Data Driven Decision Making
Analytics and Data Science Jobs that are set to be in demand in future
“Data Science a 21st Century Job Skill for Every Discipline”
Today, companies have the privilege of collecting and storing ever-increasing amounts of data in all shapes and sizes. Companies are looking at extracting value from the data that they generate and turn it into a source of competitive advantage. This has increased the demand for data science, analytics and data-driven decision making skills.
Data science and analytics careers are very lucrative and rewarding. They have become one of the highest paying jobs over the last decade. Building the right skills will help you to start or advance your career in Data science and analytics.
Data Science continues to be the number one job in the market today. According to AIM Research, in India, there were 137,780 jobs in Data Science in June 2021 and witnessed a 47.1 percent increase in open jobs. India contributed to 9.4 percent of the total global analytics job openings, a rise from 7.2 percent in January 2020.
In addition, recruitment services firm Michael Page India’s “The Humans of Data Science” report revealed that data science will create roughly 11.5 million job openings by 2026. The LinkedIn’s Emerging Jobs report ranked data science as the fastest growing globally and has witnessed a growth of over 650 percent since 2012 and the market is slated to grow from $37.9 billion in 2019 to $230.80 billion by 2026.
What is Data Science?
Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.
The Data Science Life Cycle basically can be divided into the following five stages:
- Data acquisition and understanding
- Customer acceptance
Who is a Data Scientist?
Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover solutions to business challenges.
A data scientist’s work typically involves making sense of messy, unstructured data, from sources such as smart devices, social media feeds, and emails that don’t neatly fit into a database.
Since a large part of a data scientist’s job requires them to communicate data insights to other departments, they need to have exceptional communication skills and interpretative skills. Industry knowledge and contextual understanding are also required to make accurate observations and meet business challenges.
A Data scientist’s responsibilities are not limited to data processing and analyzing. Data science roles vary from company to company, creating overlaps between data science and business analysis roles. Expert data scientists usually come with years of experience and expert knowledge of multiple industries. Given how they work with multiple stakeholders and facilitate crucial decision making for the company, data scientists are one of the most well-compensated professionals in the market.
Benefits of a Data Science Career
The various benefits of Data Science are as follows:
1. Increasing Demand
Data Science is in huge demand and is increasing every year. Job opportunities have many opportunities across Industries and Functions. According to a research by Linkedin, Data Science is predicted to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector.
2. High number of openings
The increasing demand for Data Scientists and the shortage in supply gives a lot of opportunities with the right Data Science skills. This makes Data Science less saturated as compared with other IT sectors.
Therefore, Data Science is a vastly abundant field and has a lot of opportunities. The field of Data Science is high in demand but low in supply of Data Scientists.
3. A Highly Paid Career
Data Science is one of the most highly paid jobs. According to Glassdoor, Data Scientists make an average of $116,100 per year. This makes Data Science a highly lucrative career option.
According to a study by Burtch Works, work experience is the largest factor in data science salaries. Mid-career data science professionals who have at least seven years of experience can expect to earn an average of $120,000. Highly experienced data scientists who hold managerial roles can earn upwards of over $150,000. However, education, company size, and sector are also important factors when determining data science salaries.
4. Data Science is Versatile
There are numerous applications of Data Science. It is widely used in health-care, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Therefore, you will have the opportunity to work in various fields.
5. Opportunity to Solve Complex Problems
If you enjoy solving complex, real-world problems, you’ll never be bored as a data science professional. The primary responsibility of your job is to find answers and insights by analyzing and processing vast amounts of raw data. A few examples of business problems that you’ll get to solve are:
- Finding ways to increase sales.
- Discovering features that distinguish a target audience segment.
- Finding potential opportunities in disparate data sets.
- Identifying unrecognized problems in current business operations.
- Building infrastructure that helps an organization ingest and centralize all the data.
Data Science Skills:
1. Problem solving intuition
Being good at problem solving is very important to being a good data scientist. As a practicing data scientist, you don’t just need to know how to solve a problem that’s defined for you, but also how to find and define those problems in the first place. It starts with becoming comfortable with not knowing the exact steps you will need to take to solve a problem.
2. Critical thinking
With this skill, you will:
Objectively analyze questions, hypotheses, and results
Understand what resources are critical to solve a problem
Look at problems from differing views and perspectives
Critical thinking is a valuable skill that easily transfers to any profession. For data scientists, it’s even more important because in addition to finding insights, you need to be able to appropriately frame questions and understand how those results relate to the business or drive next steps that translate into action. It’s also important to objectively analyze problems when dealing with data interpretations before you form an opinion. Critical thinking in the field of data science means that you see all angles of a problem, consider the data source, and constantly stay curious.
3. Effective communication
With this skill, you will:
Explain what data-driven insights mean in business-relevant terms
Communicate information in a way that highlights the value of action
Convey the research process and assumptions that led to a conclusion
Effective communication is another skill that is sought just about everywhere. Whether you’re in an entry-level position or a CEO, connecting with other people is a useful trait that helps you quickly and easily get things done. In business, data scientists need to be proficient at analyzing data, and then must clearly and fluently explain their findings to both technical and non-technical audiences. This critical element helps promote data literacy across an organization and amplifies data scientists’ ability to make an impact. When data offers a solution to various problems or answers business questions, organizations will rely on data scientists to be problem solvers and helpful communicators so that others understand how to take action.
4. Business sense
With this skill, you will:
- Understand the business and its special needs
- Know what organizational problems need to be solved and why
- Translate data into results that work for the organization
Data scientists perform double duty: not only must they know about their own field and how to navigate data, but they must know the business and field in which they work. It’s one thing to know your way around data, but data scientists should deeply understand the business—enough to solve current problems and consider how data can support future growth and success.
5. Multivariate Calculus & Linear Algebra
Most machine learning, invariably data science models, are built with several predictors or unknown variables. Knowledge of multivariate calculus is significant for building a machine learning model. Here are some of the topics of math you can be familiar with to work in Data Science: Derivatives and gradients, Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function, etc.
6. Programming, Packages and Software
Data Science essentially is about programming. While there is no specific rule about the selection of programming languages, Python and R are the most favored ones. Data Scientists choose a programming language that serves the need of a problem statement in hand. Python, however, seems to have become the closest thing to a lingua franca for data science.
7. Data Wrangling
Often the data a business acquires or receives is not ready for modeling. It is, therefore, imperative to understand and know how to deal with the imperfections in data. Data Wrangling is the process where you prepare your data for further analysis; transforming and mapping raw data from one form to another to prepare the data for insights. This is the most important data science skill that one must-have.
8. Database Management
With heaps and large chunks of data to work on, it is quintessential that a data scientist knows how to manage that data. Database Management quintessentially consists of a group of programs that can edit, index, and manipulate the database. The DBMS accepts a request made for data from an application and instructs the OS to provide specific required data.
Data Scientist Job Description:
A data scientist is someone who makes value out of data. Such a person proactively fetches information from various sources and analyzes it for better understanding about how the business performs, and to build AI tools that automate certain processes within the company.
Data scientist duties typically include creating various machine learning-based tools or processes within the company, such as recommendation engines or automated lead scoring systems. People within this role should also be able to perform statistical analysis.
- Selecting features, building and optimizing classifiers using machine learning techniques
- Data mining using state-of-the-art methods
- Extending company’s data with third party sources of information when needed
- Enhancing data collection procedures to include information that is relevant for building analytic systems
- Processing, cleansing, and verifying the integrity of data used for analysis
- Doing ad-hoc analysis and presenting results in a clear manner
- Creating automated anomaly detection systems and constant tracking of its performance
Skills and Qualifications
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc
- Excellence in at least one of these is highly desirable
- Great communication skills
- Experience with data visualization tools, such as D3.js, GGplot, etc.
- Proficiency in using query languages such as SQL, Hive, Pig Experience with NoSQL databases, such as MongoDB, Cassandra, HBase
- Good applied statistics skills, such as distributions, statistical testing, regression, etc.
- Good scripting and programming skills
- Data-oriented personality