preloader

Data Science

Data Science

Learn Data Science through a practical, real-world approach. Master Python, statistics, SQL, and data visualization to analyze and manage data. Build smart models using Machine Learning and Deep Learning. Work with real-time datasets and case studies. Understand the full data journey—from collection to deployment. Get job-ready for careers in AI, Data Science, and Analytics.

img
Element Element
DATA SCIENCE WITH AI

Master the essentials of Data Science combined with cutting-edge AI technologies. Build intelligent models using Python, machine learning, and neural networks. Work on real-time projects to solve practical business problems.

Data Science with SQL

Master SQL to query, manipulate, and analyze structured data effectively. Learn how to integrate SQL with Python for real-world data science workflows. Gain hands-on experience with databases used in analytics and reporting.

Machine Learning

Learn to build predictive models using real-world datasets. Understand key algorithms like regression, classification, and clustering. Apply Python and popular ML libraries to solve data-driven problems. Work on real-time projects to solve practical.

Deep Learning

Leverage deep learning techniques to solve complex data science problems. Use neural networks to analyze large datasets for patterns and predictions. Apply frameworks like TensorFlow and PyTorch for real-world AI solutions. Work on real-time projects

Overview of Data Science

img

Master Data Science with a hands-on, project-based approach. Learn to analyze and visualize data using Python, NumPy, and Pandas. Gain expertise in statistical analysis and data-driven decision making. Explore machine learning algorithms for prediction and classification. Understand deep learning using TensorFlow and PyTorch. Work with real-world datasets from business, healthcare, and more. Get mentored by industry experts with live sessions and coding demos. Learn SQL for database querying and data extraction. Master tools like Jupyter Notebook, Git, and data visualization libraries. Perfect for beginners and professionals aiming for a data science career.

What You Will Learn

This Data Science course helps you master data analysis, visualization, and machine learning techniques. You'll learn Python, NumPy, Pandas, Matplotlib, Scikit-learn, and SQL to work with real-world data. The course covers statistical modeling, predictive analytics, and AI fundamentals. You'll also gain skills in building and evaluating machine learning and deep learning models. Real-world projects provide hands-on experience to solidify your understanding. By the end of the course, you'll be job-ready with in-demand data science skills tailored to today's data-driven industries.

  • Understanding data science and its applications
  • Overview of data science tools: Python, R, Jupyter
  • Basics of statistics and probability
  • Data types and structures
  • Setting up a data science environment
  • Introduction to data ethics and privacy
  • Python fundamentals: variables, loops, functions
  • Data manipulation with Pandas and NumPy
  • Data visualization with Matplotlib and Seaborn
  • Working with CSV, JSON, and Excel files
  • Automating data cleaning tasks
  • Exploratory data analysis (EDA)
  • Descriptive and inferential statistics
  • Hypothesis testing and p-values
  • Correlation and regression analysis
  • Probability distributions
  • Time series analysis
  • A/B testing methodologies
  • Neural networks and deep learning
  • Building models with TensorFlow and Keras
  • Hyperparameter tuning and cross-validation
  • Feature engineering and selection
  • Handling imbalanced datasets
  • Deploying ML models with Flask
  • Introduction to Hadoop and HDFS
  • Processing data with Apache Spark
  • Working with distributed databases
  • Real-time data processing with Kafka
  • Using PySpark for big data analytics
  • Cloud-based big data solutions (AWS, GCP)
  • Creating dashboards with Tableau and Power BI
  • Advanced visualizations with Plotly and D3.js
  • Communicating insights effectively
  • Storytelling with data for stakeholders
  • Designing interactive reports
  • Best practices for data presentation
  • Working on end-to-end data science projects
  • Building a professional portfolio
  • Preparing for data science interviews
  • Resume building and LinkedIn optimization
  • Participating in hackathons and competitions
  • Certification guidance (e.g., AWS Certified Data Analytics)

Students Opinions

  • img
    Sajith

    Nexro EduTech Academy's Data Science course provided a strong foundation in Python and statistics. The real-world datasets used in training made the learning process impactful.

    • img
      Anushka

      I enjoyed the practical sessions on machine learning and data visualization. The instructors were highly supportive, and the capstone project helped me build confidence.

  • img
    Gowtham

    The Data Science curriculum at Nexro was structured perfectly for beginners. I learned Python, SQL, and key ML algorithms through hands-on exercises and case studies.

    • img
      Megana

      Amazing course! From data preprocessing to deep learning, every module was practical and industry-relevant. This training helped me secure my first analytics job.