# Data Science Modelling: 8 Easy Steps

If you take a fast look around today’s business and commercial world, you’re bound to come across the terms Data Science and Data Analytics. This is because Data has become an essential component of any successful 21st-century business. Data Science is a branch of information technology that focuses on extracting knowledge and actionable insights from data (both Structured and Unstructured) and applying that knowledge to solve issues.

On a complete walkthrough of this article, you will gain a decent understanding of Data Science along with its needs and applications, and skills involved in a Data Science Project along with various steps involved in the Data Science Modelling process.

## Introduction to Data Science

Data Science is an interdisciplinary field that uses Statistics, Computer Science, and Machine Learning techniques to extract information from both Structured and Unstructured Data. Data Science is one of the fastest-growing, most complex, and well-paying professions in the last decade. Following the recognition of its significance, a slew of Data Science firms has sprung up to deliver Data-driven solutions across a variety of industries.

### Key Skills Required in Data Science

To begin with Data Science Modelling, the Data Science Companies expect the ideal candidate to know some skills. Below, are the skills one should know before carrying out Data Science Modelling:

#### 1) Statistics and Probability

The underpinnings of Data Science are Statistics and Probability. When it comes to making Predictions, Probability Theory comes in handy. Estimations and Projections play a big role in Data Science. Statistical techniques are used by Data Scientists to make Estimations for future investigation. As a result, Probability Theory is frequently used in statistical methodologies. All the Statistics and Probability is based on Data.

#### 2) Programming Skills

Python is the most prevalent coding language leveraged in the Data Science profession, however other programming languages such as R, Perl, C/C++, SQL, and Java are also used. Data Scientists can use these programming languages to arrange Unstructured Data Collections.

#### 3) Data Visualization Skills

The most important stories in the newspaper are skimmed and skipped, but Sketches are largely read. Seeing something and having it registered in one’s mind is a human idea. The entire Dataset, which could be hundreds of pages long, can be condensed into two or three Graphs or Plots. To make a graph, you must first view the Data Patterns. Microsoft Excel is a terrific tool that generates the right charts and graphs for your needs. Other Data Visualization and Business Intelligence solutions include Tableau, Metabase, and Power BI.

#### 4) Machine Learning and Deep Learning

Machine Learning is a must-have ability for any Data Scientist. Predictive Models are created using Machine Learning. For example, if you want to forecast how many clients you’ll have in the coming month based on the previous month’s Data, you’ll need to employ Machine Learning techniques. Machine Learning and Deep Learning techniques are the backbones of Data Science Modelling.

#### 5) Communication Skills

You must communicate your findings to a group of Teammates or Senior Management. Communication allows us to rise above what everyone is fighting for. Being a competent communicator, further allows you to convey ideas and identify any Data Contradictions. In a Project, presentation skills are crucial for showcasing Data Discoveries and planning future strategies.

## Understanding the Need for Data Science

All Data Science Companies understand the need for Data Science and try to provide their services across all industries and their verticals. Below are the two main use cases which non-Data-driven companies should look upon:

• Historical Data: With Data Science housing strong tools, it assists us in extracting insights from past Data. It aids in optimizing your business strategies, hiring the right people, and generating more money, as Data Science enables you to make better business decisions in the future.
• Business Strategy: Data Science Companies can better create and advertise their products by narrowing down their target market. Consumers can leverage Data Science to find better products, especially on E-Commerce websites that use a Data-driven recommendation engine.

## Understanding Data Science Modelling

A Data Scientist’s ability to structure problems is crucial. A smart Data Scientist may build and represent an informative visualization, showcasing the raw Data and business activities, associated with the Key Performance Indicators and business use cases, such as new Customer Acquisition, Product Design, desk location to reduce distraction, and so on. All these factors are considered while carrying out the process of Data Science Modelling.

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## Steps Involved in Data Science Modelling

The key steps involved in Data Science Modelling are:

### Step 1: Understanding the Problem

The first step involved in Data Science Modelling is understanding the problem. A Data Scientist listens for keywords and phrases when interviewing a line-of-business expert about a business challenge. The Data Scientist breaks down the problem into a procedural flow that always involves a holistic understanding of the business challenge, the Data that must be collected, and various Artificial Intelligence and Data Science approach that can be used to address the problem.

### Step 2: Data Extraction

The next step in Data Science Modelling is Data Extraction. Not just any Data, but the Unstructured Data pieces you collect, relevant to the business problem you’re trying to address. The Data Extraction is done from various sources online, surveys, and existing Databases.

### Step 3: Data Cleaning

Data Cleaning is useful as you need to sanitize Data while gathering it. The following are some of the most typical causes of Data Inconsistencies and Errors:

• Duplicate items are reduced from a variety of Databases.
• The error with the input Data in terms of Precision.
• Variables with missing values across multiple Databases.

### Step 4: Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a robust technique for familiarising yourself with Data and extracting useful insights. Data Scientists sift through Unstructured Data to find patterns and infer relationships between Data elements. Data Scientists use Statistics and Visualisation tools to summarise Central Measurements and variability to perform EDA.

If Data skewness persists, appropriate transformations are used to scale the distribution around its mean. When Datasets have a lot of features, exploring them can be difficult. As a result, to reduce the complexity of Model inputs, Feature Selection is used to rank them in order of significance in Model Building for enhanced efficiency. Using Business Intelligence tools like Tableau, MicroStrategy, etc. can be quite beneficial in this step. This step is crucial in Data Science Modelling as the Metrics are studied carefully for validation of Data Outcomes.

### Step 5: Feature Selection

Feature Selection is the process of identifying and selecting the features that contribute the most to the prediction variable or output that you are interested in, either automatically or manually.

The presence of irrelevant characteristics in your Data can reduce the Model accuracy and cause your Model to train based on irrelevant features. In other words, if the features are strong enough, the Machine Learning Algorithm will give fantastic outcomes. Two types of characteristics must be addressed:

• Consistent characteristics that are unlikely to change.
• Variable characteristics whose values change over time.

### Step 6: Incorporating Machine Learning Algorithms

This is one of the most crucial processes in Data Science Modelling as the Machine Learning Algorithm aids in creating a usable Data Model. There are a lot of algorithms to pick from, the Model is selected based on the problem. There are three types of Machine Learning methods that are incorporated:

#### 1) Supervised Learning

It is based on the results of a previous operation that is related to the existing business operation. Based on previous patterns, Supervised Learning aids in the prediction of an outcome. Some of the Supervised Learning Algorithms are:

• Linear Regression
• Random Forest
• Support Vector Machines

#### 2) Unsupervised Learning

This form of learning has no pre-existing consequence or pattern. Instead, it concentrates on examining the interactions and connections between the presently available Data points. Some of the Unsupervised Learning Algorithms are:

• KNN (k-Nearest Neighbors)
• K-means Clustering
• Hierarchical Clustering
• Anomaly Detection

#### 3) Reinforcement Learning

It is a fascinating Machine Learning technique that uses a dynamic Dataset that interacts with the real world. In simple terms, it is a mechanism by which a system learns from its mistakes and improves over time. Some of the Reinforcement Learning Algorithms are:

• Q-Learning
• State-Action-Reward-State-Action (SARSA)
• Deep Q Network

For further information on Advance Machine Learning techniques, visit here.

### Step 7: Testing the Models

This is the next phase, and it’s crucial to check that our Data Science Modelling efforts meet the expectations. The Data Model is applied to the Test Data to check if it’s accurate and houses all desirable features. You can further test your Data Model to identify any adjustments that might be required to enhance the performance and achieve the desired results. If the required precision is not achieved, you can go back to Step 5 (Machine Learning Algorithms), choose an alternate Data Model, and then test the model again.

### Step 8: Deploying the Model

The Model which provides the best result based on test findings is completed and deployed in the production environment whenever the desired result is achieved through proper testing as per the business needs. This concludes the process of Data Science Modelling.

## Applications of Data Science

Every industry benefits from the experience of Data Science companies, but the most common areas where Data Science techniques are employed are the following:

• Banking and Finance: The banking industry can benefit from Data Science in many aspects. Fraud Detection is a well-known application in this field that assists banks in reducing non-performing assets.
• Healthcare: Health concerns are being monitored and prevented using Wearable Data. The Data acquired from the body can be used in the medical field to prevent future calamities.
• Marketing: Marketing offers a lot of potential, such as a more effective price strategy. Pricing based on Data Science can help companies like Uber and E-Commerce businesses enhance their profits.
• Government Policies: Based on Data gathered through surveys and other official sources, the government can use Data Science to better build poli==cies that cater to the interests and wishes of the people.

## Conclusion

This article teaches you about the steps needed to carry out Data Science Modelling. The first step in implementing any Data Science algorithm is integrating the Data from all sources. However, most businesses today have an extremely high volume of Data with a dynamic structure stored across numerous applications. Creating a Data Pipeline from scratch for such Data is a complex process as businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased Data Volume and Schema variations. Businesses can instead use automated platforms like Hevo.

Hevo Data, a No-code Data Pipeline, helps you transfer Data from a source of your choice in a fully automated and secure manner without having to write the code repeatedly. Hevo, with its strong integration with 150+ data sources & BI tools, allows you to not only export & load Data but also transform & enrich your Data & make it analysis-ready in a jiff.

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Research Analyst, Hevo Data

Harsh is a data enthusiast with over 2.5 years of experience in research analysis and software development. He is passionate about translating complex technical concepts into clear and engaging content. His expertise in data integration and infrastructure shines through his 100+ published articles, helping data practitioners solve challenges related to data engineering.