Data Science Training With Placement in Chennai - Online & Classroom Training Courses and Certification | ACTE

Data Science Course with 100% Job Guarantee

  • Training for Experienced and New Learners.
  • With 14+ Years of Certified Data Science Expertise.
  • Professional Training With 100% Placement Assurance.
  • 445+ Clients for Recruiting and 10315+ Trained Students.
  • Access videos, study materials, and interview questions.
Hands On   40+ Hrs
Projects   4 +
Placement Support   Lifetime Access

    Course Fees on Month ₹8999 ₹18000
    (Lowest price in chennai)

    See why over 25,000+ Students choose Acte

    Data Science Course Curriculam

    Curriculam Designed By Experts

    Expertly designed curriculum for future-ready professionals.

    Industry Oriented Curriculam

    An exhaustive curriculum designed by our industry experts which will help you to get placed in your dream IT company

      30+  Case Studies & Projects
      9+  Engaging Projects
      10+  Years Of Experience
  • Overview of Data Science and its applications
  • Importance of Data Science in various industries
  • Introduction to the Data Science life cycle
  • Review of basic math concepts (linear algebra, calculus, etc.)
  • Descriptive statistics and exploratory data analysis
  • Probability theory and distributions
  • Statistical inference and hypothesis testing
  • Introduction to Python programming language
  • Data manipulation and exploration with Pandas library
  • Data visualization with Matplotlib and Seaborn libraries
  • Introduction to NumPy and SciPy libraries
  • Introduction to supervised and unsupervised learning
  • Linear regression and logistic regression
  • Decision trees and random forests
  • Support Vector Machines (SVM)
  • Clustering algorithms (k-means, hierarchical clustering)
  • Data cleaning and preprocessing techniques
  • Handling missing data and outliers
  • Feature extraction and transformation
  • Handling categorical variables
  • Evaluation metrics for regression and classification models
  • Cross-validation and overfitting
  • Hyperparameter tuning
  • Model selection techniques
  • Deploying models in a production environment
  • Creating web interfaces for model deployment
  • Communicating findings to non-technical stakeholders
  • Creating data-driven reports and visualizations
  • Image Classification
  • Time Series Forecasting
  • Customer Segmentation
  • Advanced regression models (e.g., multiple regression, polynomial regression)
  • Time series analysis and forecasting
  • Nonparametric statistics
  • Bayesian statistics
  • Introduction to deep learning and neural networks
  • Convolutional Neural Networks (CNN) for image classification
  • Recurrent Neural Networks (RNN) for sequential data analysis
  • Generative models, such as Variational Autoencoders (VAE) and
  • Generative Adversarial Networks (GAN)
  • Introduction to NLP and its applications
  • Text preprocessing techniques
  • Text classification and sentiment analysis
  • Named Entity Recognition (NER) and Part-of-Speech (POS) tagging
  • Topic modeling and text summarization
  • Introduction to big data and distributed computing frameworks (e.g., Hadoop, Spark)
  • Processing and analyzing large-scale datasets
  • Scalable machine learning algorithms
  • Real-time streaming analytics
  • Advanced model deployment techniques (e.g., containerization)
  • Building scalable and robust production systems
  • A/B testing and model versioning
  • Monitoring and maintaining models in production
  • Object Detection
  • Fraud Detection
  • Time Series Forecasting
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    Data Science Training Projects

    Become a Data Science Expert With Practical and Engaging Projects.

      Practice essential Tools
      Designed by Industry experts
      Get Readl-world Experience

    Image Classification

    Develop a model that can classify images into different categories, such as identifying objects or recognizing handwritten digits.


    Time Series Forecasting

    Use historical data to predict future patterns and trends, such as predicting stock prices or forecasting demand for a product.


    Customer Segmentation

    Segment customers into distinct groups based on their behavior, demographics, or purchase history to better understand their preferences and needs.


    Color Detection

    Build an algorithm that can detect and extract colors from images. Use computer vision techniques, image processing, and clustering algorithms to identify and segment colors present in an image.


    Diabetic Retinopathy

    Create a model to detect and classify diabetic retinopathy in retinal images. Use image processing, feature extraction, and deep learning techniques to identify the severity of the disease.


    Brain Tumor Detection

    Develop a model to detect brain tumors from MRI scans. Use image processing, feature extraction, and deep learning techniques to accurately identify the presence and location of tumors.


    Object Detection

    Develop a model that can detect and classify objects within images, using techniques like deep learning and convolutional neural networks (CNNs).


    Fraud Detection

    Build a model to detect fraudulent transactions in a financial dataset, using techniques such as anomaly detection or supervised classification.


    Time Series Forecasting

    Use historical time series data to develop a model that predicts future values, such as stock prices, weather patterns, or energy consumption, using techniques like ARIMA, LSTM, or Prophet.

    Key Highlights




    Doubt Solving




    20+ Case studies
    & Projects

    Resume Building


    Career Opportunities After Data Science Training

    Data Analyst

    Entry-level role that involves collecting, cleaning, and analyzing data to provide insights for decision-making. Often focuses on descriptive statistics and data visualization.

    Business Analyst

    Examines corporate data to detect trends, patterns, and potential enhancements in operational workflows, aiding in the formulation of strategic choices.

    Data Scientist

    Develops predictive and prescriptive models using data to solve complex business problems. Requires expertise in data analysis, machine learning, and statistical modeling.

    Machine Learning Engineer

    Focuses on designing, building, and deploying machine learning models. Involves a strong background in programming and machine learning algorithms.

    AI Researcher

    Engaged in groundbreaking AI research, actively propelling the evolution of novel algorithms and models, advancing the forefront of artificial intelligence.

    Data Engineer

    Works on data collection, storage, and processing to ensure data availability for analysis. Involves skills in data warehousing, ETL (Extract, Transform, Load), and database management.

    Skill to Master
    Data Science
    Data Analysis
    Data Wrangling
    Story Telling
    Machine Learning
    Data visualization
    Prediction Algorithms
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    Tools to Master
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    Our Instructor

    Learn from the certified and real time working professionals.

    Data Science Certification


    Data Science certifications can enhance your credibility and employability in the field. They demonstrate to employers that you have the necessary skills and knowledge to work on data-related projects.

    Some popular Data Science certification programs include:

  • Certified Data Scientist (CDS) by DASCA
  • IBM Data Science Professional Certificate
  • Microsoft Certified: Azure Data Scientist Associate
  • Google Data Analytics Professional Certificate
  • Cloudera Certified Data Scientist (CCDS)
  • While a certification is not always required, it can be beneficial, especially for those starting their careers or looking to switch to Data Science. It helps you stand out in a competitive job market.

    Prerequisites vary depending on the certification program, but they often include a basic understanding of statistics, programming (e.g., Python or R), and mathematics. Some advanced certifications may require prior work experience.

    The duration varies based on the program. Some certificates can be completed in a few weeks, while others may take several months. Full-time or part-time options are often available.

  • Data analysis
  • Machine learning
  • Data visualization
  • Statistics
  • Data preprocessing
  • Tools like Python, R, SQL, and more
  • Yes, it’s possible to get a job with a Data Science certification and relevant skills, but having a degree (e.g., in Computer Science, Statistics, or a related field) can be an advantage for certain positions.

    Certifications like the IBM Data Science Professional Certificate, Google Data Analytics Professional Certificate, or Microsoft Certified: Azure Data Fundamentals are suitable for beginners.

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      Get hands-on training from experts.



      Experts help you in building the best of your portfolio.



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    • Placements

      Facilitating career success through strategic industry placements

    100+ Organizations Trust Us With Their Openings

    ACTE has earned the trust of over 100+ organizations as their preferred partner for identifying top talent to fill job openings. Our extensive experience and proven track record have solidified this trust, making us the go-to choice for businesses seeking the right candidates for their vacant positions. In this article, we will explore the factors that underpin this trust and how our unwavering dedication to excellence consistently delivers outstanding outcomes.

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    Career Transition


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    Data Science Reviews


    Job Opportunities in Data Science

    More Than 35% Of Developers Prefer Data Science. Data Science Is The Most Popular And In-Demand Programming Language In The Tech World.

    Data Science Training FAQ's

    Elevate Your Coding Skills - Data Science Training for Beginners and Beyond!

    Data science training can benefit a wide range of individuals, including students, professionals from diverse fields (e.g., business, healthcare, finance), researchers, and anyone interested in working with data.

  • Programming skills (e.g., Python or R)
  • Statistics knowledge
  • Data manipulation techniques
  • Data visualization methods
  • Machine learning concepts
  • Domain-specific knowledge
  • Prerequisites can vary depending on the program. Some may require a background in mathematics or computer science, while others are designed for beginners with no prior experience.

    The duration of data science training programs varies widely. Short courses or bootcamps may take a few weeks to a few months, while full-degree programs can span several years.

    Building a portfolio involves working on data-driven projects, participating in data competitions (e.g., Kaggle), contributing to open-source projects, and showcasing your work on platforms like GitHub or personal websites.

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    Self-paced training can be effective for individuals who are disciplined, self-motivated, and able to manage their own learning. Success depends on your commitment to consistent practice and self-assessment.

    Advantages include flexibility, the ability to tailor your learning to your specific needs and interests, cost-effectiveness (many resources are free or low-cost), and the ability to learn at your own pace.

    You can cover a wide range of topics, including statistics, programming (e.g., Python or R), machine learning, data visualization, data manipulation, and domain-specific applications. The choice of topics depends on your goals.

    Yes, self-paced learning can be suitable for beginners. Many online resources cater to beginners and offer introductory courses. Start with foundational topics like statistics and basic programming before advancing to more complex concepts.

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  • Flexibility in scheduling and location
  • Accessibility from anywhere with an internet connection
  • Lower costs when compared to traditional degrees
  • Self-paced learning to suit individual preferences
  • A wide variety of courses and programs to choose from
  • Online data science training can range from individual courses and tutorials to full-fledged degree programs. You can find single courses on specific topics (e.g., machine learning, data visualization) or complete programs like master’s degrees in data science.

    Many online courses include discussion forums, live webinars, and opportunities for direct communication with instructors. This interaction can vary depending on the course or program.

    To build a portfolio, work on personal projects, participate in online data science competitions (e.g., Kaggle), contribute to open-source projects, and collaborate on real-world projects with others in the field.

    Prerequisites can vary depending on the course or program. Some courses may have specific prerequisites in terms of programming skills or prior knowledge in mathematics and statistics, while others may be suitable for beginners.

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    Corporate data science training refers to educational programs and initiatives provided by companies to enhance the data science skills of their employees. It can include workshops, courses, seminars, or ongoing training efforts.

    Companies invest in data science training to harness the power of data for informed decision-making, gain a competitive edge, improve business processes, and drive innovation.

    Corporate data science training can be delivered in various formats, including in-person workshops, online courses, on-the-job training, mentoring, and bootcamps.

  • Acquisition of valuable skills
  • Enhanced career prospects
  • Increased job satisfaction
  • Improved ability to contribute effectively to company goals
  • Topics can range from data analysis, machine learning, and statistical modeling to data visualization, programming (e.g., Python or R), and data ethics. The curriculum is often tailored to the company’s needs.

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    Benefits of Learning at ACTE

    Embrace the benefits of education here, and watch your future flourish!!

    • Flexibility: Online, weekends & more.
    • Hands-on: Projects & practical exercises.
    • Placement support: Resume & interview help.
    • Lifelong learning: Valuable & adaptable skills.
    • Full curriculum: Foundational & advanced concepts.

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