What you need to know about 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

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:

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

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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