1. Who is a Data Scientist?
2. Data Science Job Description
3. Data Science Skills
4. Data Scientist Salary trends:
– Salary by Experience
– Salary by Job title
– Salary by Company size
5. A day in the life of a Data Scientist
6. How to become a Data Scientist?
Who is a Data Scientist?
Data scientists are professionals who source, gather and analyse huge sets of data. Today business decisions are powered by insights drawn from analysing data so you can imagine how crucial data scientists are for any organisation. Data science roles typically demand a background in computer science, mathematics, and statistics. Apart from modelling and processing structured and unstructured data, data scientists also interpret the findings into actionable plans for stakeholders.
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 analysing. 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.
Data Scientist Job Description
We are looking for a data scientist to model and analyse huge amounts of data and draw inferences to support our product and services team. The ideal candidate will be required to collaborate with marketing, sales, and product teams to aid business decisions. He/she should be efficient in handling large amounts of data and extract valuable business insights with product and process optimisation in mind. They should be adept in using tools and platforms for data mining, creating simulations and analysing. Candidates with machine learning skills will be preferred.
Responsibilities of Data Scientist
- Assess new data analysing tools and prepare reports on their effectiveness
- Develop custom data models and algorithms to analyse product specific data
- Mine and analyse data to optimise and improve products, marketing techniques, business strategies and more
- Work with stakeholders to identify and leverage data optimisation opportunities
- Create predictive modelling to improve product engagement, revenue generation, and stakeholder communication
- Build A/B testing framework and run tests for quality check
- Develop tools and processes for performance and quality management
- Coordinate with stakeholders to implement new tools and monitor outcomes
Data Science Skills
Data science demands knowledge of multiple tools to extract information from data and answer various kinds of operational questions. However, even before an aspiring data scientist can master those tools and techniques there are few basic skills that they need to acquire since those lay the foundation for a promising career in data science.
- Problem Solving Intuition: A data scientist must have a natural inclination towards problem-solving. They need to be able to identify and define a problem and lay out a structure for approaching the solution.
- Statistical Knowledge: Statistics is vital for data scientists. Familiarity with mathematical and statistical concepts like linear algebra, calculus, statistical distribution, probability theory, statistical significance, likelihood estimators and more is required for data scientists. They also need to identify valid techniques and approaches from lesser effective ones while working on any particular project.
- Programming Language: Programming knowledge is crucial for data scientists as it can be used to manipulate data for extracting exact insights. Two of the most commonly used programming languages, Python and R help data scientists to clean and process data. They can also be used to scrape websites for data and use APIs. Python and R have a number of packages available for numeric and scientific computing, making it easier for data scientists to apply machine learning algorithms on data sets.
- Data Wrangling: Data needs to be cleaned before data scientists can analyse it. Data imperfections can make analysing a tough job and hence it becomes important for data scientists to take care of it. Data imperfections like missing values, inconsistent date formatting, and string formatting need to be fixed for accurate data analysis. Data wrangling is especially relevant for fast-growing companies where formatting data might be an issue.
- Data Extracting and Transforming: Using multiple data sources can lead to having differential formatting structures. Data scientists need to extract data properly and transform it into a uniform format and structure before analysing or querying it. A background in ‘Extract Transform and Load’ can help aspiring candidates to structure and analyse data efficiently.
- Data Visualisation and Communication: Data visualisation is extremely important to help stakeholders interpret data inferences. Data scientists are often required to communicate the data inferences to other teams and help them make business decisions accordingly. Data visualisation helps in representing the information for both technical and non-technical audiences. Hence, data scientists must be knowledgeable in visualisation tools like matpotlib, tableau, d3.js, ggplot and more.
- Database Management: Data scientists are often required to don multiple hats, taking care of the end-to-end database management system. They should be familiar with the database management programs to edit, index and manipulate databases. Database management systems help users to receive data in a specific format. It also helps users store and retrieve data according to their requirement.
- Machine Learning: Machine learning and deep learning have emerged as preferred skills to have for data scientists. Machine Learning techniques like random forest, KNN, ensemble methods and more can help data scientists to train and model data to fit a particular format. ML algorithms can come in handy while working on custom data.
Data Science Salary Trends in 2021
If you wish to gain insights about your current salary and learn how it can grow over the span of 5-10 years, check out Great Learning’s Salary Builder! Plan your career success and find out how you can earn more.
By experience level
|Beginner (1-2 years)||₹ 6,11,000 PA|
|Mid-Senior (5-8 years)||₹ 10,00,000 PA|
|Expert (10-15 years)||₹ 20,00,000 PA|
By Job Title
|Data Scientist||₹ 8,00,000 PA|
|Data Science Engineer||₹ 9,76,133 PA|
|Data Analyst||₹ 6,02,784 PA|
By Company Size
|Microsoft||₹ 1,500,000 PA|
|Accenture||₹ 10,55,500 PA|
|Tata Consultancies||₹ 5,94,050 PA|
A Day in the Life of a Data Scientist
A typical day in the life of a data scientist starts like that of any other professional – by checking and replying to emails. S/he then connects with the rest of the team for a quick update on all the major tasks at hand. With businesses going global today, teams often function across geographical boundaries and timelines. Data scientists connect with all the stakeholders to ensure everyone’s work is in sync. Depending on the role, a data scientist might spend 40% of their time on research and simulation of data. They spend their day developing and testing algorithms to simplify data problems. The analysis results are kept confidential or shared with the stakeholders depending on the algorithm. 30% of their time goes in communicating with other teams and building relations across departments to seek new projects. This process is crucial not only to identify potential problem areas and scopes of improvement but also to provide a comprehensive view of operations. They spend the remaining 30% of their time performing data analysis and reporting. On an average day, they can be found using tools and techniques like predictive models, forecast models and data mining for subgroups and trends within a given dataset. Tableau, Python and R are also some of the commonly used tools and programming languages.
How to Become a Data Scientist?
As you might be already aware, Data Science offers a lucrative career for data enthusiasts. Even though candidates with a background in maths, statistics, and computer science might find it easier to transition to data science, it is not an absolute requirement. Professionals eager to build a career in data science can do so even without any prior knowledge of programming and mathematics.
However, before you decide to become a data scientist, ask yourself the following questions. Afterall, the path towards becoming a data science expert is long and you must ensure that you are doing it for the right reasons.
- Does Programming language excite you?
- Do complex data sets excite you?
- Are you always trying to find patterns in random data structures?
- Do you have a background in computer science, mathematics, statistics, information technology or a similar branch?
Even if you are from a non-technical background, getting into data science wouldn’t be that difficult. All you need to have is an aptitude for maths and statistics. Developing skills in applied mathematics and statistics will pave a way for you in data science.
Pick up Maths and Statistics Skills
If maths and statistics are the fundamentals of data science, machine learning follows closely. Learn the basics of machine learning since it is used for many data science applications like creating forecasts and data modelling patterns. Knowledge of machine learning will enable you to design and use algorithms for data modelling.
One of the many requirements of data science is programming so you need to brush up your coding skills. Languages like Python, R, SAS help data scientists to read and analyse data sets. Thanks to its flexibility, Python is one of the most widely used programming languages in data science. For querying, you will benefit from learning SQL.
Data Munging and Reporting
Aspiring data scientists should also learn data munging and reporting. While data munging helps to identify and discard redundant data, reporting ensures that it’s put into readable and actionable format.
Courses like Great Learning’s Data Science and Engineering, prepare a candidate for all kinds of data science roles and challenges. It starts by familiarizing a candidate with the basic programming and statistical models and gradually teaches them fundamentals of the domain. It also allows them to work on capstone projects to understand industry insights and practices. Depending on your dream job, you can also specialize in M.Tech in Data Science and Machine Learning. In the end, curiosity and a passion for driving data powered results are all that is required for becoming successful in this field.4