Everyday Life Experience – The Role of Data Science
DATA! The moment we hear this word, our mind gets wired to terms like data privacy, data leaks, data breach, data science, data analytics, statistics, databases, and so on… All these terms are not meant for only data science professionals. The common man experiences the role of data science in everyday lives in almost every situation. You might have come across new friend suggestions on social media platforms such as Facebook, Instagram, Telegram, Twitter, etc. to Google completing a search phrase to help you with different products and vendors on E-commerce sites based on your preferences to the weather forecast, and many more… Data science is being experienced by you and me in almost every situation. We are proud to use the latest technologies, but we do not acknowledge the role of data science in making them happen.
No one can overlook the role of data science in almost every aspect of life. Anything in this world that generates data is within the boundaries of this field. Data grows with the increase in the number of devices. More and more people need to transform these data into meaningful knowledge. Today, data science is creating a positive wave around us and will become even more widespread and integrated into everyday life in the coming future.
What is Data? The dictionary meaning of data is “facts and statistics collected together for reference or analysis.” In computing, data is information that has been translated into a form that is efficient for movement or processing. Also, data is information converted into binary digital form. The data in its most basic digital format is called raw data. When you fill in digital forms for registering for an online program or a social media application, and physical forms at colleges, banks, etc. data is generated.
- Data are plural. So what is the singular form? Datum.
- 33 percent of the data is images, 15 percent is video, and 11 percent is audio.
- Text data comprise 91% of the data used in data science.
- 41 percent of the data in the data science pipeline comes from public data.
- Less than 0.5% of all data we create are ever analyzed and used.
- Every second, 60,000 search queries are performed on Google and 1.2 trillion searches per year.
What is Data Science?
In 1974, Peter Naur proposed Data Science as an alternative name for Computer Science. In 1996, the International Federation of Classification Societies became the first conference to specifically feature data science as a topic. Data science is the study of data. It includes developing methods for recording, storing, and analyzing data to efficiently extract useful information. The goal of data science is to gain insight and knowledge from both types of data, structured and unstructured, for better decision-making
What is Data Science used for?
Primarily, data science is used to gain knowledge about behaviors and processes. It is also used to write Algorithms to process any size of information swiftly and efficiently, increase the security and privacy of sensitive data, and guide data-driven decision-making. To achieve all these, there are different concepts and tools. Some of the essential concepts are statistics, visualization, deep learning, machine learning modelling, and essential tools are R programming, SQL, Python, SAS, etc.
Why Data Science?
To understand the need for data science let us explore a few used cases. Data science plays an important role in the healthcare industry. Predictions can be made with the help of patient data, whether a person can get infected by a certain disease in the future. This information can help you to take some precautions and save yourself from the disease. In this case, data science can find the relationship between different characteristics that cause a disease.
The retail industry is also using data science. A recommendation system is developed to analyze the shopping history of the customers. It can then recommend a set of products that were bought earlier by the customers together. For example, if any customer buys one or two products from that set, then he or she will be recommended along with the other products to be bought in the set.
The different roles that data science plays in banking and finance are – risk mitigation by analyzing the creditworthiness of a customer, stopping fraudulent transactions like credit card usage, online shopping, and insurance claims, and promoting services based on the customers’ spending patterns.
Clients play a key role in the success and profit of a company. With the help of data science, companies can find the requirements of their clients and ensure better products and quality for them. Almost all industries, including the industries mentioned above, have benefitted from Data Science because now they can predict client requirements and behavior by analyzing past data. Today, almost all industries collect data and apply different data science concepts and tools. This is the reason why it is booming.
What are the Data Science Skills?
Let us understand the skills that will be required to get a job in the field of data science.
Statistics: For a career in data science, one must know statistical techniques. Statistics can find the hidden pattern in data and correlation between different features in data. Two main statistical methods that are used in data analysis are:
- Descriptive statistics: summarizes data using indexes such as mean and median. For example, Measures of Frequency shows how often something occurs
- Inferential statistics: It concludes data using statistical tests. For example, the relationship between SAT (Scholastic Assessment Test) scores and family income.
Computer Science: A Data Scientist must be able to apply different principles of Computer Science, including software engineering, database system, Artificial Intelligence, Machine Learning, and numerical analysis.
Analytical Thinking: A Data Scientist must think analytically to solve business problems.
Critical thinking: A data scientist must have the critical thinking ability to analyze the facts before concluding.
Interpersonal Skills: A data scientist must have excellent communication skills to interact with different audiences across the organization. In addition, they must be able to communicate with clients to understand the problems.
Why Machine Learning is important in Data Science? In Data Science Machine Learning is used for quality predictions and estimations. Machines are taught and trained by feeding them data, to automate processes to make the right decisions and smarter actions in real-time without any human intervention. Computers learn, grow, adapt, and develop themselves when fed new and relevant data, without being dependent on plain programming. Online recommendation engines use Machine Learning Technology.
Why is AI important in Data Science? Artificial Intelligence focuses on collecting, categorizing, strategizing, analyzing, and interpreting data. Algorithms allow making sense of more and more amount of information in real-time. It imparts autonomy to the data model. It is used in the development of data-driven solutions, data visualization tools, and techniques to analyze big data. Chatbots and voice assistants are popular applications of AI.
.Which Tools do Data Scientists Use? There are many tools that data scientists use in their everyday work. These tools can be programming tools, data analysis tools, or Statistical Programming Tools.
Excel: This is the stepping stone for a Data Science Program. Topics like Complex functions, Pivot tables, Power Query, Power View, Slicer, charts, forecast sheets, and Data Models are used in data analysis.
Python: Python is the most used programming language by Data Scientists. Its most important application is used in the field of Machine Learning. It has many libraries such as Numpy, Scipy, Scikit-learn, TensorFlow, Keras, PyTorch, Pandas, etc.
R programming: It is one of the indispensable statistical programming tools that is mainly used to perform a detailed analysis of large data to find insights.
SQL: It helps data scientists to work on DBMS and structured data. A Data Engineer also uses this tool. The different databases are MS SQL, MySQL, MongoDB, Oracle Database, PostgreSQL, SAP HANA, MariaDB…
Tableau: This is a data visualization tool, popular among Data Scientists because of its amazing reporting capabilities. This tool makes it easy to visualize the data and present the results to clients.
Hadoop: Apache Hadoop is an open-source framework. It is used to efficiently store and process large datasets ranging in size from gigabytes to petabytes of data. Instead of using one large computer to store and process the data, Hadoop allows clustering multiple computers to analyze massive datasets parallelly and quickly.
SAS: SAS (Statistical Analysis System) is an advanced tool for analysis used by data analysts. It has powerful features, such as analyzing, extracting, and reporting. Also, it has a great GUI that anyone can use easily. Data Scientists use it to convert the data into business insights.
ho should take this program? A Data Science job requires a mix of experience, tools, and technologies. It is a very prolific career choice for both new entrants and experienced professionals. Candidates from any educational background with an analytical mind are most suited to pursue the Data Science certification Program. IT Professionals, Analytics Managers, Business Analysts, Banking and Finance Professionals, Marketing Managers, Supply Chain Network Managers… can do great work in the field of Data Science.
Can any student do a Data Science program after the 12th? Yes, one can join data science after the 12th. Students from a science background, Undergraduates, Graduates, Diploma holders, B.A., B.Com. B.Sc., M.Sc., BCA, MCA, B.E., B.Tech., M.E., M.Tech, and Engineering students of all semesters with basic computer knowledge can join a Data Science program.
Is data science easy? Any program becomes easy or difficult based on the inclination one has towards that program. Like any other field, with proper guidance and training, Data Science can become an easy field in which to make your career. However, one should not get defocused from completing the program thereby finishing a lucrative career before starting it.
What types of job are available after completing a data science program? A learner who completes the Data Science program can look for job profiles such as Data Analyst, Data Scientist, Analytics Manager/Lead, Machine Learning Engineer, Python Programmer, Data Engineer, Business Intelligence Analyst, Marketing Analyst, and much more.
What kind of salary can you expect in a Data Science Career? The average salary for a learner who has completed Data Science Program in India is around 5 lacs per annum. This salary can go up to 10 lacs per annum for a learner with 1-4 years of experience depending on additional skills, location, and Company he is working for.
To get you started for a career in this direction, IANT offers one of the top programs – “Data Science, Machine Learning, & AI,” which is also suitable for beginners. The topics covered in this program are Introduction to Data and Data Analysis in Excel, Python Basics, Python Packages, and Manipulating Data in Python, Statistics Basics, R Programming, Data Science with R Programming, Big Data Programming & Analytics, Introduction to AI & ML, and Data Science with AI & ML.
One can appear for International certification in Python, AI, & ML from Star Certification, at the world’s lowest price. At IANT the training is conducted by certified trainers. The curriculum includes case studies and projects. Students can opt for classroom, online, or hybrid models of training sessions according to their convenience. Job assistance is provided to all students who have completed the Program successfully in collaboration with the job portal https://allitjob.com. Register on this portal and receive alerts on suitable Data Science job openings. So, if you are an enthusiast learner, explore this domain for continuous growth prospects and a superior future ahead. Join IANT and make a successful career in IT!