Data Science is a paradigm that allows you to extract value from data. It is a combination of statistics, computer science, and business analytics.
Data Science has gained momentum in recent years as it is an integral part of many businesses. This is because datasets are being generated at an unprecedented rate.
The demand for Data Scientists is growing exponentially as companies realize the potential of this field.
Data Science has become an integral part of many businesses and organizations, whether it involves the analysis of customer behavior or building predictive models using customer data.
Data Science is considered the most popular job in 2022. Doesn't that spark curiosity in you to know more about this field!
To become a good data scientist, you will need to develop strong analytical skills and gain experience working with complex data sets. To do this, you should follow a carefully planned curriculum that includes both theoretical knowledge and practical experience.
In this article, we'll learn how to set up a clear path for a successful career in data science.
Why Data Science?
Before you plan your roadmap for a career in data science, you must know exactly why you should do data science. Data Science is not meant for everyone, it certainly is a popular job field, but it is most definitely not an easy one.
And only after knowing the details about the field will you be able to figure out whether or not data science is the right choice for you.
Let's take a look at what data science is and how it might be a perfect career choice for you.
Data science is the study of data in all its forms, which is why it's a grand topic right now. Data is everywhere — even if you don't see it or touch it in your day-to-day life, the chances are that some company somewhere has it on you.
And with so much information about us available, companies are looking for ways to use this data to their advantage.
As more people become aware of how data can be used and abused, there's growing concern about how our personal information is being used by companies like Facebook and Google.
If you are one of those people who are amazed by how google and Facebook show you those ads and templates in which you have an interest, then data science is a jackpot for you. It will not just help you understand how they do it. You can learn how to do it yourself!
Job Opportunities & Salary Package in Data Science
Data science is an exciting field to be in because it combines elements of mathematics, computer science, statistics, and business knowledge into one career path. This means that you can use your skills across many different industries — which is why it's one of the fastest-growing careers right now.
The field has grown at an incredible rate, and it's projected that the employment market for data scientists will increase by 33 percent by 2024.
With this rising demand, there are also tons of different fields and job roles that you can work for after doing your data science education.
Let's take a look at some of the best and most preferred job roles in the field of data science:
Data Scientist
Job overview: One of the most common positions in data science is data science. Data scientists are responsible for analyzing large datasets and extracting meaningful information from them. They interpret their findings through visualization and provide recommendations based on this insight.
Skills required: Data Scientists need to know statistics, mathematics, machine learning, and programming languages such as Python or Java. They also need to have expertise in database management systems (DBMS) like PostgreSQL and Spark.
Average Salary: $95,000/yr.
Business Analyst
Job Overview: BI analysts work with business executives or management team members who have been given the responsibility to gather information about their business performance through various channels, which include customer feedback forms, surveys, etc.
To give them more insights into what is working well and what needs improvement, they need someone who can synthesize all that raw data into some meaningful information which they could then use for making decisions on how best they should run their business to achieve their goals.
Skills required: Business analysts need to have a critical thinking and business-oriented mindset. They should be good at using predictive analytics or data mining software to extract useful information from large sets of data. Good communication skills and a good grip on programming languages such as python, java, etc., are also important for this job.
Average Salary: $77,218 /yr
Data Engineer
Job Overview: Big data engineers help businesses store and manage huge amounts of information from different sources (e.g., databases, social media posts, etc.). They design solutions that allow companies to collect, store and analyze this data efficiently. Big data engineers are responsible for designing scalable solutions for storing large amounts of structured and unstructured data in real-time.
Skills Required: A data engineer must have a thorough knowledge of database tools, Data Transformation tools, data mining tools, real-time processing framework, machine learning skills, and data visualization skills. Data Engineers should also have good organization skills as well as communication skills.
Average Salary: $86,000/yr.
Data Analyst
Job Overview: Data analysts work with data collected from different sources and provide insights to their employers. They can perform analysis on structured and unstructured data sets to identify patterns, trends, and relationships between variables and link them to benefit their organization.
They use statistical techniques like regression analysis, clustering analysis, classification analysis, etc. to find out hidden information from the raw data sets.
Skills Required: Data analysts are required to have skills such as statistical programming, machine learning, probability and statistics, data management, and econometrics at their disposal. Those working as data analysts should also have good communication skills to communicate their findings properly with the stakeholders of the company.
Average Salary: $65,000/yr.
Machine Learning Engineer
Job Overview: Machine learning engineers are in charge of creating machine learning models using algorithms, and other programming languages like Python or R. Machine learning engineers may also be asked to build new machine learning models from the ground up or improve existing ones in response to user and analyst feedback.
Skills Required: Being a good machine learning engineer requires you to master a lot of skills. You should successfully be able to apply ML Libraries and algorithms and have a good grip on programming fundamentals and CS. Good statistical knowledge, data modeling, software design, applied mathematics, and good evaluation skills are also in-demand skills for a machine learning engineer.
Average Salary: $1,31,001/yr.
In-Demand Skills for Data Science
Now that you have got all pumped up after seeing the vast opportunities that the field of data science presents you with and the sparkling salary packages that these different data science job roles offer must have got your adrenaline rush to kick in.
If you've set your mind on becoming a data wizard, this is the first step that you need to take to move ahead in this field.
As we've already pointed out earlier in this blog, this might be a popular field, but it certainly is not an easy one, and you need to be highly skilled to get on the hiring list as a data scientist.
As you must have noticed in the above section that different job roles require different skills from an individual, we've made a list of the common skills that mostly all job roles of data science require from a candidate.
Statistical Knowledge:
Statistics is the science of making inferences from data sets based on their size and complexity. Data scientists employ statistical approaches across sectors to extract insights from massive amounts of data generated during trials or surveys.
Statistics is an extremely valuable ability to have, and recruiters expect every applicant to possess it. So, before applying for any data science jobs, make sure you have a decent understanding of statistics.
SQL & SQL Server:
The most used language for processing data in databases is SQL. It stands for Structured Query Language, and it retrieves data from database tables using commands like SELECT and WHERE.
SQL Server is a database management system that enables users to create tables that store data as rows with columns that reflect various aspects of each row (e.g., name, address). It also allows users to build relationships between tables using foreign keys, allowing them to delete rows from related tables when one is deleted from one table.
Before following this career route, you need to have a fundamental understanding of how SQL works.
Data Wrangling and Munging:
These are two abilities that allow you to alter data into the format you wish. This is significant since data is rarely in the proper format for analysis, necessitating data cleaning before analysis.
For instance, if you wanted to know what size shoes each individual at an event wears, you'd have to manually enter each person's shoe size into a spreadsheet.
If there are a lot of people attending, this will take a long time! Instead, data scientists employ data wrangling tools like Pandas(Python Library), which let them apply functions like "fillna()" and "remove duplicates()" on their dataset, allowing these programs to handle all of the inputs for them.
Data Visualization:
Data visualizers assist individuals with varying levels of technical understanding to understand complex data by converting it into charts, graphs, and other visual representations.
This ability aids in the simplification of material and makes it much easier to comprehend. Companies use these talents for a variety of reasons; for example, it assists marketing teams in better understanding data and developing strategies based on it.
Communication Skills:
A skilled data science candidate should have strong communication skills so that he or she can properly communicate his findings to clients or colleagues without losing interest or confusing them with technical jargon.
He should also be able to communicate with employees from other departments who may require his assistance in better understanding particular areas of their duties or increasing their work performance.
Python programming skills:
Python is a popular programming language among data scientists because it offers a vast library of pre-built modules that make it simple to construct data manipulation and analysis tools.
Python also supports object-oriented programming and boasts advanced features like dynamic typing and list comprehensions, making it perfect for handling complicated issues.
Creative thinking:
Data scientists must think outside of the box to discern the accurate meaning. They should be able to think differently about problems and solutions. A data scientist must be interested in comprehending data and interpreting it in a variety of ways.
Machine Learning:
Machine learning algorithms are used by data scientists to predict outcomes based on past data sets. They also utilize similar algorithms to create models and classifications for analyzing unstructured data like text and photos. A background in computer science or engineering will aid your comprehension of these topics, but it isn't required if you already have some programming expertise.
Today's world is extremely tech-savvy, and the amount of data being generated is always increasing; nonetheless, every organization wants to save time when processing this data.
As a result, implementing their algorithms for each operation is extremely time-consuming. The relevance of machine learning is highlighted here, and it has become a "must-have" skill.
Where to learn Data Science
Now you know what skills this field demands, you must have realized that it is not easy to master any of these skills. If you're oozing with confusion about where to start learning these skills and move towards a bright career in data science, then don't worry. We've got that covered for you.
The increasing popularity of data science has motivated a lot of educational institutions to focus on education in this field. Today there are so many universities that offer higher education in data science that it can almost be too confusing for you to choose which will be the best place for you to start.
When you look for a place to educate yourself about data science, the most important thing that you should focus on is the curriculum of that institute, other things should come second on the priority list.
The curriculum is the most important thing that is going to help you get desired as a data scientist in the long run. A good data science curriculum is a combination of thorough theoretical knowledge with equally good exposure to practical experience in the field.
There are two ways to get a good education in this field, either you study from multiple programs present online, or you can study on-campus at renowned and well-established universities.
Both modes have their pros and cons, but ultimately, they both will get you ready for the data science sector if you choose the right institution to learn from.
Online
Post-Covid, almost a lot of things shifted to online mode, this led to widespread of the online market over all sectors including education. There is a lot of good content available to study data science online, and if you pick the right courses, then they can equip you with the right set of skills.
But that's the twist! Finding the right course can be very troublesome. Opening and running an institute or business online is much easier than running it offline. As you already know, data science is already a very popular field. Therefore, the number of courses being offered online for data science is huge.
This creates a lot of confusion among students, and often people end up doing useless courses that provide no good guidance.
This is the biggest drawback of pursuing online education. Many reputed institutions like MIT, Harvard, etc., also offer online courses.
But it is often quoted by students that "The online course of even reputed universities are not at par with the offline courses", so figuring out what course to study online can be stressful and confusing. But this does not mean that every course offered online is not good.
If you can find a decent course online, then by the end of it, you'll be equipped with an amazing set of skills in the comfort of your home and at a comparatively lower price than what a full-time on-campus course would have cost you.
If you wish to study data science online, here are some institutions that provide the best education for this field in online mode.
Coursera:
Coursera has come to become a very popular digital learning platform in recent years and for good reason. Coursera provides excellent online learning programs on several different subjects.
They provide you with well-structured university-level courses, and a professional certificate after your successful completion of the course. Coursera has some excellent courses for Data Science Aspirants
Let's examine the pros and cons of learning data science on Coursera.
Pros: Professional service with study materials for higher education. Flexible timetables. There are other features to choose from (for example, course auditing and specialization). Degrees are available from prestigious universities.
Cons: Even though cheaper than on-campus studying, certification. Programs and degrees are still comparably expensive. Some instructors are self-conscious in front of the camera. For total beginners, the courses may be a little challenging.
EDx:
You've come to the right place if you're looking for formal higher education. EDx is a trustworthy educational and learning portal. It was founded by Harvard and MIT professors, and it presently has over 34 million users. Its courses have been developed and taught by some of the world's most prestigious schools and businesses.
EDx offers superior higher education in data science that will prepare you to work in the data science industry after graduation. EDx offers completion certificates in collaboration with other reputable institutions.
Let's examine the pros and cons of learning data science on EDx.
Pros: Edx is well-known in the market and offers a variety of options to pick from. This platform is user-friendly and offers university-level courses.
Cons: Courses have a hefty price tag that is out of reach for most people, and the platform is poorly built and crashes occasionally.
DataCamp:
As you might get from the name, DataCamp specializes in data science & analytics courses. It is well-known for the quality of education they provide in the data science field.
Not only paid courses, but their free courses in Data Science are also very good and helpful for beginners. Their teaching method is highly engaging and interactive.
Let's examine the pros and cons of learning data science on DataCamp.
Pros: The platform is simple to use and provides high-quality data science information. Their pricing is quite clear and corresponds to the high quality of their courses. In addition, the classes are suitable for beginners.
Cons: Instead of films or other visual resources, some of the courses have a lot of walls of text. Users with a free account have a limited number of learning options for courses and other content.
Udemy:
Udemy is an online platform for learning which has a library of over 50,000 courses. It offers a wide range of courses in Data Science, including courses on Python programming language, R programming language, Machine Learning, and Deep Learning.
If you are looking to gain valuable industry experience in data science or want to build up your skills portfolio, then a course from Udemy will be very beneficial for you.
Let's examine the pros and cons of learning data science on Udemy.
Pros: Udemy offers a wide range of courses and has a large user base, which can be both useful and reassuring. The Udemy platform appears to be very user-friendly and functions without glitches.
Cons: Platform price, like the data they provide (number of users, instructors), can be misleading and inconsistent at times. It's also difficult to assess the quality of a course before purchasing due to a dearth of eLearning reviews.
Offline
Data Science is a field that requires a perfect blend of practical experience as well as theoretical knowledge to get ahead in this field. Online learning sure can help you in learning those skills, but it won't provide you the real opportunity to put them to test in everyday projects and internships and on-campus case studies and research projects.
On-campus education also provides you a better chance to interact with your mentor and solve your queries personally with him. You can also use the campus facility like; library, research facility, labs, etc., to further enhance your understanding of the subject.
The best university across the globe for studying data science are:
Stanford University:
The Master's in Data Science program at Stanford University is one of the most prestigious graduate degree programs in the university, but it's also one of the most expensive. It provides specialization in many fields of data science and has a very astonishing curriculum in data science, and offers many different specializations. The most popular amongst their specializations are:
MSDS (Master of Science in Data Science) - This course is for candidates with a bachelor's degree in computer science or a related subject. Students will study software systems, machine learning, big data management systems, and other topics necessary for becoming competent data scientists analysts
Business Analytics (MSBA) - Students who want to work as business analysts who have a good background in statistics, forecasting, and forecasting models should take this course. Students will understand how to tackle current and future business challenges using analytics tools and methodologies.
Program Structure
It is a two-year, full-time program that is offered solely through the Department of Statistics. The coursework is designed to provide students with the skills and knowledge necessary to be successful in the puzzling field of data science.
Students will take courses on topics such as machine learning, computer vision, natural language processing, bioinformatics, and computational biology. There are also electives available for students who want to focus more specifically on one area of interest.
After completing their coursework, students have the option of taking a year-long capstone project course or pursuing an internship opportunity.
Massachusetts Institute of Technology (MIT):
Massachusetts Institute is a world-famous institution that offers a Masters in Data Science. The program emphasizes using data science to solve real-world problems, including courses on statistics, databases, machine learning, and more.
The program also includes a research project wherein students analyze a dataset to address an important business question or challenge. For students is a fantastic chance for students ate their problem-solving skills and mastery of the material they've learned during their studies.
Students can choose from several options within the program, including big data analytics, data mining and machine learning, computational modeling and simulations, health informatics, human-computer interaction (HCI), information security policy and management (ISPM), intelligence analysis and security policy (IASP), natural language processing (NLP), network science, social computing systems engineering (SCSE).
Program Structure
The program at MIT can be completed in 12 months, but students must take two courses during their first semester and three more during their second semester.
After completing two semesters at MIT, students are required to take two elective courses from over 100 options. Courses consist of lectures, labs, and group projects that require students to apply the concepts that they have learned in class.
In addition to its core curriculum, MIT also offers optional concentrations for students interested in specific areas of study such as finance or health care. These concentrations allow students to tailor their master's degree so that it fits their career goals after graduation.
Harvard University:
The Institute for Applied Computational Science (IACS) administers the Data Science master's program, which is jointly directed by the Computer Science and Statistics faculties.
The master's in a data science program at Harvard University offers a comprehensive interdisciplinary curriculum taught by world-class faculty across the university who bring a common goal: to train future leaders who can work across disciplines and industries to solve complex problems with data science.
Program Structure
The program is 12 months long and includes four required courses, two required research projects, and two electives. Students complete independent studies with faculty members to address issues relevant to their interests and career goals.
Courses: Introduction to Statistics; Introduction to Database Systems; Statistical Computing; Seminar in Data Science Research Methods; Policy Analysis for Data Scientists; Data Science Project 1; Introduction to Machine Learning Algorithms; Capstone Research Project 1
Research: Independent research project related to the student's area of interest.
Conclusion
Data is the most powerful weapon in the age of information. And having thorough control over this weapon gives you the power to manipulate and develop this world even further.
Data Science is not an easy path to follow, you will have to go through many hard phases and long study nights to be able to establish yourself in this field.
But this field is surely one of the most interesting and prospering ones of all. It has a high future scope, and there is always something new to learn that will ensure that you don't get bored doing your job.
This blog was your guide to a perfect start to this amazing data science journey. Don't wait long. Begin your data science adventure now!
