Difference among between data Analysis, data Analytics and Data Science
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Data
Analytics
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Data Analysis
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Data Science
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Super
set Process of Data Analysis
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Sub Set Process of Data Analytics
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Set Process
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Past
and Present study and future e prediction
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Past discovery
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What
will happen in future
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What happened
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Tools:
Tableau, Zoho Analytics,Talend, Hadoop, Xplenty,
Kafka,Python,R-Language, Cassandra, MongoDB, HPCC, Spark, Datawrapper,
PowerBI etc.
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Python, R, PowerBI, Tableau, Excel
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Data analytics predicts ‘what will happen next or what is going to be
next?’
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Data analysis is actually studying past data to understand ‘what
happened?’
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All
analysis and prediction.
Data
analytics life cycle consists of Business Case Evaluation, Data
Identification, Data Acquisition & Filtering, Data Extraction, Data
Validation & Cleansing, Data Aggregation & Representation, Data
Analysis, Data Visualization, and Utilization of Analysis Results
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DA involves Querying, wrangling, statistical modelling, analysis and
visualization.
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DS involves data sourcing,
cleansing, modelling, result evaluation and result testing and deployment
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Data Analytics and data Science
Data Science is a combination of multiple disciplines – Mathematics,
Statistics, Computer Science, Information Science, Machine Learning, and
Artificial Intelligence.
2. Difference between mutable and immutable objects in python
In Python, Built-in projects like int, float, bool, string, tuple are
immutable.
In Python, Built-in projects like list, dict, set are mutable.
