What is Data Scientist: Courses, Skills, Salary & Career

Data scientists are professionals who bridge the gap between data and actionable business strategies. They work closely with business heads and other stakeholders to understand the business goals and determine how businesses can use data to achieve the goals. A data scientist is responsible for collecting a big dump of data, studying it, sifting relevant data using tools like SAS, R programming, and Python, etc., to extract insights that can be used to improve the organization's efficiency and output. This role requires a blend of technical expertise, analytical thinking, and business acumen, making it one of the most sought-after professions today.

What is a Data Scientist?

Data scientists are analytical data experts with technical skills to solve complex problems. They work with several elements related to mathematics, statistics, and computer science and collect, analyze, and interpret large amounts of data. They are responsible for providing insights beyond statistical analyses.

The role of data science is highly transferable, and data scientist jobs are available in both private and public sectors, including finance, consulting, manufacturing, pharmaceuticals, government, and education.

What does a Data Scientist do?

Some of the primary job responsibilities of a data scientist are listed below:

1. Identify business challenges and opportunities to improve products/services.
2. Collecting a large chunk of data from various sources
3. Using programming tools to structure the data, convert it into usable information, and make strategic or tactical recommendations.
4. Apply expertise in data cleaning and handling, quantitative analysis, and data mining.
5. See beyond the numbers and understand how users interact with consumers and products.
6. Collaborate with Product and Engineering teams to solve problems and identify trends and opportunities.
7. Building a blueprint or model of a project from the insights
8. Creating data visualizations for stakeholders to understand data better
9. Maintaining and analyzing the data and gathering insights
10. Utilizing machine learning frameworks for numerical computation 
11. Extending the company’s data with third-party sources of information when needed
12. Prediction and goal setting of the product team, designing and evaluating experiments.
13. Monitor key product metrics, and understand the causes of changes in metrics.
14. Create and analyze dashboards and reports.
15. Creation of key data sets to enhance operational and exploratory analysis.
16. Enhancing data collection procedures for building analytic systems
17. Creating automated anomaly detection systems and tracking their performance
18. Creating data dashboards, graphs, and visualizations

Also Read>>Top Reasons to Learn Python and Hadoop 

Data Scientist Salary

The salary of a data scientist varies basis various factors such as education, work experience, location, industry, and the specific company.

As per Ambitionbox, the salary of a data scientist in India ranges between Rs. 3.8 - 26 LPA, and the average salary is Rs. 10 LPA. 

Apart from the basic salary, some companies also offer bonuses, ESOP, and other forms of compensation. It's also worth noting that the demand for skilled data scientists with the right data scientist qualifications is high, particularly in the tech, finance, and healthcare industries. 

How to Become Data Scientist in India - Core Skills

An advanced data science certification is the key to remaining more relevant and competitive in the market and having a well-paid career. However, you need to possess some data scientist skills, which are mandatory to have a successful career in Data Science - 

Ability to prepare data for effective analysis

1. Provision, collect, organize, process, and model data
2. Analyze large volumes of structured or unstructured data
3. Prepare and present data in the best possible ways for decision-making and problem-solving

Ability to leverage self-service analytics platforms

1. Understand the benefits and challenges of using data visualization
2. Have a basic understanding of market solutions
3. Know and apply best practices and techniques when creating analytics
4. Have the ability to share results through dashboards or self-service applications

 Ability to write efficient and maintainable codes 

1. Deal directly with programs that analyze, process, and visualize data
2. Create programs or algorithms to analyze data
3. Collect and prepare data through API

Ability to apply mathematics and statistics appropriately

1. Perform exploratory data analysis and identify important patterns and relationships
2. Apply rigorous statistical thinking to extract signals from the noise
3. Understand the strengths and limitations of different test models and why they fit a given problem

Ability to leverage machine learning and artificial intelligence 

1. Understand how and when machine learning and artificial intelligence are right for business.
2. Train and implement models to implement productive artificial intelligence solutions.
3. Explain models and predictions in useful business terms.

Must Read - Online Master’s in Data Science – Your Essential Guide

Data Scientist Eligibility

To be eligible for a career as a data scientist, typically, you would need a combination of education, skills, and experience. A bachelor's degree in a relevant field, such as computer science, statistics, mathematics, or engineering, is usually required for entry-level positions. Talking about the data scientist requirements, some companies may prefer candidates with a Master's or a PhD in a related field.

Data Scientist Technical Skills

SAS

SAS is a software suite for information management, advanced analytics, and reporting. It is used by more than 60,000 companies in over 135 countries and is a market leader in analytics. It is the most commonly used software in the Indian analytics market despite its price monopoly.

Explore Big Data Courses

MATLAB

A statistical computing software developed by Mathworks, MATLAB has various add-ons and functionalities that help in various data analyses. It allows matrix manipulations, plotting of functions and data, implementation of algorithms, and creation of user interfaces.

R Programming

R is an open-source programming language and software environment for statistical computing and graphics. It is widely used by statisticians and data miners. Its popularity has increased over the years. According to O’Reilly Survey in 2014, it was the most-used data science language after SQL and is one of the highest-paid key skills for data scientist. Data scientists in many big companies like Facebook and Google already use R.

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Python

Python is one of the most commonly used programming languages in data science roles. According to KDNuggets, it is the second-most in-demand skill in the job market after SQL. It is also the official language of Google; the Google App Engine was originally built on Python. Other big companies that use Python are Quora and IBM.

SQL

SQL is among the in-demand data science technical skills and one of the most powerful tools for many expert data scientists. SQL is a database management language for relational databases. SQL, along with R and Python, forms the triumvirate of programming languages that any data scientist worth his/her salt is expected to be proficient in.

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Hadoop

Not a necessity for being an efficient Data Scientist but with the growing popularity of Hadoop in processing Big Data, it is one of the key skills for data scientists worth having. It is a Java-based programming framework that processes large data in a distributed computing environment.

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Data Scientist Non-technical Skills

Apart from the required technical qualification for data scientist, some business skills are also required to succeed in this field. Check them out.

Business Acumen

You can analyze the data of the organization. This data is going to be highly important in growing the business. If you understand the company's business problems, you can help the company by leveraging this data to provide useful solutions.

Communication Skills

Soft skills are always important when you are working in an organization. You will regularly contact the sales, marketing, and other non-technical teams. Your data findings can only be useful when translated into proper business opportunities. You must be able to communicate your findings in a manner that other non-technical people can understand.

If you lack in some areas, there is always a way to acquire those top data science skills. There are various online courses on these topics, and get the data scientist skills you need to start your dream career in Data Science.

Who Can Become A Data Scientist?

Software Developers

Suppose you are a software developer interested in a career in Data Science. In that case, you need to know how business challenges are solved, how data is crunched, and how different algorithms work!

You can leverage your software development skills and learn new skills relevant to the data science business. Suppose you know data analysis tools and data science programming languages such as SQL, R, Python, SPSS, and SAS. In that case, it will be easier to cope with the complications of Data Science. Besides, you would also need to learn about –

1. Contingency tables, Chi-squared tests, T-tests, Pearson correlation
2. Different types of regression models and decision trees
3. Neural networks, clustering algorithms, and expert systems
4. Logic programming, linear programming, data parsing, and data profiling
5. Artificial intelligence and machine-learning algorithms
6. Various metrics of model performance evaluation

Data Analysts 

As a Data Analyst, you must have experience collecting, processing, and applying statistical algorithms to structured data for better decision-making. That would be very helpful if you wish to step ahead and become a data scientist. As a data science professional, you need to learn-

1. Specialist fields like NLP, OCR, and Computer Vision
2. SQL databases and database querying languages like MySQL, PostgreSQL, MongoDB, etc.
3. Programming languages like Python/R, C/C++ Java, Perl
4. Big data platforms like Hadoop, Apache Spark, Hive & Pig
5. Cloud tools like Amazon S3, GCP, Azure
6. R and/or SAS
7. Machine learning models like Regression, Boosted Trees Support Vector Machines (SVM), Nearest Neighbor (NN), etc.

Explore the most popular machine learning frameworks in this blog - Top Machine Learning Frameworks

Business Analysts

A business analyst is responsible for offering recommendations on process improvement, software, and solution design, while data science involves algorithm development, data inference, and other technological processes. Shifting a career from a business analyst to a data scientist is not tricky as the person already has domain expertise and industry knowledge. However, specific skills are needed to be learned, such as –

1. Basic understanding of SQL, NoSQL, MPP databases, and Hadoop

1. Knowledge of algorithms such as recommendation engines, K Means Clustering, Linear and Logistic regression, Time series analysis, text analysis, decision trees, and NLP
2. Knowledge of tools like Python, R, Django, D3j visualizations, Talend Open studio, and Splunk
3. Cloud tools like Amazon S3, GCP, Azure
4. R and/or SAS languages
5. Machine learning models like Regression, Support Vector Machines (SVM), Nearest Neighbor (NN), etc.

System/Database Administrators

System and Database Administrators are looking to boost their careers by switching to a data scientist profile. With the requisite skills, they can work within their organization, as most companies move towards using data to make business decisions.

Why Data Science is a Good Career Option?

The world is going through an era of data explosion. Every day, there are –

1. 3.5 Billion Google searches globally
2. 277,777 stories on Instagram
3. 694,444 hours of videos on Netflix
4. 4,500,000 videos on YouTube
5. 4,800,000 gifs
6. 1,400,000 swipes on Tinder

These shocking numbers are why we need data science to sort structured and unstructured data efficiently. Data Science is currently one of the top career options for people from a technology background. Given that the world is moving towards digitized operations for all big and small tasks, this profession is here to stay. This is why not only fresh graduates but also IT professionals are opting for data scientist courses.

A study claims - Data Science industry, which has grown to $3.03 billion in size in the past few years, is expected to double by 2025.

Other reasons why data science has been topping the charts of professionals for the past few years include –

Be a Part of the Smart Workforce- Data science has contributed towards creating better products explicitly tailored for customer experiences. Starting from relevant and customized recommendations for OTT platforms to self-driven cars, from recommendation systems used by e-commerce websites to humanoids, data science has redefined the conventional norm of creating products for the consumers.

Contribution to Decision-Making in the Company- Organizations use data scientists to maximize their analytical capabilities. They facilitate better decision-making processes through measuring, tracking, and recording performance metrics and other relevant information.

High Demands- 3,00,000+ Data scientists would be required across different industries, including banking, financial services, and insurance (BFSI) sector, media, and entertainment, healthcare, retail, telecommunications, and automobile, among others by 2024, with an increase of 3400 positions every month.

Work With the Best– Get an opportunity to work with top companies like Google, Amazon, Deloitte, IBM, and Accenture.

Excellent Remunerations- The average data scientist's salary in India is Rs. 10 LPA, as per Ambitionbox. Skills in Machine Learning, Python, R, and Statistical Analysis contribute to an above-average payout.

1. 1 - 4 years of experience - Average total compensation Rs. 9 LPA
2. 5 - 9 years of experience - Average total compensation Rs. 13 LPA
3. 10 - 19 years of experience - Average total compensation Rs. 20 LPA

Booming Market- More and more data scientists are anticipated by 2030 for data science jobs like research work, data engineering, data infrastructure, machine learning, full-stack data science, and many others.

Must Read – How Data Science is Revolutionizing the Finance Industry?

How to Become a Data Scientist?

There are four general steps to becoming a Data Scientist:

Reinforce Your Mathematical and Programming Fundamentals

It is important to be very aware of the importance of mathematics in the data scientist career. Data Science requires an advanced understanding of mathematics and a general knowledge of the most popular programming languages. 

Become Proficient in SQL

68% of data scientists use SQL as a relational database. SQL is the domain-specific language used to extract data from databases. SQL is more complex than most programming languages, but it's a must-learn for anyone working with large data sets and analytics.

Study Machine Learning

Data Science is the foundation of Machine Learning, and therefore, Data Scientists must demonstrate and reinforce their knowledge of Machine Learning algorithms, artificial Intelligence architectures, and statistics.

Explore Popular Online Data Analysis Courses

Learn Data Analysis

Learning the fundamentals of identifying trends in data is crucial to becoming a successful Data Scientist. Remember that many Data Scientists begin their careers as data analysts and continue their programming education as they go.

To learn more about becoming a data scientist, read our exclusive blog. 

Industries Hiring Data Scientists

Many industries hire data scientists to leverage the availability of the massive amount of data and contribute towards data-driven decision-making. Let us take a look at the top industries that hire data scientists:

1. Technology: Being at the forefront of data science, technology-based companies hire data scientists to develop algorithms and machine learning models for diverse applications.
2. Finance: Banks, insurance companies, and other financial institutions use data to mitigate risks, identify market trends, and make investment decisions. Data scientists play a role in helping finance teams by analyzing huge datasets, building predictive models, and drawing insights from data.
3. Healthcare: Data science has become core to help manage patient databases, run drug trials, streamline operations, detect internal health issues, collect and manage data from all types of medical imaging, etc. 
4. Retail: Retail companies implement data science to optimize supply chains, analyze customers' buying patterns, develop targeted marketing campaigns, and develop products.
5. Government: Governments use data science to make public policies, enhance public services, analyze crime patterns, and detect fraud. 

To learn more about industries and domains that have adopted data science methodologies and created job opportunities for data scientists, read our blog on - Top Industries Hiring Data Scientists.

Career Outlook for Data Science Professionals

The need to organize and, above all, monetize the information is imperative. It is often said that data is the oil of the 21st century. However, according to Dell Technologies, less than 0.5% of the world's data is being analyzed. The wave of digitalization that runs through the entire business fabric and that has been favored by COVID-19 has put data scientists in the crosshairs of companies, especially medium and large corporations. 

According to IBM, about 700,000 openings will be generated in this field in the coming years. Another study claims that the Data Science industry is forecasted to grow by $249147.71 million during 2022-2027 and is projected to reach $79.7 billion by 2030. It is also expected to create over 11.5 million job openings globally by 2026.