Today, Data science is an integral part of any form of the industry due to the massive and advanced amounts of data that is produced in the market and businesses. Data Science is one of the most talked-about things in the industries. It is slowly and steadily gaining its popularity rather say ‘importance’, and many companies, sectors, and industries have started implementing data science techniques to help expand their business and improve customer satisfaction.
Data science in today’s time is one of the most promising and in-demand career paths for millions of skilled professionals. Successful data professionals know how to acquire their desired skills to analyse large data, data mining, and other programming skills. To be a key asset for the organizations, it’s vital for data scientists to master the full spectrum of the data science life cycle and adapt flexibility & knowledge to maximum returns. In this blog, we will learn all about data science, its benefits, and how you can become the finest data scientist for the company or business.
Origin Of Data Science
The term “Data Scientist” was first originated in the year 2008 when companies realized the desperate need for data professionals who are skilled enough in searching, organizing, and analysing massive volumes of data.
As a brilliant data scientist, you should identify relevant questions, collect data from multiple sources, organize the information, turn results into impeccable solutions and communicate their findings in a way that will positively impact your business decisions. These professional skills are required in nearly every industry, adding value to skilled data scientists in various companies. Thankfully, you will find hundreds of apps and platforms that offer Data science online courses for young and dynamic professionals. Ekeeda is a leading EdTech platform that provides exclusively designed Data science online courses from industry professionals.
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What Is Data Science?
Technically speaking, Data science is the study that deals with vast volumes of data with the help of modern & sophisticated tools and techniques to figure out the unseen patterns, derive meaningful information, and take profitable business decisions. Data Science uses complex machine learning algorithms that help you to build a predictive model. The data used for analysis is fetched through multiple sources and present in various formats.
Now that you got an idea of what is data science, let’s see why data science is so important in the current market scenario.
Why Data Science?
Data science helps companies take better decisions, do predictive analysis, and pattern discovery. It lets you:
Identify the leading cause of a problem by triggering the right questions
Perform exploratory study on the data
Model the data using various algorithms
Communicate and visualize the results via graphs, dashboards, etc.
For instance, if you consider an airline industry, here is how you can optimize operations in many ways such as:
Line-up routes and decide whether to schedule direct or connecting flights
Build predictive analytics models to forecast flight delays based on climates, jetlags, or global events
Give personalized promotional offers based on the customers booking & ratings
Decide which planes to buy for fuel efficiency and better performance
In a layman’s term, let’s say you want to buy new sneakers. So, when you look online for the best option and deal, you need to answer some critical questions before making your decision such as:
Does the site sell sneakers
What is the range you’re looking for?
What is the size you’re looking for?
What colours you’re finding?
Is there any festive or seasonal discount?
Is there any promotional coupon code?
It will help companies apply advanced filters that will perfectly match your needs and give a smooth customer purchase experience.
Data Science Techniques & Tools
Data scientists use operational methods known as data science techniques through various software termed DS tools. It's a combination to acquire data, refine it, manipulate & labelling, and then examine the results for the best possible outcomes.
Data Science Fundamentals
It's very easy to say, "I do data science". However, when it comes to the fundamentals of data science, we have to understand: What exactly is data, what we can do with data, and how to apply the scientific principles to achieve our marketing goals.
Few Essentials Fundamentals To Learn Data Science
Probability and Statistics
Effects of Computation on Data
Prototype Coding / Programming
In today's competitive world, Data science allows us to make data-driven insights. In this Data Science online course, you will get an overview of how data science is used in the business and financial world. We will discuss data analysis, feature selection & engineering, and other common methods to understand and prepare the data for analysis. Let's explore different algorithms used for both unsupervised/supervised machine learning models with us. You can even learn the evaluation techniques to assess the model’s performance and improve it based on the evaluation results. Clear your Data Science fundamentals now!
Data Science Techniques
In order to learn to make efficient use of data science techniques, you will first have to study mathematical and statistical techniques. There are a number of techniques used for data collection, modification, storage, analysis, insights, and representation. Here are a few techniques, data analysts and scientists mostly work on:
Probability and Statistics
Data Science Tools
Data can be collected through various methods, which include online surveys, interviews, forms, etc., and the information gathered have to be transformed into a readable form. Here are the most essential Data Science Tools:
1) Data Collection Tools
2) Data Storage Tools
3) Data Extraction Tools
4) Data Cleaning / Refining Tools
5) Data Analysis
6) Data Visualization
7) Google Fusion Table
Things To Know Before You Learn Data Science
Here are some of the technical concepts you should know before you study Data Science:
It’s the backbone of data science and as a data scientist, you need to have a good grasp of the ML concepts along with the basic knowledge of statistics.
Mathematical models will help you make quick calculations and predictions on things you know about the vast data. Modelling also comes under Machine learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.
Statistics are at the core of data science. A good grasp of statistics will help you extract more intelligence and obtain better meaningful results.
A certain level of programming language is also required to complete your data science project. One of the common languages would be Python and R. Python is quite popular because it’s easy to learn, and it supports multiple libraries for data science and ML. Find out a data science course for you with Python – Clickhere
As a capable data scientist, you will have to understand how databases work, how to manage them, and how to extract data from them.
What Does A Data Scientist Do?
In the last couple of years, data scientist has been an integral part of any organization and its marketing strategy. It has become a necessary asset and is present in every form of business, sector, or industry. These professionals are brilliant minds, data-driven with hi-end technical skills who are capable of creating quantitative algorithms to organize and synthesize large amounts of information. It's then coupled with the communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.
Data Scientists are curious and result-oriented individuals with excellent industry-relevant knowledge and communication skills to explain highly technical charts, statistics, and forecasts for any business or venture. They possess a strong quantitative background in statistics and linear algebra, sound programming knowledge with focuses on data warehousing, mining, and modeling to build and analyse algorithms.
As A Data Scientist you will:
Ask the right questions to understand the problem
Gather data from multiple sources—enterprise data, public domain, etc
Process raw data and convert it into a format suitable for analysis
Feed the data into the analytic systems —ML algorithm or a statistical model
Prepare the results & insights to share with the appropriate stakeholders
Therefore, you should be aware of some machine learning algorithms which are beneficial in understanding data science.
Other than this, some key technical tools and skills you need to be savvy with include: R, Python, Apache Hadoop, MapReduce, Apache Spark, NoSQL databases, Cloud computing, D3, Apache Pig, Tableau, iPython notebook, and GitHub.
Must Knowledge Of Machine Learning Algorithms
The most basic and essential ML algorithms that data scientist uses include:
Regression - It’s an ML algorithm based on supervised learning techniques. The output of regression is a real or continuous value. For example, predicting the temperature of a room.
Clustering - It’s an ML algorithm based on unsupervised learning techniques. It works on a set of unlabelled data points and groups each data point into a cluster.
Decision Tree - It refers to a supervised learning method used primarily for classification. The algorithm classifies the various inputs according to a specific parameter. The most significant advantage of a decision tree is that it is easy to understand, and it clearly shows the reason for its classification.
Support Vector Machines (SVMs) - It is also a supervised learning method used primarily for classification. SVMs can perform both linear and non-linear classifications.
Naive Bayes - It is a statistical probability-based classification method best used for binary and multi-class classification problems.
What Role You Can Play In Data Science?
Data has hidden all around and it's crucial for your business or industry, or sectors to tap it. A variety of terms related to mining, cleaning, analysis, and interpreting data are used in this professional sphere; however, you can actually involve in different skill sets and data complexity levels like:
As a data scientist, you will examine which questions need answers and where to find the related data. You will have business acumen and strong analytical skills with the ability to do data mining, clean and present data in a structured form. Businesses often hire data scientists to source, manage, and analyse vast amounts of unstructured data. You will synthesize and communicate the results to key stakeholders and help them drive strategic decision-making in the firm.
Key Skills Required: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning
As a Data analyst, you will mitigate the gap between data scientists and business analysts. You will be provided with the questions that have to be answered by the firm, later organize and analyse data to find results that are aligned with high-level business strategies. Data analysts translate technical analysis to qualitative actions and effectively communicate their findings to diverse stakeholders.
Key Skills Required: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization.
Data engineers learn to manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data-to-data scientists for queries.
Key Skills Required: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)
Applications Of Data Science
Data science is the need-of-the-hour and found in almost every industry
Healthcare - Healthcare companies use data science to build sophisticated health instruments to detect and cure diseases.
Gaming - Video and computer games are designed using data science which has taken the gaming experience to a whole new level.
Image Recognition - Identifying patterns in images and detecting objects is one of the most popular data science applications.
Recommendation Systems - Netflix, Hotstar, and Amazon suggest movies, shows, and product recommendations based on what you like to watch, purchase, or browse on their platforms.
Shipping & Logistics - Data Science is used by logistics firms to optimize routes for speeder product delivery and increased operational efficiency.
Fraud & Spam Detection - Banking and financial institution take the help of data science and related algorithms to detect fraudulent transactions.
Airline Systems - It uses data science to line up routes, decide flights, forecasts, give promotional offers based on travelling, global events, and more.
Data Science As A Career
Over the last few years, the job opportunities for data science and its related roles have increased exponentially in India and across the globe. It’s the most promising and in-demand job and experts predict the rise of data science needs will create 12-15 million jobs by 2026 worldwide.
There are several job roles you can play under data science. To name a few:
Machine Learning Engineer
Top Reasons To Start As A Data Scientist
Data science has become the ‘talk-of-the-town' and it's hailed as the ‘Coolest Job’ of the 21st century. It’s a buzzword with very few people who know how to tap the true potential of the technology in its true sense.
Data Science is in great demand and prospective job seekers have numerous opportunities. It is the fastest-growing job and is predicted to create 12+ million jobs by 2026.
Best Salary Package
It is one of the highly paid jobs across the globe. As per sources, the average salary of a data scientist in India is ₹907,000 per annum. Thus, it makes data science a highly lucrative career option.
Highly Prestigious Job
Data Scientists help companies to make smarter business decisions. Many companies, processes, or industries rely on Data Scientists and use their expertise to provide the best results to their clients. It gives Data Scientists an important position in the company.
Opportunities in Several Verticals
There has been a wide range of applications of Data Science. It is used in healthcare, banking, airlines, consultancy services, logistics, and e-commerce industries. Thus, Data Science is a very versatile field.
Boost Your Career
Data Science will help you design a great career and let you personally grow your intellectuals. You will develop a problem-solving attitude. As a data scientist, you will bridge IT and Management, you will be able to enjoy the best of both worlds.
On a concluding note -
Data is like a lubricant for companies, businesses, and processes that will help them run the marketing mechanism swiftly and effortlessly. Companies can book profits along with enhanced customer experience. By incorporating data science techniques in your business, companies or process can predict their future growth and analyse their SWOT – Strength, Weakness, Opportunities & Threats. Now, it’s the right time for you to start your career in data science, if you’re interested.
Do you have any questions regarding this ‘What is Data Science’, fret not! Just drop your query on [email protected] and we are here to help you. Our team will help you solve your queries at the earliest.
So, folks if you wish to advance your career into data science and be one of the finest data scientists, then why wait? Enrol with Ekeeda today.
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