Data science is an essential part of any industry today, given the massive amounts of data that are produced. Data science is one of the most debated topics in industries these days. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what data science is, and how you can become a data scientist.
A brief introduction to Data Science
Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:
A fuel of 21st Century
In the 21st century, the new driving force behind industries is Data. As a matter of fact, even automobile industries are using data to impart autonomy and improve the safety of their vehicles. The idea is to create powerful machines that think in the form of data.
Data Science is also the electricity that powers the industries of today. Industries need data to improve their performance, make their business grow, and provide better products to their customers. In the scenario of the data science section, we took an example of a commercial industry that wants to maximize its sales. In order to do so, it requires a thorough analysis of data behind sales, an understanding of the purchasing patterns of the clients, and using their suggestions to improve the product. To perform all these tasks, a Data Scientist is required.
Similarly, take an example of a Business Intelligence company that is required to analyze its potential customer base. It requires a Data Scientist to utilize the data they breathe on the internet to track their daily trends and analyze their behavioral patterns.
Data Science vs. Data Analytics
Data Science and Data Analytics deal with Big Data, each taking a unique approach. Data Science is an umbrella that encompasses Data Analytics. Data Science is a combination of multiple disciplines – Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence.
It includes concepts like data mining, data inference, predictive modeling, and ML algorithm development, to extract patterns from complex datasets and transform them into actionable business strategies. On the other hand, data analytics is mainly concerned with Statistics, Mathematics, and Statistical Analysis.
While Data Science focuses on finding meaningful correlations between large datasets, Data Analytics is designed to uncover the specifics of extracted insights. In other words, Data Analytics is a branch of Data Science that focuses on more specific answers to the questions that Data Science brings forth.
Data Science seeks to discover new and unique questions that can drive business innovation. In contrast, Data Analysis aims to find solutions to these questions and determine how they can be implemented within an organization to foster data-driven innovation.
What do you need to get started in Data Science?
Getting Started with Data Science and Python: The start of your journey to becoming a data scientist! Understand what a data scientist does, the various terms associated with data science, and start getting acquainted with the Python programming language
Statistics and Mathematics: The backbone of data science. Some of the key concepts you’ll cover are probability, inferential statistics, and get a hang of how to perform exploratory data analysis (EDA). This will also include the basics of linear algebra (another core machine learning topic)
Machine Learning Basics: Welcome to the world of machine learning! This section is all about introducing you to the basic machine learning algorithms and techniques, including linear regression, logistic regression, decision trees, Naive Bayes, support vector machines (SVM), among others
Ensemble Learning: Time to deep dive into advanced machine learning topics. Understand what assembling is, the different ensemble techniques, and start working on datasets to gain hands-on practical experience.
Tools you must master for Data Science
Soft Skills for Data Science
Top 9 Data Science Certifications
A Lucrative Career
The average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary.
This makes Data Science a highly lucrative career choice. It is mainly due to the dearth in Data Scientists resulting in a huge income bubble.
Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.
A Data Scientist enjoys the position of prestige in the company. The company relies on his expertise to make data-driven decisions and enable them to navigate in the right direction.
Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales.
Data Science is without a shadow of a doubt, a bright and promising career choice
The field of data science is growing more quickly than ever, and more and more employers are getting to know the value of professionals skilled in data science. Because being a data scientist is a lucrative career option, many people are increasingly opting for it. If you are thinking about whether to go for a career in Data Science or not,
Here is a list of reasons why you should choose a career in data science:
How Is Data Science Shaping The Future Of Tech?
Data science powers the digital world and data is the most valuable commodity in the tech industry. Data informs business decisions and enhances our daily lives, so it's gold dust for the tech giants to deliver their products and services free. The only expense is the non-financial one allowing the data to be processed by those companies. And they need it, because advertisers can offer targeted ads. It's a symbiosis that is brought about by evolution and it's unavoidable.
The Internet of Things (IoT) will start pushing the Data revolution, with every computer hooked up to the internet in our lives. Medical implants can send data to carers for someone with a disease, monitoring and treating their condition effectively by analyzing their data: an example of using Data science for something more altruistic than just selling and marketing.
As computing power continues to grow and data grows larger, Voice Recognition and Artificial Intelligence will soon advance to a point where computers are better able to deal more humanistically with the daily inquiries that humans make.
So Big Data science is bound to be the most important asset in modern life as it continues to change the lives of people of today.
Conclusion
Data Science is amongst the number one jobs in the U.S. As per the labor statistics report of the U.S, demand for skilled data scientists will rise at the rate of 27.9 percent by the year 2026. If you have analytical skills and are passionate about computers, then an advanced degree in data science is a perfect goal for your carrier. You can join any of BIT’s Data Science Courses to accelerate the chances of getting an excellent job as a Data Scientist.
The demand for data science is huge and employers are investing significant time and money in Data Scientists. So taking the right steps will lead to exponential growth. This guide provides tips that can get you started and help you to avoid some costly mistakes.
All the best for your career!
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