Data Science Course in Maraimalai Nagar Python & AI Training | Updated 2025

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

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Data Science Training in Maraimalai Nagar

  • Get a Data Science Certification along with complete Job Placement and Career Guidance.
  • Enroll in the Top Data Science Training Institute in Maraimalai Nagar to Master Data Skills.
  • Our Data Science training in Maraimalai Nagar includes Excel, SQL, Python, and Power BI.
  • Work on real-time industry projects and gain practical experience with guidance from experts.
  • Choose a learning schedule that fits your routine with weekday, weekend, or fast-track options.
  • We help you craft your Resume, Preparing for Interviews, and Building a Successful Career.

WANT IT JOB

Become a Data Scientist in 3 Months

Freshers Salary

3 LPA

To

8 LPA

Affordable High-Quality Training in Maraimalai Nagar!
INR ₹28000
INR ₹18500

11090+

(Placed)
Freshers To IT

5526+

(Placed)
NON-IT to IT

7895+

(Placed)
Career Gap

4478+

(Placed)
Less Then 60%

Our Hiring Partners

Overview of Data Science Course

Our Data Science Training in Maraimalai Nagar is designed specifically for beginners, providing a step-by-step introduction to the core concepts of data science. You'll get hands-on experience with tools like R, Tableau, Jupyter Notebook, and Pandas while working on real-world projects. The program covers key skills such as data collection, cleaning, and analysis to help you extract meaningful insights. After completing the course, you'll earn a certification and be fully equipped to start your career in data science.

What You'll Learn From Data Science Training

The Data Science Course in Maraimalai Nagar is ideal for fresh graduates and working professionals looking to develop their data skills from scratch.

You will gain practical experience with Python, SQL, Machine Learning, and visualization tools like Power BI and Tableau to deepen your data knowledge.

The program emphasizes recognizing data patterns, addressing real-world challenges, and supporting data-driven business decisions.

Under the mentorship of skilled trainers, you will work on live projects to gain hands-on, industry-relevant experience.

By the conclusion of the course, you will have a strong grasp of essential data science methods and earn a valuable certification.

This training paves the way for lucrative career opportunities in data science or business intelligence with leading organizations.

Additional Info

Course Highlights

  • Start your Data Science career by mastering Python, R, SQL, Machine Learning, and Data Visualization tools all within one comprehensive program.
  • Enjoy robust job placement support and access to openings at leading companies that are actively recruiting data science talent.
  • Become part of a community of over 11,000 learners who have been trained and placed through our network of more than 350 hiring partners.
  • Learn from experienced instructors who bring over 10 years of practical industry knowledge.
  • Benefit from beginner-friendly lessons, hands-on real-time projects, and full career guidance throughout your course.
  • Take advantage of affordable fees, flexible batch timings, and guaranteed placement assistance perfect for beginners and freshers.
  • Build practical expertise and gain valuable real-world experience to confidently launch your career in data science.

Exploring the Benefits of Data Science Course

  • Python Programming: Python is the leading language for data science due to its simplicity and extensive libraries such as Pandas, NumPy, and Scikit-learn. It supports data manipulation, analysis, and predictive modeling. Its easy-to-read syntax suits both beginners and advanced users, providing a strong base for automation and data handling.
  • SQL (Structured Query Language): SQL is critical for querying and managing data in relational databases. It helps data scientists retrieve large datasets efficiently for analysis. Mastering SQL enables effective data joining and aggregation, making it essential for accessing business data.
  • Excel: Excel remains a powerful tool for quick data analysis, visualization, and reporting, especially with small to medium datasets. Features like pivot tables and charts allow users to identify patterns easily. Many organizations still rely on Excel during initial data processing.
  • Tableau or Power BI: These tools turn raw data into interactive dashboards and visuals that help communicate insights to non-technical audiences. Learning them enhances your ability to clearly present data-driven findings. Visual storytelling is a key skill in data science workflows.
  • Jupyter Notebooks: Jupyter Notebooks offer an interactive environment for writing and running Python code. They combine code, results, and notes in one document, ideal for collaboration and documentation. Data scientists use notebooks for experimentation, visualization, and sharing reports, making them essential for hands-on learning.

Essential Tools Covered in Data Science Training in Maraimalai Nagar

  • Python Programming: Python is the leading language in data science thanks to its simplicity and powerful libraries like Pandas, NumPy, and Scikit-learn. It supports data manipulation, analysis, and model development, making it perfect for learners at any level.
  • SQL (Structured Query Language): SQL is vital for querying and managing data stored in databases. It helps data professionals retrieve, join, and organize large datasets efficiently, making it a core skill for real-world data analysis.
  • Excel: Excel continues to be widely used for quick data exploration, visualization, and reporting. With features like pivot tables and charts, it remains a go-to tool for businesses handling small to medium-sized data.
  • Tableau or Power BI: These tools help turn raw data into engaging dashboards and charts. They enable you to communicate insights effectively to non-technical stakeholders, making them essential for clear and impactful storytelling.
  • Jupyter Notebooks: Jupyter Notebooks provide a collaborative environment where you can write, run, and document Python code. They’re commonly used for data exploration, visualization, and sharing analysis, making them an important part of any data science toolkit.

Key Frameworks Every Aspiring Data Scientist Should Learn

  • TensorFlow: An open-source library by Google, TensorFlow is widely used in machine learning and deep learning. It supports building neural networks for large-scale data and is adaptable across various platforms including mobile and cloud.
  • PyTorch: Developed by Facebook, PyTorch is known for its dynamic computation and user-friendly interface. It’s popular in academia and research, especially in areas like natural language processing, and is great for fast experimentation.
  • Scikit-learn: A versatile Python library for data analysis and machine learning. It supports a range of algorithms like classification and clustering, and is beginner-friendly due to its simple interface and compatibility with other Python tools.
  • Apache Spark: Apache Spark is a fast, scalable computing framework designed for big data processing. Its in-memory processing and MLlib module make it ideal for handling large datasets in real-time across various industries.
  • Keras: Built on TensorFlow, Keras provides an easy-to-use interface for building deep learning models. It’s highly modular and intuitive, making it ideal for rapid development, especially in areas like image and speech recognition.

Core Skills You Will Gain from a Data Science Course in Maraimalai Nagar

  • Data Analysis: Learn to explore, clean, and interpret data to uncover patterns and insights. This foundational skill helps you turn raw data into meaningful information that drives decision-making.
  • Programming with Python or R: Develop your coding skills in Python or R to create scripts for data analysis, automation, and modeling. Both languages are essential for analytics and widely used in the industry.
  • Machine Learning Fundamentals: Gain knowledge of algorithms such as linear regression, clustering, and decision trees. Learn how to train and test models, apply the right techniques, and evaluate outcomes effectively.
  • Data Visualization: Build your ability to represent data visually using tools like Tableau, Power BI, Matplotlib, and Seaborn. These skills are critical for making complex information clear and engaging for any audience.
  • SQL and Data Management: Master SQL to extract and manage data efficiently. Learn how to work with large datasets, combine different sources, and maintain high data quality for reliable results.

Roles and Responsibilities in a Data Science Career Path

  • Data Science Manager: Manages data teams and ensures alignment with business goals. They coordinate tasks, set priorities, and translate data insights into strategic decisions. Also mentors team members to enhance analytical capabilities.
  • Data Science Consultant: Collaborates with clients to address specific challenges using data. Consultants gather and analyze data, apply models, and offer expert recommendations across various industries.
  • Machine Learning Engineer: Focuses on designing and optimizing algorithms that improve automatically. They develop and deploy models into production, often working closely with developers and data scientists.
  • Data Analyst: Specializes in examining datasets to find trends and insights. Uses tools like SQL and Excel to create reports that inform decision-making across departments. Collaborates with teams to optimize operational efficiency through data-driven strategies.
  • Data Engineer: Builds and maintains the infrastructure needed for data storage and analysis. They ensure data pipelines are efficient and scalable, enabling smooth access to high-quality data.

Why Data Science is a Promising Career Choice for Freshers

  • Growing Demand Across Sectors: Data science roles are available in nearly every industry, from finance to healthcare. Freshers can secure stable jobs such as Junior Data Analyst or Data Scientist in this expanding field.
  • Attractive Entry-Level Salaries: Even beginner roles offer strong compensation. As demand for data expertise grows, so does the earning potential, making it a rewarding field financially. Career growth is rapid with continuous learning and upskilling.
  • Wide Range of Career Paths: Whether you're interested in technical development or business strategy, data science offers roles like AI Developer, Data Engineer, or Machine Learning Consultant, providing long-term growth.
  • Tangible Impact: Freshers get the chance to work on meaningful problems like customer retention or risk assessment. Seeing real results from your work adds purpose and motivation to your career.
  • Ongoing Learning and Development: The field constantly evolves, offering freshers continuous opportunities to grow through certifications, new tools, and real-world projects. Early roles like Junior Data Strategist or Analyst provide a solid learning foundation.

How Data Science Expertise Enables Remote Work

  • Driving Business Decisions from Anywhere: With data interpretation skills, you can contribute as a remote Data Analyst or BI Consultant, supporting global teams in making informed decisions.
  • Automation and AI Implementation: Build intelligent systems that run with minimal human input. As a remote ML Engineer, your work contributes to scalable automation solutions, regardless of your physical location.
  • Cloud Platform Proficiency: Mastery of tools like AWS, Azure, or Google Cloud allows you to work from anywhere. Positions such as Cloud Analyst or Data Science Manager often involve fully remote responsibilities.
  • Clear Communication Through Visuals: Visualizing data insights for non-technical teams is crucial, especially remotely. As a Visualization Specialist, your ability to present findings through Tableau or Power BI enhances global collaboration.
  • Cross-Functional Teamwork: Data scientists often work with multiple departments. Remote roles like Data Consultant or Strategist involve using collaboration platforms to coordinate and deliver insights to distributed teams.

What to Expect When Starting Your First Data Science Job

  • Data Cleaning is Key: A large part of your early work will involve cleaning and organizing raw data, ensuring accuracy and consistency before analysis begins. This foundation is crucial for building reliable models and generating meaningful insights.
  • Solving Real Business Issues: Your tasks will revolve around finding insights to address real company problems. Whether it's forecasting or customer behavior, your work must have practical value.
  • Teamwork is Crucial: Collaboration with engineers, analysts, and domain experts is routine. Communicating your results clearly to non-technical stakeholders is just as important as the analysis itself.
  • Learning Never Stops: Every organization uses different tools and methods. Be prepared to learn platforms like SQL, Python, or AWS on the job as you adapt to different environments. Flexibility and continuous learning are key, as technology stacks and data needs can vary widely between industries and roles.
  • Embracing Feedback: Your initial outputs may need revisions. Constructive feedback is part of the process, helping you refine your models and grow your expertise. Over time, you'll develop stronger problem-solving skills and a deeper understanding of data workflows.

Top Companies Actively Hiring Data Science Professionals

  • Google: Employs data scientists to refine search, personalize content, and power AI tools. Projects span NLP, analytics, and large-scale data systems. It offers exposure to cutting-edge research and large-scale data challenges.
  • Amazon: Leverages data science for recommendations, pricing strategies, and logistics optimization. Offers valuable experience with big data in a customer-focused environment. The fast-paced setting encourages rapid innovation and experimentation.
  • Microsoft: Uses data to enhance cloud services, productivity tools, and platforms like LinkedIn. Data scientists work on fraud detection, AI applications, and business intelligence. Cross-functional collaboration is key, offering diverse project opportunities.
  • IBM: Known for innovations in AI and analytics, IBM provides roles focused on healthcare, finance, and enterprise solutions, with strong emphasis on continuous learning. Its rich legacy in tech offers a stable yet evolving career path.
  • Accenture: Offers opportunities in digital transformation and data strategy across industries. Data scientists here work on visualization, machine learning, and predictive modeling projects for global clients.
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Upcoming Batches For Classroom and Online

Weekdays
01 - Sep- 2025
08:00 AM & 10:00 AM
Weekdays
03 - Sep - 2025
08:00 AM & 10:00 AM
Weekends
06 - Sep - 2025
(10:00 AM - 01:30 PM)
Weekends
07 - Sep - 2025
(09:00 AM - 02:00 PM)
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INR ₹18500
INR ₹28000

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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

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 Maraimalai Nagar

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

Students enrolling in the Data Science Course in Maraimalai Nagar have the option to select a specialized track that matches their interests and career objectives, boosting their prospects of securing roles with leading companies. This flexible learning approach enables them to focus on areas such as machine learning, data visualization, or statistical modeling, all while building a strong foundation in core data science skills.

  • Data Science with Python – Teaches to use Python and popular libraries like Pandas, NumPy and Matplotlib to handle, analyze and visualize data effectively.
  • Data Science with R – Focuses on using the R programming language to perform data analysis, create graphs and carry out statistical tasks in different fields.
  • Business Data Science – Uses tools like Excel, Power BI and SQL to understand business data, find patterns and help in making smart decisions.
  • Machine Learning in Data Science – Provides practical training on to build models, work with data and make predictions using tools like Python and scikit-learn.
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

Get Practical Experience with Real-World Data Science Projects

Placement Support Overview

Today's Top Job Openings for Data Science Training in Chennai

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 Chennai

    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.
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    Skill Development Workshops

    • 1. Participate in focused sessions on trending technologies and tools.
    • 2. Learn directly from industry experts through guided practical exercises.
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    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.
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    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.
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    Soft Skills & Career Readiness

    • 1. Improve communication, teamwork, and time management skills.
    • 2. Prepare for interviews and workplace dynamics with mock sessions and guidance.
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    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 an interdisciplinary field focused on extracting valuable insights from large volumes of data by integrating principles from computer science, statistics, and subject matter expertise. It includes processes like gathering data, cleaning it, analyzing it, and visualizing the results, utilizing techniques such as machine learning, predictive modeling, and big data tools.

    Ans:

    • Supervised Education: This approach involves using labeled data to train models, where each input is paired with a known output. The objective is for the algorithm to learn the relationship between inputs and outputs to make accurate predictions.

    • Unsupervised Education: In this case, the data lacks labels. The algorithm tries to discover underlying structures, such as clusters or associations, by analyzing the similarities and patterns in the data.

    Ans:

    The bias-variance tradeoff is the challenge of choosing a model that balances underfitting and overfitting.

    • Bias: Describes mistakes brought on by the learning algorithm's unduly basic assumptions.
    • Variance: Indicates mistakes brought on by the learning algorithm's excessive complexity.

    Ans:

    Overfitting occurs when a model learns not only the true patterns but also the noise and anomalies in the training data. While it performs very well on the training dataset, its performance deteriorates on new, unseen data because it doesn’t generalize well.

    Ans:

    Popular Python libraries like Matplotlib and Seaborn are widely used for creating a variety of visualizations, including static, interactive, and animated charts.

    Ans:

    Precision it measures how well optimistic predictions come true. It is calculated as the proportion of accurately anticipated positive observations to all projected positive observations. Sensitivity, also known as recall, gauges how well the classifier can identify every good example. It is the proportion of accurately forecasted positive observations to all of the dataset's actual positive observations.

    Ans:

    • This table compares actual and expected classifications in order to assess well a classification model is performing.
    • Positive instances that are anticipated to be positive are known as true positives (TP).
    • Instances that are negative and anticipated to be negative are known as true negatives (TN).
    • False Positives (FP): Negative cases that were anticipated to be positive.

    Ans:

    • Deletion: Remove rows with missing values. It’s straightforward but may reduce data volume if used excessively.
    • Mode Imputation: For categorical variables, fill in missing values using the most frequent category.
    • Predictive Imputation: Use models such as KNN or decision trees to estimate missing values using patterns in the other available data.

    Ans:

    A decision tree is a model that uses a branching structure resembling a flowchart. Each internal node represents a feature test, each branch corresponds to a decision rule, and each leaf node signifies a predicted outcome. It’s widely used for both classification and regression problems.

    Ans:

    Regularization is a method to prevent overfitting by adding a penalty term to the model’s loss function. This penalty discourages the model from fitting noise in the training data. The two primary types are L1 regularization (Lasso), which promotes sparsity, and L2 regularization (Ridge), which shrinks coefficients.

    Company-Specific Interview Questions from Top MNCs

    1. How is Data Science Different from Traditional Data Analysis?

    Ans:

    Data science focuses on extracting actionable insights from vast and complex datasets using advanced technologies like Python, machine learning, and statistical methods. In contrast, traditional data analysis mainly deals with examining past data trends and creating basic reports, while data science goes further by forecasting future outcomes and solving intricate problems through algorithms and programming.

    2. What distinguishes supervised learning from unsupervised learning?

    Ans:

    Supervised learning involves training a model on labeled data where both input and output are known, much like learning under direct supervision. Unsupervised learning works with unlabeled data, allowing the model to detect hidden patterns or clusters, similar to identifying groups of people with common interests without prior information.

    3. What is overfitting in machine learning, and how can you prevent it?

    Ans:

    Overfitting occurs when a model learns the training data too precisely, including noise and errors, leading to poor generalization on new data. Common ways to avoid overfitting include using cross-validation, simplifying the model, and applying regularization techniques.

    4. Explain the bias-variance tradeoff.

    Ans:

    Bias is error from making wrong assumptions; variance is error from being too sensitive to small changes in data. A good model balances both too much bias leads to underfitting, while too much variance leads to overfitting.

    5. What Are the Key Differences Between Python and R in Data Science?

    Ans:

    Python is versatile and widely used for building data-driven applications and machine learning models, favored by many industries. R is specialized in statistical analysis and data visualization, making it a preferred choice in academia and research.

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

    Ans:

    Missing data can be addressed by deleting records with missing values, filling in missing spots with averages or the most common values, or using sophisticated techniques like interpolation or predictive modeling to estimate the missing information.

    7. Explain the concept of feature engineering.

    Ans:

    Feature engineering means creating new input variables (features) from existing data to help the model learn better. It involves cleaning data, transforming values, and combining features to improve prediction accuracy.

    8. What is the difference between a classification and a regression problem?

    Ans:

    In classification, you predict categories like “spam” or “not spam.” In regression, you predict continuous values like a house price or temperature. Both are types of supervised learning but solve different problems.

    9. What is a confusion matrix in classification?

    Ans:

    A confusion matrix shows how well a classification model performs by comparing actual values with predicted ones. It breaks results into categories like true positives, false positives, true negatives, and false negatives.

    10. What are precision and recall?

    Ans:

    Precision tells you how many of the predicted positives are actually correct. Recall indicates how many of the real benefits were correctly predicted. They help measure how well a model finds the right results.

    11. What is cross-validation, and why is it important?

    Ans:

    Cross-validation tests a model’s accuracy by dividing data into parts, training on some, and testing on others. It helps make sure the model performs well on unseen data and avoids overfitting.

    12. What is regularization used for in machine learning?

    Ans:

    Regularization adds a penalty for complexity in a model’s loss function to discourage overfitting. It promotes simpler models that generalize better to new data.

    13. What is a decision tree, and how does it work?

    Ans:

    A decision tree is a predictive model that splits data based on feature values through a series of decision rules arranged like a flowchart, ultimately leading to classification or regression outputs at its leaves.

    14. What are the differences between bagging and boosting?

    Ans:

    Bagging builds multiple models independently and combines them to improve accuracy. Boosting builds models one after another, each one learning from the mistakes of the previous one to improve performance.

    15. What is dimensionality reduction, and why is it important?

    Ans:

    Dimensionality reduction involves reducing the number of input variables while retaining essential information, which speeds up training, reduces noise, and improves model performance, especially in high-dimensional datasets.

    1. What is Data Science?

    Ans:

    Data Science is the discipline of using data to analyze challenges, detect patterns, and make informed decisions by combining math, computer science, statistics, and domain expertise to convert raw data into actionable insights.

    2. What constitutes data science's essential elements?

    Ans:

    The main parts of Data Science include:

    • Data Collection – Gathering data from various sources
    • Data Cleaning – Fixing or removing incorrect or missing data
    • Data Analysis – Exploring and understanding the data
    • Model Building – Algorithms to make predictions
    • Interpretation – Making sense of the results to guide actions

    3. What is a confusion matrix?

    Ans:

    A table called a confusion matrix is employed to evaluate a machine learning model's performance is performing. It shows:

    • True Positives (TP) – Correct positive predictions
    • True Negatives (TN) – Correct negative predictions
    • False Positives (FP) – Incorrect positive predictions
    • False Negatives (FN) – Incorrect negative predictions

    4. What are some common metrics used to evaluate model performance?

    Ans:

    Some popular metrics are:

    • Accuracy – How often the model is right
    • Precision – How many positive predictions were actually correct
    • Recall – How many actual positives were correctly predicted
    • F1 Score – An equilibrium between recall and precision

    5. What is feature engineering?

    Ans:

    Feature engineering is the process of creating or modifying input variables to improve the predictive accuracy of machine learning models by selecting, transforming, or combining features.

    6. How do you handle missing data?

    Ans:

    There are a few ways to deal with missing data:

    • Remove rows or columns with too many missing values
    • Fill in missing values using the mean, median, or mode
    • Use algorithms that handle missing data automatically
    • Predict the missing values using other data

    7. What is overfitting, and how can it be prevented?

    Ans:

    When a model learns too much from the training data, it is said to be overfit. noise and doesn’t work well on new data. You can prevent it by:

    • Using simpler models
    • Cross-validation
    • Regularization techniques
    • More training data

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

    Ans:

    Random forest is an ensemble technique that builds many decision trees using random subsets of data and features, then combines their predictions to enhance accuracy and reduce overfitting.

    9. Describe the steps in the Data Science workflow.

    Ans:

    Here’s a basic flow:

    • Define the problem
    • Collect data
    • Clean and prepare the data
    • Explore the data
    • Build and train models

    10. How do you ensure the quality of your data?

    Ans:

    To ensure data quality:

    • Remove duplicates
    • Fix errors and inconsistencies
    • Fill in or remove missing values
    • Standardize formats
    • Check data sources for reliability

    11. What are some popular libraries used in Data Science with Python?

    Ans:

    Common Python libraries include:

    • Pandas – for data handling
    • NumPy – for numerical operations
    • Matplotlib & Seaborn – for data visualization
    • Scikit-learn – for machine learning
    • TensorFlow & PyTorch – for deep learning

    12. Explain the concept of dimensionality reduction.

    Ans:

    Dimensionality reduction reduces the number of features while preserving essential information, helping to speed up model training, minimize noise, and improve accuracy.

    13. What is A/B testing, and how is it used?

    Ans:

    A/B testing compares two versions of a product or webpage (A and B) to determine which performs better, commonly used in marketing to optimize user engagement and conversion rates.

    14. What is the difference between big data and traditional data?

    Ans:

    Big data refers to extremely large, fast-moving, and complex datasets that traditional tools cannot handle efficiently. Traditional data tends to be smaller, well-structured, and easier to process. Big data requires specialized technologies like Hadoop or Spark for analysis.

    1. What are the responsibilities of a Data Scientist in an organization?

    Ans:

    A Data Scientist helps organizations make data-driven decisions by collecting, preparing, and analyzing data. They identify trends and patterns that provide actionable insights, enabling businesses to increase revenue, cut costs, or improve their products and services.

    2. How do structured and unstructured data differ?

    Ans:

    Structured data is organized in a clear format, usually in tables, like names, dates, or sales figures. Unstructured data lacks this format and includes items such as videos, emails, social media content, and customer reviews, which are harder to analyze.

    3. What are the key steps in a data science project?

    Ans:

    The main steps are:

    • Understanding the problem
    • Collecting data
    • Cleaning the data
    • Exploring the data
    • Choosing a model

    4. How is missing data in a dataset handled?

    Ans:

    You can handle missing data by:

    • Removing the rows with missing values
    • Filling them with an average, median, or a guessed value
    • Using models that can handle missing data directly

    5. How does supervised learning differ from unsupervised learning?

    Ans:

    • Supervised learning uses labeled data (you know the answers, like whether an email is spam or not).
    • Unsupervised learning uses data without labels to find hidden patterns (like grouping similar customers).

    6. Explain the concept of cross-validation in model evaluation.

    Ans:

    Cross-validation is a method to evaluate how a model will perform on unseen data by dividing the dataset into multiple parts, training on some, and testing on others multiple times.

    7. Explain overfitting and how to avoid it.

    Ans:

    Overfitting occurs when a model learns the training data too precisely, including noise, which reduces its performance on new data. It can be prevented by using simpler models, increasing training data, and applying techniques such as cross-validation and regularization.

    8. What is a confusion matrix? Explain its components.

    Ans:

    A confusion matrix shows how well a classification model performs:

    • True Positive (TP): Correctly predicted positive cases
    • True Negative (TN): Correctly predicted negative cases
    • False Positive (FP): Incorrectly predicted as positive
    • False Negative (FN): Incorrectly predicted as negative

    9. How do you select important features in a dataset?

    Ans:

    You can pick key features by:

    • Looking at correlation with the target
    • Using feature selection methods (like backward elimination)
    • Trying models that rank features, like decision trees or Lasso regression

    10. Explain the working of the k-nearest neighbors (KNN) algorithm.

    Ans:

    KNN classifies a new data point based on the majority label of its ‘k’ nearest neighbors in the feature space. It’s a simple yet effective method, especially for smaller datasets.

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

    Ans:

    Overfitting happens when a model fits the training data, including noise, too closely, causing poor performance on new data. Avoid it by using simpler models, gathering more data, and applying regularization or cross-validation.

    2. What is Cross-Validation?

    Ans:

    Cross-validation is a way to test how well a model will perform on new data. It splits the dataset into parts some for training, others for testing and repeats this process several times. One popular method is k-fold cross-validation, where the data is split into k parts.

    3. Steps in the Data Science Process

    Ans:

    • Understand the problem – Know what you’re trying to solve.
    • Collect data – Gather the information you need.
    • Clean the data – Fix or remove errors, missing values, etc.
    • Explore the data – Find patterns or relationships.
    • Build models – Use machine learning algorithms to make predictions.

    4. What is Feature Engineering?

    Ans:

    Feature engineering involves creating new features from raw data to improve model learning, such as deriving an ‘age’ feature from a date of birth.

    5. What is a Confusion Matrix?

    Ans:

    A confusion matrix shows how well a classification model is working. It compares actual results with predictions.

    • True Positive (TP): Correctly predicted yes
    • True Negative (TN): Correctly predicted no
    • False Positive (FP): Predicted yes, but it was no
    • False Negative (FN): Predicted no, but it was yes

    6. Difference Between Precision and Recall

    Ans:

    • Precision: Out of all predicted "yes", how many were actually "yes"?
    • Recall: Out of all actual "yes", how many did we predict correctly?

    7. What is a Decision Tree, and How Does It Work?

    Ans:

    A decision tree is a model that makes predictions by asking a series of yes/no questions, branching based on answers, similar to a flowchart leading to a conclusion.

    8. What is Regularization, and Why Is It Used?

    Ans:

    Regularization is used to reduce overfitting by adding a penalty to complex models. It helps keep models simple and better at generalizing to new data. Common types: L1 (Lasso) and L2 (Ridge) regularization.

    9. Purpose of PCA (Principal Component Analysis)

    Ans:

    PCA reduces the number of features in large datasets while preserving most important information, which helps with visualization and speeds up model training.

    10. What is Time Series Analysis?

    Ans:

    Time series analysis deals with data points collected over time (e.g., stock prices, weather). It helps find trends, patterns, and make forecasts based on past behavior.

    1. What is backpropagation?

    Ans:

    Backpropagation is a training method for neural networks where errors between predicted and actual outputs are sent backward through the network to update weights and improve the model.

    2. What is the difference between a crossover and a straight-through?

    Ans:

    Crossover usually refers to swapping or combining inputs, commonly seen in genetic algorithms or crossover network cables connecting similar devices. Straight-through cables connect inputs directly to outputs without switching, typically linking different device types.

    3. What is SMTP?

    Ans:

    SMTP stands for Simple Mail Transfer Protocol. It’s the set of rules computers use to send and receive emails. When you send an email, SMTP moves it from your email program to the email server before sending it to the email address of the addressee server.

    4. What is clustering support?

    Ans:

    Clustering support means setting up multiple computers or servers to work together like a single system. If one fails, the others can take over, keeping the system running smoothly. It’s often used to increase speed, manage more users, and ensure systems stay online even during hardware problems.

    5. What is the role of IEEE in computer networking?

    Ans:

    IEEE (Institute of Electrical and Electronics Engineers) develops networking standards that ensure devices can interoperate, such as the Wi-Fi standard IEEE 802.11.

    6. What do you know about machine learning?

    Ans:

    Computers can learn from data through a process called machine learning instead of being directly programmed. For example, you can train a computer to recognize photos of cats by showing it thousands of pictures. Over time, the system improves its accuracy by learning patterns from the data.

    7. Can you explain function overloading?

    Ans:

    Function overloading allows multiple functions to share the same name but differ in input parameters. The appropriate function is chosen based on the arguments passed, improving code clarity.

    8. What do you know about the Python language?

    Ans:

    Python is a simple, easy-to-read programming language utilized in numerous domains, including automation, data research, and web development. It uses clean, English-like syntax, which makes it great for beginners. Python is known for being powerful yet beginner-friendly.

    9. What do you understand about tunnelling protocol in Computer Networks?

    Ans:

    Tunneling is the process of encapsulating one type of data within another to securely transmit it over a network, commonly used in VPNs to protect data privacy.

    10. Explain the DDL, DML, and DCL statements in SQL.

    Ans:

    • Data Definition Language, or DDL, is used to develop or change the structure of a database (like CREATE, ALTER, DROP).
    • DML (Data Manipulation Language) is for working with data inside tables (like INSERT, UPDATE, DELETE).
    • DCL (Data Control Language) manages permissions and access (like GRANT, REVOKE).

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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

    Kickstart Your Data Science Career with Our Maraimalai Nagar Program

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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.

    • Google Data Analytics Certificate
    • Microsoft Certified Azure Data Scientist Associate
    • SAS Certified Data Scientist
    • Certified in Data Science Specialization

    While a Data Science certification can greatly enhance your visibility to recruiters, it does not ensure job placement. Certifications demonstrate your knowledge and commitment, but employers also look for practical experience, problem-solving skills, and the ability to apply knowledge effectively. Hiring decisions usually consider a combination of education, hands-on experience, and communication skills.

    The time required to earn a Data Science certification varies depending on the course and your learning pace. Part-time or shorter programs typically take around 3 to 6 months, while more intensive or comprehensive courses may last up to a year. With regular study and practice, certification can be achieved within a few months.

    • Validates your full-stack development skills
    • Increases job opportunities in top tech companies
    • Builds confidence to work on front-end and back-end projects
    • Provides structured learning with real-time project experience
    • Enhances your resume and LinkedIn profile visibility
    • Study the official course materials and guidelines
    • Practice with real-world datasets regularly
    • Work on hands-on projects to strengthen your portfolio
    • Revise key concepts like statistics, Python/R, SQL and ML
    • Take mock exams or quizzes to test your understanding

    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.

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    Lowest Data Science Fees in Maraimalai Nagar

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

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    Why Choose ACTE’s Data Science Course in Maraimalai Nagar?

    Feature

    ACTE Technologies

    Other Institutes

    Affordable Fees

    Competitive Pricing With Flexible Payment Options.

    Higher Data Science Fees With Limited Payment Options.

    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.

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    Placement Support

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

    Basic Placement Support

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    Strong Ties With Top Tech Companies for Internships and Placements

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    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 Are the Requirements for Becoming a Data Scientist?

    To pursue a career as a Data Scientist, a strong foundation in mathematics, statistics, and programming languages such as Python or R is essential. A bachelor’s degree in engineering, computer science, or related fields is advantageous. Additionally, skills in analytical thinking, data interpretation, database management, and machine learning are important.
    The demand for Data Scientists is consistently strong across industries like tech, finance, healthcare, manufacturing, and e-commerce. As data-driven strategies become more central to business operations, skilled professionals in data science, machine learning, and AI are increasingly sought after, offering attractive pay and long-term job stability.

    The training covers backend and frontend technologies, such as:

    • Python and R programming
    • Pandas, NumPy, Matplotlib and Seaborn
    • SQL and databases
    • Machine Learning with scikit-learn
    • Data visualization using Tableau or Power BI
    Yes, the course includes real-time, hands-on projects designed to simulate industry scenarios. These assignments assist you in putting your theoretical knowledge into practice situations, such as building predictive models, performing data analysis and solving business problems using data.
    Yes, resume support is part of the course. We help craft a professional, role-focused resume that highlights your projects and showcases your skills effectively for job applications.
    Anyone with an interest in data, analytics, and solving complex problems can join the program. It’s open to students, professionals, and career switchers from any educational background, as long as they have basic computer skills.
    While degrees in computer science, mathematics, statistics, or engineering are beneficial, they are not always mandatory. Many professionals enter the field through online courses, certifications, and hands-on project experience without formal degrees.
    • Mathematics and statistics
    • Logical thinking and problem-solving
    • Basic programming (preferably Python)
    • Familiarity with Excel or spreadsheets
    No prior knowledge of frontend or backend development is necessary. Data Science focuses more on data analysis, programming, machine learning, and visualization. However, understanding how data flows through applications can be helpful.

    1. What placement assistance is available after completing the Data Science course?

    We provide comprehensive Data science placement support includes one-on-one career counseling, aptitude training, resume preparation and practice interviews. Additionally you will receive assistance with interview preparation and career referrals specific to data science positions.

    2. Will I Get Access to Real-Time Projects for My Resume?

    Yes, the training includes industry-relevant projects that you can showcase in your resume and portfolio to demonstrate your practical skills to potential employers.

    3. Can I Apply for Jobs in Top IT Companies After Completing the Course?

    Absolutely. With the skills, certification, and project experience acquired, you will be eligible to apply for data science positions in top IT companies, multinational corporations, and startups across various industries.

    4. Is Placement Support Available for Freshers with No Experience?

    Yes, we offer full placement assistance for beginners. The program is designed to build job-ready skills from scratch, and our placement team actively helps freshers secure their first role in data science.
    Yes, once you complete the Data Science course After finishing successfully, you will be given a certificate that says validates your learning and skill development. This certification is a formal recognition of your expertise and can strengthen your resume applying for data centric roles in various industries.
    Learning Data Science is a highly rewarding investment for your career. In today’s digital age data is one of the most valuable assets for businesses. Businesses in every industry are aggressively looking for experts can interpret data and extract actionable insights. With the growing demand for data-driven decision-making, skilled data scientists are in high demand, making this field both lucrative and future-proof.
    Most courses welcome beginners and don’t require advanced prior knowledge. However, having a basic understanding of math, statistics, and programming (especially Python or R) helps. Familiarity with Excel, databases, or analytical thinking also accelerates learning.
    Certification opens doors to opportunities in sectors like healthcare, finance, retail, and technology. It proves you have practical skills in handling large datasets, creating predictive models, and supporting business decisions through data-driven insights.
    Throughout the course, you’ll develop a range of technical and analytical skills, including data wrangling, statistical analysis, machine learning, data visualization, and programming in Python or R. You'll also work with real-world datasets using tools like Jupyter Notebook, SQL, and Tableau. These skills will prepare you to analyze complex data, draw meaningful conclusions, and support data-driven decisions in a professional setting.

    1. Will I Get Support for Job Placement After the Course?

    Yes, we provide comprehensive placement assistance including resume building, interview coaching, mock interviews, and access to job opportunities through our placement network.
    Fees vary due to differences in course duration, instructor expertise, infrastructure, access to live projects, certifications, and placement services. Premium centers may charge more for advanced resources and experienced trainers.
    Yes, the course is designed to be affordable for beginners, offering step-by-step training, hands-on practice and support without requiring prior experience in data science.
    Yes, our pricing is consistent and affordable across all locations. Whether you study in a major city or a smaller town, the quality and cost of the course remain the same to provide equal learning opportunities. We maintain transparent and fair pricing regardless of location.
    Learn (Statistical Analysis + Hypothesis Testing, EDA + Linear & Logistic Regression + ML Algoritham + Machine Learning models) at 18,500/- Only.
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