Data Science and Artificial Intelligence
About Data Science and Artificial Intelligence
Data science is a comprehensive process that involves preprocessing, analysis, visualization and prognosis. On the other hand, AI is the implementation of a prognosis model to forecast future events.
Who Uses
Data science is used as Identify and prognosisdisease, personalized healthcare recommendations; imprint out tax fraud, automating digital ad placement.AI is also retail, shopping and fashion, security and surveillance .sports analytics and activities, manufacturing and production.
SCOPE
There is a scope in developing the machines games, speech recognition machine, language detection, computer vision, expert systems, robotics and etc.AI enables the execution of hitherto complex tasks without significant cost outlays. The data scientist is able to define the problem statement, project objectives in line with the business achievements.
Eligibility
A candidates can pursue any of the data science courses after completing 12 standard from recognized board 50% of data scientists in India have a Master’s degree;34% a Bachelor’s degree; and a small 6% hold a Ph.D. Now, regarding academic background: a degree in computer studies, economics, finance, business studies is certainly considered an advantages. The best stream to select if you want to develop a career in artificial intelligence would be CS.
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JOB OPPORTUNITIES
- The job world needs very professional and highly skilled
- Artificial Intelligence jobs are stream demands all across the globe.
- It is going to expand as AI would be in every field and make it better for human beings to work together and enlarge all together.
Course Syllabus
- Introduction to Data Science
- What is Data Science?
- Mathematical & Statistical Skills
- How it will be works?
- Understanding Exploratory Data Analysis
- Machine Learning
- Model selection and evaluation
- Data Warehousing
- Data Mining
- Data Visualization
- Cloud Computing
- Business Intelligence
- Storytelling with Data
- Communication and Presentation
- Scholastic Models
- Experimentation, Evaluation
- Project Deployment Tools
- Predictive Analytics
- Segmentation using Clustering
- Applied Mathematics and Informatics
- Algorithms used in Machine Learning
- Coding
- Statistical Foundations for Data Science
- Data Structures & Algorithms
- Scientific Computing
- Optimization Techniques
- Matrix Computations
- Data handling and Visualization
- Features of Data Science
- Information Security and Privacy
- Statistical Foundations of Data Science
- Optimization for Data Science
- Mathematical Foundations of Data Science
- Introduction to Data structures and Algorithms
- Matrix Computations for Data Science
- Computing for Data Science
- Introduction to Statistical Learning
- Exploratory Data Analysis
- Business Acumen & Artificial Intelligence
- Introduction of AI
- Definition of AI
- Future of Artificial Intelligence
- Characteristics of Intelligent Agents
- Typical Intelligent Agents
- Problem Solving Approach to Typical AI problems.
- Problem solving Methods
- Search Strategies
- Heuristics
- Local Search Algorithms & Optimization Problems
- Constraint Propagation
- Backtracking Search
- Alpha Beta Pruning
- First Order Predicate Logic
- Prolog Programming
- Unification
- Forward Chaining Backward Chaining
- Resolution
- Knowledge Representation
- Ontological Engineering
- Categories and Objects
- Events
- Mental Events and Mental Objects
- Architecture for Intelligent Agents
- Agent communication
- Trust and Reputation in Multi-agent systems
- AI applications
- Language Models
- Information Retrieval
- Information Extraction
- Natural Language Processing
- Machine Translation
- Speech Recognition
- Robot & Hardware
- Perception & Planning
