How to become a Data Scientist?
Data Science has made technology even simpler and easier. With the advancement of data science, machine learning has also become very simple. Do you also want to make a career in data science and want to know how to become a data scientist and how to get achievements in this field.
What is Data Science?
In simple words, Data Science is a study of data, which includes algorithms, principles of machine learning and various other tools. It is used to record, store and analyze data to obtain important and useful information. Data scientists extract and examine data from a wide range of sources such as log files, social media, sensors, customer transactions.
Why do Data Science?
The information about why to do a data science course is given below-
Data scientists in India can earn INR 19-23 Lakhs per year, which is much higher than any other profession like engineering.
Students with a computer science, engineering, statistics, or math background can choose to pursue a data science course, as it is a better paying alternative to B Tech or mainstream engineering courses.
Even students from management background can choose data science instead of MBA.
An IBM report predicts over 59% demand for data scientists in the fields of finance and insurance, thus with the right skills, even finance and management students can experience a career boost. can.
Indeed, a popular job listing website records a 29% increase in data scientist job postings every year, which means that data science is one of the in-demand professions.
Important Elements of Data Science
Being vast in nature, the field of data science is divided into three main elements. Let's take a detailed look at these three components-
Business Intelligence: Computing techniques are used to operate every single business. Every organization produces huge amount of data every day. After careful analysis of this produced data, data scientists present it in various graphs and charts. It then helps the management to make the best business decision based on the representation of the data.
Machine Learning: Incorporating mathematical and statistical model algorithms, machine learning is adopted by almost all business organizations to make machines understand and respond to daily situations and progress. Through the use of machine learning in the field of data science, a machine can predict various trends in markets or financial systems based on historical data patterns.
Big Data: With exponential growth in Internet users every day, viewers or subscribers generate multiple clicks in the form of videos, photos, articles, comments, instructions, etc. Usually, all these activities result in the production of untransmitted data. Data scientists convert that untransmitted data into transmitted data.
Technical skills to become a data scientist
Being an interdisciplinary field, Data Science requires not only one or two, but a diverse set of technical skills and knowledge in the field of Computer Science. Here are the following skills that a data scientist must possess, irrespective of the experience gained.
Python Coding: Python is the most important coding language in data science as it helps in developing models for data mining, machine learning or web scraping. Taking on different formats of data, Python can help you create and search datasets and import SQL tables into your code.
R Programming: R is a program, usually designed for data analysis, and provides formulas and methods for information processing and statistical analysis.
Machine Learning and AI: Learning various techniques of Machine Learning like Logistic Regression, Decision Trees, Supervised Machine Learning, Time Series, Computer Vision, Outlier Detection, Survival Analysis, Natural Language Processing etc. are inherently important to tackle many challenges in the field Is.
Hadoop Platform: Whenever the amount of data is very high, it can exceed the memory of the system and Hadoop platform helps the data scientist to send or transfer the remaining data to different servers. This platform is also useful for data filtration, data sampling and summarization, exploration etc.
SQL: Structured Query Language (SQL) is a programming language that helps you communicate, access and manage databases by adding, subtracting or removing data. The Data Scientist needs to be proficient in SQL, as it is specially designed through its compact commands, to save time and reduce the amount of programming for difficult queries.
work and responsibilities
After completing your education and gaining the required technicalities in data science, here are some of the most common roles and responsibilities that you will play as a data scientist-
Staying up-to-date with all the emerging tools and techniques in Statistical Modelling, Machine Learning etc.
Mine the data and generate a hypothesis to help achieve high-level business objectives.
Analyze and mine large data sets using the hadoop platform.
Work in collaboration with IT managers, statisticians, programmers and other experts to make business decisions or create products and services.
To solve various analytical problems with incomplete datasets, data scientists also develop specially designed algorithms.
Data Science vs Machine Learning
The main differences between Data Science vs Machine Learning are given below-
Particulars Data Science Machine Learning
Objectives Data science courses help in analyzing information from multiple sources to get maximum insights which gives a company a competitive advantage. It is used to design and develop tools and algorithms that can be used to find any trends or insights from a set of data.
Top Specialization Computer Science, Statistics, Mathematics Electrical Engineering, Mechanical Engineering, Computer Science
Top Certificates Associate: Data Science Version 2.0, Certified Analytics Professional (CAP) Machine Learning Online Courses and Python Courses on Udemy
Top Skills Data Science, Data Analytics, R-Programming, Python for Machine Learning Machine Learning, Python, Tensorflow, Spark, C++
Top Job Profiles Data Scientist, Data Analyst, Application Architect Machine Learning Engineer, Software Engineer, Product Engineer, Mechanical Engineer
Data Science vs Data Analytics
The main differences between Data Science vs Data Analytics are given below-
Particulars Data Science Data Analytics
Objectives Data science courses help in analyzing information from multiple sources to get maximum insights which gives a company a competitive advantage. Organize company data on finance, market trends and alike and give inputs for better performance.
Top Job Profiles Data Scientist, Data Analyst, Application Architect Data Analyst, Data Architect, Data Engineer
Top Specialization Computer Science, Statistics, Mathematics Business, Computer Science, Finance
Top Certifications Associate: Data Science Version 2.0, Certified Analytics Professional (CAP) Project Management and PMP Certification
top skills data science, data science, data analytics, r-programming, python for machine learning data analysis, processes, python, data management, r-programming
Step by Step Guide to Become a Data Scientist
To know how to become a data scientist, it is important to know its step by step guide, which is given below-
Earn a Bachelor's degree: Most data scientists earn a bachelor's degree in science streams such as engineering, mathematics, IT or any other related field. Having a Bachelor's degree in the quantitative field can help you enhance your problem-solving abilities through analytical and critical thinking, which are some of the most important skills as a data scientist. You may also consider doing an internship during your bachelor's degree, which can help you land an entry-level role in this field. It can also help in getting a first hand knowledge of the corporate setup and day-to-day responsibilities of a data scientist.
Get Certification: Earning a certification can help you enhance your skill set and show your interest and seriousness in becoming a data scientist to potential employers. There are many courses available online on data science and data analysis on various websites. You may consider joining any of these courses to improve your skills and improve your chances of securing a job as a data scientist. You can also consider getting certified by various educational institutions in India that offer courses in data science. Some of these institutes can also help you in getting placement as data scientist in different companies under their program.
Gain work experience: Data scientists are in high demand across all industries. You may consider gaining relevant experience as a data scientist by doing an internship or entry-level job as a junior data scientist or junior data analyst. Gaining relevant work experience can help you enhance your skills and gain a firsthand knowledge of the corporate setup. Gaining work experience can help make your resume more attractive to employers and increase your chances of being shortlisted for a data scientist position. In addition, working in an entry-level position, companies can provide on-the-job training and certification that can lead to promotions.
Build Your Portfolio: You may want to build a portfolio of some of the major projects you have worked on in the past in order to demonstrate your abilities to recruiters and gain an advantage over other candidates. You can include examples of projects you've managed in your past organizations in a visual format to provide evidence of your abilities and skills and to demonstrate the impact you've made in the past. You can take help of various online resources on things like templates to use to make your portfolio exciting and attractive to potential employers.
Earn a master's degree: Earning a master's degree in data engineering, computer science, business administration, or a related field can help you gain a leadership position in the data science field. Having a master's degree can also give you an advantage over other data scientist candidates when applying for high-paying jobs. You can check the details and requirements of various programs offered by various institutions to see if they align with your desired career path and whether they can provide you with the necessary skills and exposure to excel in this field.
Pursue a doctorate. Some data scientists also pursue doctorates in computer science, data science, or another related field to gain leadership positions in their organizations and find high-paying jobs. Having a doctorate can earn you a teaching position or a high-paying role as an executive within an organization with industry expertise. Doctorates are highly skilled and knowledgeable in their respective domains and are often in high demand across industries for bringing innovation and excellence.
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