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Data science is a multidisciplinary blend of data inference, algorithmm
development, and technology in order to solve analytically complex problems.
At the core is data. Troves of raw information, streaming in and stored in
enterprise data warehouses. Much to learn by mining it. Advanced capabilities we
can build with it. Data science is ultimately about using this data in creative
ways to generate business value:
Data science discovery of data insight
This aspect of data science is all about uncovering findings from data. Diving
in at a granular level to mine and understand complex behaviors, trends, and
inferences. It's about surfacing hidden insight that can help enable companies
to make smarter business decisions. For example:Netflix data mines movie
viewing patterns to understand what drives user interest, and uses that to make
decisions on which Netflix original series to produce.
Target identifies what
are major customer segments within it's base and the unique shopping behaviors
within those segments, which helps to guide messaging to different market
audiences.
Proctor & Gamble utilizes time series models to more clearly
understand future demand, which help plan for production levels more optimally.
How do data scientists mine out insights? It starts with data exploration. When
given a challenging question, data scientists become detectives. They
investigate leads and try to understand pattern or characteristics within the
data. This requires a big dose of analytical creativity.
Then as needed,
data scientists may apply quantitative technique in order to get a level deeper e.g. inferential models, segmentation analysis, time series forecasting,
synthetic control experiments, etc. The intent is to scientifically piece
together a forensic view of what the data is really saying.This
data-driven insight is central to providing strategic guidance. In this sense,
data scientists act as consultants, guiding business stakeholders on how to act
on findings. Atlanta Computer Institute Conducts batches for Data Science ,
Advance Data Science , Machine Learning and R Programming..
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Module 1 : Introduction to Data Science
· Introduction to Data science
· Feeler of Data
· Big Data basics
· Deep Dive into Data science
Module 2 : Combination of Foundations
(Algebra, Statistics & Probablity)
· Introduction to Algebra & its connection with Data science
· Foundation Statistics
· Probablity & distribution
· Hypothesis Testing Basics
Module 3 : Advanced Statistics (8 Hrs)
· ANOVA , Chi-sqaure test
· Principal Component Analysis, Factor Analysis
· Exploratory Data Analysis
· Statistics Quality control
· Regression Analysis
· Time Series Analysis.
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· Module 4 : Tool know how - Python
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· Introduction to Python Programming
· Python Programming Concepts
· Data Manupulation in Python
· Stastics & Data Visualization in Python
Module 5 : Data Mining & Machine learning Concepts
· Data Mining Concepts
· Introduction to Machine learning
· Concepts of Machine learning Algorithms 1
· Concepts of Machine learning Algorithms 2
· Module 6 : Predictive Analytics in R
· Concepts of Predictive analytics.
· Connecting knowledge gained to Practical Models.
· Real time Model Building in Python
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