Data Science is an increasingly important field, with an ever-increasing demand for data scientists. It
is used for a variety of tasks, from predictive analysis like predicting delays in airlines or predicting
demand for certain products, to creating promotional offers and choosing the most efficient routes
for certain journeys. Mohan Mohan discussed the need for data science and definitions, as well as
the differences between business intelligence and data science. He also discussed the prerequisites
for learning data science. Lastly, he mentioned how data science can be used in politics to create
personalized messages tailored to the voters.
The first step in data science is asking the right questions and exploring the data. This helps to
identify the problem that needs to be solved and serves as the basis for the modelling process. After
modelling, results need to be visualized and communicated to those who need to know them.
Business intelligence relies heavily on structured data, while data science involves much more
complexity, such as machine learning and the extrapolation of future trends like sales. Data science
goes beyond just presenting what has happened in the past and seeks to understand why certain
behavior has occurred.
Python is becoming increasingly popular in data science for its ease of use and the variety of libraries
it supports for data science, machine learning, and powerful visualization through matplotlib. SAS is
a well-established tool, and R provides excellent visualization during development. Spark is an
excellent computing engine for distributed data analysis or machine learning. Additionally, there are
standard tools such as Informatica Data Stage, Talend, and AWS Redshift that can be used for on-
the-cloud operations. Raw data is collected, processed and analyzed before being fed into the
analytic system to create output which is then formatted in a way that is useful for stakeholders.
Decision tree is primarily used for classification and can also be used for regression. It is a clustering
mechanism which determines which objects belong to which cluster based on their scores. One
advantage of decision tree is that it's very easy to understand why a certain object has been
classified in a certain way. Data scientists explore the data, looking at its structure and removing any
columns that don't add value from an analytical perspective. Data must be cleaned and prepared in
order for the system to work properly, although the way of doing this can vary from project to
project. If there are too many missing values in few records of large data sets, it's ok to get rid of
those entire rows.
Data preparation is an essential step before analyzing or applying data. Model planning follows, and
which model to use depends on the problem you're trying to solve. For example, if it is a regression
problem, 80% of the training data can be used to train a machine learning model. The training
process may have to be iterative, and MATLAB is a popular tool for educational purposes. As an
example, data scientists might build a model based on diamond carats in order to predict the price
of a 1.35 carat diamond. This would involve passing the information through a linear regression
model or creating an appropriate model for the task.
The demand for data scientists is currently huge and the supply is very low, creating a large gap.
Gaming and healthcare are two industries that are particularly reliant on data science, as it is used
for consumer-facing activities such as diagnosis, predicting, and lifecycle management. The global
demand for data scientists is also high, which further highlights the importance of these skills. To
conclude this session, it is clear that the demand for data scientists will remain high and their skills
will be highly sought after.
is used for a variety of tasks, from predictive analysis like predicting delays in airlines or predicting
demand for certain products, to creating promotional offers and choosing the most efficient routes
for certain journeys. Mohan Mohan discussed the need for data science and definitions, as well as
the differences between business intelligence and data science. He also discussed the prerequisites
for learning data science. Lastly, he mentioned how data science can be used in politics to create
personalized messages tailored to the voters.
The first step in data science is asking the right questions and exploring the data. This helps to
identify the problem that needs to be solved and serves as the basis for the modelling process. After
modelling, results need to be visualized and communicated to those who need to know them.
Business intelligence relies heavily on structured data, while data science involves much more
complexity, such as machine learning and the extrapolation of future trends like sales. Data science
goes beyond just presenting what has happened in the past and seeks to understand why certain
behavior has occurred.
Python is becoming increasingly popular in data science for its ease of use and the variety of libraries
it supports for data science, machine learning, and powerful visualization through matplotlib. SAS is
a well-established tool, and R provides excellent visualization during development. Spark is an
excellent computing engine for distributed data analysis or machine learning. Additionally, there are
standard tools such as Informatica Data Stage, Talend, and AWS Redshift that can be used for on-
the-cloud operations. Raw data is collected, processed and analyzed before being fed into the
analytic system to create output which is then formatted in a way that is useful for stakeholders.
Decision tree is primarily used for classification and can also be used for regression. It is a clustering
mechanism which determines which objects belong to which cluster based on their scores. One
advantage of decision tree is that it's very easy to understand why a certain object has been
classified in a certain way. Data scientists explore the data, looking at its structure and removing any
columns that don't add value from an analytical perspective. Data must be cleaned and prepared in
order for the system to work properly, although the way of doing this can vary from project to
project. If there are too many missing values in few records of large data sets, it's ok to get rid of
those entire rows.
Data preparation is an essential step before analyzing or applying data. Model planning follows, and
which model to use depends on the problem you're trying to solve. For example, if it is a regression
problem, 80% of the training data can be used to train a machine learning model. The training
process may have to be iterative, and MATLAB is a popular tool for educational purposes. As an
example, data scientists might build a model based on diamond carats in order to predict the price
of a 1.35 carat diamond. This would involve passing the information through a linear regression
model or creating an appropriate model for the task.
The demand for data scientists is currently huge and the supply is very low, creating a large gap.
Gaming and healthcare are two industries that are particularly reliant on data science, as it is used
for consumer-facing activities such as diagnosis, predicting, and lifecycle management. The global
demand for data scientists is also high, which further highlights the importance of these skills. To
conclude this session, it is clear that the demand for data scientists will remain high and their skills
will be highly sought after.
