Online Classroom Batches Preferred
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekend Regular
(Class 3hr - 3:30Hrs) / Per Session
Weekend Fasttrack
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Top Skills You Will Gain
- R Programmming, Python, SAS
- Artifical Intelligence
- Deep Learning
- Machine Learning
- Statistics, Naive Bayes
- Linear Algebra, CART
- Programming, Neural Networks
- Data Mining, Visualization
Data Science Course Key Features 100% Money Back Guarantee
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5 Weeks Training
For Become a Expert -
Certificate of Training
From Industry Data Science Experts -
Beginner Friendly
No Prior Knowledge Required -
Build 3+ Projects
For Hands-on Practices -
Lifetime Access
To Self-placed Learning -
Placement Assistance
To Build Your Career
Top Companies Placement
- Designation
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Annual SalaryHiring Companies
Data Science Certification Course Curriculam
Trainers Profile
Trainers are certified professionals with 11+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Data Science Certification Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
It is not always necessary for professionals to have a data science background wheeling beforehand.
You might be a student or a fresher who is developing an interest in the data science field and planning to get individual experience in the sector. Or you might be a professional who is already established in one industry but wants to enter the data science course because of the love for data or the rising interest and demand the profile offers.
Syllabus of Data Science Certification Course in New York City Download syllabus
- 1. The World Wide Web
- 2. HTML Web Servers
- 3. HTTP
- 4. Dynamic Web Pages
- 5. CGI
- 6. Data Science Web Technologies
- 7. Servlets
- 8. JSP
- 1. JSP Containers
- 2. Servlet Architecture
- 3. Page Translation
- 4. Types of JSP
- 5. Content Directives
- 6. Content Type
- 7. Buffering
- 8. Scripting Elements
- 9. JSP Expressions
- 10. Standard Actions
- 11. Custom Actions and JSTL
- 12. Objects and Scopes
- 13. Implicit Objects
- 14. JSP Lifecycle
- 1. Translation of Template
- 2. Content Scriptlets
- 3. Expressions Declarations
- 4. Dos and Don'ts
- 5. Implicit Objects for Scriptlets
- 6. The request Object
- 7. The response Object
- 8. The out Object
- 1. HTML Forms
- 2. Reading CGI Parameters
- 3. JSPs and Data Science Classes
- 4. Error Handling
- 5. Session Management
- 6. The Session API
- 7. Cookies and JSP
- 1. Separating Presentation and Business Logic
- 2. JSP Actions
- 3. Data ScienceBeans
- 4. Working with Properties and Using Form Parameters with Beans
- 5. Objects and Scopes
- 6. Working with Vectors
- 1. Going Scriptless
- 2. The JSP Expression Language EL
- 3. Syntax Type Coercion
- 4. Error Handling
- 5. Implicit Objects for EL
- 6. The JSP Standard Tag Library
- 7. Role of JSTL
- 8. The Core Actions
- 9. Using Beans with JSTL
- 10. The Formatting Actions
- 11. Scripts vs. EL/JSTL
- 1. Web Components
- 2. Forwarding
- 3. Inclusion
- 4. Passing Parameters
- 5. Custom Tag Libraries
- 6. Tag Library Architecture
- 7. Implementing in Data Science or JSP
- 8. Threads
- 9. Strategies for Thread Safety
- 10. XML and JSP
- 11. JSP for Web Service
- 1. The JSP Standard Tag Library
- 2. JSTL Namespaces
- 3. Going Scriptless
- 4. Object Instantiation
- 5. Sharing Objects
- 6. Decomposition
- 7. Parameterization
- 1. The JSTL Core Library
- 2. Gotchas
- 3. Conditional Processing
- 4. Iterative Processing
- 5. Iterating Over Maps
- 6. Tokenizing Strings
- 7. Catching Exceptions
- 8. Resource Access
- 1. The JSTL Formatting Library
- 2. Locales
- 3. Determining Locale Time Zones
- 4. Setting Locale and Time Zone
- 5. Formatting and Parsing Dates
- 6. Formatting and Parsing Numbers
- 7. Internationalization
- 8. Working with Resource Bundles
- 9. Supporting Multiple Languages
- 1. The JSTL SQL Library
- 2. Using Relational Data
- 3. Connecting with a DriverManager
- 4. Connecting via a DataSource
- 5. The Result Interface
- 6. Making a Query
- 7. Inserts, Updates and Deletes
- . Parameterized SQL Transactions
- 1. Architecture Servlets
- 2. Architecture Servlet and HttpServlet
- 3. Request and Response
- 4. Reading Request Parameters
- 5. Producing an HTML Response
- 6. Redirecting the Web Server
- 7. Deployment Descriptors
- 8. Servlets Life Cycle
- 9. Relationship to the Container
- 1. Building an HTML Interface
- 2. HTML Forms
- 3. Handling Form
- 4. Input Application Architecture
- 5. Single-Servlet Model
- 6. Multiple-Servlet Model
- 7. Routing Servlet Model
- . Template Parsers
- 1. Managing Client State Sessions
- 2. Session Implementations
- 3. HttpSession Session
- 4. Attributes
- 5. Session Events
- 6. Invalidating Sessions
- 1. JDBC
- 2. JDBC Drivers
- 3. Using JDBC in a Servlet
- 4. Data Access Objects
- 5. Threading Issues
- 6. Transactions
- 7. Connection Pooling
- 1. The Need for Configuration
- 2. Initialization Parameters
- 3. Properties
- 4. Files
- 5. JNDI and the Component Environment
- 6. JDBC Data Sources
- 7. Working with XML Data
- 1. Servlet Filters
- 2. Uses for Filters
- 3. Building a Filter
- 4. Filter Configuration and Context Filter Chains
- 5. Deploying Filters
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Mock Interviews
- Mock interviews by Learnovita give you the platform to prepare, practice and experience the real-life job interview. Familiarizing yourself with the interview environment beforehand in a relaxed and stress-free environment gives you an edge over your peers.
- Our mock interviews will be conducted by industry experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!
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Data Science Training Objectives
- Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.
- By extrapolating and sharing these insights, data scientists help organizations to solve vexing problems. Combining computer science, modeling, statistics, analytics, and math skills—along with sound business sense—data scientists uncover the answers to major questions that help organizations make objective decisions.
- As a highly practical field, data science can not be mastered with MOOCs and books alone. If you're looking to build a career in data science, you need to demonstrate that you can do data science, not just know it. Hackathons and competitions can help you achieve that.
- “Data Science is named as the sexiest job of 21st Century by Harward Business Review”. It's true that data science has been in demand since the last few years with billions of data been exchanging worldwide and the demand for data scientists has rapidly increased with the increase in data..
- You need to have knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java, with Python being the most common coding language required in data science roles. These programming languages help data scientists organize unstructured data sets..
- Data science jobs are the talk of the town! A data science internship is one sure-fire way to understand the domain as well as to get a first-hand experience in this field. ... Many final year graduate students look forward to a career in this new-age field.
- Although many aspiring Data Scientists are finding it is becoming more difficult to land a job than it was in previous years, understanding what has changed in the hiring landscape can be used to to your advantage in matching with the best organization for your goals and interests.
- Software developers
- Web designers
- Programming enthusiasts
- Engineering graduates
- Students who all want to become Data Science developers
- The big three. When you Google for the math requirements for data science, the three topics that consistently come up are calculus, linear algebra, and statistics. The good news is that — for most data science positions — the only kind of math you need to become intimately familiar with is statistics..
Exam & Certification
- Participate and Complete One batch of Data Science Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Data Science Certification course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LearnoVita Online Training Course That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
Recently Placed Students
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Software Testing, CapgeminiData Science Course FAQ's
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- On Successfully Completing a Career Course with LearnoVita, you Could be Eligible for Job Placement Assistance.
- 100% Placement Assistance* - We have strong relationship with over 650+ Top MNCs, When a student completes his/ her course successfully, LearnoVita Placement Cell helps him/ her interview with Major Companies like Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, HCL, Infosys, MindTree and MPhasis etc...
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- More than 5400+ students placed in last year in India & Globally.
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- 85% percent placement record
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- Also, LearnoVita Technical Experts Help's People Who Want to Clear the National Authorized Certificate in Specialized IT Domain.
- LearnoVita is offering you the most updated, relevant, and high-value real-world projects as part of the training program.
- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
- You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc.
- After completing the projects successfully, your skills will be equal to 6 months of rigorous industry experience.
- We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities.
- View the class presentation and recordings that are available for online viewing.
- You can attend the missed session, in any other live batch.