A.Comparison and contrast of Data Science
i) Scope: Data Science combines expertise from several domains like as Mathematics,Statistics,Compuer Science ,Domain specific knowledge,Business Analytics knowledge,specifically support discipline,decision-making and improve business performance-based
ii) Techniques and Tools :1.Excel 2.Power BI 3.SQL 4.Python 5.ML 6.DL 7.AI 8.R 9.Statistics for DS 10.Mathematics for DS
iii) Goals :
B.Step of Data Science Projects
1.Data Collection
2.Data Cleaning and preprocessing
3.Exploratory Data Analytisis (EDA)
4.Feature Engineering
5.Model Development
6.Model Evaluation and validation
7.Interpretion and Communication
C.Business Analytics
i) Descrptive Analytics
ii) Diagnostic Analytics
iii) Pradictive Analytics
iv) Prescriptive Analytics
Module of Data Science
Module-1.Data Process,Data Storage and Management ( Tools)
1.1.Excel
1.2.SQL
1.3.NoSQL
Module-2.Data Visualization and Graphics
2.1.Power BI
2.2.Tablue
Module-3.Mathematics for Data Science
3.1.Basic Arithmetic
3.2. Basic Algebra
3.3. Basic Geometry
3.4. Basic Trigonometry
3.5.Vactors
3.6. Matrices
Module -4.Statistics for Data ,Data Exploration and Hypothesis Testing in Data Science
Module-5.Data Strutures and Librarires
Programming Languages
5.1.Python
5.2.R
Module-6.Machine learning Libraries
6.1.Scikit-Learn
6.2.Tensor Flow
Module-7.Cloud Computing Platforms
7.1AWS
7.2.Azure
7.3.Google Cloud
Module-8.Domain Expertise
Module-9.Communication and Storytelling
Module-10.Real Life Case Studies
10.1.Marketing and Customer Analytics
10.2. Supply chain Optimization
10.3.Fraud Detaction and Risk Management
10.4. Healthcear and Personalized Medicine
10.5.Human Resources and Talent Management
Module-11.
