Data Science Course in Rajaji Nagar

  • Data Science expert with 12+ years of experience.
  • Reasonable pricing on Data Science Training .
  • Tailored Data Science interview preparation.
  • 362+ recruiters and 13,409 trained students.
  • Online resources, including study guides and interview questions.
Hands On   40+ Hrs
Projects   4 +
Placement Support   Lifetime Access
3K+

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

See why over 25,000+ Students choose ACTE

Data Science Training 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

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    30+  Case Studies & Projects
  •  
    9+  Engaging Projects
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    10+   Years Of Experience
  • Fundamentals of data science
  • Significance of Applications
  • R and Data Science
  • Data Manipulation and SQL
  • Methods for Cleaning Data
  • Preprocessing Data
  • Managing Missing Data
  • Normalization and Data Transformation
  • R and Python Libraries for Data Manipulation
  • Concepts for Exploratory Data Analysis
  • Descriptive Statistics
  • Techniques for Data Visualization
  • EDA with Python and R
  • Matplotlib, Seaborn, ggplot2.
  • Probability and Distributions of Probabilities
  • Testing Hypotheses
  • Regression Analysis
  • ANOVA and MANOVA
  • Statistical analysis: R and Python.
  • Overview of Machine Learning
  • Evaluation Metrics
  • Supervised vs Unsupervised Learning
  • Hyperparameter Tuning
  • Feature Engineering
  • Machine Learning Algorithms
  • Machine Learning Techniques
  • Dimensionality Reduction
  • Fundamentals of Deep Learning
  • Gradient Boosting Machines (GBM)
  • Support Vector Machines (SVM)
  • Decision Trees
  • Fundamentals of TensorFlow
  • Introduction to Neural Networks
  • Building Neural Networks
  • Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • Transfer Learning
  • Overview of NLP
  • Preprocessing Text
  • Analysis of Sentiment
  • Word Embeddings
  • Text Classification
  • Named Entity Recognition (NER)
  • Introduction to Big Data
  • Fundamentals of Apache Hadoop
  • Fundamentals of Apache Spark
  • Working with Distributed Datasets
  • Spark MLlib for Machine Learning
  • Show More

    Data Science Training Projects

    Become a Data Science Expert With Practical and Engaging Projects.

    •  
      Practice essential Tools
    •  
      Designed by Industry experts
    •  
      Get Real-world Experience

    Titanic Survival Prediction

    Analyze the Titanic dataset to predict passenger survival using basic classification algorithms like Logistic Regression and Decision Trees

    Stock Price Analysis

    Collect historical stock data, visualize trends, and implement simple moving averages to predict future prices, gaining insights into market behavior.

    Movie Recommendation

    Build a basic recommendation engine using collaborative filtering to suggest movies based on user ratings and learning about data preprocessing.

    Customer Segmentation

    Customer Segmentation with K-Means Use clustering techniques on customer data to segment a retail market, helping businesses tailor marketing strategies to different customer groups.

    Spam Email Detection

    Develop a spam classifier using Natural Language Processing (NLP) and machine learning, focusing on text data preprocessing, feature extraction, and model evaluation.

    Predicting House Prices

    Create a model to predict house prices based on features like location, size, and amenities, using regression techniques and feature engineering for improved accuracy.

    Image Classification with CNNs

    Implement a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset, optimizing the model with techniques like dropout and data augmentation.

    Time Series Forecasting with LSTM

    Develop a Long Short-Term Memory (LSTM) model to forecast sales or stock prices, Time Series Forecasting with LSTM handling sequential data and learning about recurrent neural networks.

    Reinforcement Learning for Game AI

    Reinforcement Learning Create an AI agent that learns to play a game (like Tic-Tac-Toe or a simple maze) using reinforcement learning, focusing on policy gradients and Q-learning.

    Key Features

    Practical Training

    Global Certifications

    Flexible Timing

    Trainer Support

    Study Material

    Placement Support

    Mock Interviews

    Resume Building

    Batch Schedule

    Weekdays Regular (Class 1Hr - 1:30Hrs) / Per Session

    • 17-Feb-2025 Starts Coming Monday ( Monday - Friday) 08:00 AM (IST)
    • 19-Feb-2025 Starts Coming Wednesday ( Monday - Friday) 10:00 AM (IST)

    Weekend Regular (Class 3Hrs) / Per Session

    • 22-Feb-2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)

    Weekend Fast-track (Class 6Hrs - 7Hrs) / Per Session

    • 23-Feb-2025 Starts Coming Saturday ( Saturday - Sunday) 10:00 AM (IST)

    Enquiry Form

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    • Learning strategies that are appropriate and tailored to your company's requirements.
    • Live projects guided by instructors are a characteristic of the virtual learning environment.
    • The curriculum includes of full-day lectures, practical exercises, and case studies.

    Data Science Training Overview

    Goals Achieved in a Data Science Training :

    A Data Science Training aims to equip learners with a robust understanding of data analysis, including techniques for data cleaning, manipulation, and visualization. Participants develop proficiency in programming languages such as Python and R, essential for implementing data-driven solutions. The course also focuses on building skills in statistical analysis and machine learning, enabling students to create predictive models and derive actionable insights. By working on real-world projects, learners gain hands-on experience with data-driven decision-making processes. Additionally, the course fosters an understanding of big data technologies and tools, preparing students to handle large-scale datasets. Effective communication of findings through data visualization and reporting is emphasized to ensure insights are accessible and actionable. Ultimately, the course prepares individuals for diverse roles in data science, including data analyst, data scientist, and machine learning engineer.

    Future Works for Data Science:

    • Advanced Machine Learning Models: Developing and refining sophisticated machine learning algorithms, such as deep learning and reinforcement learning, to solve complex problems and improve predictions.
    • AI and Automation Integration: Leveraging data science to enhance artificial intelligence (AI) and automation systems, optimizing processes and decision-making in various industries.
    • Ethical AI and Data Privacy: Addressing challenges related to AI ethics and data privacy, ensuring responsible use of data, and building fair, transparent systems.
    • Real-Time Data Analytics: Implementing real-time analytics solutions to process and analyze data as it is generated, providing immediate insights and enabling faster decision-making.
    • Personalized Customer Experiences: Using data science to deliver highly personalized experiences in marketing, healthcare, and other fields, based on individual preferences and behaviors.
    • Interdisciplinary Applications: Applying data science techniques to new fields such as genomics, climate science, and smart cities, driving innovation and solving interdisciplinary challenges.

    New Data Science Frameworks:

    Recent advancements have introduced several innovative frameworks in data science. PyTorch has gained popularity for its dynamic computational graph and ease of use in developing deep learning models. TensorFlow 2.0 offers an improved and more user-friendly interface for building and deploying machine learning models. Apache Spark continues to be a leading framework for big data processing, with its capability to handle large-scale data analysis efficiently. Dask provides parallel computing with a focus on scaling Python’s data science stack for larger datasets. H2O.ai offers a suite of tools for building and deploying machine learning models, emphasizing automated machine learning (AutoML). KubeFlow integrates with Kubernetes to streamline the deployment and management of machine learning workflows, enabling scalable and reproducible data science operations.

    Trends and Techniques Used in Data Science:

    • Explainable AI (XAI): Enhancing model transparency and interpretability, allowing users to understand and trust the decisions made by complex machine learning models.
    • Automated Machine Learning (AutoML): Simplifying the process of building and tuning machine learning models, making advanced techniques more accessible to non-experts.
    • Edge Computing: Performing data analysis on devices at the edge of the network, reducing latency and bandwidth usage for real-time applications.
    • Synthetic Data Generation: Creating artificial datasets to supplement real data, addressing issues of data privacy and scarcity in training machine learning models.
    • Natural Language Processing (NLP) Advances: Improving language models and applications, such as chatbots and sentiment analysis, through techniques like transformer architectures.
    • Data Privacy and Security: Implementing advanced techniques to protect sensitive data and ensure compliance with regulations, such as differential privacy and secure multi-party computation.

    Data Science:

    Data science is a multidisciplinary field that combines statistical analysis, machine learning, and data visualization to extract insights and inform decision-making from complex datasets. It involves collecting, processing, and analyzing large volumes of data to uncover patterns, trends, and relationships. Data scientists use a variety of tools and techniques, including programming languages like Python and R, to build predictive models and perform in-depth analyses. The field spans numerous applications, from business intelligence and financial forecasting to healthcare and artificial intelligence. Data science also emphasizes the importance of communicating findings effectively through visualizations and reports, making data-driven insights accessible to stakeholders. As technology evolves, data science continues to play a crucial role in driving innovation and solving real-world problems across diverse industries.

    Add-Ons Info

    Career Opportunities  After Data Science Training

    Data Scientist

    Analyzes complex datasets to derive insights, build predictive models, and support data-driven decision-making across business functions.

    Data Analyst

    Interprets data, creates visualizations, and generates reports to support business decisions and identify trends and patterns.

    Machine Learning Engineer

    Designs and implements machine learning models and algorithms, deploying them to solve business problems.

    Data Engineer

    Develops and maintains data pipelines and infrastructure, ensuring data quality and availability for analysis and reporting.

    Business Intelligence Analyst

    Uses data visualization tools and techniques to create dashboards and reports, providing actionable insights to drive business strategy.

    Data Science Consultant

    Advises businesses on data science strategies, helping them leverage data to improve processes, make informed decisions.


    Skill to Master
    Statistical Analysis
    Data Visualization
    Machine Learning Algorithms
    Data Cleaning and Preparation
    Programming (Python, R)
    Big Data Technologies (Hadoop, Spark)
    Predictive Modeling
    Data Warehousing
    SQL and Database Management
    Data Mining Techniques
    Business Intelligence Tools
    Advanced Analytics
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    Tools to Master
    Python (Anaconda, Jupyter Notebook)
    R
    TensorFlow
    Scikit-Learn
    Apache Spark
    Hadoop
    SQL
    Tableau
    Power BI
    Jupyter Lab
    Dask
    H2O.ai
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    Our Instructor

    Learn from certified professionals who are currently working.

    instructor
    Training by

    Monisha , having 7+ yrs of experience

    Specialized in: Machine Learning Algorithms, Predictive Analytics, Data Mining, Big Data Technologies, Deep Learning, and Natural Language Processing (NLP).

    Note: Monisha is an expert in machine learning and data mining techniques. She has led numerous data science projects in various industries, from finance to healthcare. Her expertise in developing and implementing machine learning algorithms and deep learning models makes her a key asset for students aiming to excel in advanced data science techniques.

    Job Assistant Program

    We are proud to have participated in more than 40,000 career transfers globally.

    Data ScienceCertification

    Certificate
    GET A SAMPLE CERTIFICATE

    Pursuing a Data Science certification is advantageous as it validates your skills and knowledge in data analysis, machine learning, and statistical techniques, making you a more attractive candidate to employers. It provides a structured learning path and demonstrates your commitment to the field, which can lead to better job opportunities, higher earning potential, and career advancement. Additionally, it helps you stay updated with the latest tools and methodologies, enhancing your ability to tackle complex data challenges and contribute effectively to organizational data-driven decision-making.

  • No strict requirement for real-world experience
  • Experience enhances practical understanding
  • Recommended for better exam performance
  • Can be beneficial for job prospects
  • Some certifications may suggest prior experience
  • Certification alone does not guarantee employment but significantly enhances your job prospects by demonstrating your expertise and commitment to the field. However, securing a job also depends on other factors such as your experience, networking, interview performance, and the overall job market conditions.

  • Basic understanding of data science concepts
  • Familiarity with programming languages (e.g., Python, R)
  • Knowledge of statistical methods
  • Completion of recommended coursework or training
  • Some exams may require professional experience
  • To prepare for the Data Science certification exam, focus on studying the core topics such as data analysis, machine learning, and statistical methods. Engage in hands-on practice through real-world projects or case studies to apply theoretical knowledge. Join study groups or forums for additional support and clarification. Consistent and comprehensive preparation will help you build confidence and improve your chances of passing the exam.

  • Yes, many certification exams offer online options
  • Check the specific certification provider’s website
  • Online exams often require secure proctoring
  • Availability may vary by region
  • Practical experience is not always mandatory for obtaining a Data Science certification but is highly recommended. Hands-on experience with data analysis, machine learning, and real-world data projects can significantly enhance your understanding and application of concepts.

    Investing in an ACTE Data Science Certification can be worthwhile as it provides high-quality training and preparation for certification exams. ACTE’s courses offer comprehensive coverage of data science topics, practical experience, and expert instruction. This investment can enhance your skills, improve job prospects, and potentially lead to higher earning potential and career growth. The certification can help you stand out in the competitive job market by demonstrating your expertise and commitment to the field.

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    Frequently Asked Questions

    • Yes, you can access the course materials online, which typically includes lecture notes, reading materials, video tutorials, and additional resources. Online access allows for flexible learning, enabling you to review the content at your own pace and revisit materials as needed.
    • The Data Science course at ACTE is offered through various modes including online live classes, self-paced online courses, and in-person training sessions. Each mode provides different levels of interaction and flexibility to accommodate different learning preferences and schedules.
    • Access to Recorded Sessions You may be able to access recorded versions of the missed class.
    • Make-Up Classes Some programs offer the opportunity to attend make-up classes or sessions.
    • Course Material Review You can review course materials and resources provided online.
    • Instructor Support Contact the instructor for any questions or clarifications regarding missed content.
    • Peer Assistance Reach out to classmates for notes or insights from the missed class.
    • If you have any more questions, you should contact the course coordinator or support team at ACTE. They can provide detailed information, address specific concerns, and assist with any additional queries related to the course.
    • Visit the ACTE Website.
    • Select the Course.
    • Fill Out the Registration Form.
    • Make Payment.
    • Confirm Enrollment.

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    • 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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