Basics Of Data Science For College Students 


Knowledge of data science is the utmost in-demand skill of this century, and so is the demand for highly skilled data science professionals.  Data Science has now emerged as one of the most in-demand jobs of the 21st century. Today colleges, universities, and educational institutions are introducing data science courses in their curriculum. Enormous volumes of data are being generated every second around the globe, and this large volume of data is known as Big Data. 

Data scientists are highly compensated professionals who can work in almost any industry, where data is being used to derive insights. This helps an organization make effective and revenue-generating decisions, improve efficiency, better planning, improve customer satisfaction, and grow the business. 

If you are a college student and would like to enroll in an online data science coursethen this guide is curated just for you. Let us understand the basics of Data Science.

 What is Data Science?

Data Science is an interdisciplinary field that uses different scientific methods, algorithms, processes, and systems to produce insights from a bulk of structured and unstructured data and then apply this knowledge and actionable insights to a wide range of application domains. This data emerges from various sources, and you can discover hidden trends and patterns and make important business decisions. 

Data science has emerged with a concept to combine computer science, mathematics, statistics, domain knowledge, Big Data, and Information science. 

What is the need to learn Data Science?

Data Science plays a vital role in the emerging cloud-based and data-driven industries. With the right tools, procedures, algorithms, an organization can use the data to convert it into meaningful information to the advantage of growing its business. You can make effective and quick decisions by studying your business’s emerging trends and patterns by collating historical and current data. You can do predictive analysis to determine the future performance of your business by diving deep into meaningful insights. With the proper information harmonization, you can avoid monetary losses, build intelligent systems and even detect instances of fraud. 

Who can learn Data Science?

Anybody and everybody can learn data science. Three primary job roles in Data Science are Data Analyst, Data Engineers, and Data Scientists. Data Analysts make the entry-level position responsible for transforming numbers into sensible and comprehendible information. Data Engineers form the next level, who further break down the data for further analysis. Data Scientists are high-level experts who use different tools and techniques to prepare meaningful insights used for business decisions. Other data science job roles are Statistician, Data Architect, Data Administrator, Business Analyst,  Data Manager. 

Basic Components of Data Science

Below are the basic components of data science that make the overall framework of the data science and engineering domain. 

  • Statistics: This is the most crucial component of data science. It is the science of collecting, analyzing, presenting, and interpreting large numerical data to get meaningful insight. The data scientist will use descriptive statistics and Inferential statistics to solve problems.
  • Visualization is a technique that helps access big data in an easy-to-understand and sensible depiction. 
  • Machine Learning: This involves the building and studying algorithms with the historical and current data to make predictive analyses for the future. 
  • Deep Learning is the latest machine learning method through which the algorithm helps select the analysis model that needs to be followed.

Basic Features of Data Science:

  • Sources of Data: Structured Data, Unstructured Data (SQL, NoSQL, Cloud Data, Log files, Sensor data, Texts)
  • Methods Used: Machine Learning, Graphical Analysis, Statistics, NLP 
  • Primary Focus- Predictive analysis for futuristic result

Machine Learning is the heart of Data Science; hence one needs to be proficient in statistics and mathematics. You need to acquire different good communication skills and hard skills for a successful career in Data Science. 

Tools Required in Data Science

Data scientists use different statistical methods, which predominantly form the backbone of Machine Learning algorithms. Deep learning algorithms are used to make strong predictions. Below are the tools and programming languages used by data scientists:

  1. R: It is a scripting language used for statistical computation and operations. It is mainly used for statistical analysis, modeling, clustering, and forecasting of multiple industries.
  2. Python: It is a versatile and interpreter based on a very high-level programming language. It is widely used in Data Science for Data Analysis and Natural Language Processing, Data Mining, and building predictive models.
  3. SQL: Also known as Structured Query Language, manages and queries massive data that is stored in the database. Extracting information from the database is the first step in analyzing data.
  4. Hadoop: It is the most popular Big Data software. It is open-source software with a scalable architecture used to quickly process vast volumes of data. There are different packages in Hadoop like Apache, HBase, Hive, etc.
  5. Tableau: It is a data visualization software used to create interactive dashboards. It shows different trends and insights of the data. Examples are Bar graphs, treemaps, box plots, etc. 
  6. Weka: It is used for data mining and different machine learning operations. It is also an open-source software using a GUI interface, making it easy to use and interact with. 
Examples of Data Science in Real Life

Data Science has penetrated different industries like Banking, Manufacturing, Healthcare, Transportation, etc.  Let us take a quick look at some of the applications of Data Science.

  • Healthcare: Doctors are using Image recognition software to detect diseases like cancer in their early stages. The genetic industry also uses data science to analyze and classify genomic sequences.  The Healthcare industry has enormous data availability, which emerges from clinical trials, patient records, genetic information, billing, database, internet research, etc.
  • Banking and Financial Services: Banks make smart decisions through customer segmentation, managing customer data, and customer lifetime value prediction. Risk modeling helps banks to assess their performance. Financial institutions can make quick data-driven decisions. Machine learning algorithms and techniques enhance cost efficiency, identifying, continuously monitoring, and prioritizing risks.
  • The E-commerce industry has benefited from the help of data science. Data science helps identify a potential customer base, analyze the customers’ online cart, and display meaningful recommendations to the customer based on their search and preferences.
  • Gaming Industry: Data science technology is being used by gaming companies. They use machine learning techniques to develop new games and automatically update themselves to higher levels.

The Bottom line: To conclude, data science engineering is an enormous subject. However, by following the right approach, anyone can acquire the skills. Data Science offers long-term and lucrative career opportunities in different industry domains. You need to have a keen interest in problem-solving skills and love to experiment with data.

This is the right time for college students to begin planning their careers in data science. This is an ever-growing field that is increasingly becoming the lifeline of various businesses to predict problems, make informed decisions, come up with practical solutions, and make the future of the business seamlessly rewarding and profitable.