Data Science Life Cycle | Life Cycle Of A Data Science Project | Data Science Tutorial | Simplilearn
Summary
TLDRIn this session on data science, Mohan introduces the life cycle of a data science project, starting with the concept study to understand the business problem and available data. He then discusses data preparation, including data gathering, integration, and cleaning. Mohan explains model planning and building, highlighting various algorithms and exploratory data analysis techniques. The session covers training and testing models, deploying them, and communicating results to stakeholders. Finally, he summarizes the process, emphasizing the importance of presenting and operationalizing the findings to solve business problems effectively.
Takeaways
- 📚 The first step in a data science project is the concept study, which involves understanding the business problem and available data, and meeting with stakeholders.
- 🔍 Data preparation, also known as data munching or manipulation, is crucial for transforming raw data into a usable format for analysis.
- 🔧 Data scientists explore and clean the data, handling issues like missing values, null values, and improper data types.
- 📈 Data integration, transformation, reduction, and cleaning are all part of the data preparation process to ensure data quality for analysis.
- ⚖️ Handling missing values can involve removing records, filling them with mean or median values, or using more complex methods depending on the dataset's size and importance.
- 📊 Exploratory data analysis (EDA) uses visualization techniques like histograms and scatter plots to understand data patterns and relationships.
- 🤖 Model planning involves selecting the right statistical or machine learning model based on the problem, such as regression for continuous outcomes or classification for categorical outcomes.
- 🛠️ Model building is the execution phase where the chosen algorithm is trained with the cleaned data to create a predictive model.
- 📉 Testing the model with a separate dataset ensures its accuracy and reliability before deployment.
- 🛑 If the model fails to meet accuracy expectations during testing, it may need to be retrained or a different algorithm may be required.
- 📑 Communicating results effectively to stakeholders and operationalizing the model to solve the initial business problem is the final step in the data science lifecycle.
Q & A
What is the first step in the life cycle of a data science project?
-The first step is the concept study, which involves understanding the business problem, meeting with stakeholders, and assessing the available data.
Why is it important to meet with stakeholders during the concept study phase?
-Meeting with stakeholders helps to understand the business model, clarify the end goal, and determine the budget, which are all crucial for the project's success.
What are some examples of data issues that might be encountered during data preparation?
-Examples include missing values, null values, improper data types, and data redundancy from multiple sources.
What is the purpose of data munching or data manipulation in the data preparation phase?
-Data munching or manipulation is necessary to transform raw data into a usable format for analysis, addressing issues like data gaps, structure inconsistencies, and irrelevant columns.
How can data scientists handle missing values in a dataset?
-They can handle missing values by removing records with missing data if the percentage is small, or by imputing values using the mean, median, or mode of the dataset.
Why is it essential to split data into training and test sets during model preparation?
-Splitting data ensures that the model is tested on unseen data, providing a more accurate measure of its performance and preventing overfitting.
What is exploratory data analysis, and why is it important?
-Exploratory data analysis is the initial examination of data to discover patterns and understand the data types and distributions. It's important for identifying data issues and guiding the choice of models.
What are some common tools used for model planning and building in data science?
-Common tools include R, Python with libraries like pandas or numpy, MATLAB, and SAS, each offering capabilities for statistical analysis, machine learning, and data visualization.
Can you explain how linear regression works in the context of model building?
-Linear regression works by finding the best-fit straight line that represents the relationship between an independent variable and a dependent variable. The model training process determines the slope (m) and y-intercept (c) for the given data.
What is the final step in the data science project life cycle after obtaining results?
-The final step is operationalizing the results, which involves communicating the findings to stakeholders, getting their acceptance, and putting the model into practice to solve the stated problem.
Outlines
📚 Introduction to Data Science Lifecycle
The script introduces the concept of a data science project lifecycle, beginning with the 'concept study' phase. This phase involves understanding the business problem, engaging with stakeholders, and assessing available data. The importance of asking questions, identifying specifications, and previous problem-solving examples are highlighted. The script sets the stage for a deeper dive into the subsequent steps of a data science project.
🔍 Data Preparation and Exploration
This paragraph delves into the intricacies of data preparation, also known as 'data munching' or 'data manipulation'. It discusses the challenges of working with raw data, such as gaps, structure inconsistencies, and redundancy. The paragraph outlines subtopics like data integration, transformation, reduction, and cleaning. It also touches on handling missing and null values, and the importance of data cleaning for accurate analysis. Strategies for dealing with large datasets and missing values are suggested, emphasizing the variability in approaches based on the project's specific needs.
📈 Model Planning and Building
The script moves on to model planning, where the type of model or algorithm to be used is decided based on the problem at hand. It explains the iterative process of model training using cleaned data and the importance of exploratory data analysis for understanding data relationships and preparing for model building. The paragraph also introduces the concept of splitting data into training and test sets to ensure the model's accuracy. Tools for model planning, such as R, Python, MATLAB, and SAS, are mentioned, highlighting their roles in statistical analysis and machine learning.
💬 Communicating Results and Operationalizing Solutions
The final paragraph focuses on the importance of communicating the results of data analysis to stakeholders and the process of operationalizing the findings. It emphasizes that presenting the results effectively and getting them accepted is crucial for solving the initial problem stated. The paragraph summarizes the entire data science lifecycle, from concept study to data preparation, model planning, building, and finally, the presentation and implementation of the solution.
Mindmap
Keywords
💡Data Science
💡Life Cycle
💡Concept Study
💡Data Preparation
💡Data Munching
💡Model Planning
💡Exploratory Data Analysis (EDA)
💡Machine Learning
💡Training Data
💡Testing Data
💡Operationalization
Highlights
Introduction to the life cycle of a data science project by Mohan.
Concept study involves understanding the business problem and meeting with stakeholders.
Examples of concept study include understanding specifications, end goals, and budget.
Data preparation involves data gathering, exploration, and manipulation.
Data munching is the process of making raw data usable for analysis.
Handling missing and null values as part of data cleaning.
Data integration addresses conflicts and redundancy in merged data sets.
Data transformation ensures consistency when merging data from multiple sources.
Data reduction techniques for managing large data sizes without losing information.
Exploratory data analysis to understand relationships between variables and data appropriateness.
Visualization techniques such as histograms and scatter plots for exploratory data analysis.
Model planning includes deciding on the type of statistical or machine learning model to use.
Model building involves training the chosen model with cleaned data.
Iterative training process for models to achieve good accuracy.
Tools used for model planning include R, Python, MATLAB, and SAS.
Linear regression as an example of model building for predicting diamond prices.
Communicating results to stakeholders through presentations or dashboards.
Operationalizing the model by putting it into practice to solve the stated problem.
Summary of the data science project life cycle from concept study to operationalization.
Transcripts
hello and welcome to this session on
data science my name is mohan and today
we are going to take a look at what this
buzz is all about so now let's talk
about the life cycle of a data science
project okay the first step is the
concept study in this step it involves
understanding the business problem
asking questions get a good
understanding of the business model meet
up with all the stakeholders understand
what kind of data is available and all
that is a part of the first step so here
are a few examples we want to see what
are the various specifications and then
what is the
end goal
what is the budget is there an example
of this kind of a problem that has been
maybe solved earlier so all this is a
part of the concept study and another
example could be a very specific one to
predict the price of a 1.35 carat
diamond and there may be relevant
information inputs that are available
and we want to predict the price the
next step in this process
data preparation data gathering and data
preparation also known as data munching
or sometimes it is also known as data
manipulation so what happens here is the
raw data that is available may not be
usable in its current format for various
reasons so that is why in this step a
data scientist would explore the data he
will take a look at some sample data
maybe there are millions of records pick
a few thousand records and see how the
data is looking are there any gaps is
the structure appropriate to be fed into
the system are there some columns which
are probably
not adding value may not be required for
the analysis very often these are like
names of the customers they will
probably not add any value or much value
from an analysis perspective the
structure of the data maybe the data is
coming from multiple data sources and
the structures may not be matching what
are the other problems there may be gaps
in the data so the data
all the columns all the cells are not
filled if you're talking about
structured data there are several blank
records or blank columns so
if you
use that data directly you'll get errors
or you will get inaccurate results so
how do you either get rid of the data or
how do you fill this gaps with something
meaningful so all that is a part of data
munching or data manipulation so these
are some additional
sub topics within that so
data integration is one of them if there
are any conflicts in the data that may
be data may be redundant data resident
redundancy is another issue there may be
you have let's say data coming from two
different systems and both of them have
customer table for example customer
information so when you merge them there
is a duplication issue so how do we
resolve that so that is one data
transformation as i said there will be
situations where data is coming from
multiple sources and then when we merge
them together they may not be matching
so we need to do some transformations to
make sure everything is similar we may
have to do some data reduction if the
data size is too big you may have to
come up with ways to reduce it
meaningfully without losing information
then data cleaning so there will be the
wrong values or you know values or there
are missing values so how do you handle
all of that a few examples of very
specific stuff so there are missing
values how do you handle missing values
or null values here in this particular
slide we are seeing three types of
issues one is missing value then you
have null value you see the difference
between the two right so in the missing
value there is nothing blank null value
it says null now the system cannot
handle if there are null values
similarly there is improper data so it's
supposed to be numeric value but there
is a string or a non-numeric value so
how do we clean
and prepare the data so that our system
can work flawlessly so there are
multiple ways and there is no one common
way of doing this it can vary from
project to project it can vary from what
exactly is the problem we are trying to
solve it can vary from data scientist to
data scientist organization to
organization so these are like some
standard practices people come up with
and and of course there will be a lot of
trial and error somebody would have
tried out something and it worked and
will continue to use that mechanism so
that's how we need to take care of data
cleaning now what are the various ways
of doing you know if values are missing
how do you take care of that now if the
data is too large and
only a few records have some missing
values then it is okay to just get rid
of those entire rows for example so if
you have a million records and out of
which 100 records don't have full data
so there are some missing values in
about 100 cards so it's absolutely fine
because it's a small percentage of the
data so you can get rid of the entire
records which are missing values but
that's not a very common situation very
often you will have multiple or at least
a large number of a data set for example
out of million records you may have 50
000 records which are like having
missing values now that's a significant
amount you cannot get rid of all those
records your analysis will be inaccurate
so how do you handle such situations so
there are again multiple ways of doing
it one is you can probably if a
particular values are missing in a
particular column you can probably take
the mean value for that particular
column and fill all the missing values
with the mean value so that first of all
you don't get errors because of missing
values and second you don't get results
that are way off because these values
are completely different from what is
there so that is one way then a few
other could be either taking the median
value or depending on what kind of data
we are talking about so something
meaningful we will have put in there if
we are doing some
machine learning activity then obviously
as a part of data preparation you need
to split the data into training and test
data set the reason being if you try to
test with a data set which the system
has already seen as a part of training
then it will tend to give a reasonably
accurate results because it has already
seen that data and that is not a good
measure of the accuracy of the system so
typically you take the entire data set
the input data set and split it into two
parts and again the ratio can vary from
person to person individual preferences
some people like to split it into 50 50
some people like it as 63.33
and 33.3 is basically two-thirds and
one-third and some people do it as 80 20
80 for training and 20 for testing so
you split the data perform the training
with the 80 percent and then use the
remaining 20 for testing all right so
that is one more data preparation
activity that needs to be done before
you start analyzing or applying the data
or putting the data through the model
then the next step is model planning now
this models can be statistical models
this could be machine learning model so
you need to decide what kind of models
you're going to use again it depends on
what is the problem you're trying to
solve if it is a regression problem you
need to think of a regression algorithm
and come up with a regression model so
it could be linear regression or if
you're talking about classification then
you need to pick up an appropriate
classification algorithm like logistic
regression or decision tree or svm and
then you need to train that particular
model so that is the model building or
model planning process and the cleaned
up data has to be fed into the model and
apart from cleaning you may also have to
in order to determine what kind of model
you will use
you have to perform some exploratory
data analysis to understand the
relationship between the various
variables and see if the data is
appropriate and so on right so that is
the additional preparatory step that
needs to be done so a little bit of
details about exploratory data analysis
so what exactly is exploratory data
analysis is basically to as the name
suggests you're just exploring you just
receive the data and you're trying to
explore and
find out what are the data types and
what is the is the data clean in in each
of the columns what is the maximum
minimum value so for example there are
out of the box functionality available
in tools like r so if you just ask for a
summary of the table it will tell you
for each column it will give some
details as to what is the mean value
what is the maximum value and so on and
so forth so this exercise or this
exploratory analysis is to get an
understanding of your data and then you
can take steps to during this process
you find there are a lot of missing
values you need to take steps to fix
those you will also get an idea about
what kind of model to be used and so on
and so forth what are the various
techniques used for exploratory data
analysis typically these would be
visualization techniques like you use
histograms uh you can use box plots you
can use scatter plots so
these are very quick ways of identifying
the patterns or a few of the trends of
the data and so on and then once your
data is ready you you decided on the
model what kind of model what kind of
algorithm you're going to use if you're
trying to do machine learning you need
to pass your 80 percent the training
data or rather you use that training
data to train your model and the
training process itself is iterative so
the training process you may have to
perform multiple times and once the
training is done and you feel it is
giving good accuracy then you move on to
test so you take the remaining 20 of the
data remember we split the data into
training and test so the test data is
now used to check the accuracy or how
well our model is performing and if
there are further issues let's say and
model is still during testing the
accuracy is not good then you may want
to retrain your model or use a different
model so this whole thing again can be
iterative but if the test process is
passed or if the model passes the test
then it can go into production and it
will be deployed all right so what are
the various tools that we
use for
model planning r is an excellent tool in
a lot of ways whether you're doing
regular statistical analysis or machine
learning or any of these activities are
in along with our studio provides a very
powerful environment to do data analysis
including visualization it has a very
good integrated visualization of plot
mechanism which can be used for doing
exploratory data analysis and then later
on to do
analysis detailed analysis and machine
learning and so on and so forth then of
course you can write python programs
python offers a rich library for
performing data analysis and machine
learning and so on matlab is a very
popular tool as well especially during
education so this is a very easy to
learn tool so matlab is another
tool that can be used and then last but
not least sas sas is again very powerful
it is a preparatory tool and it has all
the components that are required to
perform very good statistical analysis
or perform data science so those are the
various tools that would be required for
or that that can be used for model
building and
so the next step is model building so we
have done the planning part we said okay
what is algorithm we are going to use
what kind of model we are going to use
now we need to actually train this model
or build the model rather so that it can
then be deployed so what are the various
uh ways or what are the various types of
model building activities so it could be
let's say in this particular example
that we have taken you want to find out
the price of 1.35 carat diamond so this
is let's say a linear regression problem
you have data for various carets of
diamond and you use that information you
pass it through a linear regression
model or you create a linear regression
model which can then predict your price
for 1.35 carat so this is one example of
model building and then a little bit
details of how linear regression
works so linear regression is basically
coming up with a relation between an
independent variable and a dependent
variable so it is pretty much like
coming up with equation of a straight
line which is the best fit for the given
data so like for example here y is equal
to mx plus c so y is the dependent
variable and x is the independent
variable we need to determine the values
of m and c for our given data so that is
what the training process of
this model does at the end of the
training process you have a certain
value of m and c and
that is used for predicting the values
of any new data that comes all right so
the way it works is we use the training
and the test data set to
train the model and then validate
whether the model is working fine or not
using test data and
if it is working fine then it is taken
to the next level which is put in
production if not the model has to be
retrained if the accuracy is not good
enough then the model is retrained maybe
with more data or you come up with a
newer model or algorithm and then repeat
that process so it is an iterative
process once the training is completed
training and test then this model is
deployed and we can use this particular
model to determine what is the price of
1.35 carat diamond remember that was our
problem statement so now that we have
the best fit for this given data we have
the price of 1.35 carat diamond which is
10 000. so this is one example of how
this whole process works now how do we
build the model there are multiple ways
you can use python for example and use
libraries like pandas or numpy to build
the model and implement it this will be
available as a separate tutorial a
separate video in this playlist so
stay tuned for that moving on once we
have the results the next step is to
communicate this results to the
appropriate stakeholders so which is
basically taking this results and
preparing like a presentation or a
dashboard and communicating these
results to the concerned people so
finishing or getting the results of the
analysis is not the last step but you
need to as a data scientist take this
results and present it to the team that
has given you this problem in the first
place and explain your findings explain
the findings of this exercise and
recommend maybe what steps they need to
take in order to overcome this problem
or solve this problem so that is the
pretty much once that is accepted and
the last step is to operationalize so if
everything is fine your data scientists
presentations are accepted then they put
it into practice and thereby they will
be able to improve or solve the problem
that they stated in step one okay so
quick summary of the life cycle you have
a concept study which is basically
understanding the problem asking the
right questions and trying to see if
there is enough data to solve this
problem and then even maybe gather the
data then data preparation the raw data
needs to be manipulated you need to do
data munching so that you have the data
in a certain proper format to be used by
the model or our analytics system and
then you need to do the model planning
what kind of a model what algorithm you
will use for a given problem and then
the model building so the exact
execution of that model it happens in
step four and you implement and execute
that model and
put the data through the analysis in
this step and then you get the results
these results are then communicated
packaged and presented and communicated
to the stakeholders and once that is
accepted that is operationalized so that
is the final step so with that we come
to the end of this session thank you
very much for watching this video and if
there are any feedback any comments
please or any questions please put it
below and we will get back to you
provide your contact information or
email so that we can respond to you and
thank you very much once again and have
a good day bye bye
hi there if you like this video
subscribe to the simply learn youtube
channel and click here to watch similar
videos turn it up and get certified
click here
5.0 / 5 (0 votes)