2020 is supposedly the year to generate 50 times the data than in 2011. How do we navigate around with this humongous amount of data? Do we call up “data” experts? Yes, we do. In fact, their expertise is what’s driving business in prominent industries.
It is a trivial fact that data scientists have been voted to have the most desired jobs, as per the Harvard Business Review. But is that what’s driving the interest? Apparently, no. A Career in data science is seemingly creating intrigue since all businesses and governments are using enormous amounts of data to improve on what they know.
If you are stuck at home in 2020, thinking what is the eligibility for data science? How long does it take to become a data scientist? Then you have landed yourself at the right place. This piece will guide you on your path on becoming a data scientist.
Related reading: What is Data Science? With Examples
Career in Data Science 101
Data Science relies on investigation, helping you uncover usable insights like trends, consumer behavior, retention, usage, etc. The investigation is aided by tools, algorithms, statistics, mathematics, and machine learning processes, as well as technology.
Example? Take Google, Amazon, or Flipkart. They record and store cookies and your personal data like location, gender identity and location to improve your consumer experience. The same engine also shows you products or services you are likely to use. So, businesses today use data science to outperform and increase retention through smartly fixated business choices.
Data Science propels automated methods to assess and analyze vast volumes of data, extracting insights to progress the informed business planning.
Now, when you are looking at a data science degree in the context of academics, you have to learn what entails this interdisciplinary discourse. Since data science requires a spectrum of skills and tools, you might ask – are you into maths or stats or are you into software engineering.
The right way to approach the definition of a data scientist would be – a data scientist is likely to be better at stats than a software engineer while being better at software designing than a statistician.
As you have gathered already, data scientists are analytical experts who are scientists and technical experts. They have the tenacity to conduct holistic research and the technical skills to solve complex issues.
They don’t just understand the language of data, they have mastered the art of storytelling via data. This has made data mining, data management, and data refining more comprehensive for stakeholders and management, helping them make informed choices.
Here are some of tasks they normally handle:
- Framing and identifying data analytics related problems that directly impact the clientele and the company.
- Collecting, transforming and cleansing the unstructured and structured data acquired from different data sources.
- Building statistical models and automating machine learning algorithms to commit in-depth analysis of the data processed.
- Interpreting data models to locate patterns or address solutions and opportunities for the better retention and growth.
- Communicating the discoveries comprehensively, more like storytelling. This helps the stakeholders in having a clear purview of the situation.
Here’s a graph informing the pay scale of a data scientist in India.
On an average a data scientist earns ₹708,012. If you are an entry-level scientist then the average LPA is around 5lacs. If you are having an experience of 1 to 4 years, then the LPAs will be around ₹610,811/annum. A mid level data scientist can earn up to ₹1,004,082 per annum. A senior data scientist can easily get a whopping ₹1,700,000/year in India. Although the steps to become a data scientist are not linear, it can be quite rewarding once you start off your professional journey.
The rates, however, vary depending on your geopolitical location. Here’s a report published by PayScale.com defining the starting LPAs one can get as a data scientist depending on their location.
- Mumbai – ₹7,88,789
- Chennai – ₹7,94,403
- Bangalore – ₹9,84,488
- Gurgaon – ₹8,90,476
- Hyderabad – ₹7,95,023
- Pune – ₹7,25,146
- Kolkata – ₹4,02,978
Here’s a list of some of the reputed companies who recruit data scientists and their respective payscale. These job profiles also enjoy an incremental percentage of 15% annually.
- IBM Corp: INR 14,68,040
- Accenture: INR 19,86,586
- JP Morgan Chase and Co: INR 9,97,500
- American Express: INR 13,50,000
- McKinsey and Company: INR 10,80,000
- Impetus: INR 19,00,000
- Wipro Technology: INR 17,50,000
How Hard Can it be to Become a Data Scientist?
To answer that question, you need to know the purpose data scientists and a data science degree serves.
To reiterate, data scientists extract, wrangle, pre-process, and scrutinize data. After this, they make supposed outcomes and predictions. So, the purpose of a data scientist is to derive conclusions through which they can assist the companies in making informed decisions.
Now, what to learn to become a data scientist? It entirely depends on the kind of data scientist you want to be. Many standard courses include advanced curriculum which you might find redundant. Next would be the academic discipline you are hailing from. For instance, if you are coming from disciplines like maths, data analysis, stats, and applied computer science, your transition would be fairly easy.
Most of the data science projects that you will come across will involve design algorithms, various statistical techniques which might or might not require prior mathematical or statistical knowledge. So, as a data scientist you will be coming up with original models which can either be based on statistics or completely data driven.
Although data science relies on a steep learning curve, it is not entirely unfathomable. Even if you do feel the inadequacy of not being able to keep up with data science, you should at least know why this job profile can be challenging.
What are the Data Scientist Qualifications?
Here are a few key things you should be wary about before deciding your career as a data scientists:
- Data science deals with variations and given that it’s a nascent field of study, you’d be running a trial and error method with your acquired methodologies. But that should not just limit you to deny this pursuit, be open to challenges.
- Managing bulky data is tiresome. Data you collect might not be structured which adds to your challenge. But that naturally falls under the responsibilities of a data scientist. But if, as a data scientist you have prior knowledge of tools like Sparks or Hadoop, then you can calibrate your workflow.
- As mentioned already, data science is an interdisciplinary field. So, the academic appetite for this discipline requires you to be more versatile. This is also why most data scientists hold expertise over quantitative fields like stats, finance, or natural sciences.
- You need to have domain knowledge that can only be acquired through experience. Domain knowledge is required to locate variables and develop models in the context of your problem. Domain knowledge is imperative since it also helps a data scientist to re-evaluate and eliminate biases.
- Mathematical concepts can be daunting but that lays the foundation of data science. Concepts are then implemented in practical fields. So, it remains that your theoretical concept would require an auxiliary implementation which otherwise will vaporize.
A number of universities have started to address this governing concern. AnalytixLabs is one such institute that has adapted to this demanding learning curve.
What are the Steps to Become a Data Scientist?
Some of the recurrent questions like “how to become a data scientist?” or “who is eligible for data science?” etc keep cropping up.
This is why this guide on steps to become a data scientist exists.
So, with the data scientist qualifications intact with you (please refer to topic 4), here’s how to become a data scientist
Is it really for you?
Truth to be told, data scientists are usually lifiting the weight of nuanced education both in terms of economic and intellectual investment. However, platforms like AnalytixLabs provide a wide range of courses that will help you train as a data scientist across this field’s multiple domains. This will also help you to have a comprehensive understanding of what you will encounter in your professional field as a data scientist.
Choosing an academic path
A study revealed that it is impossible to attain the skills required for this field without a formal degree which is applicable to 9 out of 10 data scientists. Data scientists are opting for master’s degrees where almost 1 out of 4 professionals are holding a PhD. degree with less than 3 years of experience. 44% of working professionals have a Ph.D. But if you refer to Topic 4, you’d see alternative approaches that can streamline your costs. There are lots of internet resources and massive open online courses available. Data science bootcamps can be taken into account to sharpen your skills.
Choosing a specific area of expertise
You can have a lucrative career in the Data Science field. Traditionally, you can start from a bachelor’s level degree in data science, which can land you a job like data visualization specialist, market research analyst, and management analyst. From there, you can choose to do a specific Masters program on ML algorithm developer, data engineer or stats. To further your expertise, you may pursue a doctorate degree as a data scientist.
If you get certified, it makes you more marketable as a data scientist. Data Science Certification from a reputed institute like AnalytixLabs is highly regarded in the industry and under the aegis of seasoned mentors prove to be a worthy career investment with high ROI.
After you are through with your academic pursuits, the only way to test your skills out is through a career in data science. Data Science is a field that defies typecasting, so given its specificity, there are existing forums, job boards and networks dedicated to this field. You can start with forums like Kaggle or make networking via your linkedin connections. Another alternative is to find networking grounds in iCrunchData.
Who is Eligible for Data Science? What to Learn to Become a Data Scientist?
Topic 2 elaborates on the job profile of a data scientist. And, it would seem that the path to being a data scientist requires versatility. That being said, data scientists are coming from a wide spectrum of academic backgrounds. So, what is the eligibility for data science? There aren’t any, but there are some disciplines that make your transition to being a data scientist easier. These include:
- Computer Studies
- Business Studies
However, research shows that these are not data scientist education requirements to acquire a position in this field. So, how to become a data scientist? focus on the skills that data scientists require to be employable.
Some of the fundamental data scientist education requirements that you need will also help you in having a competitive edge. So, what to learn to become a data scientist? Here are some of the skills you should be concerned about learning:
- Excel navigation and proficiency
- Knowledge of business statistics
- Background of mathematics, specifically in the areas governing algebra, calculus
- Proficient in programming languages like SQL, R and Python.
- Must have working knowledge in using visualization tools like Tableau
- Machine Learning and Deep Learning techniques
You may also like to read: Basic Statistics Concepts for Data Science
In addition to these technical and educational skills, a data scientist also requires a consolidated business acumen to locate and address the problems at hand.
But if you worried about starting from scratch, there are alternative approaches to learning data science. For instance, you can always enroll in a crash course with good mentorship. If you can’t afford to invest time in school, then an online certificate program would open a number of professional opportunities for you.
Research conducted recently on 1001 LinkedIn data scientist profiles unveiled that 43% of them have at least one online course in their CV. On average they have about 3 certificate programs on their resume too, even if they are from ivy league colleges. This can only indicate that there are no such restricted criteria to be a data scientist since part of this journey relies on a proactive approach and self-preparation.
Best Data Science Courses in 2020
With over 200+ hours worth of course content, hands-on assignments, and designated courses, you can easily opt for data science courses available online. These 7 most sought after data scientist qualifications courses in 2020 will answer the most asked question i.e. where can I study data science?
This program covers basic statistical predictive modeling, data analytics, and machine learning via a series of projects and examples. This is one of the best R training in Delhi NCR and Bangalore. You can opt for their live online class or their self-based video modules.
Course Duration: 180 hours / 3 Months
This highly coveted training program entails visualization, machine learning, data handling and statistical modeling. Industry experts provide both offline and online classes with course work that includes hands-on projects.
Course Duration: 220 hours / 4 Months
- Data Science Specialization — JHU (Coursera)
This program really strikes a balance between the breadth and depth of its specialization using the R language. Prior knowledge of programming is required to attend this program and you need to have a good grip over Algebra, Calculus and Linear Algebra
Course Duration: 11 months
- Introduction to Data Science — Metis
Taught by data scientists from top-tier companies, this course covers up almost everything that the process of data science entails. Moreover, the instructors offer a flexible time frame for those who require extra attention.
Course Duration: 6 Weeks
- CS109 Data Science — Harvard
This program also manages to strike a balance between application and theory. It is a great program for beginners. Although it offers no certification, it is free of any cost.
Course Duration: 13 weeks
Now that the question about “where can I study data science?” has been answered, the next concern is the duration since most data science courses are time intensive and intellectually exhausting.
Although a UG and PG course in data science takes about 2-3 years, you can also learn them in a matter of 6-11 months by dedicating 6-7 hours daily with the courses mentioned above.
You may also like to read: Top Data Science Courses & Free Learning Resources
Virtually any industry can benefit from data science, from retail to real estate. These industries can leverage their existing data and weaponize to their competitive advantage. So, if you are an aspiring data scientist willing to exercise your positional authority to make decisions for corporations, then go for it. Jokes aside, you will be a key player with that job profile where you will be the sieve through which structured, semi-structured and unstructured data will be passing through for obtaining insights.
A career in data science is obviously a lucrative option since it really reciprocates your intellectual and economic needs. Although challenging, data scientists are in higher demand – which is likely to explode in the upcoming decade. So, learn with curiosity and retain with optimism. Keep working and the rest will permeate accordingly.
Related reading: Top 20 Data Science Projects Ideas