Data Science Courses at Srijan Institute and Training Center

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

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.