Data Science Learning Path - Roles, Salary, Opportunities

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11st May, 2022
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10th Jan, 2022
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Data Science Learning Path - Roles, Salary, Opportunities

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. 

What is the Need for Data Science? 

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: 

  • Asking the right questions and finding the cause of a problem 
  • Performing an exploratory study on the data 
  • Data modeling with the help of multiple algorithms 
  • Data visualization using graphs, charts, dashboards, etc. 

What is Data Science? 

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.

What is the Role of a Data Scientist? 

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. 

What are the 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: 

  • Management – 

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. 

  • Analytics – 

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. 

  • Strategy or Design – 

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. 

  • Collaboration – 

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. 

  • Knowledge – 

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. 

What are the Top Industries Hiring 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: 

  • Retail – 

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.  

  • Banking and Finance – 

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. 

  • Medicine –  

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.   

  • Communication, Media, and Entertainment – 

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. 

  • Education –  

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. 

  • Transportation – 

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.  

  • Outsourcing – 

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. 

What are the Key Skills to Master to Become a Data Scientist?
key skills to master to become a Data Scientist

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. 

1. Education 

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. 

2. Fundamentals

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: 

  • What is the difference between Machine Learning and Deep Learning? 
  • What is the difference between Data Science, Data Analysis, and Data Engineering? 
  • What are fundamental tools and terminologies relevant to Data Science? 
  • What is the difference between Supervised and Unsupervised Learning? 
  • What are Classification and Regression problems? 

3. Mathematics

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: 

  • Probability – 

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. 

  • Statistics – 

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. 

  • Multivariable Calculus – 

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. 

  • Linear Algebra – 

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: 

4. Python & R programming 

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: 

6. Hadoop Platform 

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 l

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Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.