Data Science Life Cycle | Life Cycle Of A Data Science Project | Data Science Tutorial | Simplilearn

Summary

In 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

00:00

📚 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.

05:00

🔍 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.

10:01

📈 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.

15:02

💬 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.

Keywords

💡Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. In the video, it is the central theme, with the focus on the life cycle of a data science project, which includes various stages from understanding business problems to deploying solutions.

💡Life Cycle

The life cycle in the context of the video refers to the series of stages a data science project goes through from its inception to completion. This includes concept study, data preparation, model planning, model building, and operationalization, which are all essential for a comprehensive understanding of how data science projects are managed.

💡Concept Study

Concept Study is the initial phase of a data science project where the business problem is understood, stakeholders are met, and data availability is assessed. It sets the foundation for the project by defining the problem and determining the feasibility of finding a solution with the available data, as mentioned in the script.

💡Data Preparation

Data Preparation, also known as data munching or data manipulation, involves cleaning, integrating, transforming, and reducing raw data to make it suitable for analysis. The script explains that this step is crucial because it ensures the data is in a usable format, free from errors and gaps, which directly impacts the accuracy of the analysis.

💡Data Munching

Data Munching is a subset of data preparation where the data scientist explores and manipulates the data to fill gaps, remove unnecessary columns, and ensure the data structure is appropriate for analysis. The term is used in the script to describe the detailed work of making raw data ready for the modeling process.

💡Model Planning

Model Planning is the phase where the type of model and algorithm to be used for the data science project is decided. It depends on the nature of the problem and involves statistical or machine learning models. The script emphasizes the importance of this step in determining the approach to solving the business problem at hand.

💡Exploratory Data Analysis (EDA)

Exploratory Data Analysis is an approach to analyze data sets to summarize their main characteristics, often using visual methods. In the script, EDA is mentioned as a preparatory step to understand the data's properties, detect outliers, and discover patterns, which informs the choice of model and the subsequent analysis.

💡Machine Learning

Machine Learning is a subset of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. The script refers to machine learning models such as logistic regression, decision trees, and SVMs, which are trained to make predictions or decisions based on input data.

💡Training Data

Training Data is the subset of the data set used to train machine learning models. The script explains that the data is used to teach the model to make predictions or decisions, and it is a critical part of the model building process, where the model learns from the data to perform tasks without being explicitly programmed.

💡Testing Data

Testing Data is the portion of the data set held back from the training process and used to evaluate the performance of a trained model. In the script, it is mentioned that testing data helps to ensure the model's accuracy by providing a measure of how well it performs on unseen data.

💡Operationalization

Operationalization is the final step in the data science project life cycle where the validated model or findings are put into practice to solve the business problem. The script highlights the importance of this step as it involves communicating the results, getting acceptance, and implementing the solution in a real-world context.

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