DATA SCIENCE COURSE
COURSE OVERVIEW
Every day we create 2,500,000,000,000,000,000 (2.5 Quintillion) bytes of data. Data has becomes the most abundant thing today. So data scientist is the “Sexiest Job of the 21𝑠𝑡 century “and the concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data.
COURSE TOPICS
Introduction
- What is data science?
- Different type of analysis
- Different type of statistical methods
- forecasting methods
- R studio introduction
- data interface
- Visualization
- Statistics
- Machine Learning case study
- Artificial nerual networks case study
- Deep Learning case study
- Python Introduction
- data interface
- Visualization
- Statistics
- Machine Learning case study
- Artificial intelligence case study
- Artificial nerual networks case study
- Deep Learning case study
- Introduction to development
- Basics Programming
- HTML,CSS & java script
- Django Framework
- Excel introduction
- Data Analysis
- Visualization
- Data validation
- Financial Analysis
- Tableau Introduction
- Worksheets from Tableau
- Tableau calculations
- SQL Queries
- Types of statistical test
- Types of analysis
- SAS introduction
- data interface
- Visualization
- Statistics
- Machine Learning case study
- SPSS introduction
- data interface
- Visualization
- Statistics
- Machine Learning case study
- Machine learning introduction
- Types of machine learning
- Types of analysis method
- Introduction to NLP
- Features& Terminology
- Applications of NLP
- Introduction to AI
- Techniques & applications
- Types of intelligence
- Introduction to Artificial neural Networks
- Basic structure & working
- Applications of Artificial neural networks
COURSE SYLLABUS
- What is Data Science?
- Different types of Analysis
- Analytics Methodology
- Basic Statistical Analysis
- Basic Business Analysis
- Forecasting method
- Introduction
- Different types Data visualization using R
- Different types of test using R
- Data Manipulation
- Clustering
- Types of Regression method
- Overview of Excel
- Data visualization with different types of graphs
- Data Analysis with Different methods
- Data connections From Different Sources
- What is Python?
- What is Numpy Package?
- What is Pandas Package?
- Different types of Data Visualization methods
- Types of Regression method
- Overview of SPSS
- Different types of Data Visualization
- Types of Regression method
- SAS Introduction
- SAS Data Visualization
- SAS Regression
- What is Tableau?
- Importance of Tableau
- Different types of Data Visualization method
- What is SQL?
- SQL Functions
- SQL Keywords
- What is Machine Learning?
- Importance of Machine Learning
- Different types of Data visualization method
- Types of Regression method
- Natural Language Processing
QUALIFICATION REQUIRED
- Diploma
- Degree
- Btech
- Mtech
- MCA
- BCA
- BBA
- MBA
CERTIFICATIONS
BSS -GOVT of India approved Certification
COURSE DURATION
Ambit Automation conduct 3 to 4 months of data science course with 100% placement support. Check out the detailed syllabus and proceed to Admission.
FAQ RELATED TO DATA SCIENCE
What is Data Science course?
It is the process of manipulating the data from different filed using scientific methods, process, algorithm to extract the information and insights from structured and unstructured data. It should be actionable in various domains Data technological know-how may be described as a mix of mathematics, enterprise acumen, tools, algorithms, and gadget mastering techniques, all of which assist us in locating out the hidden insights or styles from uncooked information which may be of fundamental use withinside the formation of massive enterprise decisions.
What are the best courses for Data Science?
Advanced data scientist and computational intelligence data scientist specialization AI and Deep learning applied data science with python Data science with business intelligence.
Who is eligible for Data Science?
Data Science, loosely, is an amalgamation of ideas from Mathematics, Computer Science, and Statistics. Students need to have a diploma in one of the fields in science, technology, engineering, and mathematics (STEM background). The candidate needs to keep a BBA /MBA BCA/ B.Sc Statistics / B.Sc Mathematics / B. Sc Computer Science / B.Sc IT./ BE or BTech or equal diploma from a diagnosed university. He/ She need to have scored 50% marks withinside the qualifying examination.
