Data Science
Data Science Online Training in Hyderabad
Best Data Science Online Training Institute in Hyderabad
Now you can fast-track your career in Data Science through experts training with Data Science Training program by Ashok IT. This course is specially designed by the Data Science industry experts for budding Data Scientists. Being a perfect blend of theory, case studies, and capstone projects, this course helps you become a fully competent Data Science expert. This Data Science course provides real-time training for students and helps them develop professional skills in areas like programming with Python/R, Statistical Analysis, Data Modeling, Predictive Analysis, Data Visualization, and more.
Data Science Training course at our institute will also be equipping students with basic skills in the advanced topics related to Data Science like Artificial Intelligence, & Machine Learning.
Data Science Syllabus
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis, Data Mining, and Machine Learning
- Analytics vs Data Science
- Value Chain
- Types of Analytics
- Lifecycle Probability
- Analytics Project Lifecycle
- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data and Sources
- Data Quality, Changes and Data Quality Issues, Quality Story
- What is Data Architecture
- Components of Data Architecture
- OLTP vs OLAP
- How is Data Stored?
- What is Big Data?
- 5 Vs of Big Data
- Big Data Architecture, Technologies, Challenge and Big Data Requirements
- Big Data Distributed Computing and Complexity
- Hadoop
- Map Reduce Framework
- Hadoop Ecosystem
- What is Data Science?
- Why are Data Scientists in demand?
- What is a Data Product
- The growing need for Data Science
- Large-Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases and Data Science Project Life Cycle & Stages
- Map-Reduce Framework
- Hadoop Ecosystem
- Data Acquisition
- Where to source data
- Techniques
- Evaluating input data
- Data formats, Quantity and Data Quality
- Resolution Techniques
- Data Transformation
- File Format Conversions
- Anonymization
- Introduction to R
- Business Analytics
- Analytics concepts
- The importance of R in analytics
- R Language community and eco-system
- Usage of R in industry
- Installing R and other packages
- Perform basic R operations using command line
- Usage of IDE R Studio and various GUI
- The datatypes in R and its uses
- Built-in functions in R
- Subsetting methods
- Summarize data using functions
- Use of functions like head(), tail(), for inspecting data
- Use-cases for problem solving using R
- Various phases of Data Cleaning
- Functions used in Inspection
- Data Cleaning Techniques
- Uses of functions involved
- Use-cases for Data Cleaning using R
- Import data from spreadsheets and text files into R
- Importing data from statistical formats
- Packages installation for database import
- Connecting to RDBMS from R using ODBC and basic SQL queries in R
- Web Scraping
- Other concepts on Data Import Techniques
- What is EDA?
- Why do we need EDA?
- Goals of EDA
- Types of EDA
- Implementing of EDA
- Boxplots, cor() in R
- EDA functions
- Multiple packages in R for data analysis
- Some fancy plots
- Use-cases for EDA using R
- Storytelling with Data
- Principle tenets
- Elements of Data Visualization
- Infographics vs Data Visualization
- Data Visualization & Graphical functions in R
- Plotting Graphs
- Customizing Graphical Parameters to improvise the plots
- Various GUIs
- Spatial Analysis
- Other Visualization concepts
