practice company-based interview questions.
  • Take online assessment tests to crack interviews
  • Practice confidently with real-world interview and project-based questions.
Learn from the best

🧪 LMS Online Learning Platform

  • Explore expert trainer videos and documents to boost your learning.
  • Study anytime with on-demand videos and detailed documents.
  • Quickly find topics with organized learning materials.

Data Science Course Syllabus in T. Nagar

  • 🏫 Classroom Training
  • 💻 Online Training
  • 🚫 No Pre Request (Any Vertical)
  • 🏭 Industrial Expert

Students enrolling in our Data Science Training in T. Nagar can enjoy a flexible learning journey designed to match their interests and career goals. This structure helps them develop strong skills in key areas like machine learning, data analysis and data visualization, while covering all essential topics included in the Data Science Course in T. Nagar. The course also offers valuable Data Science Placement support and Data Science Internships for those looking to gain real-world exposure. With hands-on sessions and live projects, learners build practical experience throughout the training. After successful completion, students receive an industry-recognized certification that supports their long-term career growth.

  • Introduction to Data Science – Begin by learning to clean, explore and study data using simple tools and methods.
  • Advanced Topics in Data Science – Dive deeper into machine learning, AI and big data tools to handle complex problems.
  • Data Analytics with Excel & Power BI – Discover to make reports and dashboards that turn data into useful insights.
  • Python Programming for Data Science – Learn to use Python for analyzing, processing and visualizing data step-by-step.
  • Practical Business Applications – Use data science techniques to solve real business problems and support smart decisions.
Introduction to Data Science
Python for Data Science
Data Handling & Preprocessing
SQL for Data Management
Exploratory Data Analysis
Machine Learning Foundations
Data Science with AI Tools

Builds the base to understand the field and its core functions:

  • What is Data Science – Importance, applications and workflow
  • Data Science vs Data Analytics – Key differences in roles and outcomes
  • Tools & Technologies – Overview of Python, R, SQL, Excel, Tableau
  • Career Paths – Roles like data analyst, data scientist, ML engineer

Covers essential programming and data handling with Python:

  • Python Basics – Variables, data types, loops, functions
  • Pandas – Reading, cleaning, filtering and grouping data with DataFrames
  • NumPy – Efficient numerical operations using arrays
  • Matplotlib & Seaborn – Plotting line graphs, bar charts, heatmaps and histograms

Focuses on preparing raw data for analysis:

  • Data Collection – Importing data from files, databases, APIs
  • Data Cleaning – Handling missing values, duplicates and outliers
  • Data Transformation – Encoding, normalization, scaling
  • Feature Engineering – Creating meaningful features from raw data

Gain knowledge about to access and modify data kept in databases:

  • Basic SQL Commands – SELECT, WHERE, ORDER BY
  • Joins & Relationships – INNER JOIN, LEFT JOIN, RIGHT JOIN
  • Aggregation Functions – COUNT, SUM, AVG, MAX, MIN
  • Views & Subqueries – Organizing and optimizing data queries

Helps find insights and patterns in data visually and statistically:

  • Data Profiling – Summary statistics, distributions, data types
  • Visualization Tools – Box plots, scatter plots, pair plots
  • Correlation Analysis – Identifying relationships between variables
  • Outlier Detection – Visual and statistical methods

Introduces predictive modeling and intelligent data-driven systems:

  • Supervised Learning – Regression and classification techniques
  • Unsupervised Learning – Clustering and dimensionality reduction
  • Model Building – Training, testing and tuning machine learning models
  • Evaluation Metrics – Accuracy, precision, recall, ROC curve

Applies all learned skills in real-world scenarios:

  • Power BI / Tableau – Interactive dashboards and storytelling
  • Model Deployment Basics – Introduction to using Flask or Streamlit
  • Documentation & Reporting – Presenting insights clearly and effectively

🎁 Free Addon Programs

Aptitude, Spoken English

🎯 Our Placement Activities

Daily Task, Soft Skills, Projects, Group Discussions, Resume Preparation, Mock Interview

Hands-on Experience in Data Science Projects

Placement Support Overview

Today's Top Job Openings for Data Science Training in T. Nagar

Data Scientist

Company Code : CFS146

Chennai, Tamilnadu

₹25,000 – ₹45,000 a month

Any Degree

Exp 0-1 yr

  • We’re looking for a Data Scientist can analyze complex datasets, build predictive models and generate insights that influence business strategies. You should be skilled in statistics, machine learning and data visualization and able to work closely with cross-functional teams to communicate results effectively.
  • Easy Apply

    Machine Learning Engineer

    Company Code : THC216

    Chennai, Tamilnadu

    ₹30,000 - ₹50,000 a month

    Any Degree

    Exp 0-3 yrs

  • Join us as an ML Engineer to design, implement and maintain machine learning pipelines and models. You’ll work with Python, scikit-learn, TensorFlow/PyTorch and collaborate with software teams to deploy scalable solutions that deliver real-time value.
  • Easy Apply

    Data Engineer

    Company Code : YST316

    Chennai, Tamilnadu

    ₹20,000 - ₹35,000 a month

    Any Degree

    Exp 0-2 yrs

  • Now accepting applications for a Data Engineer proficient in building robust data pipelines using Hadoop, Spark, Kafka, along with Python or Scala scripting. Experience with SQL and NoSQL (Postgres, MongoDB) is essential for developing scalable architectures.
  • Easy Apply

    Deep Learning Engineer

    Company Code : NNL246

    Chennai, Tamilnadu

    ₹30,000 - ₹55,000 a month

    Any Degree

    Exp 0-1 yr

  • Opportunities are now open for Deep Learning Engineer to create and deploy neural networks using frameworks like TensorFlow or PyTorch. Your role will include data preprocessing, model training, performance tuning and drafting detailed technical documentation.
  • Easy Apply

    Business/Data Analyst

    Company Code : IEG346

    Chennai, Tamilnadu

    ₹25,000 - ₹40,000 a month

    Any Degree

    Exp 0-2 yrs

  • We want an Analyst who have hands on experience in SQL, Excel and Power BI/Tableau can collect and analyze business data, identify trends and create actionable dashboards. You’ll work cross-functionally to support data-driven decision-making and document your findings clearly.
  • Easy Apply

    NLP Engineer

    Company Code : LTI497

    Chennai, Tamilnadu

    ₹28,000 - ₹48,000 a month

    Any Degree

    Exp 0-3 yrs

  • In this role you will build NLP models that power chatbots, sentiment analysis tools and text classifiers. Required skills include Python, NLTK/spaCy, Transformers (e.g. BERT) and experience in model fine-tuning and deployment.
  • Easy Apply

    Analytics Engineer

    Company Code : DFS413

    Chennai, Tamilnadu

    ₹25,000 - ₹45,000 a month

    Any Degree

    Exp 0-2 yrs

  • Now hiring for an Analytics Engineer to bridge the gap between raw data and insights. In this role, you'll build and maintain scalable data models and ETL workflows using SQL and Python, while collaborating with data scientists and analysts to ensure accurate and timely data delivery.
  • Easy Apply

    Computer Vision Engineer

    Company Code : VAL143

    Chennai, Tamilnadu

    ₹30,000 - ₹60,000 a month

    Any Degree

    Exp 0-1 yr

  • Become a Computer Vision Engineer out team to design and deploy image and video analysis pipelines. You'll work with OpenCV, TensorFlow/PyTorch and deep learning architectures to develop object detection, segmentation and classification solutions for real-world applications.
  • Easy Apply

    Highlights of the Data Science Internship in T. Nagar

    Real-Time Projects

    • 1. Gain hands-on experience by working on live industry-based applications.
    • 2. Understand real-world problem-solving through Data Science scenarios.
    Book Session

    Skill Development Workshops

    • 1. Participate in focused sessions on trending technologies and tools.
    • 2. Learn directly from industry experts through guided practical exercises.
    Book Session

    Employee Welfare

    • 1. Enjoy benefits like health coverage, flexible hours, and wellness programs.
    • 2. Companies prioritize mental well-being and work-life balance for all employees.
    Book Session

    Mentorship & Peer Learning

    • 1. Learn under experienced mentor guide your technical and career growth.
    • 2. Collaborate with peers to enhance learning through code reviews and group projects.
    Book Session

    Soft Skills & Career Readiness

    • 1. Improve communication, teamwork, and time management skills.
    • 2. Prepare for interviews and workplace dynamics with mock sessions and guidance.
    Book Session

    Certification

    • 1. Earn recognized credentials to validate your Data Science skills.
    • 2. Boost your resume with course or project completion certificates from reputed platforms.
    Book Session

    Sample Resume for Data Science (Fresher)

    • 1. Simple and Neat Resume Format

      – Use a clean layout with clear sections like summary, skills, education, and projects.

    • 2. List of Technologies You Know

      – Mention skills like Python, SQL, Excel, Power BI, Tableau, Pandas, Data Cleaning.

    • 3. Real-Time Projects and Achievements

      – Add 1–2 real-time projects with a short description and the tools used.

    Top Data Science Interview Questions and Answers (2025 Guide)

    Ans:

    Data Science is field that combines computer science, statistics and subject knowledge to find useful insights from large amounts of data. It involves collecting, cleaning, analyzing and visualizing data. It also includes advanced techniques like machine learning and prediction models. These help solve real problems and support better decision-making.

    Ans:

    Labeled data with known input and output is used to train model in supervised learning. The model learns to predict results based on the given examples. Unsupervised learning tries to uncover latent patterns or groupings in data without labeling. Its useful for exploring unknown data structures.

    Ans:

    The goal of the bias-variance tradeoff is to find the ideal balance between the two categories of model errors. High bias means the model is too simple and underfits the data. High variance means the model is too complex and overfits the training data. To perform successfully on fresh data, a good model should have low variance and low bias.

    Ans:

    Overfitting happens when a model learns both the patterns and the noise in the training data. This causes it to perform very well on training data but poorly on new or test data. It usually occurs when the model is too complex or has too many parameters compared to the amount of data. The model fails to generalize properly.

    Ans:

    Precision and recall are used to evaluate classification models. Precision tells many of the predicted positive results were actually correct. Recall shows many actual positive cases the model was able to find. Both are important for understanding well a model identifies the target class.

    Ans:

    A confusion matrix is table used to evaluate the classification model’s performance. It helps in determining which predictions the model is making correctly and incorrectly. It gives clear view of overall accuracy and errors.

    Ans:

    There are various methods for dealing with missing data. One method is to remove rows with missing values, but this may lead to data loss. Another method is imputation, where missing values are filled with the mean, median or mode. More advanced techniques use models like KNN or decision trees to predict the missing values.

    Ans:

    One kind of model that resembles a tree structure is a decision tree. It makes decisions based on questions about the data at each step. Each branch leads to a possible outcome and each leaf gives a final prediction. Decision trees are easy to understand and used in both classification and regression tasks.

    Ans:

    Machine learning models may prevent overfitting by using regularization. To keep the model simple, it adds a penalty to the loss function. L1 regularization (Lasso) can reduce some weights to zero and making the model simpler. L2 regularization (Ridge) shrinks weights without removing them. Both help improve model performance on new data.

    Ans:

    Ensemble methods combine multiple models to improve accuracy. Bagging like Random Forest, builds several models from different data samples and averages their results to reduce variance. Boosting such as AdaBoost and Gradient Boosting, trains models one after another, each focusing on fixing errors made by the previous ones. This leads to better performance overall.

    Company-Specific Interview Questions from Top MNCs

    1. What is Data Science and how is it different from regular data analysis?

    Ans:

    To gain valuable insights from data, data scientists integrate statistics, programming and subject expertise. Unlike regular data analysis, which mainly looks at historical data to create reports, Data Science goes a step further by using techniques like machine learning to make predictions about future outcomes.

    2. How does supervised learning differ from unsupervised learning?

    Ans:

    Supervised learning uses labeled data where the correct answers are already known to train a model to make predictions. At the same time, unsupervised learning uses unlabeled data and looks for hidden patterns, groupings, or structures without producing any expected outcomes.

    3. What is overfitting in machine learning and how can it be prevented?

    Ans:

    When model learns the noise in training data as well as the helpful patterns, is known as overfitting. It performs well on known data but poorly on new data. To prevent overfitting, we can simplify the model, gather more training data, or use techniques like regularization and cross-validation.

    4. Can you explain bias and variance in simple terms?

    Ans:

    Bias refers to errors from overly simple models that fail to capture complex patterns this causes underfitting. Variance refers to models that are too complex and react too strongly to small changes in training data this is reason for overfitting. To perform successfully on the fresh data, good model balances the variance and bias.

    5. How do R and Python differ from one another in data science?

    Ans:

    Python is widely used to building machine learning models and applications due to simplicity and versatility. R is often preferred for deep statistical analysis and creating detailed visualizations. While Python is common industry and tech, R is popular in academic and research fields.

    6. How do you handle missing values in a dataset?

    Ans:

    To deal with missing data, we can either remove the incomplete rows or fill in the gaps using methods like replacing with the mean, median or mode. In some cases, predictive techniques like using KNN or regression can estimate the missing values.

    7. What is feature engineering and why is it important?

    Ans:

    The process of developing, altering, or choosing data characteristics to enhance a model's performance is known as feature engineering. This might involve combining columns, extracting new variables or converting data into formats that are easier for algorithms to understand.

    8. What’s the difference between classification and regression problems?

    Ans:

    Classification is used to predict categories or labels, such as “yes” or “no,” “spam” or “not spam.” Regression is used to predict continuous values like temperature, price or age.

    9. What is a confusion matrix used for?

    Ans:

    A confusion matrix is table that shows how well a classification model is performing. It displays number of correct and incorrect predictions, helping to measure accuracy, precision, recall and other performance metrics.

    10. What do precision and recall mean?

    Ans:

    Precision measures how many of the model’s favorable predictions were actually right. Recall shows how many of actual positive cases were identified correctly by the model. Both are key to evaluating a classification model’s effectiveness.

    1. Can you explain what Data Science is?

    Ans:

    Data Science is all about using data to understand problems, find patterns and make predictions. It combines skills in programming, statistics and business to help companies make smart decisions.

    2. What are the main parts that make up Data Science?

    Ans:

    The key parts of Data Science include collecting data, cleaning it, analyzing it, visualizing results, applying machine learning and understanding the final outcome. Each step helps turn raw data into useful insights.

    3. What does a confusion matrix show?

    Ans:

    A confusion matrix is a simple table that shows how well a model’s predictions match the actual results. It helps you see how many predictions were right and where the model made mistakes.

    4. Which metrics are commonly used to check model performance?

    Ans:

    To check how well a model is working, we use metrics like accuracy, precision, recall, F1-score and ROC-AUC. These tell us how correct the model is and how often it misses or mislabels things.

    5. What is feature engineering and why is it important?

    Ans:

    Feature engineering means creating or changing data columns (features) to help models perform better. Its important because the right features make it easier for the model to learn and make good predictions.

    6. How do you deal with missing values in a dataset?

    Ans:

    If data is missing, we can either remove those rows or fill the gaps with averages, common values or predictions. The approach depends on how much data is missing and how important the data.

    7. What is overfitting in machine learning and how can you stop it?

    Ans:

    Overfitting happens when model learns the training data too well even the mistakes so it doesn’t work well on new data. To fix this, we can simplify the model, use more data or apply methods like regularization and cross-validation.

    8. What is a random forest and how does it work?

    Ans:

    A random forest is group of decision trees that work together to make better predictions. Each tree gives an answer and the final result is based on the most common answer or average. This improves accuracy and reduces errors.

    9. What are the steps followed in a Data Science project?

    Ans:

    A Data Science project usually involves understanding the problem, gathering data, cleaning it, exploring it, building a model, testing the model and finally using it to make predictions or decisions.

    10. How do you make sure your data is of good quality?

    Ans:

    To keep data quality high, we remove errors, fix missing values, delete duplicates and check for anything that doesn’t make sense. Clean and accurate data is essential for getting reliable results.

    1. What is the role of a Data Scientist in a company?

    Ans:

    A data scientist helps a business make smart decisions by working with data. They collect and clean the data, study it to spot patterns and build models that can predict future trends. Their goal is turn raw data into meaningful insights that support business growth and solve real problems.

    2. How is structured data different from unstructured data?

    Ans:

    Structured data is neat and fits well into tables, like numbers, dates and categories. It's easy to store and analyze. On the other hand, unstructured data includes things like emails, videos, images and text, which don’t follow a fixed format. This kind of data needs special tools and techniques to analyze.

    3. What are the main stages of a Data Science project?

    Ans:

    A typical Data Science project begins with understanding the business problem. Then data is gathered, cleaned and explored. After that, models are created and tested. Finally, the best-performing model is deployed to make predictions or guide decisions based on data.

    4. How is missing data in a dataset handled?

    Ans:

    Missing values can either be removed or filled using techniques like replacing them with the average, median or the most common value. The method used depends on how much data is missing and whether it affects overall quality of the analysis.

    5. How does supervised learning differ from unsupervised learning?

    Ans:

    In supervised learning the model trained using data that already has labels or answers, helping it learn to make predictions. In unsupervised learning, the data has no labels and the model tries to find patterns or groupings on its own, like clustering similar items together.

    6. What does cross-validation mean in model testing?

    Ans:

    Cross-validation is a way to test how well a model will work on new data. It splits the dataset into parts some parts are used to train the model and the others to test it. This gives a better idea of how reliable the model is and helps prevent overfitting.

    7. What is overfitting and how can you avoid it?

    Ans:

    Overfitting occurs when model takes too much information from the training set, such as errors or noise. It does well on the training set but performs poorly on new data. To avoid it, we can use simpler models, get more training data, use cross-validation, or apply regularization techniques.

    8. What does a confusion matrix tell you?

    Ans:

    A confusion matrix is table shows how well a classification model is performing. It compares what the model predicted with the actual results and shows true positives, true negatives, false positives and false negatives. This helps measure accuracy and spot errors.

    9. How do you pick the most useful features in a dataset?

    Ans:

    To choose important features, we can use methods like checking correlations, using model-based scores like feature importance from a random forest, or applying statistical tests. Selecting the right features makes the model simpler and often improves its accuracy.

    10. How does the K-Nearest Neighbors (KNN) algorithm work?

    Ans:

    KNN is a basic algorithm that classifies new data by comparing it to nearby known data points. It checks the 'k' closest neighbors and assigns the most common class among them. The idea is that similar items are usually near each other in the dataset.

    1. What is overfitting and how can it be prevented?

    Ans:

    A model is said to be overfit if it collects excessive details from the training data, such as errors or noise. As a result, it does well on training data but fails on new data. To avoid this, you can use more training data, choose simpler models, apply cross validation or use regularization techniques to limit how much the model learns.

    2. Can you explain cross-validation?

    Ans:

    Cross-validation is a method used to check if a model will work well on new, unseen data. The data must be divided into several sections, some for testing and some for training. This guarantees that model is accurate and not merely fortunate with a single dataset.

    3. What are the key stages in a data science workflow?

    Ans:

    A typical data science workflow begins with understanding the business problem. Then, you gather and clean the data, explore it for insights and build models for prediction. Finally, the model is tested and the results are shared to support better decision making.

    4. What is feature engineering and why is it useful?

    Ans:

    Feature engineering is the process of creating or modifying input data to help a model perform better. For example, splitting a date into day, month and year or combining two columns into one. This makes it easier for the model to detect patterns and improve its accuracy.

    5. How does a confusion matrix help in evaluating models?

    Ans:

    A confusion matrix is a chart that compares what the model predicted with the actual results. It shows true positives, true negatives, false positives and false negatives. This helps you understand how accurate the model is and where its making mistakes.

    6. What’s the difference between precision and recall?

    Ans:

    Precision measures how many of the items the model marked as positive are actually correct. Recall measures how many actual positives were successfully identified. Recall tries to cover all true positives, whereas precision concentrates on the level of positive predictions.

    7. What is a decision tree and how does it function?

    Ans:

    A decision tree is a model that works like a series of yes/no questions. At each step, it splits the data into branches based on answers. This continues until it reaches a final result. Its simple to understand and is used to make predictions or decisions from data.

    8. What does regularization mean in machine learning?

    Ans:

    Regularization helps prevent models from becoming too complicated and overfitting the data. It adds a penalty during training to discourage the model from focusing too much on less important features. This leads to better general performance on new data.

    9. Why do we use PCA (Principal Component Analysis)?

    Ans:

    By lowering the number of features while preserving the key patterns, PCA helps to simplify big datasets. It transforms the data into new components that represent the most important trends. This helps make models faster and sometimes more accurate.

    10. What is the purpose of time series analysis?

    Ans:

    Time series analysis is used to study data that changes over time, like monthly sales or daily temperatures. The aim is to identify trends, seasonal patterns or cycles and then use that information to make predictions about future values.

    1. What is backpropagation and why is it important?

    Ans:

    Backpropagation is a technique used to train machine learning models, especially neural networks. It works by adjusting the internal weights of the model based on the errors it makes during prediction. The model compares its prediction to the actual result, calculates the error and then moves backward to tweak the weights so that it can improve performance in the next round.

    2. How does crossover differ from straight-through in algorithms?

    Ans:

    Crossover is mostly used in genetic algorithms where parts of two solutions are combined to create a new one like mixing traits of two parents to produce a child. Straight-through, on the other hand, usually means letting data or signals pass directly through layers without much alteration. This is commonly used when training models involving non-differentiable functions.

    3. What is the role of SMTP in email communication?

    Ans:

    The standard system for transmitting emails over the internet is called SMTP or Simple Mail Transfer system. When an email is sent, SMTP ensures that it reaches the recipient’s email server properly. It handles only the sending part while other protocols like POP3 or IMAP are used for receiving emails.

    4. What does clustering support mean in computing?

    Ans:

    Clustering support typically refers to a system’s ability to group similar data points or to connect multiple servers that work together as a cluster. In IT, this helps improve system performance, ensure better data handling and provide high availability by balancing the load among multiple machines.

    5. What is IEEE’s contribution to computer networking?

    Ans:

    Institute of Electrical and Electronics Engineers is plays a key role in the setting standards for technology and networking. For instance, IEEE is responsible for the 802.11 standard which defines how Wi-Fi works. Their guidelines ensure that different devices can communicate effectively using common technical protocols.

    6. How would you describe machine learning?

    Ans:

    Machine learning is branch of artificial intelligence where computers are taught to learn from data instead of being programmed with specific instructions. These systems analyze data, find patterns and use them to make predictions or decisions. Its widely used in areas like online recommendations, speech recognition and fraud detection.

    7. What is function overloading in programming?

    Ans:

    Function overloading means creating multiple functions with the same name but with different parameters. Depending on how its called, the program decides which version of the function to run. This makes the code more flexible and easy manage when performing similar tasks with different inputs.

    8. What do you know about Python as a programming language?

    Ans:

    Python is widely used programming language is renowned for being easy to understand. It is widely used in web development, automation, machine learning and data analysis. Python has vast collection of libraries and strong community, making it great choice for both beginners and professionals.

    9. What is a tunneling protocol and why is it used?

    Ans:

    A tunneling protocol is used to securely send data across public networks like internet. It wraps original data in different format to protect it during transmission like sealing a letter in envelope. This method is essential for secure services like VPNs that require private and encrypted connections.

    10. What are DDL, DML and DCL in SQL used for?

    Ans:

    In SQL,Data Definition Language includes commands like CREATE and DROP that define the structure of a database. DML (Data Manipulation Language) involves commands such as INSERT and UPDATE that modify the data within the tables. Data Control Language is used to setting permissions and includes commands like GRANT and REVOKE.

    Disclaimer Note:

    The details mentioned here are for supportive purposes only. There are no tie-ups or links with the corresponding PGs.

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    Top Data Science Job Opportunities for Freshers

    • 1. Junior Data Scientist Jobs at Startups and IT Companies
    • 2. Campus Placements and IT Service Jobs
    • 3. Internship-to-Job Programs
    • 4. Apply Through Job Portals
    • 5. Skills That Help You Get Hired

    Getting Started With Data Science Training in T. Nagar

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    8 Lakhs+ CTC
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    WFH Jobs (Remote)

    Why Data Science is the Ultimate Career Choice

    High Demand

    Companies prefer multi-skilled professionals can handle entire project cycles.

    Global Opportunities

    Open doors to remote and international job markets.

    High Salary

    Enjoy competitive salaries and rapid career advancement.

    Flexible Career Path

    Explore roles such as developer, architect, freelancer, or entrepreneur.

    Future-Proof Career

    Stay relevant with skills that are consistently in demand in the evolving tech landscape.

    Versatility Across Industries

    Work in various domains like e-commerce, healthcare, finance, and more.

    Career Support

    Placement Assistance

    Exclusive access to ACTE Job portal

    Mock Interview Preparation

    1 on 1 Career Mentoring Sessions

    Career Oriented Sessions

    Resume & LinkedIn Profile Building

    Get Advanced Data Science Certification

    You'll receive a certificate proving your industry readiness.Just complete your projects and pass the pre-placement assessment.This certification validates your skills and prepares you for real-world roles.

    • IBM Data Science Professional Certificate
    • Google Data Analytics Professional Certificate
    • Microsoft Certified Azure Data Scientist Associate
    • SAS Certified Data Scientist
    • Certified Analytics Professional (CAP)

    The duration varies based on program and your schedule. Entry-level certifications like those from IBM or Google can take 1 to 3 months while advanced certifications might require 3 to 6 months or more especially if pursued part-time.

    Absolutely. Certification guarantee a job, it significantly boosts your profile. It shows employers that you're committed and have verified skills, which is especially valuable for beginners or career switchers.

    • Reviewing the official syllabus or exam outline
    • Taking structured online courses or video tutorials
    • Practicing tools like Python, SQL, Excel and Tableau
    • Working on real-world datasets and mini-projects
    • Taking mock tests to assess your readiness
    • Enhances your professional credibility
    • Builds hands-on skills and self-confidence Demonstrates knowledge of key tools and methodologies Opens doors to better roles in data science and analytics

    Complete Your Course

    A Downloadable Certificate in PDF Format, Immediately Available to You When You Complete Your Course

    Get Certified

    A Physical Version of Your Officially Branded and Security-Marked Certificate.

    Get Certified

    Lowest Data Science Fees in T. Nagar

    Affordable, Quality Training for Freshers to Launch IT Careers & Land Top Placements.

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    How is ACTE's Data Science Course in T. Nagar Different?

    Feature

    ACTE Technologies

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    Affordable Fees

    Competitive Pricing With Flexible Payment Options.

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    Industry Experts

    Well Experienced Trainer From a Relevant Field With Practical Data Science Training

    Theoretical Class With Limited Practical

    Updated Syllabus

    Updated and Industry-relevant Data Science Course Curriculum With Hands-on Learning.

    Outdated Curriculum With Limited Practical Training.

    Hands-on projects

    Real-world Data Science Projects With Live Case Studies and Collaboration With Companies.

    Basic Projects With Limited Real-world Application.

    Certification

    Industry-recognized Data Science Certifications With Global Validity.

    Basic Data Science Certifications With Limited Recognition.

    Placement Support

    Strong Placement Support With Tie-ups With Top Companies and Mock Interviews.

    Basic Placement Support

    Industry Partnerships

    Strong Ties With Top Tech Companies for Internships and Placements

    No Partnerships, Limited Opportunities

    Batch Size

    Small Batch Sizes for Personalized Attention.

    Large Batch Sizes With Limited Individual Focus.

    LMS Features

    Lifetime Access Course video Materials in LMS, Online Interview Practice, upload resumes in Placement Portal.

    No LMS Features or Perks.

    Training Support

    Dedicated Mentors, 24/7 Doubt Resolution, and Personalized Guidance.

    Limited Mentor Support and No After-hours Assistance.

    Data Science Course FAQs

    1. What qualifications do I need to start a career in Data Science?

    You don’t need a specific degree to begin a career in Data Science. If you have basic computer knowledge, logical reasoning skills and a curiosity about data, you’re all set to start. Even without a technical background, you can succeed by learning foundational skills like Python programming and statistics.
    Absolutely. Data Science is a fast growing, future proof career. With industries relying heavily on data for decision making, the demand for the skilled data professionals is increasing steadily. Learning the right tools and techniques can open doors to a long-term, fulfilling career.

    The training covers backend and frontend technologies, such as:

    The course covers essential tools such as Python, R, SQL, Excel, Tableau, Power BI and machine learning libraries like Scikit-learn and TensorFlow. You’ll also explore topics like statistics, big data fundamentals, data preprocessing and real-world project implementation.
    Yes. You will be work on multiple real world projects that reinforce your learning. These include tasks like analyzing datasets, building predictive models and creating data visualizations. This hands-on experience makes you job ready.
    Yes, you’ll receive support with resume building, optimizing your LinkedIn profile and preparing for interviews. These services help to increase your chances of getting hired by helping you properly demonstrate your talents.
    Anyone with an interest in working with data is welcome. Whether you’re a student, a working professional or someone considering a career change even from a non-IT background this course is suitable for you.
    No, a degree is not mandatory. What matters is your ability to apply data science concepts and tools to real world problems. With proper training and certification, people from diverse fields have successfully transitioned into data science roles.
    Only basic computer skills and logical thinking are needed. While having a background in math or Excel can help its not required. The course is beginner friendly and starts from the basics.
    Not at all. You’ll be taught programming languages such as Python and R from the ground up. The course is designed for beginners, so even if you’ve never coded before, you’ll learn step-by-step.

    1. What placement assistance is offered during the course?

    You’ll receive end-to-end placement support, including job referrals, resume assistance, career counseling and interview preparation. This guidance continues until you secure a relevant job.

    2. Will I gain practical experience that I can mention in my resume?

    Yes. You’ll complete multiple hands-on projects that demonstrate your skills. These can be showcased in your portfolio or resume to highlight your practical experience to potential the employers.

    3. Can I apply to top companies after completing the course?

    Definitely. After finishing the course, you’ll be eligible to apply to companies such as TCS, Infosys, Accenture, Capgemini and many more. The training and certification will strengthen your profile.

    4. Is placement support available for fresh graduates?

    Yes, freshers receive complete placement assistance. This includes help with internships, resume preparation and mock interviews ensuring a smooth start to your career in the Data Science.
    Yes, you’ll receive a professional certification once you complete the course. This certificate validates your skills and it can be added to your resume and LinkedIn to enhance your job prospects.
    Yes. Data Science continues to be one of the most in demand and high paying fields across industries such as healthcare, finance, e-commerce and IT. The need for data professionals is growing every year.
    No, coding is not prerequisite. The course is structured to teach you Python or R from scratch ensuring you understand every concept even if you’re new to programming.
    The course gives you practical experience, a recognized qualification and skills that are in demand. With placement support included, you’ll be well-prepared for a successful career in data analytics and machine learning.
    You’ll get hands-on training with Python, R, SQL, Excel, Tableau, Power BI, Jupyter Notebook and libraries like NumPy, Pandas, Scikit-learn and Matplotlib. These tools are important for analyzing and visualizing data.

    1. Do I get job assistance after completing the course?

    Yes, you will receive full job support even after completing the course. This includes resume writing, mock interviews, job referrals and internship guidance.
    Fees can vary on the basis of institute’s reputation, trainer expertise, course content, infrastructure and placement success rate. Institutes offering live projects and personalized mentoring may charge more for added value.
    Yes, most training centers offer beginner friendly pricing. You’ll also find flexible EMI options, early bird discounts and special pricing for students and job seekers.
    Yes, our course fee is consistent across cities like Chennai, Bangalore, Pune and others. This ensures equal access to high-quality training no matter where you are.
    Learn (Statistical Analysis + Hypothesis Testing, EDA + Linear & Logistic Regression + ML Algorithm + Machine Learning models) at 18,500/- Only.
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