In this blog, you will have all the information you need to know about data science. From what data science is all about to the various data science courses to the skills required for data science, the following is a comprehensive guide for beginners who are thinking of education and a career in data science.
Table of Content:
- What is Data Science?
- Data Science with Example
- Data Science Goals and Deliverables
- Why Data Science?
- Components of Data Science
- The Data Science Life Cycle
- Technologies and Technique for Data Science
- Who is Data Scientist?
- What Does A Data Scientist Do?
- Skills Required For Data Science
- What are the Different Types of Data Science Courses?
- Who Can Do Data Science Course
- How To Learn Data Science
- Is Data Science Hard
- Is Data Science A Good Career
- Does Data Science Require Coding
- How To Start A Career In Data Science
- How To Get A Job In Data Science
- How Data Science Will Change The World
What is Data Science?
Data science is the study and processing of data and analytics through technologically advanced methods like software and tools powered by machine learning. It allows us to gain an insightful look into data and derive meaningful information that will help in making predictive models that can be used to make smart business decisions. Data science helps us tap into the power of data in a big way.
Thanks to widespread digitisation, today, humans produce a vast amount of data through their daily routine. This data can be used to make life better. Companies can use this data to drive innovation and market the right products and services. Governments can use it to become efficient and make their citizens’ lives safer. The applications are vast, and the possibilities arising from them are innumerable. Therefore, data science has emerged as the fastest rising subject of education in the world.
Data Science with Example
The best way to understand something is by seeing a live example in action. Similarly, you will truly understand the importance and application of data science through a live example that you might be familiar with, but not know that it is being powered by data science.
Here are some excellent data science examples:
1. Netflix – One of the biggest platforms that makes use of data science is Netflix. In network television, only 35% of TV shows get renewed for a second season, whereas, in 2017, 93% of Netflix’s shows got renewed for a second season. This decision was possible through the use of data science. The superhit show Stranger Things was renewed for a second season only after a thorough study of its data analytics. Needless to say, the second season was also successful.
2. Amazon – eCommerce giant, Amazon, employs the best data scientists in the world to improve sales and user experience. Once you search for a product, you start seeing ads related to that product. You even get updates on alternate products, related products, and price drops. This is possible through data science. The data you generate through online shopping is used to optimise your shopping experience. You are even shown products according to the region you live in. That’s how detailed data science can get.
Data Science Goals and Deliverables
The foremost goal of data science is to improve business decisions. Data science gives such an in-depth look into data that one can make near-accurate predictions based on this data. These predictions can be used to negate risks, improve user experience, drive innovation of products & services, and increase profits.
Data science is going to make lives better and safer. It has applications in all aspects of our lives. It will make healthcare and medicines better and more efficient. Technology will evolve faster. Industries will function better. There will be more jobs, but lesser working hours. Commutes will become more efficient and safer. Mankind will have a better understanding of the world and universe. Data science is going to make every aspect of human life better.
Why Data Science?
Data science is not a passing trend. It is an important subject that is going to be a permanent feature in education and business. But you still need to have strong reasons to pursue this field because data science is a specialised field.
Here are some reasons that are answers to the question, why data science is important:
- Data science is the fastest rising field in education all over the world. The best universities and institutes in the country and world have introduced full-time data science courses at the undergraduate and postgraduate levels.
- Currently, data scientists find themselves amongst the highest-paid professionals in the world. Data scientists are making INR 12.6 lakhs annually which is even better than software engineers, and this figure is going to get even better.
- Data science is a field like no other. It is one of the most technologically advanced and challenging fields in the world, meaning, there are not going to be any dull moments in data science careers.
- Just in the past decade, many fields of education and careers have become irrelevant, but this won’t happen to data science. The demand for data scientists is only going to get higher.
- If you want to work overseas and find high-paying jobs in multinational companies, data science is the best way to achieve that because data scientists are in huge demand in the international market.
Components of Data Science
To truly understand data science, you need to know about the components of data science. Each component is like a gear and their functioning is dependent on each other. Therefore, knowing about these components will help you understand data science as a whole:
The collection and arranging of numerical data and analysing it and deciphering important findings is statistics. Numbers are an important part of data science as it uses many statistical models for analysis and predictions. Accurate recording and analysis of statistics are one of the most integral parts of data science.
2. Data Engineering
Once the data has been acquired, it needs to be properly stored, retrieved, and processed. This is where data engineering comes to the fore. How, when, where, and what needs to be done with the data is all a part of data engineering. Data engineering also deals with metadata.
3. Domain Expertise
Data science is always used to process data of a particular domain and for this, input from a domain expert is needed. Domain expertise brings data science together and gives it a direction, and lets you decide how to best make use of all the insightful findings.
4. Advanced Computing
Advanced computing is exactly what it sounds like. At the core of data science are many computer programs running on code. The creation, running, and advancing of these source codes is part of advanced computing. Advanced computing is responsible for the comprehensive processing of the data.
Once the data is processed, it needs to be represented in such a way that it easily communicates all the findings to everyone. Visualisation also helps cover a vast amount of data.
The Data Science Life Cycle
Since data science is relatively new, there’s a lack of clarity on the processes involved and the life cycle of a process. Knowing about the life cycle processes will further help you understand data science.
Here are the 5 main processes invloved in data science life cycle:
1. Data Gathering
It all starts with gathering data from the right sources. In the data science model, to query databases, MySQL is used. ML programs like Python and R are used to read data. Data is also gathered from Web APIs, MongoDB, and PostgreSQL. Facebook and Twitter also allow the connecting web servers for data collection. But the easiest way is to directly upload files.
2. Data Cleaning
The uploaded data needs to be cleaned and filtered. There could be data sets that are missing which also need to be replaced. Data can be in just one format, but for better analysis, it needs to be converted into different formats. All this is done during the data cleaning process.
3. Exploring Data
After the data cleaning process, it will be available in different formats. The data scientists will then explore this data and convert it into corporate settings. Properties are picked out and represented and visual representations are provided for each to ease understanding post the exploration of the data.
4. Data Modelling
This is probably the most one of the most integral parts of the Data Science Life Cycle. The data scientist has to choose what features and values to represent that will directly affect the prediction model. There are many tasks in this stage but the 2 main devices the data scientist uses are prediction and regression.
5. Data Interpretation
Finally, it’s time for data interpretation. It has to be done in such a way that a person with no technical knowledge can understand the visual representations put forth. The business questions fed in during the data processing have to get answered in the representation. All the visual representations of data have to be accurate, so the predictions are also accurate.
Technologies and Technique for Data Science
Data science makes use of many technologies and techniques. They can be different based on the application of data science and the requirement of the predictive models. But it is important to know about them.
Here are the most widely used technologies and techniques in data science:
- Linear Regression – This is a linear way to showcase a relationship between the dependent and independent variable and scalar response.
- Logistic Regression – Probabilities of certain events or classes are modelled through logistic regression.
- Decision Tree – Data fitting and classification are addressed by the decision tree prediction models. Support Vector Machine (SVM)
- Clustering – Grouping data together is called clustering.
- Dimensionality Reduction – For quick computations, data complexity is reduced through dimensionality reduction.
- Machine learning – Inferencing patterns from data is done by machine learning techniques.
- Julia – One of the best languages for computational science and numerical analysis.
- R – The best language for data mining and statistics.
- Python – The most common programming language for data science
- TensorFlow – Google’s framework machine learning.
- Pytorch – Facebook’s framework for machine learning.
- Jupyter Notebook – Interactive web interface for faster experimentation.
- Apache Hadoop – Framework for processing data over distributed systems.
4. visualisation Tools
- Plotly – Gives access to multiple scientific graphing libraries.
- Tableau – Software for data visualisation.
- PowerBI – Microsoft’s business analytics service.
- Qlik – Multiple software and tools for business intelligence and data visualisation.
- Google Charts – Google tool for making elaborate graphical charts.
- Sisense – Front-end tool for data visualisations like reports and dashboards.
- Webix is a UI toolkit for data visualisation.
- RapidMiner – Widely used data science software platform.
- Dataiku – Data science software for big data.
- Anaconda – Free, open-source platform for distribution of R and Python.
- MATLAB – Popular platform used in academia and industries
- Databricks – Popular platform for collaborative data science and data engineering.
- IBM Watson Studio – Cloud platform for integrating AI into business-related applications and collaborative data science software and tools.
Who is a Data Scientist?
A data scientist is a professional whose main job is to create data science models and process data in these models. They make sense of raw data and derive useful and insightful information based on which businesses make important decisions. To be a data scientist, you have to be good in science, mathematics, statistics, and computer programming.
What Does A Data Scientist Do?
There are many tasks a data scientist does. But the main ones are data collection, cleaning, processing, interpreting, and presenting that data in a visually understandable way. They have to create predictive models for the data. The information that is derived from these modes is used by the upper management to make important business decisions. Data scientists are integral to the growth and development of a business.
Skills Required for Data Science
The education and the work of a data scientist are quite unique and specialised. Therefore, you need to have a special set of skills to become a data scientist. Even if you may not have them all, you can also work on these skills if you choose to get an education in and pursue data science.
Here are the skills required for data science:
1. Mathematical Skills
Data scientists have to work with numbers, so you need to have excellent mathematical skills. Many concepts and aspects of data science require advanced mathematics, so it is a must.
2. Analytical Skills
Data science is all about analysis, so analytical skills are a must. From analysing raw data to analysing results and providing insightful findings, each step requires deep analysis. You should be able to read between the lines and spot patterns and make accurate predictions.
3. Programming Skills
Data scientists have to create datasets and analytical and predictive models for which you need programming skills. Data science makes use of many programming languages like R and Python. So, programming and coding are a must.
4. Communication Skills
Communications skills are a must in any profession, more so in data science. Data scientists have to work with others to gather data. They also provide their findings once their work is done. Good communications skills will ensure their work, findings, and insights are all understood by the senior management.
5. Presentation Skills
Presentation is the final step in the job of a data scientist. All the findings and predictions have to be presented, and presentation skills will help you make them so easy to understand that even a layperson can make out what’s being presented. You need to have a good understanding of all the visual forms of presentations.
What Are The Different Types Of Data Science Courses?
To learn data science, you need to do a data science course. As mentioned before, data science is relatively new, so there aren’t many data science courses available in India. But the currently available ones are good. These courses will make you eligible for a career in data science. However, you can just choose any course. You have to know about these courses and choose the one that meets your education and career goals.
Therefore, here are the different types of data science courses.
1. Certification Training Course in Data Science
This is a certificate course that is mostly taught by private institutions. This is an excellent course if you want to do it as an add-on to your other educational degrees and courses. It is also very flexible and allows professionals who are already working to pursue data science. If done from the right institute, the certification training course in data science can get you quickly started in a data science career. These courses are also quite affordable and let you learn from the best teachers.
2. Graduate Course in Data Science
This is a proper degree course in data science. Offered by universities and private institutions, the graduate course is 3 to 4 years in duration. You will have to attend classes and sit for exams. Once you have this degree, you can choose to pursue a career in data science or study further and get a postgraduate or management degree.
3. Diploma Course in Data Science Online/Offline
Similar to a graduate course, the diploma course in data science is also quite valuable. The advantage with diploma courses is that they tend to be more industry focussed. This diploma course can vary in duration based on where you are doing this course. You can also do this course online from a reputed institute. Data science is so much in demand that good diploma courses can also get started in an excellent data science career.
4. PGD in Data Science Online/Offline
If you already have a degree in another stream but want to switch to data science, you can pursue a PGD in data science. However, you will have to clear the criteria set by the university or institute providing the PGD in data science course. PGD courses are usually overseen by the AICTE, so look for a course and college approved by AICTE for it to have value. This course can land you in some of the top-paying jobs in data science. It is also available online.
5. MBA in Data Science Online/Offline
The MBA in data science is one of the best data science courses you can do. Along with all the technical knowledge, you also get a lot of managerial and administrative knowledge, making you eligible for top posts in data science. This course can be anywhere between 1-3 years in duration and can also be done online from a reputed institute or university.
Who Can Do a Data Science Course?
Before you get excited about data science, you need to know who can do a data science course. You need to know about your eligibility before giving data science a serious thought. Here’s information on who can do a data science course:
Students can enrol for a degree or diploma course for data science and pursue a career in this stream. Some courses may require students to sit for entrance exams. The advantage of pursuing data science right at the student level is that you understand data science faster and better as you are not subject to any biases. This is the best time to pursue data science.
2. Working Professional
Yes, many working professionals are also pursuing data science to further their careers in their respective fields, data science has applications, and can bring value to many fields. Working professionals can pursue online courses or other flexible courses. Most working professionals go for certificate and diploma courses, but they should also consider postgraduate courses as it can give their career a huge boost.
How to Learn Data Science
Now, this is a question on everyone’s minds. You must have heard of data science, got curious, and gathered some information on data science. But now that you are seriously considering pursuing data science, you will need to know how to learn this subject.
Here’s some help on how you can learn data science:
- Love Data – If you pay close attention to data, it communicates more than what’s obvious. It not only shows you the past and present, but it also gives you a look into the future. Therefore, you have to learn to love data. Data science is no different from any other subject. If you take the time and effort to understand it, it will keep getting easier as you proceed. If you have to find the motivation to keep learning and without you knowing, you’ll become an expert in data.
- Don’t Just Learn, Apply – Most knowledge is gained by doing something. Even professionals will tell you that the most they have learned is on the job. Same with data science. There’s only so much you can learn. You have to start applying the things you have learned. Start doing data science projects that involve everything you’d do in a job. Do some data cleaning. This is the most time-consuming job. Use algorithms for data processing. Make visual representations. And repeat. The more you do, the more you will know.
- Practice Communicating Insights – Nobody asks a data scientist how they got the insights. Everyone, including your bosses, is only interested in the insights. So, you have to practice communicating and presenting insights. You can employ the help of friends and family who know nothing about data science. Present your insights and visual communication to them. You will know how to make communication easier and more effective.
- Peer Partnership – Teamwork is important in any profession, but more so in data science. So, if you are thinking of pursuing this, you also have to start working with others. Make friends with people who are also pursuing data science. Collaborate with them on projects. Make contributions to open-source packages and platforms. Connect with data science bloggers. All these will help you learn from your peers, and make learning data science easy.
- Upgrade Difficulty – Once you can do a project without hassles, you have to upgrade the difficulty level on the next one. This is the best way to learn data science and increase your skill levels. Work with a bigger set of data. Tackle more difficult issues. Scale up your algorithms. Time yourself. Do all these things to upgrade difficulty and get better. You can also work with others and help them love data science.
Is Data Science Hard
Just like it is with other subjects, if you love it, it is easy, but if you don’t like it, it will be hard. So, if you love data and analytics, data science is going to be easy for you. For data science to be easy, you need to be good in science, mathematics, and programming and need to have business acumen. These will enable you to do well in your data science course and career.
Is Data Science A Good Career
Data science is one of the best careers to have right now. With a rise in technology, big data, and organisations realising the importance of data science, data science careers are going to be in high demand all over the world. So, if you are thinking of data scienc below find the key benefits of choosing this course.
Here are 5 key reasons to consider a career in data science:
1. Massive Demand
In 2020, there were more than 1.5 lakh jobs available in the field of data science. This was a 62% growth compared to the figures of last year and experts predict the figures are going to get better in 2021. As countries and economies start reopening, there will be a surge in demand for data scientists. One fact worth knowing is that there is a demand for data science candidates with less experience.
2. High Salary
Even an entry-level data science job can get you a salary of INR 5 lakhs per annum. Data scientists count themselves among the highest paid professionals, not just in India but the world. Multinational companies are hiring teams of data scientists. Even small and mid-level companies are stretching their budgets and hiring data scientists as they know the huge positive impact they can have on their business. So, as a data science professional, you can expect a high salary and other perks and benefits with the job.
3. Part of Every Industry
Data science is not confined to a few core industries; it can enhance business across all industries. From IT and automobiles to healthcare and pharmaceuticals, every industry can benefit from the use of data science to enhance their business operations and decision-making. So, if you have an education in data science, you can choose to join the industry of your choice.
4. Technologically Advanced Field
Technological advancement is the reason why many industries are thriving today. It is going to be the main driving force behind growth and development. Therefore, industries with technology at their core are going to do really well. Therefore, data science is also going to do well as it synergizes with technology really well and is going to be one of the most advanced industries in the future.
5. Challenging Jobs
If you are looking for fun and excitement in your job, data science can definitely provide it. Data scientists face new challenges every day and turn them into opportunities to learn and grow. Therefore, there is never boredom in the data science field. Every day there is something new and exciting.
Does Data Science Require Coding?
Yes and no. Knowing R, Python, and other programming languages is important for you to learn data science, so knowing coding is going to help you a lot. But if you aren’t an expert coder or don’t know to code at all, you can get by becoming an expert in using GUI tools. Graphical User Interface tools are an excellent way to make up for your lack of coding skills. However, you have to learn a certain amount of coding if you want to become a data scientist.
How to Start A Career in Data Science
Surely by now, you have realised how excellent data science is and the amazing career opportunities it brings to you. The path is easy for students to start a career, but what about people who are already in different careers?
Here’s guidance on how to start a career in data science:
1. Work on Mathematics
If you aren’t good at mathematics, it’s time you started seriously working on it. In regards to data science, you have to be good in concepts like statistical methods & probability theory, linear algebra, statistical modelling & fitting, regression analysis, Bayesian thinking & modelling, Markov chains, multivariable calculus, etc.
2. Learn Programming Languages
Start learning a programming language. Data science requires you to do a bit of programming. Of course, you have software and tools to help you but if you know to program, it would be really helpful. The best languages to learn are R and Python.
3. Gain Experience Through Projects
If you are learning data science, you should partake in some side projects that will help you learn. You will also end up connecting with like-minded people who are already in or are starting in data science. The best knowledge is gained by doing actual work.
4. Data Analyst Should be Your 1st Career
Even if you don’t immediately get started as a data scientist, you can start your career as a data analyst. Both professions are similar. Data analyst can be an excellent stepping stone to getting the right experience and becoming a data scientist.
5. Network with the Right People
Having connections with the right people can open a lot of doors in the data science industry. So, you have to network as much as you can. Eventually, you will get your dream job, and even help someone get theirs.
6. Explain Your Employers Your Career Transition
This is for those who are switching to data science from a completely different field. Explain well to potential employers why you are making the switch. Your previous knowledge will actually make you better at data science as data science can contribute to all industries. Chances are your potential employer knows how important the data science field is, but you still have to do your part in explaining your career transition.
How To Get A Job In Data Science?
The data science field is so much in demand that it’s not going to be difficult to get a job. However, you still have to know about the right approach for searching for a job. This way, you will not only get a job, but you will also get the right job.
Here’s guidance on how you get a job in data science:
1. Research on LinkedIn
The professional’s social network is one of the best ways to get a data science job. Since everyone’s professions are stated, you can directly send connections to data scientists. You can also share blogs and articles on data science to catch attention.
2. Research on GlassDoor
GlassDoor is the best place to get all the latest information on the data scientist profession. Make note of qualifications and skills that might be missing from your resume. Also, apply for jobs through this website.
3. Build a Skill
Any data science-related skill you might be missing needs to be worked upon quickly. The skills you already have need to be showcased through online demos and tutorials. Visual communication is soon going to be your main form of communication.
Give as many interviews as you can and learn something from each interview. A new skill or technology you were asked about, a small project you were asked to work on – there’s a lot you can take away from an interview.
5. Search on Job Portals
Data science jobs are highlighted on most job portals so start there with your job search. However, make sure you read the job descriptions carefully and match them with your resume.
There are many communities for data science where there is a give and take of knowledge. You should be a part of such communities. You can also get many career opportunities through these networks.
How Data Science Will Change the World
Data science gives an opportunity to look into the future, and change the present to make it better. But this is a rather simplistic overview. Data science does a whole lot more. Through data, data science can help us get deep into the problems and failures of the past, and help us understand and plan accordingly. It can help in bringing overall improvement in all aspects of human life.
Another great example of data science changing the world is the current COVID-19 vaccines. These vaccines have been developed in record time thanks to data science and artificial intelligence. In the future, data science can also help humanity predict pandemics and other calamities. It can help us be prepared and help improve the lives of everyone. Many people only think of data science’s business applications but it can do so much more and truly change the world.
All the above-given information will give you a clear picture of data science and help you decide if it is the career for you. But most experts will tell you that data science is one of the best courses you can do.
One of the great things about data science is that currently, employers are also hiring candidates with less experience. So, if you are just starting off, or making a career switch, or adding on data science certifications to boost your career, you will get a job in a reputed company. But you have to ensure you do your data science course from the right place.
The best place for you to do a data science course is Ethans. We offer the most comprehensive and prolific online and offline data science courses taught by the best faculty and experts in India. To know more visit our website, and fill out the enquiry form. Our representatives will get in touch with you with all the information you need. All the best!