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Data Science

Data Science Training || Data Science Certification Training || Data Science Online Training || Data Science Self-Paced Training || Data Science Instructor-Led Training.

Srijan institute

Online No. of classes

Srijan institute
6 Month Srijan institute
12 (5 Rating)


Data Science Training || Data Science Certification Training || Data Science Online Training || Data Science Self-Paced Training || Data Science Instructor-Led Training


Key Features of Training:

  • 6 Month  Instructor-led Training
  • Mock Interview Session
  • Project Work & Exercises
  • Flexible Schedule
  • 24 x 7 Lifetime Support & Access
  • Certification and Job Assistance













The fee for Data Science training can vary depending on several factors such as the location, duration of the course, training format, and level of expertise. Various training options for Data

Science are available, including instructor-led courses, e-learning courses, and virtual live classrooms.


For more details, you can Register/Sign Up.



Data Science Certification FAQ's::

1. What is Data Science certification?
A: Data Science certification is a professional certification that validates an individual's knowledge and skills in using data science techniques, tools, and methodologies for data analysis, machine learning, and predictive analytics.
2. What are the prerequisites for Data Science certification?
A:The prerequisites for Data Science certification may vary depending on the specific certification program. However, in general, candidates are recommended to have a basic understanding of statistics, programming (Python, R), SQL, and familiarity with data analytics concepts..
3. Which programming language is helpful in learning Data Science, and why?
A: Python is the most popular and preferred language for building Data Science applications. It is an easy-to-use, easy-to-learn, open-source programming language. Moreover, it is a dynamic language that supports multiple paradigms. Apart from this, some other languages used in Data Science include R and SQL..
4.How do I become a Data Scientist?
A: SAP DWC certification exams are typically computer-based and consist of multiple-choice questions. The exam format may vary depending on the specific certification program.
5.Does Data Science require coding knowledge?
A: To become a data scientist, you need to have good mathematics & Python knowledge. You should be a good story teller. Tools like Power BI or Tableau is essential to present the data insights in an effective manner. The knowledge of ML and AI algorithms with hands on exposure are vital. Our data science course will help you attain the required skills.
6. Data Science vs. Data Analytics: Which is better?
A: SAP DWC certification is valid for two years. After two years, candidates must renew their certification by passing the current exam or an equivalent exam.
7. What are the levels of Data Science certification?
A: Data Science certifications may have various levels, such as Beginner, Intermediate, and Advanced. Beginner-level certifications test foundational knowledge, while Intermediate and Advanced certifications assess more complex skills and expertise in data science practices.
8. What is the format of Data Science certification exam?
A:Data Science certification exams are typically computer-based and consist of multiple-choice questions, coding assessments, or case studies. The exam format may vary depending on the specific certification program.


9. How can I prepare for Data Science certification?
A: Official training courses, online resources, and study materials can help candidates prepare for Data Science certification. Additionally, candidates can gain hands-on experience through projects and practical applications of data science concepts..
10.How long does it take to prepare for Data Science certification?
A: The time required to prepare for Data Science certification may vary depending on the candidate's prior knowledge and experience. Candidates are typically recommended to study for several weeks to a few months, depending on the complexity of the certification


Data Science Certification:

Data Science certification is a professional certification that validates an individual's knowledge and skills in using data science techniques and tools for data analysis, machine learning, and predictive analytics. The certification program is designed to test the candidate's knowledge of data science concepts, methodologies, and best practices, as well as their ability to design and implement data-driven solutions.


Data Science certifications may have different levels, such as Beginner and Advanced. The Beginner level certification tests the candidate's knowledge of foundational data science concepts and tools. The Advanced level certification is for experienced users who can demonstrate expertise in applying complex data science techniques and methodologies in real-world scenarios.


To prepare for Data Science certification, candidates are encouraged to attend official training courses and study for at least six weeks before taking the certification exam. Additionally, candidates can gain hands-on experience by working on data science projects and practicing the concepts and techniques covered in the certification program.



Month 1: Foundations Of Data Science 


Supervised Learning:


1. Introduction to Data Warehousing Concepts

  • Introduction to machine learning concepts
  • Linear Regression, Logistic Regression, Decision Trees
  • Random Forest, SVM, Naive Bayes, KNNe
  • Ensemble Techniques: Bagging, Boosting, Voting

  Unsupervised Learning 

  • K-means Clustering, Hierarchical Clustering
  • DBSCAN, PCA
  • Recommendation systems, Apriorism algorithm

Month 2: Deep Learning

  • Indtroduction to Deep Learning
  • Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN)
  • Recurrent Neural Networks (RNN)
  • NLP and Reinforcement Learning
  • GANs (Generative Adversarial Networks)

Month 3: Advanced Data and Science Tools 

  • learning Hyperparameter Tuning,Cross-validation Techniques
  • Using Automated Libraries for machine learning
  • Handling Class Imbalance Problems

4.Month 4 & 5: Internship and Live Project

  • Collaborative work with teams
  • Application of data science skills on live data projects
  • Real-world internship project

Month 6:  Interview Preparation Tips

  • Review Key Projects
  • Understand the Technologies
  • Highlight Soft Skills
  • Prepare Questions

  Perks and Benefits:

  •  5 Months Course + 1 Month Internship on a Live Project
  • Course Completion Certificate
  • Internship Completion Certificate
  • Microsoft Enabled PowerBI, Excel AI Tools, and Tableau Certification
  • Cash Prizes for Top 3 Interns


  • Q1. What is Data Science, and what are its main functions?
    A1: Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. Its main functions include data collection, cleaning, analysis, and interpretation to solve complex business problems.

    Q2. What is a data model, and what information does it contain?
    A2: A data model is a conceptual framework that organizes and standardizes how data is stored, processed, and retrieved. It contains information such as data structure, data relationships, and constraints on data.

    Q3. What is a machine learning model, and what information does it contain?
    A3: A machine learning model is a mathematical representation of a real-world process created from training data to make predictions or classifications. It contains information like input features, learned parameters, and the relationships between them.

    Q4. What is the difference between supervised and unsupervised learning?
    A4: Supervised learning involves training a model on labeled data (input-output pairs), while unsupervised learning deals with finding patterns in unlabeled data without explicit outcomes.

    Q5. What is a data pipeline, and how is it used in Data Science?
    A5: A data pipeline is a series of data processing steps that automate data extraction, transformation, and loading (ETL). It is used in Data Science to streamline the process of collecting and preparing data for analysis or machine learning models.

    Q6. How do you create a machine learning model in Python?
    A6: To create a machine learning model in Python, follow these steps:

    • Import libraries like scikit-learn or TensorFlow
    • Load and preprocess the dataset
    • Split the dataset into training and testing sets
    • Select a machine learning algorithm
    • Train the model on the training data
    • Evaluate the model on the test data
    • Fine-tune the model if needed

    Q7. What is feature selection, and how is it used in Data Science?
    A7: Feature selection is the process of choosing the most important variables from a dataset to improve model performance by reducing overfitting and simplifying the model.

    Q8. What is cross-validation, and how is it used in Data Science?
    A8: Cross-validation is a technique used to assess the generalization ability of a model. It involves splitting the dataset into multiple folds, training the model on some folds, and testing it on the remaining folds.

    Q9. What is the difference between classification and regression?
    A9: Classification is a predictive modeling task where the output is a discrete label, while regression predicts a continuous numeric value.

    Q10. What is a loss function, and how is it used in machine learning?
    A10: A loss function measures how well a machine learning model's predictions match the actual values. It is used to optimize the model's performance by minimizing the error during training.

    Q11. What is overfitting, and how is it handled in Data Science?
    A11: Overfitting occurs when a model learns the noise in the training data, resulting in poor performance on new data. It is handled by techniques such as regularization, cross-validation, and pruning.

    Q12. What is the difference between a decision tree and a random forest?
    A12: A decision tree is a single tree-like structure used for decision-making, while a random forest is an ensemble of multiple decision trees that improves prediction accuracy and reduces overfitting.

    Q13. What is the difference between training and testing data in machine learning?
    A13: Training data is used to teach the model by adjusting its parameters, while testing data is used to evaluate the model's performance on unseen data.

    Q14. What is a confusion matrix, and how is it used in classification problems?
    A14: A confusion matrix is a table that summarizes the performance of a classification model by showing the true positives, true negatives, false positives, and false negatives. It is used to evaluate the accuracy and other metrics like precision and recall.

    Q15. What is a neural network, and how is it used in Data Science?
    A15: A neural network is a computational model inspired by the human brain that is used to learn patterns and relationships in data. It is widely used in tasks like image recognition, speech processing, and natural language processing.

    Q16. What is an activation function in a neural network?
    A16: An activation function defines the output of a neuron in a neural network. It introduces non-linearity into the model, helping it learn complex patterns in the data.

    Q17. What is deep learning, and how is it different from traditional machine learning?
    A17: Deep learning is a subset of machine learning that involves neural networks with many layers. It excels at tasks like image and speech recognition, requiring large datasets, whereas traditional machine learning often involves simpler algorithms and smaller datasets.

    Q18. What is the difference between a decision boundary and a margin in classification?
    A18: A decision boundary is a line that separates different classes in a feature space, while a margin is the distance between the decision boundary and the closest data points from each class. A larger margin generally indicates a more robust model.

    Q19. What is the difference between accuracy and precision in model evaluation?
    A19: Accuracy measures how often the model makes correct predictions overall, while precision focuses on the accuracy of positive predictions (i.e., how many positive predictions are actually correct).

    Q20. What is a data warehouse, and how is it used in Data Science?
    A20: A data warehouse is a large, centralized repository of data from multiple sources, optimized for querying and analysis. It is used in Data Science to store and manage historical data for reporting and decision-making

    • Participants will have 24/7 access to our online lab, providing hands-on experience with data science tools and scenarios.

    This includes server access to Python, R, and Jupyter Notebooks for 1 year, ensuring you have ample time to practice and apply your skills in a real-world environment.

    With this extended access, you can work on projects, explore advanced data analytics and machine learning features, and solidify your understanding of data science in the latest technologies.

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    ₹20,000.00 ($270.27)
    • Category
      Data Science
    • Classes
      Online
    • Duration
      6 Month
    • Language
      English
    • Quiz & Exam
      Not Available
    • Level
      beginner
    • Mode
      Online
    • Application Date
      Dec 01, 2024
    • Last Date
      Dec 15, 2024
    All Courses

    UNIQUE FEATURES

    Data-Science Features

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    Real-World Projects

     Gain hands-on experience by working on real-world datasets and projects. This helps you apply machine learning, data analysis, and visualization techniques to solve practical business problems

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    Advanced Machine Learning Techniques

    Learn cutting-edge algorithms and deep learning techniques, including neural networks, natural language processing (NLP), and computer vision, which are essential for modern data science applications.

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    Comprehensive Tool Coverage

    Master key data science tools like Python, R, TensorFlow, and Hadoop, ensuring you're well-versed in the industry’s most widely used technologies and platforms.

    FAQs

    Frequently Asked Questions

    Upon enrolling in the program, you will get below mentioned learning support services.

    • A personal mentor to track your progress
    • Immersive online instructor-led sessions conducted by Industry Experts
    • Real-time exercises, assignments, and industry-oriented projects
    • 24/7 learning support
    • 1:1 doubt clearance by subject matter experts
    • Forum to interact with likeminded learners
    • Personalized job support & access to the job portal

    Data scientists are responsible for collecting, analysing large volume of structured and unstructured data from different data sources. They use their mathematics, statistics, and computer science skills to analyse and interpret the data. They present data insights to business teams to make informed decisions. This Applied Data Science Course will help you horn data science skills.

    Python is the most popular and preferred language for building Data Science applications. It is an easy-to-use, easy-to-learn, open-source programming language. Moreover, it is a dynamic language that supports multiple paradigms. Apart from this, some other languages used in Data Science include R and SQL.

    Here is the process to get into the placement pool:

    1. Complete the Data Science course in India.
    2. Submit all the mandatory assignments and projects within given timelines.
    3. Clear the Placement Readiness Test (PRT)

    Upon clearing the PRT learner will get access to the dedicated jobs as well as the career mentoring sessions.

    Yes, certainly this course will help you land in data science & AI related jobs upon course completion.

    To become a data scientist, you need to have good mathematics & Python knowledge. You should be a good story teller. Tools like Power BI or Tableau is essential to present the data insights in an effective manner. The knowledge of ML and AI algorithms with hands on exposure are vital. Our data science course will help you attain the required skills.

    The decision between Data Science and data analytics depends on your goals. Data Science is broader and focuses on gaining insights, creating models, and solving complex problems using various techniques. Data Science is best suited for those interested in research and innovation. On the other hand, data analysis is more about interpreting existing data to make data-driven decisions. If you enjoy working with structured data to gain useful insights and contribute to business strategies, data analytics may be a better fit. Consider your preferences and career aspirations to make an informed decision between Data Science and data analytics.