Category: data science

What is Data Science?

Data science is a field where the use of data assists us in making predictions and concluding decisions. Likewise, with the means of raw data analysis and usage of combined procedures of statistical analysis, data analysis, and machine learning algorithms, diverse insights are generated to use as knowledge.

How does Data Science work? 

It all starts with data, which helps in making easier strategic decisions.

Data Science Working Lifecycle

Data Collection:

In this process, a data team identifies the type of data that helps to get the desired results. After identifying multiple data from various sources, they are structured into a common database to analyze and simplify it.

Data Cleaning:

Raw collection of data is incomplete and has multiple points of failure. Therefore, the data team fills up the missing data in a strategic way. That’s why they remove Multi outliers of data that distort the result. Data team efforts to make the data consistent and perfect aid the further analysis step.

Data Analyzing

In this step, the data team visualizes the data to identify different underlying patterns and data gems. Moreover, multiple creative approaches to the data analysis process creation help to obtain a better solution.

Data Modelling

This process starts after the data team applies the core of data, its science, and machine learning to find the initial essential features from the sample data. Likewise, after that, the data team uses data modeling to get the best data prediction.

Data Storytelling

Finally, after the data model is ready, the data team utilizes the results to straddle the data insights to make better strategic decisions for the company.
Moreover, the insights are usually presented in user-friendly language and visualized so that the decision-taker can reference them and use them to accelerate the company’s growth.

History of Data Science

Danish Computer Scientist Peter Naur introduced the term in 1974. Before the term was named, John Tukey, a mathematician, and statistician mentioned data analysis used statistics and computers to get a measurable solution to a problem that would take longer than manual calculations.

Similarly, In 1977, the International Association for Statistical Computing was established with a mission to link traditional statistical methodology with modern computer technology to generate outcomes that are converted into knowledge to help make a decision.

In 1989, the creators established the first workshop for Knowledge Discovery in Databases, later known as the ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

In 1999, the CAO of DMWAY analytics, Jacob Zahavi noticed and realized the need to store as well as handle large-scale data. Moreover, Software-as-a-Service (SaaS) was created in 2000 to enable the new world of cloud-based applications.

Some major marks in data science history were in:

The year 2002: 

The International Council for Science: Committee on Data for Science and Technology began publishing the Data Science Journal.

The year 2006:

Hadoop, an open-source, non-relational database, was released

The year 2009:

NoSQL was reintroduced.

The Year 2011:

Job Listings for Data scientists increased by 150 times, and data science was the buzzword.

The Year 2015:

Using Machine Learning, Google’s speech recognition had a significant improvement.

Data Science Applications

In the modern digital world, there are enormous applications of the field. We have mentioned a few of the important ones below:

Health Care:

Data science plays a leading role in healthcare advancements. Likewise, different tools and machine learning solutions have helped medical operators detect several diseases before its critical. Moreover, it has helped save the lives of thousands of individuals through early diagnosis of different heart and lung diseases.

Digital Advertising

Digital advertising is upgrading to a whole new standard with the help of data along with machine learning. Similarly, the machine learning algorithm models target the audience interested in the advertised products or services. Almost every social media and search engine utilizes data to target their advertisements.

Fraud detection

Data science has helped hundreds of online businesses together with individuals by rejecting suspicious transaction attempts through fraud detection. Likewise, multiple email services use fraud detection algorithms to analyze spam emails which helps to minimize fraud.

Recommendation Systems

Social media, e-commerce, OTT, and Music Streaming software use data-generating tools with machine learning to recommend the most likely results that fulfill the user’s intent. Reportedly, with the help of the recommender system, amazon sales have increased by over 29%.
Moreover, machine learning together with Data Science can upgrade almost any digital system. Likewise, other applications of recommender systems are image detection, autopilot in cars, smartwatch health systems, etc.

Conclusion

Wrapping it up. Data science is being utilized by almost every large company to its advantage. Therefore, this field of education has great potential and a bright career path to choose if you are interested.

Do read our other blogs to understand more about data science. 

Data Science is an evolving field of this decade, and it’s a good idea to jump into it. The role of a data scientist is impactful, and you can change the world with it.

According to Glass Door, the average salary of a data scientist (2021) is $121,120 in the United States. The high pay and increasing demand for data scientists signify there is a very secure career in this field.

Let’s dive into the main topic.

How to become a data scientist after the 12th?

Being a data scientist is a high-profile job that requires you to be skillful. It requires a lot of patience, hard work, and dedication to become a good data scientist. Let’s break the steps down.

A Degree in Data Science Field

A skilled individual doesn’t lag not having a bachelor’s degree.

Although you will require academic credentials to gain initial trust, it is a rare and an undefined path to work as a data scientist with only your skills.

A data science degree can help you get an initial weight with guidance from industry experts, and learn in and out of data science tools.

Master relevant skills

As a data scientist, you’ll work with data. It is a good idea to sharpen your skills related to the field. You must be good at a few programming languages that are used frequently in the data science field.

Learn about Databases, database systems, data warehousing, ETL tools, data analytics, and a grasp of machine learning concepts. We have a detailed blog on this topic.

Get an entry-level data analytics job

A data scientist is a critical job role of a doctor. You should not expect to work as a data scientist directly. It’s advisable to join a rising data science field as an intern.

Slowly, you’ll gain trust with the company, and with guidance from experts and experience, you can be promoted to your dream job, and grow.

Prepare for data science interviews

Competition is natural in the field of data science. Prepare yourself for core questions in this field. Work on your communication skills, and learn to confidently answer the question. Revise your academic syllabus, take crash courses, and get ready to accept early rejections.

A message from Sunway College

Considering the rising demand in the data science field in Nepal. We have introduced a BSc (Hons) Computer Science and Artificial Intelligence. Our specialized course with an academic partnership with Birmingham City University (BCU), will cover topics on Computer programming, data structures, cyber security, artificial intelligence, neural networks, and all topics that will be helpful in your career as a data scientist. Moreover, our team of field experts will guide you with their industry experience. Feel free to learn more here.

What happens to all this data? Where does it go? What do we use it for? And, What do most popular data science terms resemble?

Data science is the answer to all these questions. Data science is the field of science that applies algorithms, methods, and processes to extract information from random and non-random data. Simply put, it is the process of finding patterns among the noise and using it to predict and solve various problems across a wide range of applications. I will come back to 6 data science terms that everybody must know in 2023, very shortly.

It harvests the power of statistics, data analysis, and modeling, along with the use of data structures, algorithms, and machine learning to interpret the findings from massive hordes of data. From Spotify’s recommendation algorithms to predicting human behavior, data science has a wide variety of applications in today’s fast-paced world.

At Sunway College, we primarily focus on bringing the change that is required in the current digital space. The current generation is the Internet generation. According to a report by Statista, in 2021 alone, humanity created 74 zettabytes of data. That’s 74000000000 terabytes of data, in ONE year. That’s up from 59 zettabytes in 2020, and just below the projected 94 zettabytes of data in 2022.

Most Popular Terms in Data Science

To understand data science, here are a few of the basic keywords and terms that you need to know about today.

Algorithms:

In computer science, we can define an algorithm as a specific set of processes used to solve a problem or perform computation. Algorithms are hardware and software-specific instruction lists that complete the given process in hand. For example, the simplest of algorithms perform tasks like sorting a set of numbers in descending order, while the most complex set of algorithms guide and work on a natural language processing system.

In the field of data science, professionals widely employ algorithms. Given the focus on big data, data scientists need precise algorithms to reduce processing times and operational costs significantly. Some of the common algorithms used in the field of data science include Linear Regression, Naive Bayes, Decision Trees, Gradient Boosting algorithms, etc.

Data Mining:

We can define data mining as the process of converting raw data into useful information. Data mining involves looking into large sets of data in a database, analyzing patterns and behaviors of the data entities, and using the result to create better algorithms, strategies, and plans for the business.

Data science, as the name suggests, heavily operates on data. Therefore, it means it requires a large amount of data to work with. It involves various data mining and warehousing techniques on the data with classification, clustering, and predictive analysis of the given data. Large companies use data mining to create better algorithms, understand customer behavior, and create AI and ML-powered hardware and software.

Big Data

Big Data can be defined as data, that is BIG (literally!). It defines large volumes of data that can be structured or unstructured, mined from various sources in a large quantity. Five Vs define big data: Variety, Volume, Velocity, Value, and Veracity. Big Data is sourced from large, complex sources.

Big Data poses a unique challenge addressed by data science. It is a massive capital for businesses and companies that can analyze this data to understand customer behavior, insightful analysis of products and services offered, and predict their purchase and usage behavior. Data science uses various algorithms and processes to derive insights from big data.

Deep Learning

In computer science, we can define deep learning as the process of machine learning that uses structures similar to human brains (called neural networks). Deep learning is used to make better algorithms, data sets, and AI hardware and software.

Deep learning uses powerful computers and large sets of data with output (called supervised learning) and without output (called unsupervised learning) to understand and predict patterns and outcomes.

In Data Science, we employ the use of neural networks and methodologies to study big data with massive models on a large level computational scale. Usually, we utilize deep learning to create better algorithms, and insights on specifics such as user experience of a product, facial recognition, behavior pattern processing, etc. Data science combines the power of deep learning, data, and computer science to generate new insights and value for businesses.

Web Scraping

Web scraping is the web data extraction process used to extract data from websites. Automated technology retrieves bulk unstructured data, typically HTML, from numerous websites in the process known as web scraping. A database methodically stores structured data, facilitating its utilization for analysis, training, and research purposes.

Data scientists use various existing frameworks such as Scrapy, a Python-based web crawler. Data scientists prefer Beautiful Soup, a tool akin to extracting and storing large website data in databases. Therefore, we use web scraping for price monitoring across e-commerce platforms and email marketing. Similarly, emotion analysis across social media platforms and news monitoring purposes are also done using web scraping. Moreover, this data can also be fed to machine learning algorithms for training and prediction purposes. 

Natural Language Processing (NLP):

One of the most frequent data science terms. Natural Language Processing is a subfield of deep learning that is concerned with Human Linguistics. It is the process of identifying and understanding meaningful phrases from a human language and generating meaningful phrases and sentences that look like a natural form. NLP includes two sub-sections, Natural Language Understanding (NLU), and Natural Language Generation (NLG).

Natural Language Processing involves five phases. Lexical (structure) analysis, parsing, semantic analysis, discourse integration, and pragmatic analysis. Some of the well-known areas of NLP are Speech recognition (Siri, Google Assistant), Chatbots(Maya chat, ), and Optical Character recognition (Google Lens,).

Conclusion

We can understand the importance of data science in today’s technology-infused world. Understanding data science terms broadens domains, aiding students and enthusiasts in getting serious about this field. We have introduced two new courses in a digital space. BSc (Hons) Computer Science and Artificial Intelligence.

Moreover, our Bachelor’s course in Computer Science and Artificial Intelligence solely focuses on preparing students with the required skills academically and practically to pursue a career in artificial intelligence and data science. We do have a team of field experts and academically profound professors with their guidance. You can achieve the best in the data science and artificial intelligence field.