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What is Data Science

Data science

1. What is Data science?

It’s an interdisciplinary field of study in which expertise, data of research & report purposes is used, programming skills, and knowledge of statistics & mathematics is used to extract insight data.

2. What is data science useful for?

It empowers better business decisions through interpretation, deployment, and modeling. Data science is useful for business stakeholders through visualizing data that is under consideration for building future roadmaps & trajectories.

3. What are the six steps of data science?

  • First, frame the problem
  • Secondly, require collecting the raw data for your problem
  • Analysis the data
  • Delve into the data
  • Do an in-depth analysis of your data
  • Lastly, investigate the communicate results
intelligent management scope

 Difference between data science &computer science:

A specific field of knowledge within computer data science mainly emphasizes analytics, programming, and statistics. On the other hand, computer science focuses solely on computers while dealing with building hardware and designing software.

 Common data science use cases:

Some common relevant & efficient data science used cases are the following:

  • For fraud detection
  • For network management & optimization
  • Customer sentiment analysis
  • For customers segmentation
  • Real-time analytics
  • Predictive analytics
  • A lifetime value prediction
  • For behavior analytics

 Difference between data science & AI:

Data science uses various statistical techniques for building models. On the other hand, AI uses computer algorithms for building models that emulate cognition and human understanding.

 Must-have Skills for Data science:

data science skills
  • Contextual in mathematics, computer science, and statistics
  • The expertise of analytical tools
  • Knowledge of multivariate analysis, calculus, data visualization, etc.
  • A talent to convey their complex ideas merely whether verbally & visually
  • The ability of team orientation, intellectual curiosity, and business acumen

 Different data science tools:

Here is the list of 14 best data science tools that most of the data scientists used.

  • SAS
  • Apache spark
  • BigML
  • js
  • MATALB
  • Excel
  • ggplot2
  • Tableau
  • Jupyter
  • Matplotlib
  • NLTK
  • Scikit-learn
  • TensorFlow
  • Weka

4. What are different career options in data science?

  • A Data analyst
  • A Data scientist
  • Engineer in machine learning
  • Scientists in machine learning
  • A Data Architecture
  • Architect of Enterprise
  • Statistician
  • Infrastructure architecture
  • Analyst in Business intelligence

 

 

 

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