Who's a Data Scientist?
Now, why has data scientist been referred to as the sexiest job of the 21st century by none other than Harvard Business review?
Consider this:
90% of the data in the world was created in the past two years (or so IBM has estimated). The world is slowly shifting online and the internet is becoming more accessible. It is estimated that every second 8 new people join the internet. Can you imagine the amount of data that is going to be generated in the future? All of this data can be analysed, understood and used by companies to understand their customers better. For example, if a coffee machine manufacturing company knew that you had just Google searched coffee machines, and best ones to buy, it would help them instantly target you through social media and other digital properties.
Major internet corporations like Amazon and Google want to keep an eye on the activities of the people on the internet for one simple reason- people are the currency of the internet. Meaning that data scientists work to figure out what people are doing online, what tools they are using and how these impact their behaviour.
What you do online, where you do it, what device you use- everything is a data insight. In the world of data science, there are three core problems: acquiring data, doing the math and taking action.
Want to pursue a career as a Data Scientist? Deep dive into this field with Mentoria’s experts.
What would you do?
Where would you work?
How do you get there?
What skills would you need?
Technical Skills
Technical skills refer to the specific knowledge and abilities required to perform particular tasks, often related to technology, engineering, computer science, or specialized fields. These skills encompass a wide range of competencies, including proficiency in software applications, programming languages, data analysis tools, and technical writing. Mastering technical skills allows individuals to effectively utilise tools and technologies relevant to their job roles. Strong technical skills will enable professionals to troubleshoot issues, implement solutions, and contribute to innovation within their organisations. build this skill
Analytical and Data Skills
Analytical and data skills involve the ability to evaluate information, interpret data, and draw insights to solve problems or make informed decisions. These skills require critical thinking, logical reasoning, and proficiency in working with data sets, often using tools like Excel, SQL, or statistical software. This skill set helps you break down complex problems, identify patterns, and make data-driven decisions. In the workplace, strong analytical and data skills allow you to provide insights that guide strategies, optimise processes, and drive innovation. They are essential for roles involving research, business analysis, marketing, finance, and operations. build this skill
Communication Skills
Communication skills are the ability to clearly express ideas, information, and feelings, both verbally and in writing, so others can easily understand. This includes listening effectively, using the right tone, and being aware of non-verbal cues like body language. These skills help you explain your thoughts clearly, build relationships, and work better in teams. Good communication is important in almost every job, from customer service to leadership roles, because it ensures that tasks are understood and done correctly, and it helps prevent misunderstandings. build this skillHow do you make it to the top ranks?
Junior Data Analyst
Your career in data science starts as a junior data analyst. You will learn to extract data from various sources and combine all the data in the desired format. You will follow the instructions given by senior data analysts.
Senior Data Analyst
As a senior data analyst, you will follow the instructions given by the data scientist. You will train the junior data analysts and supervise their work. You will create tools and software for collecting data and get them approved by the data scientist.
Chief Data Scientist
As you progress, your responsibilities will increase. You will head a team of junior and senior data analysts and oversee their work. You will also need to conduct meetings to give your team instructions on what has to be done.
Pursuing your career locally VS abroad
Data science combines elements from computer science, mathematics and statistics. So, it is common for students to study a related bachelor’s degree in mathematics or computer science and then pursue a master’s in data science for one or two years. A data science degree can cost roughly somewhere between INR 1,00,000 – 14,00,000 in India. You can do a BSc, BTech, BBA related to the field of Data Sciences. For a masters or PG, you would require a UG degree in a related field.
Click here to look at the best data science programmes in India.
Students can choose to study an undergraduate degree in data science, which will take around three or four years, depending on the country. The average tuition fees for a bachelor’s in data science in the US will cost you around US$35,100 (INR 25 lakhs a year). In Australia, the average tuition fees is AU$ 38,900 (INR 19.29 lakhs a year) and in Canada, the average tuition fees will cost you around C$ 35,400 (INR 19.59 lakhs a year).
A master’s degree in data science is much more common, with postgraduate science degrees available in many countries around the world. A master in sciences should cost you anywhere between INR 10.9 lakhs to INR 45 lakhs a year, with the average being around INR 23.7 lakhs for a year. To apply abroad, you would be required to show that you are proficient in English through your IELTS, PTE, or TOEFL scores. Additionally, you might be asked to furnish GRE or GMAT scores to the institute.
Click here to look at the best data science programmes abroad.
How much would you get paid?
The exact number will depend on your skill set, relevant work experience, and your qualifications. But we can give you a general idea.
What are your career options?
Machine Learning Engineer
Machine learning engineers create data funnels and deliver software solutions. By combining software engineering and data analysis, you will enable machines to learn without the need for further programming. In addition to a master’s degree that has machine learning as an element, it is essential to have experience in computer programming to get into this field.
Application Architect
The job of an application architect lies in the design and analysis of software projects. You will create new applications or improve existing applications, run software tests, develop product prototypes and create technical documents and manuals related to application development. A master’s degree in data science or computer science should be enough but in some cases, application architects need to possess industry certification in programming languages and architecture design.
Enterprise Architect
An enterprise architect is responsible for the upkeep and maintenance of an organisation’s IT networks and services. As an enterprise architect, your primary duty lies in overseeing, improving and upgrading enterprise services, software and hardware. You will need around five to ten years of IT experience before you can step into the role. Most employers are looking for someone who has experience with SQL, data sourcing, enterprise data management, modelling, business strategy, auditing and compliance.
Data Architect
A data architect is required to collaborate with IT teams and management to devise a data strategy that addresses industry requirements. You will be responsible for visualising and designing an organisation’s enterprise data management framework. Some companies need data architects who specialise in data modelling techniques; others may want experts in data warehousing, ETL tools, SQL databases or data administration.
Infrastructure Architect
Infrastructure architects design and implement information systems to support the enterprise infrastructure of an organisation. You must ensure that all systems are working at optimal levels and support the development of new technologies and system requirements. To be qualified for the job, it is necessary to have many years of experience in developing network infrastructure solutions and need proficiency in ITIL strategy, network security, network components, active directory and protocols.
Data Engineers
Data engineers build reservoirs for data and play a key role in managing the reservoirs as well as the data churned out by digital activities. They develop, construct, test, and maintain data-storing architecture — like databases and large-scale data processing systems. Much like constructing a physical building, a big data engineer installs continuous pipelines that run to and from huge pools of filtered information, from which data scientists can pull relevant datasets for their analyses. In addition to a background in computer science, engineering, applied mathematics or a degree in other related IT fields, you’ll need experience with multiple programming languages, including Python and Java, and knowledge of SQL database design.
Business Intelligence Developer
The Business Intelligence (BI) developer works collaboratively with end users to develop reporting systems that provide accessible information for decision-making. The BI developer uses warehouse data to solve organisational problems through reports, analysis and data visualisation. Some of the most beneficial features of business intelligence are the ability to recognise business growth opportunities, raise profit shares, determine employee productivity, detect risks and threats, and reduce wastage and costs. While most of these skills are tech-related, a BI developer also needs to have strong communication skills to describe complex technical information to the non-BI developers in the company.
You’ve only scratched the surface.
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