According to a recent Harvard Business Review article, being a Data Scientist is the coolest job of the 21st century. Fom startups to Fortune 500 companies, everyone is looking out for the best and brightest of individuals to fill up the role of a Data Scientist.
There are several questions that arise for anyone wanting to become a Data Scientist. What is Data Science? What are the roles and responsibilities of a Data Scientist? How does one become a Data Scientist and what are the skills required for it? And many more. In this article, we will answer all the questions about a career in Data Science and take you a step forward towards becoming a successful Data Scientist.
Let us first get an understanding of why Data Science is important.
Traditionally, the data that was generated was small in size and structured in its outlook. Simple Business Intelligence could be used to analyze such datasets. With time, data has become significantly unstructured or semi-structured. This is because the data generated in recent times is vast and collected from multiple sources like text files, financial documents, multimedia data, sensors, etc. BI tools are not able to process this huge and varied amount of data. In order to gather insights from these data, we need advanced analytical tools and algorithms. This is one of the major reasons for the growth in popularity of Data Science.
Data Science allows an individual to make better decisions by performing predictive analysis and finding significant patterns. Some of the key things you can do with the help of data science are:
Data Science is a practice that helps you to generate insights from structured, semi-structured, and unstructured datasets with the help of various scientific techniques and algorithms which in turn allows you to make predictions and plan out data-driven solutions. In coordination with different statistical tools, it works on a huge amount of data to provide such meaningful insights for better decision making.
Let us understand Data Science better with an example. Consider your sleep quality, for instance.
The kind of sleep you had last night tends to be 1 data point for every day. On day 1, you had an excellent sleep of 8 hours. You did not move much, nor did you awaken much. That’s a data point. However, on day 2, you slept lightly for just 7 hours. That is another data point.
By collecting and analyzing such data points for a whole month, you can gather insights about your sleeping pattern. Maybe, around the weekdays, you have 6 - 7 hours of sleep and on the weekends, you have 8 hours of sleep. Also, you can gather other insights, say, around 2 a.m. every night, you have most of the short awakenings in a week.
If you work on the data of your sleep quality for a year, you can gather more complex analyses. You can learn what would be the best time for you to go to sleep and wake up or you could identify the worst sleeping time of the year and correlate it to work pressure. Further, you can even predict such stressful parts of the year and allow yourself to be prepared beforehand.
The data we use in Data Science projects is usually gathered from numerous sources ranging from surveys, social media platforms, e-commerce websites, browsing searches, etc. We are able to access all these data because of the latest and advanced technologies used in recent times for data collection. Small and big businesses both benefit from this data because they allow the organizations to make predictions about the products and make informed decisions which in turn gives huge profit returns to the business giants.
The role of a data scientist is becoming more significant since most businesses are dependent on data science to drive their decision-making and most of the IT strategies lean on machine learning and automation.
Data Scientists are considered to be big data wranglers. They gather and analyze large chunks of structured and unstructured data. Their role involves the collaboration of computer science, statistics, and mathematics. They perform analysis, processing, and modeling of the data and finally interpret the results in order to create actionable plans for business organizations.
In basic terms, a Data Scientist organizes and analyzes large amounts of data with specific software designed for a specific task. They discover insights from data to meet specific business needs and targets. They are mostly analytical experts who utilize their industry knowledge, contextual understanding, and skills in technology and social science to find trends and uncover solutions to business problems.
The data on which a typical Data Scientist works is usually unstructured and messy, collected from multiple sources like social media platforms, smart devices and emails and their task is to make sense out of that data. However, technical skills are not the only thing that is required in a Data Scientist.
A Data Scientist is also expected to be an effective communicator, leader, team member, and work as a high-level analytical thinker. This is because they usually belong in business settings and are given the duty to communicate complex ideas and make business decisions depending upon data trends.
Experienced data scientists are often tasked to work with other teams in their organization, such as marketing, operations, or customer success. They perform tasks starting from cleaning up data, to processing it, and finally storing the data.
A Data Scientist is one of the most highly sought after job roles at present, in a tech-dependent economy; and their salaries and the growth of job is clearly a reflection of that.
Let us look at the basic responsibilities of a Data Scientist.
An important step towards becoming a Data Scientist is to understand the numerous responsibilities they need to undertake in their journey. Some of the most common responsibilities are as follows:
A Data Scientist plays a managerial role by supporting the construction of the pillars of the technical abilities within the Data and Analytics field which allows them to provide assistance to many planned and active data projects.
A Data Scientist plans, implements, and assesses statistical models and strategies which are applicable in the most complex issues of a business. They develop models for numerous problems such as projections, classification, clustering, pattern analysis, etc.
In order to understand the trends of consumers, a Data Scientist plays a significant aspect in the progress of innovative strategies. It also helps data scientists to provide solutions to difficult business challenges, for example, how to optimize the process of product fulfillment and the entire profit generation.
The key role of a Data Scientist is to enhance an organization’s performance scale and help in better decision making. To achieve these, a data scientist needs to collaborate with other experienced data scientists and discuss various obstructions and findings with the relevant stakeholders.
A Data Scientist takes initiative to look into numerous other tools and technologies so that they can create innovative meaningful insights for the business organization. They also assess and make use of new and enhanced data science methods to pace up the business tactics.
Let us now take a look at the most popular industries that hire Data Scientists.
Since the evolution of Data Science, it has been helpful in tackling many real-world challenges and is in great demand across a wide range of industries which allows business giants to become more intelligent and make better-informed decisions. This is the reason why Data Science and big data analytics are at the cutting edge of every industry.
The top industries that hire data scientists are as follows:
Big Data and Analytics provide meaningful insights to the retail industry which is a major reason behind their customer’s happiness and it allows them to retain their customers. According to a study by IBM, around 62% of retail respondents claim that information provided by Data Scientists allowed them to have advantages over other business giants.
In the recent era, bankers have started to use technologies to drive their decision-making process. The Bank of America has created a virtual assistant named Erica using natural language processing and predictive analytics, which helps customers to access information about their forthcoming bills and also view previous transactions. They also believe that it will gradually be able to suggest financial schemes to customers at appropriate times by studying the habits of a customers’ banking.
This industry is making use of data and analytics to improve healthcare in a lot of ways. One example of such is the use of wearable trackers. It provides meaningful information to physicians who in turn can provide better care for their patients. It also provides data about whether the patient is taking the medication or not and also whether the patient is following the proper treatment plan or not.
Data Science is being used in this field to gather data from social media platforms and mobile content and understand real-time media usage patterns. One such example is Spotify, which is an on-demand music streaming service application. It collects and analyzes data from its users to render them with their specific taste of music.
Data Science in this sector can be used for a number of tasks. A data science model can measure a teachers’ effectiveness by measuring against many factors like each individual subject, the number of students, aspirations of students, student demographics, and other related variables. The University of Tasmania developed a learning and management system model with the help of 26,000 students. This particular system can track the time of a student login, students’ overall progress, and time spent on other different pages.
Data Science is used by transportation providers to help people reach their destinations on time. They enhance the chances of successful trips by gathering data such as traffic time, rush hours, etc. The Transport department of London has devised a statistical model which gives them information about customer journeys and how they can manage unpredicted situations and provide people with individual transport details.
The Outsourcing industries make use of Data Science to automatize the back-office processing, controls price-checking, and also helps to reduce the turnaround time. Flatworld Solutions is a business company who have incorporated Data Science in their systems to automate processes like classification and indexing of documents, naming and processing of PDF files, discovering related documents, and also for inventory management.
Let us now take a look at the most popular industries that hire Data Scientists.
Data Science has taken over the corporate world and every tech enthusiast is eager to learn the top skills to become a Data Scientist. It is one of the fastest-growing career fields with a job growth of around 650% since the year 2012 and a median salary of around $125,000.
Data Science helps you to extract the knowledge from data to answer your question. In layman’s terms, it is a powerful tool that businesses and stakeholders use to make better choices and to solve real-world problems.
So, as we learn new technologies and more difficult challenges come our way, it becomes significant for us to build a strong base. Let us learn in detail about the key skills you need to have to become a Data Scientist in the 21st century.
According to a study, Data Scientists are usually highly educated with around 88% of them having a Master’s degree and around 50% of them PhDs. Though there are a number of exceptions, in order to develop the deep knowledge necessary for a Data Scientist, you need to have a very strong educational background.
You can have a lot of options in choosing your field. You can earn a Bachelor’s degree in Computer Science, or Statistics, or you can even opt for Social Sciences and Physical Sciences. The most popular fields of study that will provide you the skills to become a Data Scientist are Mathematics and Statistics (32%), Computer Science (19%), and Engineering (16%).
However, earning a bachelor’s degree is not enough. Most Data Scientists working in the field enroll themselves into a number of other training programs to learn an outside skill, for example, Hadoop or Big Data querying, alongside their Master’s degree and PhDs. So you can do your Master’s program in any field like Mathematics, Data Science, or Statistics and allow yourself to engage in learning some extra skills which in turn will help you to easily shift your career to being a Data Scientist.
Apart from your academic degree and extra skills, you can also learn to channel your skills in a practical way by taking on small projects such as creating an app, writing blogs, or even exploring data analysis to gather more information.
As a beginner in the field of Data Science, you would be suggested by many to learn machine learning techniques like Regression, Clustering, or SVM without having any basic understanding of the terminologies. This would be a very bad way to start your journey in the field of Data Science since promises of “Build your ML model in just 5 lines of code” are far-fetched from reality.
The first and the most essential skill you need to develop at the beginning of your journey is to know about the fundamentals of Data Science, Artificial Intelligence, and Machine Learning. To understand the basics, you should focus on topics that answer the following questions:
Data Science is all about using algorithms to extract insights from data and make data-driven informed decisions. This is because making inferences, estimating, or predicting is a significant part of Data Science.
Data Scientists need to have a very strong foundation of the following mathematical concepts:
In order to work as a Data Scientist, you need to learn about concepts such as Bayes’ Theorem, Distribution functions, Central Limit Theorem, expected values, standard errors, random variables, and independence. These concepts of probability will help you to perform statistical tests on data and uncover insights from it.
Data Scientists should be well aware of the key concepts in Statistics which include mean, median, mode, maximum likelihood indicators, standard deviation, distributions, and sampling techniques. You should also learn about Descriptive statistics and Inferential statistics which will help to get a brief idea of the data through charts and graphs and also you can make predictions using that data respectively.
As you aspire to be a Data Scientist, you need to brush up your concepts on mean value theorems, gradient, derivatives, limits, Taylor series, and finally beta and gamma functions. These concepts will help you to understand logistic regression algorithms and also help in solving different calculus challenges in interviews.
It is considered to be the backbone of the essential machine learning algorithms and concepts like matrices and vectors will help you in the long run.
Other than the above concepts, some of the complimentary topics you can learn are step function, sigmoid function, logit function, Rectified Linear Unit function, cost function, and tensor functions. Plotting of functions is also an important skill to learn alongside all these
You can refer to the following links to learn about the Mathematics for Data Science:
According to a survey by O’Reilly, 40% of respondents claim that they use Python as their major programming language. It is considered to be the most commonly used and most efficient coding language for a Data Scientist along with Java, Perl, or C/C++.
Python is a versatile programming language and can be used for performing all the tasks of a Data Scientist. You can collect a lot of data formats using Python and can easily import SQL tables into your code. Data Scientists can also create datasets using Python.
According to another study, 43% of Data Scientists solve various statistical problems with the help of the R programming language. R is the most preferred programming tool to gather a deep knowledge of any analytical tools and you can use it to solve any problem while working with Data Science. However, in comparison to Python, R has a very steep learning curve.
You can refer to the following links to learn about Python and R:
Hadoop is an open-source software library created by the Apache Software Foundation. According to a survey performed by CrowdFlower on around 3490 LinkedIn data science jobs, Hadoop was claimed to be the second most important skill for a Data Scientist with a rating of 49 percent.
It is basically used to distribute big data processing across a range of computing devices. The Hadoop platform has its own Hadoop Distributed File System (HDFS) to store large data and is also used to stream the data to user applications like MapReduce. Though it does not come under a requirement, having some user experience with software like Hive, Pig, or Hoop is a strong point in your resume. You should make yourself familiar with cloud tools like Amazon S3.
The role of Hadoop comes when you are faced with a circumstance where the memory of your system is being exceeded by the large volume of data or in other situations such as when you need to send data to a number of different servers. It can also be used for data exploration, data sampling, data filtration, and summarization.
You can refer to the following links to learn about Hadoop:
SQL or Structured Query Language is a programming language that allows a user to store, query, and manipulate data in relational database management systems. You can perform operations like addition, deletion, and extraction of data from a database and also carry out analytical functions and modification of database structures.
Though NoSQL and Hadoop have resulted to become essential components of data science, a candidate aspiring to be a Data Scientist should learn to write and execute complex SQL queries.
SQL software is particularly designed to help individuals access, communicate, and work on data easily. It provides insights and consists of comprehensive commands that help to reduce work time and also decrease the amount of programming needed for difficult search queries.
As a Data Scientist, you need to be quite proficient in the use of SQL, and learning it will develop a better understanding of relational database systems and also boost your profile as a Data Scientist.
You can refer to the following links to learn about SQL:
Apache Spark is a processing engine and is becoming one of the most renowned big data technologies in the global market. It can easily integrate with Hadoop and can work with large and unstructured datasets. However, the only difference between them is that Spark is much faster than Hadoop. The reason behind this is that Spark stores its computations in its memory while Hadoop reads and writes to disk, making it slower.
Data Scientists work with Spark due to its specific design and it helps to run complex algorithms much faster than other tools. They use it to handle large chunks of complex unstructured datasets and disseminate the data processing. Spark can be used on a single machine or a cluster of machines.
Performing data analytics and distributing computing is a simple task in Spark. The X factor of this software lies in its speed and platform which allows Data Scientists to prevent any loss of data and also carrying out Data Science projects becomes easier.
You can refer to the following links to learn about SQL:
Although all data science roles are not required to have knowledge of deep learning, data engineering skills, or Natural language Processing, if you want to stand out in a crowd of data Scientists, you need to be acquainted with the techniques of Machine Learning like Supervised Machine learning, decision trees, logistic regression, k-nearest neighbors, random forests, ensemble learning, etc.
According to a survey by Kaggle, a small percentage of Data professionals are competent in advanced machine learning skills which include Supervised and Unsupervised machine learning, Time series, Natural language processing, Outlier detection, Computer vision, Recommendation engines, Survival analysis, Reinforcement learning, and Adversarial learning.
If you are interested in working with big data and want to solve different data science problems, you should make yourself familiar with Machine Learning techniques.
You can refer to the following links to learn about Machine Learning:
A popular idiom says “A picture is worth a thousand words”. Data Visualization is a graphical representation of data and an essential skill to learn for a Data Scientist. People understand visuals in the form of charts and graphs much better in comparison to raw data. So the huge amount of data being produced by businesses needs to be transformed into a form that is easily understood by people.
You need to learn different Data Visualization tools such as ggplot, Power BI, Matplotlib, and Tableau. As a Data Scientist, these tools will allow you to convert your raw form of data into a certain format for people to understand more easily. Most people do not understand datasets of serial correlation or p values, so it becomes essential to translate these data which is visually more representable so that you can make comparisons and predictions out of it.
Almost all business organizations make use of data visualization tools to grasp meaningful information which in turn helps them to work on new business innovations.
You can refer to the following link to learn about Data Visualization tools:
Curiosity is the desire of an individual to acquire more knowledge, not just about a particular subject but about a wide range of topics and ideas. An intellectually curious person is someone who has a love for learning. As a Data Scientist, you are expected to ask a lot of questions regarding data since the main job of a Data Scientist is to spend most of the time discovering and preparing the data.
Curiosity is one such skill that you need to develop from the beginning to succeed as a Data Scientist. To achieve that, you need to update yourself with all the relevant books, articles, and blogs that are published on the internet about the trends in data science. It is quite essential for you to make sense out of the vast amount of knowledge hovering around the internet. In early stages, you might not be able to extract many insights from your collected data. However, with a curious approach, you will eventually sift through the data to find more answers.
As a Data Scientist, you need to have a clear understanding of how businesses operate so as to make sure your efforts are channeled in the right direction. Having a precise perspective of the industry you are working in is essential so that you can solve the business problems of your company.
It is important as a Data Scientist to be able to recognize which problems to solve for your organization and identify new techniques as to how your problem-solving can be beneficial for the business.
Good communication skills can help you to easily and clearly translate technical insights for non-technical people like a member from the Marketing or Sales department. It is essential for a data scientist to understand the needs of their non-technical fellow workers. They perform this task by wrangling the data in a suitable manner and generate critical insights enabling the businesses to take informed solutions.
Storytelling around the data is another skill you need to learn as a Data Scientist to make it easy for others to understand. It is important as it allows you to properly convey your findings to other team members. For example, sharing information from your data in a storytelling method is much more effective to understand and gather in comparison to a simple table of data.
As a Data Scientist, you have to work with literally everyone, from company executives to develop strategies, designers to create better products, marketers for better product campaigns, and clients and developers to create data pipelines and improve the flow of work.
Last but not the least, as a Data Scientist, you need to develop use cases with your fellow members so as to gather information about the business goals and data to solve the real-world challenges. You have to keep in mind the right approach to address the use cases and how you can convey your result in a way that can be easily understood by everyone involved in the process.
According to a report by Glassdoor, Data Scientist has been named the number one job in the US for four years in a row. Furthermore, the U.S. Bureau of Labor Statistics stated that the data science skills will boost a 27.9 percent rise in employment by the year 2026. Although the demand for Data Scientists is high, there is a shortage of qualified data scientists globally.
In recent times, every business organization extracts information from sales or marketing campaigns and uses this data to gather insights. These insights allow the business to answer questions like what worked well, what did not, and what to do differently in the future. Thus, businesses can make more informed decisions with the right and organized data.
The salaries of Data Scientists depend on several factors like which industry they are working in, how many years of experience they have, what is the size of the organization, and so on. However, one big advantage of being a Data Scientist is they are always in demand globally and if you get bored of working in a particular city or a particular country, you always have the option of moving somewhere else because of the freedom and flexibility that this role offers.
Let us now look at the highest paying countries and the average annual salary of a Data Scientist:
The average annual Data Scientist salary in India is over ₹698,412.
The average annual Data Scientist salary in the USA is around USD 120,122.
The average annual Data Scientist salary in Germany is around €55,440.
The average annual Data Scientist salary in the UK is around £40423.
The average annual Data Scientist salary in Canada is around CAD 79123.
The average annual Data Scientist salary in Australia is over AUD 115,000.
The average annual Data Scientist salary in Denmark is around DKK 44,344.
The average annual Data Scientist salary in Singapore is around SGD 70,975.
According to Glassdoor, Data Scientists in India have a base pay ranging between 3 – 10 Lakhs. A Data Scientist in India with experience between 1 – 4 years has a net earning of around ₹6,10,811 per annum. On the other hand, an individual with experience of 5 – 9 years makes up to 10,04,082 per annum and someone with more experience than that can earn up to 17,00,700 per annum in India. However, there are several factors that are also associated while deciding the salary of a Data Scientist.
Every company, big or small, around the world now considers data science as an important sector and looks upon its potential to be able to change the market trends. The decision-making authorities of the companies are focusing more on technology and consumers.
Now, let us understand what are the significant factors that affect the salary of a Data Scientist in India.
According to a survey by Linkedin, an entry-level Data Scientist having a Master’s degree and experience of 1 – 5 years can get an annual salary of around 9 lakhs and can earn up to 11 lakhs for another couple of years of experience. A senior Scientist gets an annual salary of around 20 lakhs or more with experience of 6 – 14 years. However, someone with a specialization in the field can get a salary of around 23 lakhs or more.
Let’s see how experience affects the salary of a Data Scientist in India:
Every industry around the world recruits Data Scientists, due to which, there has been a significant increase of individuals choosing this career path which in turn adds a lot of value and enhances the progress of different industries.
In an organization, the Data Scientists are directly responsible for most of the decision-making process and they achieve this with the help of meaningful information using statistical tools like Power BI, Tableau, and SQL. The progress impacts the salaries of these Data Scientists which range between $80,000 to $107,000 at their entry level.
Financial companies hire Data Scientists to predict the company’s performance by gathering knowledge about the macroeconomic and microeconomic trends. The Scientists in this industry are responsible for creating economic data models and forecasts. Data Scientists working in this sector have an average annual salary ranging between $60,500 to $72,000.
Marketing research Scientists use sales data, customer surveys, and competitor research to optimize the targeting and positioning efforts of their products. This industry has a pay scale ranging from $61,490 to $75,000 at the entry-level.
Similarly, the Data Scientists working in the healthcare industry, whose job is to maintain the daily administrative advancements and operations gets an average annual salary of $60,000 to $85,000.
The highest number of Data Scientists and the average annual data salary in India is the highest in the Silicon Valley of India, a.k.a Bangalore.
Bangalore, Pune, and Gurgaon offer 22.2%, 10.5%, and 10.5% more than the average annual salary in India respectively. On the other hand, Data Scientists working in Mumbai get a salary ranging between 3.5 lakh to 20 lakh per annum which is less than the national average. Hyderabad and New Delhi receive 7.65 and 4.7% less than the national average respectively.
The top recruiters of Data Scientists in India are tech giants like Tata Consultancy Services, Fractal Analytics, Accenture, and Cognizant whereas according to reports, salaries offered are highest at Microsoft which is around 7 Lakhs – 28 Lakhs per annum.
Source link: Payscale.com
Skill is an important factor while deciding the salary of a Data Scientist in India. You need to go beyond the qualifications of a Master’s degree and Ph.D. and gather more knowledge of the respective languages and software.
Source link: Payscale.com
Some useful insights are as follows:
According to a global study by Capgemini, almost half of the global organizations have agreed that the gap between the skilled and the not-so-skilled is not only huge but is widening with the years.
With the increase in the application of Machine Learning and Artificial Intelligence, there is a spike in demand for skilled IT professionals across the globe. As the demand for data science has emerged, there has been a shortage of skills in this sector making this a huge concern for the tech giants.
As the demand and the supply gap has widened, there has been a plethora of opportunities for data scientists all over the world. Let us see some of the top geographies where Data Scientists are in high demand.
Almost every major tech hub in Europe, from Berlin, Amsterdam, London, Paris, to Stockholm, has a great demand for data science professionals. The most rigorous technical jobs in Europe include Artificial Intelligence, Machine Learning, Deep Learning, Cloud Security to Robotics, and Blockchain technologies. Among the leading digitally-driven countries in Europe, Sweden has the highest demand for Data Science professionals.
The demand for IT skills and the shortage of data science professionals has compelled European countries to fill the vacancies from outside the European nations. According to a German study, by the year 2020, they will face a shortage of 3 million skilled workers, with an appreciable number of them being IT professionals.
The United Kingdom has a vast demand for Machine Learning skilled professionals, which has nearly tripled in the last five years reaching around 231%. According to a survey, recruitment specialists in the United Kingdom claim that the demand for Artificial Intelligence skills is growing much faster than in countries like the US, Australia, and Canada.
In 2018, the number of AI vacancies in the United Kingdom was 1300 out of every million. This was double the vacancies produced in Canada and almost 20% more than in the US. Different regions saw different growth rates, for example, in Wales, it rose to 79% and to 269% in the North West regions in the UK.
India is considered to be the testing ground of most of the applications of Data Science starting from security to healthcare to media. The IT industry of India is expected to have a requirement of around 50% of professionals with data skills.
The ratio of skilled individuals to the jobs available in the Deep Learning field is around 0.53 and for machine learning, the figure stands at 0.63. This shows the demand for professionals with skills in Artificial Intelligence, Machine Learning, and user interface.
The regions in India where data professionals are highest in demand are Mumbai, Pune, Delhi, Bangalore, Chennai, and Hyderabad and the hiring industries include IT, healthcare, e-commerce, retail, etc.
China is one of the top countries that have a high demand for professionals in the Artificial Intelligence field. They are active participants in this sector and investing heavily in innovations such as facial-recognition eyewear for police officers which will help them to locate wanted criminals.
Although the demand for AI professionals is high in China, they face an acute shortage due to which the job market is unable to fill up vacant job positions. Data Science professionals who have at least 5 years of experience in the field are a rare sight, so companies in China are continuously looking for skilled individuals all over the world and are readily active to give much higher average salaries than most countries.
Canada aspires to reach the top position in the development of Artificial Intelligence in the global market. They have started investing heavily to create a framework on ethics, policy, and the legal inference of AI.
The topmost demanding data science jobs in Canada are Machine Learning Engineer, Full Stack Developer, and DevOps Engineer. Professionals with experience of around 1 – 5 years can earn a salary of $70,000 to $90,000 per annum. Furthermore, an individual with more than 5 years of experience can earn up to $130,000 or more.
Learning data science skills is a way to overturn your journey in this field. But landing your dream job can take some time, even if you have mastered your skills in Python, R, SQL, and other technical tools. You need to invest time, effort, and build requisite knowledge to find a job that’s right for you.
The first step in the process is to identify the different types of jobs that you should be looking for.
Let us talk about some of the in-demand roles in the data science world which you can undertake starting from a Data Scientist.
The average salary of a Machine Learning Engineer in the US is $144,800.
What is a machine learning engineer?
All machine learning engineers are needed to have at least some of the data science skills and a good advanced understanding of machine learning techniques. At some companies, this title means an individual who is a data scientist having some specialization in machine learning whereas, at some other companies, it might mean a software engineer performing data analysis and turning it into some deployable software. There is always an overlap between a machine learning engineer and a data scientist.
The average salary of a Quantitative Analyst in the US is $127,400.
What is a Quantitative Analyst?
Quantitative Analysts are also referred to as “quants”. Their main job is to make predictions related to finance and risk using advanced statistical tools. A strong foundation of statistics is essential in this field and most of the data science skills are vastly beneficial for a Quantitative Analyst. Knowledge of Machine learning models and how they can be used to figure out financial challenges are increasingly common these days.
Business Intelligence Analyst
The average salary of a Business Intelligence Analyst in the US is $95,800.
Who is a Business Intelligence Analyst?
A business intelligence analyst is essentially a data analyst whose job is to analyze data to gather meaningful market and business trends. This particular position is required to have knowledge on how to use software-based data analysis tools, for example, Power BI and Tableau. Most of the data science skills are also significant for a business intelligence analyst along with solid foundational skills in Python and R programming.
Data Warehouse Architect
The average salary of a Data Warehouse Architect in the US is $134,373.
Who is a Data Warehouse Architect?
A data warehouse architect is essentially in charge of a company’s data storage systems. Although it is a sub-category within Data Engineering, SQL and database management skills are quite crucial for this position. You will not be hired as a data warehouse architect solely on the basis of your data science skills. If you want to work as a data warehouse architect in the data engineering sector, you need to have a command over different technical skills.
The average salary of a Statistician in the US is $99,300.
Who is a Statistician?
‘Statistician’ is the name of the job title that data scientists were called before the term ‘data science’ even existed. The necessary skill required for all statisticians is a strong foundation of probability and statistics, although it might vary from one job to another. Knowledge of any statistical-based programming language like R will also be beneficial for this job role. Although they are expected to have an understanding of the mathematical techniques of different machine learning models, they are not required to build and train machine learning models.
The average salary of a Systems Analyst in the US is $79,470.
Who is a Systems Analyst?
The main task of a Systems Analyst is to discover organizational challenges and then plan and examine the changes or the new systems that are important for problem-solving. For this job role, you need to be familiar with programming skills, data science skills, and few statistical skills. All these skills combined will help you identify the issues in your company's technical system and allow you to make decisions about what to implement and what not to.
The average salary of an Operation Analyst in the US is $67,250.
Who is an Operational Analyst?
The main task of an Operational Analyst is to examine and organize the internal processes of a business organization. All operational analysts are not required to make use of data science skills, but in most cases, their major focus is on cleaning, analyzing, and visualizing the data. It allows them to determine which of the company systems are working efficiently and which of the parts require improvements.
With a steady rise in demand and popularity, a lot of young professionals want to pursue a career in data science. It helps that the field offers perks and a plethora of job openings all over the world. Organizations are trying to stay ahead of their competitors by investing heavily towards acquiring data science talent.
That said, the transition into Data Science has its own set of challenges. Let us look at how individuals working in other career domains like IT, Sales, Finance, HR, or Healthcare can transition into the world of Data Science.
If you really enjoy working as a software engineer, you should consider the most common role of a Data Engineer or a Machine Learning Engineer.
However, if you are keen on working as a Data Scientist, you need to acquire these skills:
Learn the fundamental concepts of probability and statistics.
As a software engineer, you have already learned database management. You now need to learn about concepts like window functions, CTEs, triggers, and style guides of SQL, etc.
You need to study some good data models and also learn how and when to use them. You can take the help of e-documentations and tutorials available on the internet. Make sure to have an understanding of the domain you are working in, such as healthcare, logistics, manufacturing, etc.
You should learn how to visualize your data with the help of graphs, charts, time series, or other visualization tools.
After you have gathered insights, learn how to compile and organize them into a report, for example, a document or a dashboard.
It is one of the most important skills you need to develop in the process. Your fellow workers should understand your analysis in a very easy and efficient manner.
If you’re from a finance background, you are very close to your dream of becoming a data scientist. It is a field of numbers and easily blends with the data science space.
However, if you are willing to work as a Data Scientist in finance, you need to acquire these skills:
Other than these skills, you need to work on your mathematical skills both verbally and visually and how to solve commercial challenges using them.
Also, have a strong foundation of concepts like optimization, statistical inference, multivariate analysis, and so on.
UX researchers have already been using low hang data science tools like Google Analytics, Excel, JSON, user testing data, etc. These tools and techniques are significant in doing UX designs and finding insights into the data.
The role of an application developer is to develop a webpage that is understandable by the stakeholders. On the other hand, a Data Scientist's job is to give an output in numbers and present these numbers to the customers with a visual aid.
However, to transition from an application developer to a data scientist, the best way is to start learning the fundamentals of Data Science, Machine Learning, Statistics, and Database Management and work your way up in the field.
Amongst all other career domains mentioned earlier, a Marketing and Sales professional is widely different. However, a Marketing and Sales team is mostly dependent on data and gets the opportunity to work closely with data analysts. So, a transition from this field into data science can be a natural changeover.
Apart from all these factors, you need to keep in mind some of the realities about an analytics job when switching to a role in Data Science:
You need to be a keen and fast learner and be able to work with terminologies like regression, decision trees, graphs, and charts most of the time.
This is one of the most common and popular question asked everywhere - Can I become a Data scientist without a technical or an engineering background?
The simple and short answer is Yes!
According to experts in the field, you actually do not require any background as such to become a Data Scientist. The only thing you require is a keen interest in the subject and asking yourself the question of whether you want to work with data and make an effect in your organization’s decision-making process.
However, as a beginner in the field, without any prior experience, you can follow this learning process:
Data science is a multidisciplinary study of data where mathematics, statistics, and computer science collaborate in a single place. It had emerged as the most sought-after job in the 21st century mainly because of lucrative pay and a multitude of job positions.
Let us take a look at the key advantages of data science:
Data Science is a highly employable and appealing field according to the latest industry trends and claims to create approximately 11.5 million jobs by the year 2026.
According to Glassdoor, a Data Scientist can earn up to $116,000 on an average per annum. As Data Analytics takes the middle stage in the decision-making process, the demand for data scientists is booming at a high pace and different kinds of job positions are coming up day by day. There is an abundance of data science roles all over the globe.
With advanced machine learning algorithms and robotic science, more and more manual and day-to-day tasks are getting automated. Technologies have allowed training models to perform iterative chores and the critical thinking and problem-solving roles are taken up by humans.
With the help of machine learning, e-commerce sites are now able to customize their products and enhance consumer experiences. Companies like Amazon and Flipkart use recommendation systems to refer products and give personalized suggestions to users.
Data scientists extract useful information from large chunks of data and provide crucial insights to their senior staff members so that they can take better decisions for the organization. Some of the industries benefiting from this are healthcare, finance, management, banking, and e-commerce.
Predictive analytics and machine learning algorithms have allowed Data Scientists to develop systems which can detect early tumors, anomalies of organs, etc. It is also helping farmers from all over the world by adopting new scientific methods to deal with agricultural pests and insects.
KnowledgeHut has various courses by which you can enhance your knowledge on the field of Data Science and which will help you grab a role of Data Scientist in any of the popular industry. Here are some of the Data Science tutorials offered by KnowledgeHut along with their key learning points and ratings:
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