What is Data Science? History, Tools and Much More

The tech world is home to endless possibilities. While it’s fascinating to see them in action, it could sometimes be a bit challenging to fully understand these technologies.

One such example that is the talk of the town these days is data science. So what is data science in reality? This is exactly what we are going to talk about today. So stick around.

According to Grand View Research, the data science market is estimated to reach a value of $25.94 billion by 2027 with a CAGR of 26.9%. 

Reports by Statista state that the big data market will generate a revenue of $103 billion by 2027.

Promising stats, right? So how is it crunching these staggering numbers? What’s all this hype about? Let’s get into the meat & potatoes of the subject.

What is Data Science?

Data science is not just about making complicated models & graphs, fancy visualizations, or using programming languages like Python for writing code.

It is about using data to add value to your company, help them make informed decisions, and create as much positive impact as possible.

Now, value addition & impact can take various forms. It could be valuable insights, product, or product/service recommendations.

It could essentially be anything. To be able to do all that you need data science skills, fancy visualizations, and coding as well.sE1`

 

Knowing what data engineers do will give you an understanding of what data science is. 

Data science engineering is about solving real company problems using data and there are no restrictions as to what tools are used.

There are a few misconceptions about the subject and one reason is that there’s a huge disharmony between what’s popular to talk about and what actually goes on in the industry.

History of Data Science

Before popularizing data science, the term “data mining” was mostly used. In the scholarly article “From Data Mining to Knowledge Discovery”, published in 1996, data mining refers to the overall process of discovering valuable information through data.

In 2001, Willian S. Cleveland took data mining to another level by combining it with computer science. He basically made mining data a lot more technical because he believed that would pave the way for more possibilities and powerful innovation. 

This is also the time when the Web 2.0 revolution surfaced. In it, websites were not just a digital platform for accessing information but a medium for shared experiences among millions of users around the world. These revolutionary websites include MySpace (2003), Facebook (2004) & YouTube (2005).

Big data opened doors to a whole new world of possibilities in finding valuable insights using electronic information. It also meant that new & more sophisticated infrastructure was required to support handling this unimaginable data.

It required powerful computing technologies like MapReduce, Hadoop, and Spart. So the rise of big data sparked the rise of data science to support the growing business needs in drawing insights.

Why is Data Science Important?

Data science has emerged as a critical component in today's digital era, holding immense importance across various domains. Its significance stems from several reasons. Firstly, data science empowers organizations to make informed and data-driven decisions. 

By analyzing vast amounts of data, businesses gain valuable insights that shape strategic planning and facilitate better decision-making at all levels. 

Additionally, data science techniques such as machine learning and predictive analytics enable organizations to forecast future trends and outcomes, helping them anticipate customer behavior, optimize processes, and make proactive adjustments to stay ahead in the market.

Another crucial aspect is process optimization and efficiency. Data science helps businesses identify inefficiencies, bottlenecks, and areas for improvement by analyzing data, thereby streamlining processes and enhancing operational efficiency. This, in turn, reduces costs and boosts productivity.

What Does a Data Scientist Do? 

While most data scientists work on more technical aspects, GAFA companies have so many low-hanging fruits to improve that they don’t need advanced ML engineers only.

Again, a good data scientist isn’t just someone who can make exceptional data models & visuals. It is about the impact you can have with your skills. You’re not just crunching numbers.

 

You are a problem solver and a strategist. The employers will present you with problems and you are expected to guide them in the right direction.

Ensuring that data scientists possess an industry-recognized Data Scientist certification is crucial for validating their skills and expertise. Such certifications provide professionals with the recognition they need to stand out and demonstrate their commitment to excellence in the field. This not only benefits individual careers but also boosts organizational credibility.

Data Science Applications

Today, data science is almost everything that has something to do with data such as data collection, analysis, & modeling.

The most important aspect, however, is the application of data science. The most interesting & popular ones are AI & ML. So let's briefly talk about them.

Data Science and Artificial Intelligence Scope

Big data made it possible to train machines with a data-driven approach instead of a knowledge-based one. Something that has the potential to influence the way humans make decisions and perceive the world.

Deep learning is no longer a concept. It is now a reality and affects us daily. Machine Learning & Artificial Intelligence are dominating the world of data science and it is belittling other aspects such as exploratory analysis, experimentation, and even skills like business intelligence.

There is a general perception that data science is a bunch of researchers joining heads and focusing on AI & ML only. But in reality, companies are hiring data scientists as analysts.

Examples of Data Science in Daily Life

Now that we have a better understanding of what data science is, let’s conclude with some real-life examples of what Silicon Valley expects from a data scientist. 

But first, let’s take a moment to look at and examine this chart. 

Source: Hackernoon

 

It's a very comprehensive chart that describes all the basic elements of data engineering. At the foot, we have the “collect” step. This is obvious, we need to first collect before we can do anything.

The lesser-known step lies in between the "learn/optimize" and "aggregate/label" steps.

Everything that’s here is one of the most important for companies. It’s the step where the data engineer is guiding companies about what to do with the product.

In this context, the "analytics" provide you with insights regarding the behavior of your users, such as their interactions with your product and what is currently happening to them.

The “metrics” will tell you if you’re successful or not. Then we have “A/B testing” & “experimentation”, which allows us to see things like which product version is the best.

These aspects hold immense importance in the field of data science and are highly sought-after by most Silicon Valley executives. However, the "AI/Deep Learning" component is the one that significantly contributes to its widespread popularity.

However, upon careful research and consideration, it becomes evident that most companies do not prioritize it as the highest importance. Alternatively, it fails to generate the maximum outcome in proportion to the input invested.

That’s why Artificial Intelligence sits at the top of the chart.

Most Popular Data Science Programming Languages

Data science relies on several programming languages to handle data manipulation, analysis, and modeling. Let’s explore some of the top most popular data science programming languages that are making waves across the globe.

1. Python

Python has emerged as one of the leading programming languages for data science due to its simplicity, versatility, and powerful libraries. With libraries like NumPy, Pandas, and Matplotlib, Python provides robust tools for data manipulation, analysis, and visualization. 

Its extensive ecosystem, combined with easy integration with machine learning frameworks like TensorFlow and PyTorch, makes Python an ideal choice for data scientists, enabling them to efficiently tackle complex data challenges and build advanced models.

2. R Language

R is a widely adopted programming language in the field of data science, known for its strong statistical capabilities and rich collection of packages. It offers a comprehensive set of libraries and functions specifically designed for data manipulation, exploratory data analysis, and statistical modeling. 

R's intuitive syntax and interactive environment make it an ideal choice for data scientists who prioritize statistical analysis, data visualization, and research-oriented tasks. Its active community support and a vast collection of user-contributed packages further enhance its capabilities, making R a valuable tool for data scientists.

3. SQL

SQL (Structured Query Language) plays a crucial role in data science as a language for managing and querying databases. It allows data scientists to extract, manipulate, and analyze large volumes of data efficiently. 

SQL provides powerful features for data aggregation, filtering, sorting, and joining multiple tables, enabling data scientists to perform complex data transformations and extract meaningful insights. 

Its ability to handle structured and relational data makes SQL an essential tool for data wrangling and data preparation tasks in data science workflows. 

Data scientists often use SQL to perform data extraction, transformation, and loading (ETL) processes, as well as to create custom queries for data exploration and analysis.

4. C/C++

While C/C++ is not as commonly used as Python or R in the realm of data science, it still holds significance in certain scenarios. C/C++ is a powerful and efficient programming language, known for its speed and low-level control. 

It is often utilized for building high-performance computational algorithms and implementing machine learning libraries. 

Data scientists may use C/C++ for optimizing critical sections of their code or developing custom algorithms that require fine-grained control over memory management and execution speed. 

Additionally, C/C++ can be employed in the development of data processing pipelines or in integrating data science models with existing software systems. 

However, due to its lower-level nature, C/C++ may involve more complexity and longer development cycles compared to higher-level languages.

Empower Your Data-Driven Insights With Popular Data Science Tools

Data science tools are essential for professionals in the field to effectively analyze, manipulate, visualize, and draw insights from data. Check out a few of the most popular data science tools that the tech community loves globally.

1. Apache Spark (Data Analytics Tool)

Apache Spark is a popular data analytics tool known for its efficient and scalable processing of large datasets. With in-memory computing and versatile APIs, Spark supports batch processing, real-time streaming, machine learning, and graph processing. 

Its distributed architecture and optimization techniques make it ideal for big data analytics in various industries.

2. Apache Hadoop (Big Data Tool)

Apache Hadoop has gained significant popularity as an open-source framework designed to facilitate the distributed storage and processing of vast amounts of big data. With its scalable architecture and components like HDFS and MapReduce, Hadoop enables efficient storage and parallel processing of large datasets. 

It is widely used for data-intensive applications, providing organizations with the ability to handle and analyze massive amounts of data.

3. KNIME (Data Analytics Tool)

KNIME is a powerful data analytics tool known for its visual workflow interface and extensive capabilities. It offers a wide range of built-in tools for data preprocessing, exploration, modeling, and visualization. 

With its modular architecture and user-friendly interface, KNIME is a popular choice among data scientists and analysts for designing and executing data analysis workflows.

4. Microsoft Excel (Data Analytics Tool)

Microsoft Excel is a popular data analytics tool known for its spreadsheet interface and versatile features. It offers functions, formulas, and pivot tables for data manipulation and analysis. 

With its statistical tools and ease of use, Excel is widely used for basic to intermediate-level data analytics tasks in various industries.

5. Microsoft Power BI (Business Intelligence, Data Visualization, and Data Analytics Tool)

Microsoft Power BI is a versatile business intelligence tool that combines data analytics and visualization. This tool enables users to connect to diverse data sources, transform and model data, and create interactive dashboards and reports.

With advanced analytics features and cloud-based collaboration, Power BI is a comprehensive solution for data-driven decision-making.

6. MongoDB (Database Tool)

MongoDB is a popular NoSQL database tool known for its flexibility and scalability. Its document-oriented structure allows for dynamic schema design, while its distributed architecture ensures high availability. 

With powerful querying capabilities and support for horizontal scaling, MongoDB is widely used for modern applications with large and complex datasets.

7. Qlik (Data Analytics And Data Integration Tool)

Qlik is a comprehensive data analytics and data integration tool that helps organizations gain valuable insights from their data. It offers intuitive visualizations, powerful data discovery, and robust data integration capabilities, allowing users to explore and analyze data from multiple sources. 

With its user-friendly interface and interactive dashboards, Qlik enables data-driven decision-making across various industries.

8. QlikView (Data Visualization Tool)

QlikView is a powerful data visualization tool that enables users to create interactive dashboards and visualizations. With its drag-and-drop interface and associative data model, QlikView allows for easy exploration and analysis of data from multiple sources. 

It provides dynamic and real-time insights, making it a popular choice for businesses seeking to visualize and understand their data effectively.

9. SAS (Data Analytics Tool)

SAS is a powerful data analytics tool used for data manipulation, statistical analysis, and predictive modeling. It offers advanced analytics features and integration with various data sources, enabling organizations to uncover valuable insights and make data-driven decisions.

10. Scikit Learn (Machine Learning Tool)

Scikit-learn is a widely used machine-learning library in Python that offers a rich set of tools for various machine-learning tasks. It provides efficient implementations of popular algorithms, such as classification, regression, clustering, and dimensionality reduction.

With its user-friendly interface and extensive documentation, Scikit-learn simplifies the process of building and evaluating machine learning models.

11. Tableau (Data Visualization Tool)

Tableau is a leading data visualization tool that allows users to create interactive and visually appealing dashboards and reports. It enables businesses to explore and communicate data effectively, making it easier to uncover insights and make informed decisions.

12. TensorFlow (Machine Learning Tool)

TensorFlow is a popular open-source machine-learning framework that provides a comprehensive set of tools for developing and deploying machine-learning models. It offers a flexible and scalable platform for building various types of neural networks and deep learning models.

With its extensive library of pre-built functions and APIs, TensorFlow simplifies the implementation of complex machine learning algorithms.

Navigating the Data Science Lifecycle: From Data to Insights

The Data Science Lifecycle encompasses the step-by-step process of transforming raw data into valuable insights. It provides a structured framework for data scientists to follow, ensuring that data analysis and interpretation are conducted in a systematic and effective manner. 

Let's explore the key stages of the Data Science Lifecycle:

1. Data Acquisition

The journey begins with acquiring relevant and reliable data from various sources. This involves identifying data requirements, gathering data from databases, APIs, or external sources, and ensuring data quality and integrity.

2. Data Preparation

Once the data is collected, it needs to be processed and prepared for analysis. This stage involves cleaning the data, handling missing values, dealing with outliers, and transforming the data into a suitable format for further analysis.

3. Exploratory Data Analysis (EDA)

In this stage, data scientists perform exploratory analysis to understand the characteristics, patterns, and relationships within the data. EDA techniques such as data visualization, statistical summaries, and correlation analysis are employed to gain insights and identify potential trends or anomalies.

4. Feature Engineering

Feature engineering involves selecting, creating, or transforming variables (features) that will be used to build predictive models or perform analysis. This stage aims to enhance the predictive power of the data by extracting meaningful features and reducing noise or redundancy.

5. Model Building

The next step is to develop models that can uncover patterns, make predictions, or provide insights. Data scientists employ various machine learning algorithms or statistical techniques to build models based on the nature of the problem and available data. This stage includes model selection, parameter tuning, and performance evaluation.

6. Model Deployment

Once a suitable model is developed, it is deployed into production, where it can be used to generate insights or make predictions on new, unseen data. This stage involves integrating the model into the existing infrastructure, ensuring scalability, and monitoring its performance over time.

7. Model Evaluation and Iteration

The final stage of this entire data science process involves evaluating the performance of the deployed model and iterating on the process to improve its accuracy and effectiveness. 

This may involve retraining the model with new data, incorporating feedback, and making necessary adjustments to enhance the model's performance.

Benefits and Challenges of Data Science

The benefits of data science are vast, including improved decision accuracy, enhanced productivity, and the ability to gain a competitive edge. 

 

However, data science also comes with its fair share of challenges, such as data quality issues, privacy concerns, and the need for skilled professionals. Now, let's swiftly examine both of them below.

Benefits of Data Science

Data science offers organizations the ability to extract valuable insights from data, make data-driven decisions, and gain a competitive edge. 

It enables businesses to optimize processes, enhance customer experiences, and drive innovation by uncovering hidden patterns and trends in large datasets.

Challenges of Data Science

Data science also presents certain challenges that organizations must navigate. These include data quality and consistency issues, data privacy and security concerns, and the need for skilled data science professionals. 

Additionally, managing and analyzing large volumes of data can be complex and time-consuming, requiring robust infrastructure and advanced analytics capabilities.

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Take your data-driven decision-making to the next level and drive business growth with InvoZone's remote data scientists. 

Feel free to reach out to us today to initiate a conversation about your project and begin the process.

 

Frequently Asked Questions

1. What is Data Science Used For?

Data science is used to extract valuable insights from data, enabling informed decision-making and problem-solving. It finds applications in business analytics, predictive modeling, fraud detection, customer segmentation, recommendation systems, risk analysis, and optimization. 

By leveraging data science, organizations can enhance operational efficiency, improve customer experiences, identify market trends, and drive innovation.

2. How to Outsource Data Scientist From InvoZone?

To outsource a data scientist from InvoZone, start by outlining your data science needs and project goals. Reach out to InvoZone to discuss your requirements, and we will guide you through the process. 

Our experienced team will help you select the right data scientist(s) for your project and define the scope of work. With clear communication and collaboration, you can utilize InvoZone's expertise to outsource data scientists and achieve your desired outcomes efficiently.

3. What does data science do?

Data science involves applying scientific methods, algorithms, and tools to extract insights and knowledge from structured and unstructured data. 

Data scientists use their expertise in statistics, programming, and machine learning to analyze and interpret data, identify patterns, build predictive models, and solve complex problems.

4. Does data science require coding?

 

Yes, data science requires coding. Proficiency in programming languages like Python, R, or SQL is essential for data scientists to collect, clean, analyze, and manipulate data, as well as develop and implement machine learning models. Coding skills are crucial for working with large datasets and extracting insights efficiently.