What is Data Science? Lifecycle, Applications with Example

In the current times, data is crucial for every firm. Raw data does not serve any purpose until and unless it is processed. Data science transforms raw and unstructured data into actionable insight, which can then be used for decision-making and planning. According to Forbes, data scientists have to spend about 80% of their time cleaning and preparing data. This points out how critical data quality is for understanding information.  

In this blog, we will discuss what data science is, its processes, and how you can follow a proper roadmap to excel in this field.

Watch this Data Science Tutorial:

What Is Data Science?

Data science is the process of extracting useful insights from raw data. It includes statistical analysis, data analysis, machine learning, data modeling, and data preprocessing. Data can be used from various sources and presented in different formats. For example: A case study that also went on to become a Hollywood feature film, “Moneyball.”

In the movie, they show how an underdog team went on to compete at the highest level of the baseball tournament by analyzing the statistical data points of each player and quantifying their performances to win the game. It can be aligned with how data science works.

Why Data Science?

Humans generate approximately 330 million terabytes of data each day.  Modern organizations manage and process these data to find patterns. These patterns help them make forecasts or predictions. 

Data science plays an important role in drawing insights or finding these patterns from data using several tools and techniques such as Pandas, Numpy, Matplotlib, etc.

Consider an e-commerce platform that uses predictive software to anticipate purchases of different products. Within this system, when analyzing past sales figures to uncover trends and predict future demand, the algorithm can incorporate a keyword suggestion tool.

For example, if the forecasting module identifies boosting sales of outdoor furniture during the summer months, it may make use of the recommendation system to suggest relevant keywords such as “patio chairs,” “outdoor dining sets,” or “garden furniture”. These suggested keywords are based on prior sales data and recognized seasonal patterns.

The prediction engine integrated into the forecast module helps sellers optimize their listings by giving them insights on how they can align their marketing activities according to the expected changes.

Data science is all about using information to help us make better choices and solve problems. It is like having a superpower for decision-making.

• Smart Decisions: With data science, we can analyze data to make smart decisions in business, health, and many other areas.
• Solving Problems: It helps us solve tricky problems. For example, it can help doctors find better ways to treat diseases.
• Discovering Patterns: Data science helps us find patterns in data that we might not see on our own. It’s like finding hidden treasures.
• Saving Time and Money: By using data, we can save time and money. For businesses, this means more profit.
• Endless Possibilities: Data science is a powerful tool with endless possibilities. It’s like having a magic wand for understanding the world.

In a nutshell, data science is all about using data to make life better, easier, and more exciting.

Look at the following infographic to better understand the scope of data science.

Google is by far the biggest company that is on a hiring spree for trained data scientists. Since Google is mostly driven by Data Science and Artificial Intelligence these days, it offers one of the best salary packages to its data science employees.

Have a look at the difference between Data Science Vs Data Analytics

Data Science Life Cycle

The data science life cycle is an iterative set of processes that can be followed to complete a data science project from planning to deployment. The Data Science lifecycle comprises the following steps:

Let’s discuss each of these steps in detail:

1. Problem Definition: It’s crucial to have a well-defined goal. For example, if you want to predict customer churn for a business or detect fraud in financial transactions, it should be clear, otherwise, it will consume a lot of time and we will fail to achieve a positive result.

2. Data Collection: Once you understand the problem, you can initiate the project by gathering the data. The data can be collected from various sources, such as databases, and a variety of sensors. In the steel industry, they use multiple sensors to check on the quality of steel. 

Also,  online sources like GPS, user live interaction (such as live streaming video data), etc., are a few examples of data sources.

3. Data Cleaning: The data that we collected in the previous step can be messy. So, in this step, we clean our data. Cleaning involves removing errors, duplicates, and irrelevant information. Processed data can then be sent for the next step, which is EDA (Exploratory Data Analysis).

4. Exploratory Data Analysis: In this step, we understand our data and its characteristics. It is a process of discovering patterns, locating outliers, and identifying relationships between variables. We also create a visualization or perform statistical analysis to get insights from it.

5. Data Preprocessing: Data preprocessing is the process of making data suitable for analysis. This involves manipulation, filtration, encoding of the data, or augmentation before the analysis, making it suitable for modeling.

6. Model Building: In model building, we create models using various algorithms to analyze the data. We can choose from a variety of algorithms. We can use a regression algorithm for forecasting; we can use classification algorithms for voice recognition, spam mail detection, clustering, pattern recognition, etc.

7. Model Evaluation: After building the model, the next step is to evaluate it. In this step, we check how our model is performing. We use metrics to see how well they can solve our problem. We check for its accuracy, like how accurate results it is producing and is our model reliable or not.

8. Deployment: Once the model evaluation process is done, we then move on to the deployment. In this step, we deploy the model to work in the real world. For example, let’s say we build a recommendation system that recommends movies to users. To make it available to users on the web, we will have to deploy it.

9. Feedback and Improvement: Deployment is not the last step; after the deployment step, we then do continuous monitoring of our models and gather feedback. If there is any issue with their (model) performance or if they are not performing as per our expectations, then we make improvements. There can be various factors that affect our model’s performance.

Data Science Prerequisites

Several prerequisites must be fulfilled to efficiently drive data science solutions in an organization. Here are some technical terms you should know before learning data science:

1. Programming Knowledge

For the statistical analysis and computations that are required for the Data Science processes, the professionals must be familiar with programming languages such as Python or R. The library support and scripting knowledge help you create machine-learning models from scratch with ease. 

Also, check out Python Vs R in Data Science.

2. Statistics, Probability, and Linear Algebra

Knowledge of descriptive statistics and inferential statistics is a must if you want to make a career in data science. With the help of statistical analysis, you can draw various inferences and understand the data at hand. One example would be how we discussed performing hypothesis testing to test whether a time series is stationary or not.

Probability and linear algebra also play an important role in shaping the understanding of complex machine-learning algorithms. If you’re familiar with these concepts, it will be easier for you to understand the internal functioning of various machine learning algorithms.

3. SQL, Excel, and Visualization Tools

Visualization tools such as PowerBI, Tableau, etc. can provide a great interactive interface to represent various data points, which can help in performing initial analysis or just understanding the data.

SQL and Excel, on the other hand, can help you understand the representation of data in tabular format or data frames that help in data manipulation, wrangling, etc.

4. Big Data and the Cloud

A machine learning model deployed at scale is where the cloud comes into the picture. To be able to magnify the learnings and outcomes for any business problem, we use machine learning on the cloud. And big data gives a better perspective on how to handle large and complex data for our business problems.

Have a look at the Project Ideas for Beginners

What Is the Data Science Process?

Let’s understand what is data science process with an example:

Step 1: Gathering Raw Data

Let’s say a company wants to understand public sentiment toward its brand on social media. They decided to gather data from the Twitter API, which provides a stream of tweets related to their brand.

Step 2: Data Modeling

Using statistical analysis and machine learning approaches, the data scientists preprocess and clean the Twitter data. They extract relevant features such as sentiment scores, user demographics, and engagement metrics. The data is then transformed into a structured format suitable for analysis.

Step 3: Actionable Insights

The data scientists analyze the structured data to derive insights. They identify patterns, trends, and correlations within the Twitter data. For example, they may discover that positive sentiment is higher among younger demographics and during certain events. These insights provide the company with actionable information on how to improve its brand perception and engagement strategies.

Importance of Data Science

Data is a highly valuable asset for a wide range of industries, serving as a cornerstone for making well-considered and informed business decisions. Data science, as a field, possesses the remarkable capability to transform raw data into meaningful and actionable insights.

A data scientist has the expertise to extract valuable information from whatever data they have access to. Their proficiency lies in the art of converting numbers, statistics, and data points into practical recommendations. Instead of merely working with data, they sculpt it into a compass, guiding organizations along the path of data-driven success.

Data scientists serve as trusted navigators, steering companies through the constantly changing data landscape. They ensure that every decision and suggestion is firmly anchored in robust and insightful data analysis. This, in turn, empowers businesses to remain agile, seize opportunities, and maintain their competitive edge in an increasingly data-centric world.

Uncover the distinctions between BI & Data Science to optimize your business strategy.

Now that you know what data science is, let’s focus on the data science life cycle.

Applications of Data Science

The below-listed applications showcase how data science drives operational efficiency, cost savings, and improved decision-making across diverse sectors.

Below mentioned are some of the applications of Data Science:

1. Fraud and Risk Detection

Over the years, financial organizations have learned to analyze the probabilities of risks and defaults through customer profiling, past expenditures, and other variables available through data.

2. Healthcare

Data science makes it possible to manage and analyze very large, diverse datasets in healthcare systems, drug development, medical image analysis, and more. Recently, data science approaches were brought in to combat the COVID-19 pandemic. Data scientists helped with digital contact tracing, diagnosis, risk assessment, resource allocation, estimating epidemiological parameters, drug development, social media analytics, etc.

3. Internet Search

All search engines, including Google, use data science algorithms to deliver the best results for searched queries within seconds.

4. Targeted Advertising

Digital ads have a higher call-through rate (CTR) than traditional ads because targeted advertising is based on a user’s past behavior with the help of data science algorithms.

5. Recommendation Systems

Major online companies, and countless other businesses, have enthusiastically adopted recommendation engines. These engines are all about providing personalized suggestions to users based on their past interests and searches. It’s like offering users ideas and products they might like. This not only keeps users engaged but also creates a more tailored and enjoyable experience for customers.

Interested in Data Science? Check out this blog on How to Become a Data Scientist to grow in your career.

6. Advanced Image, Speech, or Character Recognition

Facial recognition algorithms on Facebook, speech recognition products such as Siri, Cortana, Alexa, etc., and Google Lens are all perfect examples of data science applications in image, speech, and character recognition.

7. Gaming

Today, games use machine learning algorithms to improve or upgrade themselves as players move up to higher levels. In motion gaming, the opponent (computer) can analyze a player’s previous moves and accordingly shape up its game.

8. Augmented Reality (AR)

Augmented reality (AR) is a technology that combines digital information or virtual elements with the real world, typically viewed through a device like a smartphone or AR glasses. It enhances your perception of reality by adding computer-generated images, sounds, or other data to what you see, creating an interactive and enriched experience.

Examples of Data Science

Let’s take a look at some data science examples:

• Amazon: Amazon uses a personalized recommendation system to improve customer satisfaction. This is heavily dependent on predictive analytics. Amazon analyzes the user’s purchase history to recommend more products.
• Spotify: Spotify utilizes Data Science to offer personalized music recommendations to users. In 2013, Spotify made predictions about the Grammy Award winners by analyzing what music its users listen to; out of the 6 predictions, 4 were true.
• Uber: Uber utilizes big data to gain better insights and provide better service to its users. With its huge database of drivers, it can suggest to users the most suitable one. Uber charges customers based on the time it takes to get to their destination. This prediction is helped by various algorithms.

Also, check the difference between Data Science Vs Machine Learning

Data Science vs. Data Scientist

Data science uses scientific methods, and algorithms to extract insights from data, whereas a data scientist is a professional who uses these data science techniques for the analysis and interpretation of complex data.

Data Science processes include using statistics, machine learning, data analysis, and data visualization, while a data scientist is involved in designing and implementing algorithms, models, and statistical analyses. The responsibilities include identifying data sources, cleaning data, and creating / testing models. Data science is widely applicable across industries such as finance, healthcare, marketing, technology, etc while a data scientist applies data science techniques to solve specific problems within an organization.

Data Science vs. Business Intelligence

Data science helps us extract insights and knowledge from data to make informed decisions and predictions, while business intelligence will help in decision-making to support the business by analyzing historical and current data. Business intelligence primarily focuses on reporting, querying, and visualizing data.

Data science mainly involves extensive data cleaning, and preprocessing and often deals with unstructured data, while business intelligence deals with structured data and focuses on aggregation, filtering, and sorting. 

Data science involves long-term strategic decision-making based on future prediction, while business intelligence supports short-term tactical decision-making. While data science deals with a wide range of data sources, including structured and unstructured data, business intelligence focuses on structured data from databases, spreadsheets, and flat files.

Data science helps us generate predictive models, data visualizations, and actionable insights while business intelligence produces reports, dashboards, and key performance indicators for monitoring.

Data Science	Business Intelligence
In Data Science, we use math, statistics, and ML models to identify hidden trendlines in the data.	In Business Intelligence, we use tools like PowerBI, Tableau, and Qliksense to identify hidden trends in the data.
Data Science is more focused on the  future	Business Intelligence is more focused on the past and the present.
It deals with both structured and unstructured data.	It mainly deals with structured data.

What Is Data Science Used for?

In this section, let us understand, with an example, what data science is used for. Here we have taken the scenario of an e-commerce company, VirtuCart. Let’s understand their current issue and how they found the prime reason.

VirtuCart faces the critical challenge of high cart abandonment rates on its e-commerce platform. To resolve this issue, VirtuCart made a strategic decision to use data science tools and techniques to uncover the underlying reasons contributing to the problem. 

They began by recruiting skilled data science professionals to conduct a thorough analysis and arrive at a conclusive understanding. The professionals first obtained data through data collection methods and did exploratory data analysis using Python and, R, among other tools. 

Then, they prepared a machine learning model through which they conducted predictive analysis. This analysis helped them identify the reason for cart abandonment, which was surprise shipping costs and a complex checkout process.

According to the results, Informed by such findings, interventions were carried out, with A/B testing being done for optimal results. They deployed two different UI for the same endpoint (the cart page) and later selected the one with a better conversion rate. This method fixed the immediate issue while also setting up a continuous, evidence-based structure for optimizing VirtuCart conversion rates. Continuous improvement was ensured through real-time monitoring systems that were supported by data science visualization tools. This method fixed the immediate issue while also setting up a continuous evidence-based structure for optimizing VirtuCart conversion rates.

What are Different Data Science Tools?

In this section, we are going to explore different tools and languages we use in data science, including Python and its libraries, R, SQL, Excel, Tableau, PowerBI, Apache Spark, Hadoop, Jupyter Notebook, Cassandra, and Kafka.

1. Python:

Python is a popular programming language widely used in Data Science.

These are some of the popular Python data science libraries that you should explore.

• NumPy:  It is a library using which you can create a multidimensional array in Python.
• Pandas: It is used for data manipulation. We can manipulate our numerical tables and time series using pandas.
• Matplotlib: It is a Python library used for creating interactive visualization.
• Scikit-learn: It is a free Python library used for predictive data analysis
• TensorFlow: It is a free and open-source Python library used for ML and AI.
• PyTorch: It is a library used for computer vision and natural language processing.

2. R :

R is a high-level programming language and software environment. It is used for statistical computing and graphics. It is not a general-purpose programming language, unlike Python. It comes with a variety of in-built functions that can be used for data visualization and analysis.

3. Excel:

Excel is a software widely used for data entry and basic data analysis. In Excel, you can create visualizations like charts and graphs.

4. Tableau:

Tableau is a data visualization and business intelligence tool that allows us to create interactive dashboards and reports. It is used for data visualization and analysis.

Also check out how Data Scientists use Tableau.

5. Power BI:

Power BI is a business analytics service provided by Microsoft. It allows us to connect to different data sources, transform and model data, and create interactive visualizations, and dashboards.

6. Apache Spark:

Apache Spark is an open-source, distributed computing framework for big data processing. It is a multi-language engine that can be used for executing data engineering, data science, and machine learning tasks. 

7. Jupyter Notebook:

Jupyter Notebook is an open-source web application that allows us to create and share documents containing live code, visualizations, and narrative text. Using jupyter, we can create and share computational documents.

8. Cassandra:

Cassandra is a distributed, open-source NoSQL distributed database management system designed to handle large amounts of data across many servers.

9. Apache Kafka:

Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming applications. It is designed to handle high-volume data streams with low latency and high throughput.

Uses of Data Science

Data science finds extensive applications in various domains, revolutionizing decision-making processes and enhancing operational efficiency. 

• In healthcare, it aids in early disease detection, drug discovery, and personalized treatment plans.

• E-commerce platforms use data science for product recommendations, targeted marketing, and Supply Chain Optimization. 
• In finance, data science is used for credit risk assessment, fraud detection, and stock market analysis.
• In the manufacturing Industry, it can be used for predictive maintenance, quality control, and process improvement.
• In the transportation and logistics industries, it is used for optimizing the route for transport trucks and can be used to manage fleets. 

Also check the blog on SQL for Data Science.

Where Do You Fit in Data Science? 

There are several job roles in the data science field. Based on your qualifications and skills, you can choose any one of them as your career. Let’s discuss these roles in detail, which will help you understand where exactly you fit in the data science field:

Data Scientist

Data scientists are experts in data analysis with technical skills for solving tough problems. They deal with various factors related to mathematics, statistics, and computer science, and they obtain, process, and understand the meaning of massive data. They are proficient in developing statistical models, machine learning algorithms, and predictive analytics solutions to address business challenges.

Data Scientist Responsibilities:

• Analyze huge amounts of complicated data to extract insights that will influence decision-making.

• Design experiments to test hypotheses and measure the effectiveness of solutions.

• Collecting and preparing data, as well as constructing and maintaining a system for data management.

Data Engineer

Data engineering can be used in different places to set up systems that gather, store, and convert raw data into meaningful information for interpretation by business analysts and data scientists. The main task of these engineers is to make the data available for organizational use in assessing its performance. 

Data Engineer Responsibilities:

• Gather relevant data sets

• Help in building up real-time streaming platforms

• Develop new solutions for data analytics and business intelligence

• Build reports for corporate managers based on processed data

• Design algorithms to translate information into well-calculated results

Data Analyst

A data analyst is a professional who collects, processes, and analyzes vast statistical datasets. This position requires using various methods to deduce important findings from the given figures, which are often communicated to decision-makers. 

Responsibilities of a Data Analyst:

• Ensure the availability of quality, accurate, and consistent data by collecting and pre-processing multiple sources.

• One is to analyze complex data and identify trends or patterns that can inform decision-making in business.

• Use dashboards and visualization tools to communicate findings and insights to stakeholders.

Machine Learning Engineer:

Machine learning engineers build as well as deploy machine learning models. They work closely with the data scientist, identifying appropriate algorithms and models, which they later build and implement within production settings. Also, they monitor model performance and update it when necessary.

Machine Learning Engineer Responsibilities:

• Create scalable machine learning models/systems geared at supporting business objectives.
• In addition, these professionals have to work with their colleagues in data science as well as those working in data engineering so that relevant information can be collected in advance before processing it through pipelines.
• This includes designing data infrastructure, such as storage for all kinds of information required in ML workflows, besides feature engineering till model training phases.

Also, know the difference between Data Science vs. Deep Learning vs. Machine Learning vs. Artificial Intelligence.

What Is a Data Scientist?

Data scientists are IT professionals whose main role in an organization is to perform data wrangling on a large volume of data—structured and unstructured—after gathering and analyzing it. Data scientists need this voluminous data for multiple reasons, including building hypotheses, analyzing market and customer patterns, and making inferences.

What Does a Data Scientist Do?

The role and responsibilities of a data scientist can vary from organization to organization; based on this, we can segregate them in the following manner:

A data scientist’s role in any organization will involve the following:

1. Data Extraction, Loading, and Transformation
2. Exploratory Data Analysis
3. Data Manipulation
4. Statistical Analysis
5. Visualization
6. Data Modeling
7. Gathering Actionable Insights

This modified data is further used for the prediction of results that can help organizations come up with efficient plans that need to be executed for their growth.

Since we have discussed what data scientists do, let us also discuss why becoming a data scientist is good for your career.

Why Should You Become a Data Scientist?

• High Demand: Data scientists are required in every industry because companies are relying more and more on data to make choices. This means there are plenty of job options, and they often come with good pay.
• Exciting Career Prospects: If you decide to be a data scientist, you’ll get to work with new and cool technologies, deal with tough problems, and bring out new ideas. You’ll always have chances to learn and grow in this field.
• Meaningful Contributions: Data scientists can find important information in data that helps companies make smart decisions, improve how they work, and stay competitive. You’ll feel good about the impact you’re making on businesses and society.
• Diverse Skill Set: Data science gives you a set of skills that you can use in many jobs. You’ll be good at things like looking at data, programming, and understanding different subjects. This means you can choose from a lot of different careers.

• Learning all the Time: The world of data science is always changing. This means there’s always something new to learn and discover. 
• Working with Different People: Data scientists often work with people from different fields, like business, engineering, marketing, and healthcare. This mix of ideas makes work interesting and lets you see things from new angles.
• A Career That’s Ready for the Future: As more and more companies use digital tools and data, data scientists will be in demand. Therefore, if you become a data scientist, you’ll have a safe job.

Top Companies Hiring Data Scientists

We will look at how top industry players, such as Google, Amazon, and Visa, use data science. IT organizations need to address their complex and expanding data environments to identify new value sources, exploit opportunities, and grow or optimize themselves efficiently.

Here, the deciding factor for an organization is what value they extract from their data repository using analytics and how well they present it. Some of the biggest companies that are hiring data scientists at competitive salaries are listed below:

Google

Google is by far the biggest company that is on a hiring spree for trained data scientists. Since Google is mostly driven by data science, artificial intelligence, and machine learning, it offers one of the best salary packages to its employees.

Amazon

Amazon is a global e-commerce and cloud computing giant that is hiring data scientists on a large scale. Amazon needs data scientists to find out customer mindsets and enhance the geographical reach of both e-commerce and cloud domains, among other business-driven goals.

Visa

Visa, an online financial gateway for most companies, does transactions worth millions in a single day. Due to this, the need for data scientists is huge at Visa to generate more revenue, check fraudulent transactions, customize products and services as per customer requirements, etc.

Salaries and Jobs Available in Different Countries

Data science is expanding at a mind-blowing rate, resulting in increased demand for skilled data scientists around the globe. According to PayScale, the average annual salary of a skilled data scientist is US$94,491. However, the salary offered may differ based on location and experience.

Below are the  five countries that provide many opportunities for data scientists:

• United States (US): The US has the highest demand for skilled data scientists. In the US, companies have spent more than a billion dollars to acquire data scientists from different countries. The average annual salary of an entry-level data scientist in the US is US$85,000; the salary can go up to US$136,000 p.a. based on your expertise and experience in the field.
• Germany: Data scientists in Germany can earn about €5,960 per month. The salary of a data scientist in Germany ranges from €2,740 per month to €9,470 per month. Germany offers the most lucrative salary packages for the role of a data scientist.
• United Kingdom (UK): Similar to Europe and the US, various industries in the UK are now hiring skilled professionals to manage, maintain, and analyze large amounts of data. A data scientist in the UK can earn up to £50,000 p.a.
• China: China is planning to lead the world in artificial intelligence by the year 2030 by investing in IT industries and making government policies more accommodating. An experienced data scientist in China can earn up to ¥350,000 p.a.
• India: India has the fastest-growing industries in several sectors, such as healthcare, defense, logistics, and artificial intelligence. Similar to the rest of the world, India too is facing acute challenges in finding skilled data scientists. So, if you have the right skills and experience as a data scientist, you can earn up to ₹1,000,000 p.a.

How Does Intellipaat Help in Making a Career in Data Science?

Intellipaat provides many opportunities to learners who are willing to establish themselves as all-rounders in the domain of data science. We offer a wide range of courses dedicated to providing you with end-to-end knowledge about trending and in-demand data science skills.

Conclusion

Today, if any digitally-driven organization is starved of data, even for a short duration, then the organization loses its competitive edge. Data scientists help organizations make sense of their business, customers, and markets.

If you want to become a Google Data Scientist with the best salary, then you need to be at the top of your game. If you are wondering how to learn data science and the scope of Data Science, then Intellipaat is the right place to start your incredible Data Science journey.

To learn more about data science, check out Intellipaat’s Data Science training.

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