Data Science is a field of study that aims to use a scientific approach to extract meaning and insights from data. The basic concepts of Data Science are explained in this tutorial. It is designed for students and working professionals who are complete beginners. This tutorial will enable you to begin the journey of becoming a Data Scientist and discuss various job profiles associated with Data Science that will help you attain relevant experience. Also, to become a Data Scientist, you don’t need to be from a specific background. The digital transformation across industries has increased the demand for candidates with relevant Data Science skills and knowledge.
- What is Data Science?
- Data Science Flow Chart
- Who is a Data Scientist?
- What Are The Essential Skills to Become a Data Scientist?
- Applications of Data Science
- Why Should You Build a Career in Data Science?
- Advantages of Data Science
- Disadvantages of Data Science
- Jobs in Data Science
What is Data Science?
Data Science is a sought-after topic among organisations that focus on collecting data and drawing meaningful insights to aid business growth. If processed efficiently, data is an asset to any organization. The need for storage grew multifold when we entered the age of big data. Until 2010, the primary focus was on building an advanced infrastructure to store this valuable data, which would then be accessed and processed to draw business insights. With frameworks like Hadoop that have taken care of the storage part, the focus has shifted towards processing this data. Let us see Data Science Basics and how it fits into the current state of Big Data and businesses.
Data Science Flow Chart
The chart above shows the different steps of a data scientist’s workflow. The rest of the article focuses on detailing these steps.
Step 1: Obtaining the Data
One first needs to identify what kind of data needs to be analyzed, and this data needs to be exported to an excel or a CSV file.
Step 2: Scrubbing or cleaning the data
It is essential because before you can read the data, you must ensure it is in a perfectly readable state, without any mistakes, with no missing or wrong values.
Step 3: Exploratory Data Analytics
Analyzing the data is done by visualizing the data in various ways and identifying patterns to spot anything out of the ordinary. To analyze the data, you must have excellent attention to detail to identify if anything is out of place.
Step 4: Modeling or Machine Learning
The data engineer or scientist writes down instructions for the Machine Learning algorithm to follow based on the Data that has to be analyzed. The algorithm iteratively uses these instructions to come up with the correct output.
Step 5: Interpreting or “Data storytelling’
In this step, you uncover your findings and present them to the organization. The most critical skill in this would be your ability to explain your results. Hence the term “storytelling.”
Who is a Data Scientist?
A Data Scientist identifies important questions, collects relevant data from various sources, stores and organizes data, decipher helpful information, and finally translates it into business solutions and communicates the findings to affect the business positively.
Apart from building complex quantitative algorithms and synthesizing a large volume of information, data scientists are also experienced in communication and leadership skills, which are necessary to drive measurable and tangible results to various business stakeholders.
What are the essential skills to become a Data Scientist?
- Mathematical Expertise: There is a misconception that Data Analysis is all about statistics, and there is no doubt that both classical and Bayesian statistics are crucial to Data Science. Still, other mathematical concepts are also crucial, such as quantitative techniques and linear algebra, which support many inferential techniques and machine learning algorithms.
- Strong Business Acumen: Data Scientists are the source of deriving helpful information that is critical to the business and is also responsible for sharing this knowledge with the concerned teams and individuals to be applied to business solutions. They are critically positioned to contribute to the business strategy as they have exposure to Data like no one else. Hence, data scientists should have a strong business understanding to fulfill their responsibilities.
- Technology Skills: Data Scientists are required to work with complex algorithms and sophisticated tools. They are also expected to code and prototype quick solutions using one or a set of languages from SQL, Python, R, and SAS, and sometimes Java, Scala, Julia, and others. Data Scientists should also be able to navigate through technical challenges and avoid any bottlenecks or roadblocks that might occur due to a lack of technical soundness.
Data Science Applications
Some of the popular applications of data science are:
The product recommendation technique has become one of the most popular techniques to influence customers to buy similar products. For example, a salesperson of Big Bazaar is trying to increase the store’s sales by bundling the products together and giving discounts. So he bundled shampoo and conditioner together and gave a discount on them. Furthermore, customers will buy them together for a discounted price.
Predictive analysis is one of the most used domains in data science. We are all aware of Weather forecasting or future forecasting based on various types of data that are collected from various sources.
Fraud and Risk Detection
Since online transactions are booming, losing your personal data is possible. So one of the most intellectual applications of data science is Fraud and Risk Detection. For example, Credit card fraud detection depends on the amount, merchant, location, time, and other variables. If any of them looks unnatural, the transaction will be automatically canceled,, and it will block your card for 24 hours or more.
Self Driving Car
The self-driving car is one of the most successful inventions in today’s world. We train our car to make decisions independently based on the previous data. In this process, we can penalize our model if it does not perform well. The car becomes more intelligent with time when it starts learning through all the real-time experiences.
When you want to recognize some images, data science can detect the object and classify it. The most famous example of image recognition is face recognition – If you tell your smartphone to unblock it, it will scan your face. So first, the system will detect the face, then classify your face as a human face, and after that, it will decide if the phone belongs to the actual owner or not.
Speech to text Convert
Speech recognition is a process of understanding natural language by the computer. We are quite familiar with virtual assistants like Siri, Alexa, and Google Assistant.
Why you should build a career in Data Science?
Advantages of Data Science
Data Science is in high demand in the current scenario and is predicted to create 11.5 million jobs by 2026. This makes data science a promising career in the future.
In the healthcare sector, significant improvements have occurred since data science emerged. With the advent of Machine learning, it is now easier to detect early-stage tumors.
Customized user experience:
Data Science involves machine learning, enabling industries to create better products tailored specifically for customer experiences. For example, Recommendation Systems used by e-commerce websites provide personalized insights to users based on their historical purchases.
Disadvantages of Data Science
Concerns over data privacy
Data utilized in the process may breach the privacy of customers. An individual’s personal data is visible in the parent company and may sometimes leak due to security leaks.
Too much dependence on data
When unproven data is analyzed, it does not yield the expected results. This can hinder making effective business decisions and problem-solving.
Jobs in Data Science
As mentioned above, there are a variety of different jobs and roles under the data science umbrella to choose from. Here are different job profiles that can eventually lead you to become a data scientist.
- Data Analyst
- Data Engineers
- Database Administrator
- Machine Learning Engineer
- Data Architect
- Business Analyst
- Data and Analytics Manager
- Data Scientist
Read this article on Top 9 Job Roles in the World of Data Science to understand more about jobs in Data Science.
Data Science FAQs
1. What is data science in simple words?
Data science can be defined as an interdisciplinary field of study that uses data for various research and reporting purposes to derive insights and meaning from that data.
2. What does a data scientist really do?
Data scientists create and use algorithms to analyze data. This process generally involves using and building machine learning tools and personalized data products to help businesses and clients interpret data in a useful manner.
3. What is data science example?
One of the most important examples of data science now would be its use in studying the COVID-19 virus and coming up with a vaccine or a treatment. Data science also includes fraud detection, customer care automation, healthcare recommendations, fake news detection, eCommerce and entertainment recommendation systems, and more.
4. What is data science course eligibility?
The course eligibility for Data Science is a bachelor’s degree in computer science, mathematics, IT, statistics, or any related field. Students in their final semesters of bachelor’s degree can also enroll in Data Science courses. Additionally, candidates should also have a minimum of 60% aggregate in their Xth, XII, and bachelors.
5. Is data science a good career?
Yes, Data Science is a good career path, one of the best right now. There isn’t a single industry that couldn’t benefit from data science, making data science roles rise yearly. Apart from this, high-demand candidates also meet with some of the highest salaries in the market. According to Glassdoor, Data scientists make an average of $116,100 annually.
6. Do data scientists code?
Depending on the role, data scientists are required to code for various process-related tasks. They need to have good knowledge of different programming languages like C/C++, SQL, Python, Java, and more.
7. What problems do data scientists solve?
From addressing climate change problems to creating recommendation systems in streaming services, data scientists are practically solving every kind of problem around the world.
9. Can I learn Data Science on my own?
Yes, but in order to become an expert, you must enroll in a course that offers you proper training, guidance, and mentoring.
Data Science tools and techniques contribute a lot to the growth of a business. Every business is undergoing a digital transformation, and there is an increasing demand for candidates with relevant skills and knowledge companies offer competitive salaries for the right talent. If you want to pursue a career in Data Science or shift your career to roles such as Business Analysts, Data Analysts, Data Engineers, Analytics Engineers, etc. Check out Great Learning’s postgraduate program in Data Science and Engineering, which will help you acquire relevant data science tools, techniques, and hands-on applications through industry case studies.