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Know The Ins And Outs Of Data Science And Analytics Career

You are currently viewing Know The Ins And Outs Of Data Science And Analytics Career

Last Updated on: September 21, 2022

Data science and data analytics are two sides of the same coin. Both help drive business innovation with various types of data cleaning, collection and analysis for more informed decision making. With growing digitalisation, massive volumes of data are at the disposal of companies, in the form of texts, emails, tweets, user searches, social media chatter and IoT. This data needs processing and analysis, which traditional systems are unable to perform due to the enormity, diversity and complexity of the data being generated. This is where data science and analytics come into play.

Data science is a multidisciplinary field that works with large datasets. It involves the use of tools, algorithms, and machine learning principles to uncover hidden patterns from unstructured data. Data analytics is a focused version of this field that creates actionable insights. In other words, it converts data into information that can be put into action.

A career in these two fields can be highly rewarding. Job opportunities for data science and analytics rose by 30.1% in April 2022, compared to the same month in the previous year. India contributed to 11.6% of the total global jobs, which is a considerable jump from the 9.4% in June 2021. In fact, LinkedIn declared the data scientist career path as the most promising one due to the tremendous advancements expected in the future. So, here’s a guide for those of you wondering whether data science is a good career.

What Does A Data Scientist Do?

Data scientists are experienced professionals who are purely data-driven and have expert knowledge of technical skills required for their jobs. They know how to build complex quantitative algorithms and how to organise and synthesise large amounts of information collected through data. Their findings and inputs help answer business questions and drive transformational strategies in their respective organisations.

Curious by nature and extremely result-oriented, data scientists have excellent industry-specific knowledge and exceptional communication skills that help them to present technical results and findings in an easy-to-understand manner to their colleagues and important stakeholders.  

Data Scientist Roles And Responsibilities

As you know by now, data scientists use statistical, analytical and programming skills to collect and collate large data sets. They then develop data-driven solutions that cater to the requirements of the organisation they work for. Their responsibilities include:

  • Identifying valuable data sources
  • Extracting usable data from these data sources. This is known as data mining
  • Improving the data collection processes
  • Preprocessing structured and unstructured data
  • Cleaning the data and validating its correctness
  • Analysing huge amounts of information to find trends and patterns
  • Proposing solutions and strategies to resolve business problems and challenges
  • Presenting the results using data visualization techniques
  • Presenting the results in a clear, concise manner to stakeholders
  • Developing prediction systems and machine learning algorithms

Benefits of being a Data Scientist

To build a career in data science, one may perform a range of activities on large datasets. These activities include data mining, data processing, validating data integrity, garnering business insights with the help of algorithms, statistical analysis and machine learning. Is data science a good career? 

Here are the main advantages: 

  • The opportunity to work with industry behemoths like Apple, Amazon and Google. 
  • Rewarding career, with an average salary of ₹11 lakhs per annum. 
  • The demand for data science professionals is unlikely to fade. 
  • Work with different technologies and programming languages.
  • Get to solve real-world business problems.
  • Gain in-depth knowledge of statistical software tools like R, SAS and Python. 

How Much Can You Make As A Data Scientist?

The salary of jobs related to data sciences will depend on:

  • Your knowledge and expertise
  • Employer
  • Location in India
  • Work experience 

You will need knowledge of algorithms, statistics, mathematics, machine learning, and programming languages like SQL, Hive and Python. Good communication skills are also a key advantage. Here’s a look at how monetarily rewarding it is. 

Data Scientist Salary By Experience:

Experience Level

Approx. Years Of Experience

Average Annual Salary

Entry-level0-1₹6 lakhs
Mid-level1-4₹8 lakhs
Senior data scientist5-9₹12 to ₹14 lakhs

Data Scientist Salary By Job Title:

Job Title

Average Annual Salary

Data Analyst₹6 lakhs
Data Scientist₹8 lakhs
Data Science Engineer₹9.5 lakhs

Data Scientist Salary By Company:

Company

Average Annual Salary

TCS₹7.5 lakhs
Accenture₹12 lakhs
IBM₹12.8 lakhs
Infosys₹8.8 lakhs

Where You Can Hope To Work

Aspiring data professionals often consider which country is best for a data scientist. Although the US is one of the highest paying countries, India is the second-largest country offering data scientist jobs. This is because analytics adoption among the largest Indian firms grew to about 74.5% in 2021. Some of the most reputed Indian and foreign companies hiring data scientists include Wipro Technology, IBM Corp, Accenture, McKinsey, and JPMorgan Chase.

In India, Mumbai is the hub for this career, with around 43.4% of the analytics functions of leading companies being based here. It is followed by Delhi, at 24.3%. 

What Does A Data Analyst Do?

Now that you know everything about the roles and responsibilities of a data scientist, let’s also understand everything about a data analyst role. Data analysts play an important role in data-driven organisations. They not only mine and cleanse data but also review it to gain important insights into their organisation’s business patterns. They use this knowledge to help solve business problems. Essentially, a data analyst works on a small part of what a data scientist’s role involves.

Key Responsibilities Of A Data Analyst

A data analyst used data mining techniques to collect and organise data related to sales, logistics, customer behaviour, market research, linguistics, and any other data related to business outputs. A data analyst’s responsibilities include:

  • Extracting data from primary and secondary sources
  • Eliminating corrupted data
  • Fixing coding errors
  • Creating and maintaining databases
  • Organising data in a readable format
  • Using statistical tools to analyse and interpret patterns and trends in the complex data sets
  • Creating reports based on the findings
  • Presenting the reports in an easy-to-understand format and to the stakeholders
  • Analysing local and global trends that have the potential to impact the organisation as well as the industry as a whole

How Much Can You Make As A Data Analyst?

The salary of a data analyst in India ranges from ₹ 1.9 lakhs per annum to ₹ 11.4 lakhs per annum. The average annual salary of a data analyst in India is approximately ₹ 4.3 lakhs. If you are conflicted regarding choosing from data scientist vs data analyst, here are some salary figures that can guide and motivate you:

Data Analyst Salary By Experience

Approx. Years Of Experience

Average Annual Salary

0-2₹3.8 lakhs
3-4₹4.8 lakhs
5-6₹5.7 lakhs

Data Analyst Salary By Job Title

Job Title

Average Annual Salary

Junior Data Analyst₹3.6 lakhs
Senior Data Analyst₹7.8 lakhs
Data Research Analyst₹3.9 lakhs

Data Analyst Salary By Company:

Company

Average Annual Salary

TCS₹4.7 lakhs
Accenture₹5.1 lakhs
IBM₹7 lakhs
Infosys₹5.5 lakhs

Differences And Similarities Between Data Analyst And Data Scientist

When deciding between data scientist vs data analyst, know that both require a bachelor’s degree in a quantitative field such as statistics, mathematics, or computer science.

The career path for data analyst is full of data mining, data analysis and data visualization. On the other hand, a data science career path is full of designing efficient ways to collect, store, manipulate and analyse data in order to help with business decisions. While a data analyst tries to extract the meaning from existing data, a data scientist works towards developing new ways of capturing and examining the said data.  Simply put, a data scientist works on a more macro level and an analyst works at a micro level. Let’s understand the difference between them in detail below. 

Data Scientist vs Data Analyst: Which Is better?

There are some differences between the roles of a data scientist and a data analyst. The role of a data scientist is suitable for those who are keen on building advanced ML models and using deep learning techniques to make human tasks easier. But if analytics excites you, consider the career of a data analyst.

Knowledge Required

Data Scientist 

Data Analyst

MathematicalAdvanced statistics and predictive analyticsFoundational maths and statistics
ProgrammingAdvanced object-orienting programmingBasic fluency in R, Python and SQL
Software and tools Hadoop, MySQL, TensorFlow and SparkSAS, Excel and business intelligence software
Other SkillsKnowledge of machine learning and data modelingAnalytical thinking and data visualisation

For data analyst roles, you will need a Bachelor’s degree in a field like maths, computer science or finance. Data scientists can consider a Master’s degree in data science, statistics and information technology. There are also professional courses at GeekLurn that help to build the necessary skills.

Although there are differences in roles, a data analyst can become a data scientist by upskilling.

Prospects Of Data Science And Analytics 

Investments in AI and ML are projected to grow by 33.49% in 2022 alone. The data analytics industry is predicted to create over 11 million jobs in India by 2026. These highlight the exciting prospects of a career in data sciences and analytics. 

Data analysts can move to management positions, like senior-level analysts, analytics managers, chief data officers and directors of analytics. You can also specialise in a specific area, like business analyst, operations analyst, marketing analyst and financial analyst. 

The Bottom Line

A whopping 463 exabytes of data is expected to be created globally every day by 2025. Experts will be required to convert this massive amount of data into actionable insights using advanced technologies. Almost all industries will continue to have demand for such expertise. This highlights why data science has become an exciting and satisfying career option.

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.

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Neel Neeraj

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
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