Data science is one of the most important subjects that are changing the face of the Indian technology sector. As the subject is being applied by organizations in different sectors – from retail to finance – more professionals are learning the art of handling data. Considered the “Sexiest Job of the 21st Century”, more data science positions are opening up in firms, such as Flipkart, Amazon, Cognizant, Wipro and Infosys. It is expected that the number of data science vacancies will exceed more than 3 million in the upcoming years. Let us now understand the basics of data science…
What is Data Science?
Data science is the combination of various tools, algorithms, and models used for identifying patterns in data. Due to the emergence of technologies, such as the Internet of Things (IoT), a huge amount of data are produced every day. This data originates from sensors in electronic devices, purchase data of consumers, profile information from social media websites, financial or business data, etc. Also, this data plays a crucial role in important business decisions.
Embed Youtube Video URL here:
You can understand the basics of the subject by looking at the data science lifecycle. The phases are as follows:
Data discovery:
In this initial stage, data professionals collect data from different sources, such as text files, relational databases, images, IoT devices, and videos. As the primary aim of any organization is to enhance their sales and business, the relevant data is collected.
Preparation of data:
Here, the unstructured data is converted into a simple format so that it is easier to work on. Data is put into an analytical sandbox by the ETLT (extract, transform, load and transform) activities. Programming languages, such as R is used for cleaning it up and transformation. This helps data scientists to visualize data patterns. The relationship between data variables can be better understood with this procedure.
Data model planning:
Different tools are used in this stage for formulating the relationships between data variables. The following tools are used:
- R is used for developing interpretive models
- SQL Analysis services perform database analytics and data mining functions
- SAS is used for fetching data from Hadoop and creating model flow diagrams
Building data models:
Data scientists develop data sets for testing and training. Methods such as classification, clustering, and association are applied.
Performing data operations:
Once data models are ready, they are put to work. The models help professionals to identify the hidden patterns and trends in the data. Bugs and discrepancies are figured out and the data model has modified accordingly. They present the final reports, code, and technical documentation.
Communicating results:
The last step is producing and communicating the results of the data analysis. Data scientists convey the results to the different stakeholders, team members, clients, and senior decision-makers.
How is the data science market growing?
As per a study conducted in 2018, the Indian data science market was slated to be worth 2.7 billion dollars in 2019. However, the industry has exceeded the estimation and is more than 3 million dollars currently. The revenues are expected to be close to double by 2025. Intelligent automation, machine learning, IoT and artificial intelligence have altered the businesses of domestic and international companies. The domestic analytics industry has increased by 12% in revenues.
For the outsourcing industry, tech giants, such as Wipro, Genpact, TCS, and Tech Mahindra form 35% of the market. This industry is the main source of revenue, accounting for more than $25 billion. Out of this, TCS receives almost 2 billion of the revenue. Consulting firms like Deloitte and McKinsey account for 10% of the analytics market. Moreover, more than 45% of the data analytics revenue comes from the U.S.
If we look at the sectors obtaining revenue from data science the most, BFSI comes first. This industry is followed by marketing and advertising and e-commerce.
Demand for Data Science Professionals in India
Data science professionals are in huge demand in companies of different sectors like IT, finance, e-commerce, and retail. After the U.S., India is the second-largest job market for data science professionals having more than 95,000 vacancies. As per the most recent study, this number is expected to rise up to 2 lakh by 2020. Notably, 1 out of every 10 advanced analytics jobs is in India. BFSI, e-commerce, and telecom sectors are the major recruiters of data science professionals. More than 97% of the jobs are offered on a permanent basis.
Furthermore, data professionals earn more than the employees at the traditional IT positions, such as System Administrator or Junior Developer. On average, data scientists in India earn more than INR 10,19,000 annually. Thus, the pace at which the companies are adopting data analytics into their decision-making process is creating a supply gap for skilled professionals. Therefore, it is time that you consider building a career in data science.
Data science as a career
The following skills are required to be a data scientist:
- Knowledge of descriptive statistics and probability theory
- Know-how of programming languages, such as R or Python
- Knowledge of MongoDB, Google Analytics and MySQL DB along with ETL (Extract, Transform and Load) operations
- Basic experience in machine learning and deep learning
- Know-how of big data frameworks, such as Hadoop and Spark
Here are the popular data science roles and their respective salaries per annum:
- Entry level data scientist – INR 297,000 to INR 1,200,000
- Mid level data scientist – INR 590,000 to INR 2,070,000
- Senior data scientist – INR 972,106 to INR 2,927,745
- Data analyst – INR 3.5 lakh
- Data architect – INR 19,52,674
- Business analyst – INR 6,09,953
- Data engineer – INR 839,112
There is no time better than now to start learning the basics of data science to build a career around it. As a beginner, it may seem overwhelming as there is a lot to cover. Start with basic statistics and probability, then move on to linear algebra. You must then start learning the basics of Python and R. Having done that, learn data exploration, visualization, and analysis using these languages. To follow a proper learning path, you can enrol in an online or offline data science course for beginners. But, you have to remember that it is a continuous learning process that requires you to learn and practice the concepts daily.
