What is data science: Importance, tools, benefits and future scope

Data science and Data Analytics have become an essential part of every industry in the last decade. They have helped companies reduce costs, conduct thorough market analysis and mainly predict outcomes using predictive models. But if you were asked what Data Science is, or better yet, who is a Data Scientist, what would you say? Let’s find out.

What is Data Science?

Data Science is the field of study that is mainly used to deal with extremely large data sets. In this field, Data Scientists program complex algorithms and use other modern tools to discover new patterns and key insights for their organization. Data Science is used in almost every field now, be it Medicine, Defence, Banking or even Weather forecasting, it plays a crucial role in the way the world works today. But where did Data Science emerge from exactly?

History of Data Science

The term ‘Data Science’ was coined in the 70’s to describe a field of study that would draw insights from the vast amounts of data being collected at the time. Data Analysis was a precursor to Data Science, and first gained popularity in 1962 when famous statistician John Turkey wrote the article ‘The Future of Data Analysis’. Turkey was talking in relation to the addition of computers to the field of statistics. Fast forward to 1974, 12 years after the coining of Data Analytics, Peter Naur used the term Data Science repeatedly in his book ‘Concise Survey of Computer Methods’. Naur was one of the first people to write a definition of Data Science. Fast forward to 2008, DJ Patil of Linkedin and Jeff Hammerbacher of facebook, caused a massive buzz around the word ‘Data Scientist’. This inturn created a massive boom in the industry that actually led to Harvard university to label Data Science as the sexiest job of the 21st century. Present day, Data Science is now defined, with certainty, as a field that forms valuable insights from Big Data.

Why Data Science Is Important

There is a reason they call data the new currency. Almost every industry we know today is data driven, which is why a field of study that can make sense of the unfathomable amounts of data we generate every minute. Data Science pushes industries to make the best possible decisions by providing data backed predictions. Let’s take a look at some of the applications of Data Science to better understand their importance.

• Helping with better decision making on a management level
• Setting goals by analyzing trends and directing actions accordingly
• Encouraging the team to implement effective methods and prioritize important matters
• Spotting potential opportunities
• Making decisions based on measurable, data-based evidence and evaluating their effectiveness
• Identifying and refining the target audience
• Hiring suitable individuals for the organization.

The Future of Data Science

The future of Data Science does look very promising. With the increasing amount of data being generated, Data Science is becoming a crucial tool for businesses and organizations to extract insights, make data-driven decisions, and improve their operations. As the demand for Data Science increases, many new opportunities and applications of it will emerge in various industries such as healthcare, finance, transportation, and more. The future of Data Science also includes advancements in technology such as the integration of machine learning algorithms in Data Science, Big Data, and Cloud Computing. This will enable more advanced data analysis and predictions. Additionally, with increasing focus on data privacy and security, there will be a growing need for experts who can handle and protect sensitive data and use the up and coming Data Science technologies.

What are the benefits of Data Science for businesses?

Data Science is a powerful tool that businesses can use to gain a deeper understanding of their operations and the marketplace. By making use of data science, businesses can gain valuable insights and predictions that help them to make complex decisions with more confidence and accuracy. Data Science also helps businesses make:

Discover unknown transformative patterns

Using a variety of tools such as data exploration, anomaly detection or machine learning algorithms in Data Science can help businesses find patterns from historical data. These patterns can help businesses make new innovations or revive past schemes - to better their businesses.

Innovate new products and solutions

Data science can help businesses innovate new products or solutions by providing them with valuable insights and predictions about their customers, market, and industry. Data scientists can give insights on customer needs and market trends which can aid in the innovation of new products or solutions.

Real-time optimization

Uses of Data Science include, being able to optimize product development by identifying patterns in engineering data such as design, testing and manufacturing data. This can help businesses to identify new opportunities for product improvement and increase efficiency in the product development process.

What are Data Science Processes?

Now that we have seen what Data Science does, let's take a closer look at what processes are used to derive those key insights.

• Obtain data - Collect and acquire the necessary data for the analysis.
• Scrub data - Clean and prepare the data for modeling by handling missing or incorrect values, and dealing with outliers.
• Explore data - Analyze the data and identify patterns, trends, and relationships. This step is also known as "Exploratory Data Analysis" (EDA).
• Model data - Use statistical or machine learning techniques to build a model that can make predictions or draw insights from the data.
• Interpret results - Communicate the results of the analysis to stakeholders and make recommendations based on the findings.

The Basic Principle behind Data Science techniques

Data science techniques are based on the principle of extracting insights and knowledge from data using statistical, mathematical and computational methods. The goal is to uncover patterns and trends that can inform decision making and support the prediction of future outcomes. If we were to dive deeper into Data Science concepts and techniques, this is what we would find:

Classification

Classification is a technique in data science where a model is trained to predict a category or label for a given input data. It is a type of supervised machine learning where the model is trained on labeled data and then used to predict class labels for new, unseen data.

Regression

A technique in data science where a model is trained to predict a continuous output value for a given input data. It is a type of supervised machine learning, where the model is trained on labeled data containing input-output pairs and then used to predict output values for new, unseen data.

Clustering

A technique in data science where a model is trained to group similar data points together. It is an unsupervised machine learning method, where the model does not have any labeled data and is used to discover the inherent groupings or structure in the data. Common algorithms used for clustering include k-means, hierarchical clustering, and density-based clustering.

What are different Data Science tools?

We know by now, Data Scientists help predict future trends and gain insights, but what tools do they use to make this all possible?

• Data storage - Data storage is a critical tool in data science as it enables the collection, organization, and preservation of large volumes of data. Data storage systems can range from simple file systems to complex databases, depending on the size and complexity of the data.
• Machine learning - Machine Learning algorithms in Data Science, are used to build models that can automatically learn from data and make predictions or draw insights.
• Analytics - Analytics is the process of examining data to extract useful information, draw conclusions, and support decision making. In data science, analytics in identifying trends, tracking performance, creating predictive models and making forecasts.
• Modeling - Modeling is the process of creating mathematical representations of a system in order to understand and make predictions about it. Modeling is used, by Data Scientists, to build predictive models that can automatically learn from data and make predictions or draw insights.
• Programming - In Data Science, programming is used to manipulate, analyze, and visualize data, as well as to implement machine learning and statistical models.
• Statistics - Statistics is the study of collecting, analyzing, interpreting, and presenting data. It is used to understand and make inferences about data by using methods such as descriptive statistics, inferential statistics, and statistical hypothesis testing.

Conclusion

In conclusion, Data Science is a vast field that uses various methods and tools such as Machine Learning, Analytics, Modeling etc. to gain deeper understanding and key insights from data. If you’re interested in knowing how to become a Data Scientist, and want to join this insanely cool field, check out our Data Science and Analytics course. One of the many things you will learn in this course is Python programming from scratch with specific use cases in Data Science. Our course also gives you the chance to build an impressive portfolio with multiple projects spanning from data cleaning and data manipulation to data visualization. If you want to learn what Data Scientists do or discover who a data scientist is check out our video ‘Data Science Explained’. If you liked learning all about Data Science, leave a comment below and let us know which topics you'd like us to cover next.