Data Science Frameworks: Top 7 Steps For Better Business Decisions

Data science is a vast field encompassing various techniques and methods that extract information and help make sense of mountains of data. Moreover, data-driven decisions can deliver immense business value. Therefore, Data science frameworks have become the holy grail of modern technological businesses, broadly charting out 7 steps to glean meaningful insights. These include: Ask, Acquire, Assimilate, Analyze, Answer, Advise, and Act. Here’s an overview of each of these steps and some of the important concepts related to data science. 

Data Science Frameworks: Steps

1. Asking Questions: The Starting point of data science frameworks

Like any conventional scientific study, Data science also begins with a series of questions. Data scientists are curious individuals with critical thinking abilities who question the existing assumptions and systems. Data enables them to validate their concerns and find new answers. So, it is this inquisitive thinking that kick-starts the process of taking evidence-based actions. 

2. Acquisition: Collecting the required data

After asking questions, data scientists have to collect the required data from various sources, and further assimilate it to make it useful. They deploy processes like Feature Engineering to determine the inputs that will support the algorithms of data mining, machine learning, and pattern recognition. Once the features are decided, data can be downloaded from an open-source or acquired by creating a framework to record or measure data. 

3. Assimilation: Transforming the collected data

Then, the collected data has to be cleaned for practical use. Usually, it involves managing missing and incorrect values and dealing with potential outliers. Poor data cannot give good results, no matter how robust the data modeling is. It is vital to clean the data as computers follow a logical concept of “Garbage In, Garbage Out”. They do process even the unintended and nonsensical inputs to produce undesirable and absurd outputs. 

Different forms of data

Data may come in structured or unstructured formats. Structured data is ordinarily in the form of discrete variables or categorical data, having a finite number of possibilities (for example, gender) or continuous variables, including numeric data such as integers or real numbers (for example, salary and temperature). Another special case can be that of binary variables possessing only two values, like Yes/No and True/False. 

Converting data 

Sometimes, data scientists may want to anonymize numeric data or convert it into discrete variables to synchronize it with algorithms. For example, numerical temperatures may be converted into categorical variables like hot, medium, and cold. This is called ‘binning’. Another process called ‘encoding’ can be used to convert categorical data into numerics.  

4. Analysis: Conducting data mining

Once the required data has been acquired and assimilated, the process of knowledge discovery begins. Data analysis involves functions like Data Mining and Exploratory Data Analysis (EDA). Analyzing is one of the most essential steps of data science frameworks

Data Mining

Data mining is the intersection of statistics, artificial intelligence, machine learning, and database systems. It involves finding patterns in large datasets and structuring and summarizing pre-existing data into useful information. Data mining is not the same as information retrieval (searching the web or looking up names in a phonebook, etc.) Instead, it is a systematic process covering various techniques that connect the dots between data points. 

Exploratory data analysis (EDA)

EDA is the process of describing and representing the data using summary statistics and visualization techniques. Before building any model, it is important to conduct such analysis to understand the data fully. Some of the basic types of exploratory analysis include Association, Clustering, Regression, and Classification. Let us learn about them one by one. 


Association means identifying which items are related. For example, in a dataset of supermarket transactions, there could be certain products that are purchased together. A common association could be that of bread and butter. This information could be used for making production decisions, boosting sales volumes through ‘combo’ offers, etc. 


Clustering involves segmenting the data into natural groups. The algorithm organizes the data and determines cluster centers based on specific criteria, such as studying hours and class grades. For example, a class may be divided into natural groupings or clusters, namely Shirkers (students who do not study for long and get low grades), Keen Learners (those who devote long hours to study and secure high grades), and Masterminds (those who get high grades despite not studying for long hours). 


Regression is done to find out the strength of the correlation between the two variables, also known as a predictive causality analysis. It comprises conducting a numeric prediction by fitting a line (y=mx+b) or curve to the dataset. The regression line will also help in detecting outliers – the data points that deviate from all other observations. The reason could be incorrect input of data or a separate mechanism altogether.

In the classroom example, some students in the ‘Mastermind’ group may have prior background in the subject or may have entered wrong study hours and grades in the survey. Outliers are important to identify problems with the data and the possible areas of improvement. 


Classification means assigning a class or label to new data for a given set of features and attributes. Specific rules are generated from past data to enable the same. A Decision Tree is a common type of classification method. It can predict whether the student is a Shirker, Keen Learner or Mastermind based on exam grades and study hours. For instance, a student who has studied less than 3 hours and scored 75% could be labeled as a Shirker.

5. Answering Questions: Designing data models

Data science frameworks are incomplete without building models that enhance the decision-making process. Modeling helps in representing the relationships between the data points for storing in the database. Dealing with data in a real business environment can be more chaotic than intuitive. So, creating a proper model is of utmost importance. Moreover, the model should be evaluated, fine-tuned, and updated from time to time to achieve the desired level of performance.

Our learners also read: Top Python Courses for Free

Explore our Popular Data Science Courses

6. Advice: Suggesting alternative decisions

The next step is to use the insights gained from the data model to give advice. This means that a data scientist’s role goes beyond crunching numbers and analyzing the data. A large part of the job is to provide actionable suggestions to the management about what could be to improved profitability and then deliver business value. Advising includes the application of techniques like optimization, simulation, decision-making under uncertainty, project economics, etc. 

upGrad’s Exclusive Data Science Webinar for you –

Transformation & Opportunities in Analytics & Insights

Top Data Science Skills to Learn to upskill

7. Action: Choosing the desired steps

After evaluating the suggestions in light of the business situation and preferences, the management may select a particular action or a set of actions to be implemented. Business risk can be minimized to a great extent by decisions that are backed by data science. 

Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

Read our popular Data Science Articles

Top 10 Data Science Frameworks to Learn

There are many data science frameworks available, but it is important to choose the right one that suits your needs. Like python frameworks for data science, R frameworks for data science, etc. To help you decide, we have compiled a list of the top 10 data science frameworks that are currently in use.


Keras is one of the most popular and widely used deep learning frameworks. It enables developers to rapidly create powerful neural networks that can be used for a variety of tasks, such as image recognition, natural language processing, and more. Keras is written in Python and can be integrated with other libraries like Theano, TensorFlow, and CNTK.


TensorFlow is an open-source machine-learning library developed by Google. It can be used to build powerful neural networks for a wide array of tasks, from image recognition and natural language processing to predictive analytics. TensorFlow also provides access to advanced AI tools like Google’s DeepMind and TensorFlow Serving. Besides this, Like python frameworks for data science, TensorFlow is also an ideal choice for deep learning and AI development tasks.


Pandas is one of the most popular data science frameworks. It provides convenient tools for data wrangling, analysis, and visualization. Pandas is written in Python and is integrated with other libraries such as NumPy, Matplotlib, Scikit-Learn, and Statsmodels. Mostly, the data science tools and frameworks are based on the python language, and Pandas is one of them.


Scikit-Learn is a powerful Python library that enables developers to create and deploy machine-learning models with ease. It is built on top of NumPy, SciPy, and Matplotlib and provides access to a variety of pre-built algorithms for tasks such as predictive analytics, clustering, classification, and more. These data science tools and frameworks are widely used in various data science projects.


Numpy is an open-source library that enhances the computational power of Python with robust data structures designed for number-crunching applications such as Quantum Computing, Statistical computing, signal processing, image processing, graphs and networks, astronomy processes, cognitive psychology, and more, using the high-performance capabilities of C.

Spark MLib:

Spark MLib is a framework that supports Java, Scala, Python, and R. It can be used on Hadoop, Apache Mesos, Kubernetes, and cloud services to handle various data sources. For example, it can be used to build streaming applications that process data in real-time. And it supports distributed machine-learning algorithms like decision trees, random forests, and more.


Theano is a Python library that enables developers to build complex deep-learning models with ease. It is written in Python and based on NumPy and SciPy. Theano also provides access to GPU acceleration for faster model training.


MapReduce in data science is an open-source framework used for data processing across large clusters of computers. It is built on the Hadoop Distributed File System (HDFS) which enables applications to store files in a distributed manner across multiple machines. MapReduce in data science can be used for tasks such as data aggregation, sorting, filtering, data mining, and machine learning. It divides the dataset into smaller chunks and distributes them across multiple computers, allowing them to process the data in parallel.


Data science has wide-ranging applications in today’s technology-led world. The above outline of data science frameworks will serve as a road map for applying data science to your business! 

If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science.

Is NumPy considered a framework?

The NumPy package in Python is the backbone of scientific computing. Yes, NumPy is a Python framework and module for scientific computing. It comes with a high-performance multidimensional array object and facilities for manipulating it. NumPy is a powerful N-dimensional array object for Python that implements linear algebra.

In data science, what is unsupervised binning?

Binning or discretization converts a continuous or numerical variable into a categorical characteristic. Unsupervised binning is a sort of binning in which a numerical or continuous variable is converted into categorical bins without the intended class label being taken into consideration.

How are classification and regression algorithms in data science different from each other?

Our learning method trains a function to translate inputs to outputs in classification tasks, with the output value being a discrete class label. Regression issues, on the other hand, address the mapping of inputs to outputs where the output is a continuous real number. Some algorithms are designed specifically for regression-style issues, such as Linear Regression models, while others, such as Logistic Regression, are designed for classification jobs. Weather prediction, house price prediction, and other regression issues may be solved using regression algorithms. Classification algorithms may be used to address problems like identifying spam emails, speech recognition, and cancer cell identification, among others.

Want to share this article?

Prepare for a Career of the Future

Leave a comment

Your email address will not be published. Required fields are marked *

Our Popular Data Science Course

Get Free Consultation

Leave a comment

Your email address will not be published. Required fields are marked *

Get Free career counselling from upGrad experts!
Book a session with an industry professional today!
No Thanks
Let's do it
Get Free career counselling from upGrad experts!
Book a Session with an industry professional today!
Let's do it
No Thanks