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DATA SCIENCE
Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.
Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist.
Data science training in Chennai adventure will provide the RProgramming, Machine learning, Python, Deep Learning, SAS, Artificial Training with 100% job assistance.
Scope of Data Science
Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.
Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.
Data science generally has a five-stage lifecycle that consists of:
- Capture: Data acquisition, data entry, signal reception, data extraction
- Maintain: Data warehousing, data cleansing, data staging, data processing, data architecture.
- Process: Data mining, clustering/classification, data modeling, data summarization
- Communicate: Data reporting, data visualization, business intelligence, decision making.
- Analyze: Exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis.
Eligibility
- Duration :1-2 Months
- Eligibility : B.E/B.Tech, M.E/M.Tech, M.sc
Who should attend?
- Basic knowledge with coding techniques.
- Software professionals who need gain more knowledge in that domain.
- Any aspiring candidate with a Bachelor’s degree in Computer Science, Statistics, Physical Science etc.
Career opportunities
- In the past decade, data scientists have become necessary assets and are present in almost all organizations.
- These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization.
- This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.
- This is a data-dependent world; organizations are applying the insights that data scientists give to shine in the crowd. You may question why data scientist is one of the most demanding position nowadays.
- To put it in a nutshell, there has been a huge burst in both the data produced and retained by organizations.
- Your Roles in Companies would be:
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- Data Scientist
- Data Analyst
- Data Engineer
Brochures
A right place for getting start your Career with 100% placement assistance. To know more about us click here.
Why Choose Adventure
Real-world experience by allowing the trainee to get her hands directly on whatever she is learning, creating a sense of empowerment.
Hand on practical session
Real-world experience by allowing the trainee to get her hands directly on whatever she is learning, creating a sense of empowerment.
When a student completes his/ her course successfully, Placement Cell helps him/ her interview with major companies related to their courses.
100% placement assistance
When a student completes his/ her course successfully, Placement Cell helps him/ her interview with major companies related to their courses.
Dedicated Trainers who are experienced more than 10+ years in the industry. Our trainers are recognized as a pioneering Executive Coach.
Expert Trainer
Dedicated Trainers who are experienced more than 10+ years in the industry. Our trainers are recognized as a pioneering Executive Coach.
Adventure syllabus patterns are designed by industry experts, Core job related professionals. Our syllabus is based on trending industry requirements.
Industry relevance syllabus
Adventure syllabus patterns are designed by industry experts, Core job related professionals. Our syllabus is based on trending industry requirements.
A flexible schedule allows an student to attend the classes with variable timing pattern.
Flexible timingt
A flexible schedule allows an student to attend the classes with variable timing pattern.
We offer reasonable fees structures for the students with Case studies after completing every chapter with practical assessment.
Affordable fees
We offer reasonable fees structures for the students with Case studies after completing every chapter with practical assessment.
DATASCIENCE COURSE SYLLABUS
1. Statistics and Probability Refresher, and Python Practice
* An Introduction To R * Introduction To The R Language * Programming Statistical Graphics * Programming With R * Simulation * Computational Linear Algebra * Numerical Optimization2. Data Manipulation Techniques Using R Programming
* Data In R * Reading And Writing Data * R And Databases * Dates * Factors * Subscribing * Character Manipulation * Data Aggregation * Reshaping Data
3. Statistical Applications Using R Programming
* Basics * The R Environment * Probability And Distributions * Descriptive Statistics And Graphics * One- And Two-Sample Tests * Regression And Correlation * Analysis Of Variance And The Kruskal–Wallis Test * Tabular Data * Power And The Computation Of Sample Size * Advanced Data Handling * Multiple Regression * Linear Models * Logistic Regression * Survival Analysis * Rates And Poisson Regression * Nonlinear Curve Fitting
Course Overview
1. Machine Learning Language Environment
* Object Oriented * Platform Independent * Automatic Memory Management * Compiled / Interpreted Approach * Robust * Secure * Dynamic Linking * MultiThreaded * Built-In Networking2. Machine Learning Fundamentals
* Data Types * Operators * Control Statements * Arrays * Enhanced For-Loop * Enumerated Types * Static Import * Auto Boxing * C-Style Formatted I/O * Variable Arguments * Essentials Of Object-Oriented Programming * Object And Class Definition * Using Encapsulation To Combine Methods And Data In A Single Class * Inheritance And Polymorphism3. Writing Machine Learning Classes
* Encapsulation * Polymorphism * Inheritance * OOP In Machine Learning * Class Fundamentals * Using Objects * Constructor * Garbage Collection * Method Overloading * Method Overriding * Static Members * Understanding Interface * Using Interfaces Class4. Packages
* Why Packages * Understanding Classpath * Access Modifiers And Their Scope * Exception Handling * Importance Of Exception Handling * Exception Propagation * Exception Types * Using Try And Catch * Throw, Throws, Finally * Writing User Defined Exceptions5. Exception Handling
* Importance Of Exception Handling * Exception Propagation * Exception Types * Using Try And Catch * Throw, Throws, Finally * Writing User Defined Exceptions6. I/O Operations In Machine Learning
* Byte Oriented Streams * File Handling * Readers And Writers7. Multi Threaded Programming
* Introduction To Multi-Threading * Understanding Threads And Its States * Machine Learning Threading Model * Thread Class And Runnable Interface * Thread Priorities * Thread Synchronization * Inter Thread Communication * Preventing Deadlocks8. Developing Machine Learning APPS
* Defining A Solution Without Writing Code * Organizing A Concept Solution * Creating A Program Skeleton * Defining Error Checking Requirements * Introduction To Application Security9. Network Programming
* Introduction To Networking * InetAddress * URL * TCP Socket And ServerSocket * UDP Socket * Developing A Chat Application10. Machine Learning Util Package / Collections Framework
* Collection And Iterator Interface * Enumeration * List And ArrayList * Vector * Comparator * Set Interface And SortedSet * Hashtable * Properties11. Generics
* Introduction To Generics * Using Built-In Generics Collections * Writing Simple Generic Class * Bounded Generics * Wild Card Generics12. Inner Classes
* Nested Top Level Classes * Member Classes * Local Classes * Anonymous Classes13. Abstract Window Toolkit
* Graphics * Color And Font * AWT Components/Controls * Event Handling And Layouts14.Swing Programming
* Introduction To Swing And MVC Architecture * Light Weight Component * Swing Hierarchy * Atomic Components E.G. JButton, JList And More * Intermediate Container E.G. JPanel, JSplitPane And More * Top-Level Container E.G. JFrame And JApplet * Swing Related EventsCourse Overview
1. An Introduction To Python
* Introductory Remarks About Python * A Brief History Of Python * How Python Is Differ From Other Languages * Python Versions * Installing Python * IDLE * Getting Help * How To Execute Python Program * Writing Your First Program2. Python Basics
* Python Keywords An * d Identifiers * Python Statements * Python Indentation * Comments In Python * Command Line Arguments * Getting User Input * Exercise3. Variables And Data Types
* Introduction * Variables * Data Types * Numbers * Strings * Lists, Tuples & Dictionary * Exercise4. Decision Making & Loops
* Introduction * Control Flow And Syntax * The If Statement * Python Operators * The While Loop * Break And Continue * The For Loop * Pass Statement * Exercise5. Functions
* Introduction * Calling A Function * Function Arguments * Built In Functions * Scope Of Variables * Decorators * Passing Functions To A Function * Lambda * Closures * Exercise6. Modules And Packages
* Modules * Importing Module * Standard Module – Sys * Standard Module – OS * The Dir Function * Packages * Exercise7. Exception Handling
* Errors * Run Time Errors * Handling IO Exceptions * Try…. Except Statement * Raise * Assert * Exercise8. Files And Directories
* Introduction * Writing Data To A File * Reading Data From A File * Additional File Methods * Working With Files * Working With Directories * The Pickle Module * Exercise9. Classes Objects
* Introduction Classes And Objects * Creating Classes * Instance Methods * Special Class Method * Inheritance * Method Overriding * Data Hiding * Exercise10. Regular Expressions
* Introduction * Match Function * Search Function * Grouping * Matching At Beginning Or End * Match Objects * Flags * Exercise11. Socket Programming
* What Are Sockets? * Creating Sockets * Server-Client Socket Methods * Connecting Client Server * Client-Server Chatting Program * ExerciseCourse Overview
1. Introduction To Deep Learning
* What Exactly Is Deep Learning? * Neural Network * Supervised Learning With Neural Networks * Prominence Of Deep Learning2. Neupal Networks Basics
* Bi.ary Cla3sification * Logistic Regression * Gradient Descent * Logistic Regression Gradient Descent * Vectorization3. Shallow Neural Networks
* Neural Networks Overview * Neural Network Representation * Vectorizing Across Multiple Example * Derivatives Of Activation Functions * Random Initialization4. Deep Neural Networks
* Key Concepts On Deep Neural Networks * Building Deep Neural Network * Deep Neural Network ApplicationCourse Overview
1. Introduction To SAS
* Introduction * Need For SAS * Who Uses SAS * What Is SAS? * Overview Of Base SAS Software * Data Management Facility * Structure Of SAS Dataset * SAS Program * Programming Language * Elements Of The SAS Language * Rules For SAS Statements * Rules For Most SAS Names * Special Rules For Variable Names * Types Of Variables * Data Analysis And Reporting Utilities * Traditional Output * Ways To Run SAS Programs * SAS Windowing Environment * Noninteractive Mode * Batch Mode * Interactive Line Mode * Running Programs In The SAS Windowing Environment2. How SAS Works
* Writing Your First SAS Program * A Simple Program To Read Raw Data And Produce A Report * Enhancing The Program * More On Comment Statements * Internal Processing In SAS * How SAS Works * The Compilation Phase * The Execution Phase * Processing A Data Step A Walkthrough * Creating The Input Buffer And The Program Data Vector * Writing An Observavion To Vhe SAS Data Set>/br> J* Four Types Of SAS Libraries * SAS Libparies *`Work Library * AShelp Library * SASuser Library3. Reading Raw Data Into SAS
* What Is Raw Data * Definitions * Data Values * Numeric Value * Character Value * Standard Data * Nonstandard Data * Numeria Data * Character Data * Choosing An Input Stylg * List`Input * Modified List Input * Column Input * Formatted Input * Named Input * Instream Data * Creating Multiple Records From Sinele Input Row * eading ata Froo Externcl Files * Readi.g Blank Separat%d Values (List Or Free Form Data) * Re!ding Ra7 Data S%parated`By Commas (.Csv Files) * Reading In Raw Data Separated By Tabs (.Txt Files) * Using Informats With List Input * Supplying An Informat Statement With List Input * Using List Input With Embedded Delimiters * Reading Raw Data That Are Aligned In Columns * Method 1 Column Input * Method 2 Formatted Input * Using More Than One Input Statement The Single Trailing @ * Reading Column Data That Is On More Than One Line * Mixed-Style Input * Infile Options For Special Situations * Flow Over * Missover * Trun Cover * Pad * Using Lrecl To Read Very Long Lines Of Raw Data * Checking Your Data After It Has Been Read Into SAS4. Reading Data From A Dataset
* Introduction * Set Statement Overview * Automatic Variables In SAS * Interleave Multiple SAS Data Sets * Combine Multiple SAS Data Sets * Creating & Modifying Variables * Creating Multiple Data Sets In A Single Data-Step * Sub Setting Observations * Conditional SAS Statements * Logical And Special Operators * The SAS Supervisor And The Set Statement * Efficiency And The Set Statement * Know Your Data * Set Statement Data Set Options * Drop And Keep Options * Rename Option * Firstobs And Obs Options * In Option – * Where Option – * Other Set Statement Options * End Option * Key Option * Nobs Option * Point Option * Do Loops And The Set Statement * Introduction To Retain Statement * Carry Over Values From One Observation To Another * Compare Values Across Observations * Assign Initial Values * Determining Column Order In Output Dataset * SAS System Options5. Reading Data From A Dataset
* Input SAS Data Set For Example * Selecting Observations For A New SAS Data Set * Deleting Observations Based On A Condition * Accepting Observations Based On A Condition * Comparing The Delete And Sub Setting If Statements * Methods Of Creating New Data Sets With A Subset * Sub Setting Records From An External File With A Sub Setting If Statement * Sub Setting Observations In A Data Step With A Where Statement * Sub Setting Observations In A Proc Step With A Where Statement * Sub Setting Observations In Proc Sql * Difference Between If And WhereCourse Overview
Curriculum for Artificial Intelligence Course will be taken the functional concepts such as data mining, deep learning algorithms, reinforcement learning, robotics, computer vision, NLP, IoT, and other new techniques with complete hands-on experiences on real-time projects. After the course completion, one can able to have the appropriate understanding of along with the guidance through repetitive and updated Artificial Intelligence Interview Questions and Answers. * Artificial Intelligence Fundamentals * Computational Mathematics For Learning And Data Analysis * Machine Learning * Human Language Technologies * Parallel And Distributed Systems: Paradigms And Models * Intelligent Systems For Pattern Recognition * Algorithm Engineering (KD) * Data Mining (KD) * Mobile And Cyber-Physical Systems (ICT) * Real-Time Data Warehouse Migration * Information Retrieval (KD) * Social And Ethical Issues In Computer Technology * Computational Neuroscience (ING) * Robotics * Semantic Web
CERTIFICATION
Data science certifications are a great way to gain an edge because they allow you to develop skills that are hard to find in your desired industry. They're also a way to validate your skills, so recruiters and hiring managers know what they’re getting if they hire you.
Adventure technology solutions will provide the globally accepted data science certification with 100% job assistance support.
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- Customizable Platform
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- One to one live practice session
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- Works with real time projects
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- 10+ Years of experienced trainer
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- Trained more than 2000+ Students
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- Well equipped labs real time example projects
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