People in modern businesses will agree that data science is the technology of today and the future, and there is a huge career opportunity for data scientists. There is much talk about big data and how companies can use it to become more efficient. Many organizations have started using data science to extract valuable insights that help them prepare for future events. But to exploit the job opportunities in this field, one must get past the interviews with flying colors. Let us see how to do this.
What Is Data Science?
Before we see how to ace the data science job interview, it makes sense to know what this technology is for the sake of those who have not mastered the subject yet. It is the analysis of data to discover hidden patterns. This differs from data analysis in that here, the data used is mostly unstructured. Analysts use data to find what happened and explain it to the company management. But data scientists can find patterns in the data and predict events that can occur in the future.
Data science courses online explain how experts use unstructured data and uncover hidden patterns to get valuable insights. These experts use advanced algorithms to look at data from various angles previously unknown. One of the techniques used widely in data science is machine learning. Machines are fed samples and made to understand specific patterns. They are then allowed to build models to enable large volumes to be analyzed to find valuable information.
Data science helps to make decisions and predictions using various techniques like predictive causal analytics, prescriptive analytics, and machine learning. Anyone who wishes to become a data scientist must know what these are.
Predictive Causal Analytics
Students attending the XLRI data science course know that you must use predictive causal analytics to build a model that can predict future occurrences. This is useful for companies that are regularly doing credit sales or lending money. They can use this model to analyze a customer’s payment history to determine if they will pay installments on time. Organizations can use this for analyzing and predicting sales fluctuations during particular times of the year.
Prescriptive Analytics
Sometimes it is not enough to predict what will happen in the future. One must also be able to get suggestions for actions to be taken. This model must also be dynamically able to change its own decisions as situations change. It is here that prescriptive analytics becomes essential. A classic example of this is the self-driven car. It continuously analyzes data about road and traffic conditions and keeps taking action on that basis. One can bring intelligence to the data by running certain algorithms.
Machine Learning
When enough samples are available of a cause and effect, these can be fed to the computer. The machine will use such data to learn and use that intelligence to process similar data. If there is enough sample of fraudulent transactions, feeding these can help in predicting future frauds. Machine learning is also used for discovering patterns. The machine itself will learn the patterns and help in coming up with solutions.
Also Read: How to Build a Successful Career in Data Science?
Why is Data Science Important?
The natural question in anyone’s mind is why one should learn data science using Python instead of traditional analysis methods. Companies will undoubtedly want to know the answer for this as they would be spending a lot of money appointing data scientists and upgrading their computers accordingly.
A primary reason why data science is essential today is that, unlike in traditional systems, data is now available mainly in an unstructured form. Data is generated from text files, multimedia forms, sensors, instruments, and financial logs. Conventional BI tools cannot analyze this data. There are also vast volumes of data from various sources. This is the reason why there is a need for more advanced analytical tools and algorithms.
There are other reasons for using data science. The information on customers is very high in quantity. Analyzing such data will need modern tools. With this data, it can find out customer purchase behavior. These analytical tools can use various information like customer’s age, income, purchase history, and browsing history. Analyzing these will help companies to suggest products to customers more accurately.
Students of data science languages learn about how cars are using data science to become more intelligent. Though self-driven vehicles are still in experimental status, modern cars have many smart features to make driving safer. Cars use sensors to know the distance between them and the cars in front to slow down automatically if they get too close. This helps avoid accidents. Self-driven cars collect data from various sensors in their surroundings.
Weather forecasting is an area where predictive analytics is being used effectively. Data from various sources like ships, aircraft, radars, and satellites can be collected and analyzed. Building predictive models based on such analysis will give a more accurate forecast of the weather. It will also help in predicting other natural calamities and can save a lot of life and property.
Those interested in embarking on a data science career will undoubtedly want to know how businesses use it.
Also Read: Kickstart your Career in Analytics with Data Science Program
How do Businesses benefit from Data Science?
1. Making Smarter Decisions
Companies make intelligent decisions by using business intelligence. But traditional BI was static and descriptive. Data science is making this more dynamic. It is also helping to include more functional areas under business intelligence. The availability of a large amount of data has made it necessary for businesses to use data science to process and get valuable insights.
Decision-making involves the assessment and evaluation of various aspects. The process is completed in four steps using data science.
- Understanding the nature and complexity of the problems that a company must solve
- Assessing the data and knowing the quality and quantity that can be used for analysis
- Using the correct algorithms and tools for arriving at the best solution
- Using the correct method to convey the solutions to those who must make the decision
2. Improving Product Quality
Companies should be able to attract customers to their products. To achieve this, they must improve their products so that people would prefer them to be. So, how do they find a way to guarantee complete customer satisfaction? It is by collecting data from the customers. There are a large number of product reviews on the internet. Companies can collect this data and use data science to find what customers’ requirements are.
Such information doesn’t just help in improving products. Many companies also use data from the market and understand the latest trends. This allows them to formulate better marketing strategies and influence buyers in a better way. Advanced analytical tools can update companies about market trends helping them change tactics to attract customers. Airbnb uses this science to improve facilities in their accommodations to give a better experience to their customers.
3. Improve Efficiency
Companies generate a lot of data as part of their regular functioning. Such information can be a vital source of insights that will help in improving their efficiency. Data science can analyze the success rate of various marketing strategies. It will help companies know which of their methods have earned the best returns. Companies can scrap or optimize those that are not giving results as expected.
Data science can also analyze the performance of employees in the company. It is possible to find out which employees or departments are most efficient and contribute maximum to achieving company goals. Workforce analytics will also help firms to know what works best for their employees. Analytics also helps in understanding processes that can be speeded up. This science is also helpful in finding unnecessary expenses and cutting down costs.
4. Predicting Events
There are a lot of uncertainties in business. This could be in financial matters, production expenses, sales fluctuations, etc. All these will affect a company’s work and can prevent it from achieving goals. Statistical analysis of historical data using machine learning tools can help companies predict many events and take the necessary steps to face such situations. Using such methods, these organizations can get an edge over others.
Risk management can be more effective using predictive analytics. Using past data and patterns, companies can know the risks that lay ahead. They can predict a decline in sales using this method. Fraud detection is one of the primary uses of predictive analytics. Companies can look at history and see if there is a likelihood of any fraudulent transactions. Predictive analytics can help know weather conditions that will allow companies to plan their shipments accordingly.
5. Automating Recruitments
Recruitment can be a highly time-consuming and laborious process. This is especially true for big companies that attract numerous candidates for a single post. Scanning the resumes and eliminating the non-eligible candidates themselves can take a long time. When there are so many applicants, the manual process of selecting the right person may not be accurate. This is where data science can contribute to improving this system.
Companies can automate processes like resume scanning using data science. In this way, the HR managers can avoid those resumes that are not suitable and find the most suitable candidate. Analytics is also helping companies to find the latest trend in the job market and attract the best talents. Analyzing market data also helps to know where suitable candidates are available and the best way to attract them.
6. Recommendations And Personalization
It is not just in improving product quality that data science helps. It also helps in suggesting the best products to customers. E-commerce companies have been using this for a long time. Earlier it was only possible to group customers according to broad parameters like age, sex, or location. With data science and machine learning, it is possible to narrow down the categories. Analyzing customer behavior makes it possible to recommend products to individual customers based on their purchase patterns.
Nothing can please a customer more than receiving a personalized marketing message. This could be an SMS or an email. Studying a customer’s purchase and browsing behavior, companies cannot send them highly customized messages. Such communication helps to improve customer engagement and keeps them loyal to the company. Many industries like online music companies, finance firms, healthcare organizations, and educational institutions use this method.
7. Classification Of Documents
Companies generate various kinds of documents. While they may be easy to classify as per the type of documents like contract or invoice, it is not possible to do further classification. The unstructured data category includes documents, emails, images, videos, audio files, and text. Using traditional methods, it was impossible to classify such information. Without classification, companies cannot use the information in them.
Data science and deep learning technologies are allowing companies to classify even unstructured data. Once such information is classified, it can be used for various purposes. Companies can mine such data and arrive at valuable insights. These help them make better decisions for the future. Healthcare agencies can classify data and find out some common causes for specific ailments.
8. Sentiment Analysis
Data science is allowing companies and other agencies to analyze the sentiments and behavior of people. This helps to understand the behavior and predict events well in advance. Customers keep changing their preferences as new products arrive. Their needs also change as they know the problems and issues with the items they are using. Through sentiment and behavior analysis tools, companies know if customers are satisfied with a product and what improvements they will like to have.
Law enforcement agencies are also able to predict violent eruptions and agitations by following people on their social media platforms. These agencies use analytics to go through social media posts and identify people likely to lead disruptive agitations. This analysis will also help to sense the mood against political leaders and parties. Companies can use them to assess the reaction to changes they have made to their products or other aspects.
9. Customer Communication
One of the things customers today are looking for is an immediate response to their queries. Whether they have already purchased a product or are considering it, they want to have as much information about it as possible. This information must be available to them at their convenient times. In many cases, visitors to websites have left halfway as they could not find the answers to their questions. Data science is making this happen now.
Chatbots are becoming a regular feature on many websites. These automatic assistants can answer a variety of questions from customers. They can converse like humans. They are also able to understand questions asked in a variety of ways. These bots have been trained using large amounts of data and machine learning to understand what someone asks and give suitable answers. Such systems are facilitating customer communication at any time of the day or night.
Also Read: Prosperous Data Science Careers in the Digital Age
Understanding The Data Science Life Cycle
All data science courses online will teach about its life cycle. Though the names may change in different organizations, it is broadly the same format followed everywhere. Understanding the life cycle is important because the process cannot be started or completed without defining it. These steps must be followed as it is to achieve the desired results. The problems addressed by the analysis and the data collected can be different in various firms. This is why a predefined flow is very useful to get complete clarity.
1. Defining The Problem
Every data science project is taken up to solve a specific problem. So, the first step will be to define clearly what problems must be solved with this project. For some companies, it could be to find the customer churn rate, and for others, it could be to know product acceptability. Only if the exact issue is known can the data scientist start collecting information. To complete this step successfully, it is better that the data scientist understands the business well. Only with this can the expert understand the exact problem.
2. Gathering Data
Attending an XLRI data science course can teach students that this is a critical step because only by collecting the right data can one expect to get the desired outcome. There can be various sources for the data. Internal sources like company customer records can be used. Invoices may be useful to find purchase patterns. External sources indicating market trends must also be collected. Depending on the problem, the relevant data must be collected. Analysts collect data through web scraping using Python or extracting data using third-party APIs.
3. Preparing The Data
This is the most important and time-consuming step in the data science life cycle. The collected data must be cleaned and prepared for analysis. There will be various elements that are not needed for the analysis. They must be removed. The relevant data is selected and combined by mixing datasets. In this step, missing data is identified. It is either removed, or relevant portions are added. Data scientists deal with outliers at this stage. Feature engineering helps here by creating new data or extracting new features from existing data.
4. Data Modeling
Another crucial step in the process, it is here that the cleaned and prepared data is input to get the desired result. An appropriate model will be selected depending on the required output. The experts will select the most suitable machine learning algorithm for the expected results. One must tune the hyperparameters and ensure that precise results are obtained. Once this is completed, you must test the model to see if it is possible to get the required result. The accuracy and relevance of the outcome are examined.
5. Deploying The Model
Once you are convinced that you have selected the right model, it is time to deploy it. It is crucial to select the right channel and format. Deploying the model is the final step in the data science life cycle. One must be very careful at each step before deploying the model. Any small mistake in one of the steps will give you inaccurate results. Deployment could be getting the model output on Tableau or scaling it to cloud storage for others to use.
6. Communicate Results
Companies undertake data science projects to get a specific outcome. Once data experts have deployed the project, it is time to see how the results are turning up. It is not enough to get the results, but they must be communicated to the stakeholders. It is very important that data science experts possess good communication skills so that the results can be conveyed in a language that is easily understood by everyone.
We have seen in detail how data science works and why it is beneficial for companies to undertake such projects. Those who aspire to become data scientists must ace the interview to get a good job. But before that, it is important to know what a data scientist does and what are the job opportunities available for this profession.
Also Read: Roles & Responsibilities of a Data Scientist
Who is A Data Scientist?
There is a huge amount of data that is generated by companies. There are also other data available in various other organizations. Till now, there was no way to use this data easily to find insights. With the development of new technologies like machine learning, it is possible to easily use large volumes of information and get useful insights. These can be used for improving efficiency, reducing costs, increasing sales, and a lot of other outcomes. To complete this process, one needs data scientists.
These experts process the available data with data science using Python and find out various patterns that can point to how and why certain events occurred. Such information is vital for companies to mitigate risks, understand customer behaviors and find out why employees leave their companies. Data scientists use their technical knowledge, along with an understanding of the company and industry they work in, to find solutions to specific problems.
What is The Role Of A Data Scientist?
The intention of this article is to give tips on coming out successfully in a data science interview. While there are other job titles for one who has specialized in data science, the most common one is data scientist. So, what do these professionals do? What are the person’s roles and responsibilities in an organization? The role of a data scientist is to gather and analyze data to solve specific problems. They must first understand what problems need to be solved. For this, these experts must possess some company and industry knowledge.
Data must be gathered, cleaned, and stored. The decision on what data must be collected will be made by the data scientists. But the collection and cleaning are done by business analysts if such a post exists in the company. In smaller companies, the data scientist will accomplish this job. The next job is to select an appropriate model and apply suitable algorithms. The process is repeated with optimization each time. This is done until the desired results are obtained. The results are then conveyed to the stakeholders using a language that can be understood by all.
Data scientist is a general term used to refer to anyone proficient in data science. The designations offered by companies may vary, so will the nature of the job though using the same skill sets. Let us see what are the most lucrative data science jobs in India for someone who is an expert in data science languages and techniques.
Also Read: How much does a Data Scientist Earn?
Data Analysts
This has been one of the most common and preferred data science jobs in India. These professionals collect, use, and organize data so that they can be used for analytics. They also assist higher executives in extracting valuable insights from the analysis. Data analysts must be good at A/B testing and tracking web analytics. An entry-level position in this job will require a degree with a special focus on math or statistics. Many recruiters like these candidates to possess skills like SQL, Python, Oracle, etc.
Data Scientists
This is a more technical position than data analysts. These professionals perform data cleaning, organizing, and making it ready for use. They extract highly valuable information from the data. They can discover patterns and trends from large data sets. These can be used for understanding the reasons behind various events in the past. Apart from having a good background in IT, CS and math, these specialists must also possess hands-on experience. Most companies look for those specialized in certain industries like healthcare, retail, etc.
Machine Learning Engineer
The main responsibility is to create data funnels and develop software solutions. A machine learning engineer will also need to run tests and experiments to monitor the performance and functionality of the system. Companies will hire someone with strong skills in programming and statistics. One can get practical knowledge for this job by attending online courses that provide a facility to implement projects. Such courses are available from ed-tech firms like Talentedge. They provide certifications with integrated industry mentoring.
Machine Learning Scientist
A higher position than ML engineers, these experts are hired by organizations for experimenting with new approaches. They will try new algorithms and both supervised and unsupervised learning methods. They are also given the designations of a research scientist or research engineer in some companies. Someone with an engineering degree and postgraduate certification is preferred for this job. The person must also have a wide research experience. People in this position usually work in the R&D departments.
Applications Architects
An applications architect must supervise the operation of applications in a firm and see how users are interacting with them. They must build the architecture for applications, including creating the user interface and app infrastructure. A person would need a degree in computer science along with a certification in programming and architectural designs. There is an increased demand for people to fill this position. This has made it one of the highest-paid jobs in India.
Data Architects
Another high-paying job around the world, these experts create new database systems. They use performance and design analytics to create a better-interconnected data ecosystem in an organization. The aim behind their work is to make data easily accessible to data scientists for their work. This job needs the person to be a computer engineer. Additionally, they will also need to have an excellent command of statistical and mathematical concepts. Companies prefer people who have learned data management, programming, big data development, system analysis, and technology architecture.
Enterprise Architects
A company’s strategy must be aligned with its technological solutions, and this is what an enterprise architect achieves. This person understands an organization’s goals and designs a system architecture to meet specific requirements. These professionals must ideally possess a master’s certification. Those who have undergone field instruction in enterprise architecture can enter the profession. There is a high demand for such people in India presently.
Infrastructure Architect
A company cannot adopt a new technology overnight. It needs to build the necessary capacity for the same. An infrastructure architect will check whether a company has the infrastructure to support the latest technologies. If this is not so, then they must create such a setup. Many companies also hire cloud infrastructure architects for their cloud strategies. A degree in software development or computer engineering is necessary for this job. They must also be trained in database administration, information system development, and system maintenance.
Statisticians
Their job is to collect, analyze and interpret the data. This will help the decision-makers to do their work in a more efficient manner. They must find the relationship between datasets and trends. It is also their job to communicate their findings to stakeholders in a way in which it is easy for them to understand. They will give their inputs in formulating operating strategies. A bachelor’s degree is sufficient to enter the profession. But to progress further, one must have a postgraduate diploma in mathematics, CS, or economics.
Business Intelligence Analyst
As the name suggests, they must have a good knowledge of the business. They formulate strategies for a business using available data. They also ensure that the information provided can be used with ease by everyone. They help others use the BI tools and applications developed by them with ease. These professionals must understand basic concepts of business and management apart from having technical expertise.
One can know from the above that there is excellent job potential for someone specialized in data science. But having a certificate from even the best data science courses online cannot guarantee you the job you want. One must be able to beat the competition at the interviews. Here are six resources that will help you breeze past others at the interview.
Also Read: Data Science Engineer Vs Machine Learning Engineer
Brush Up Your Knowledge And Succeed
1. Programming
Not all aspects of data science require the knowledge of coding. But programming is essential for data science applications. Its applications are available in various fields. This means that different languages can be used. Whether one is proficient in R, Python, Matlab, or Golang, it is necessary to be strong in coding if you want to enter a data science job. But in most interviews, companies allow candidates to use a program of their choice to solve interview questions.
2. Maths & Statistics
There cannot be data science without maths and statistics. You will need to use probability theory and linear algebra to work with data. You need them for finding patterns, trends, and building algorithms. You can certainly expect questions on these two subjects in a data science interview. The complexity of the questions will vary with the post one is applying for. It is better to refresh the knowledge of maths and statistics before attending an interview.
3. Data Handling
There cannot be data science without data handling. One will learn all about it in the XLRI data science course. Once the coding and maths questions are done, candidates will be asked to solve data handling problems. One will need to use Python, R, and SQL for cleaning and analyzing data. It is best to use some online sources for practicing SQL and writing queries. Whatever other skills you may possess, but this is the core of a data science job.
4. Machine Learning And Algorithms
Machine learning is another important aspect of data science. One may not necessarily be writing ML algorithms daily. But all data science candidates are expected to be familiar with such algorithms. They must be familiar with basic machine learning algorithms. You may be asked to suggest some algorithms for a specific dataset or to solve a problem. It is necessary that one doesn’t fumble at that time.
5. Project Validation
Everyone who uses data science with Python knows that no project is complete unless it can be tested and ensured that everything is working perfectly. Once someone has trained and developed a model, it is necessary to test it before deploying. If the model doesn’t produce results as expected, it is a waste of money and resources. Though this may not be an exercise at the interview, it is better to be ready for any questions on this. There are many online A/B testing question samples on the internet.
6. Knowing The Data Science Project Steps
Once specific questions are over, the interviewer will ask candidates general questions about data science projects and the procedure. One may be asked to go through all the steps of a data science project to solve a real-life problem. They will do this to test your ability to use the fundamental aspects and build a complete project. It is better to be thorough with the process and be able to answer any general question.
General Tips To Ace The Data Science Interview
1. Keep Updated
It is essential to remain updated with the latest developments and trends in this field. Keep looking for new data science languages or other such innovations. It is best to keep abreast of these developments and try to acquire as much knowledge as possible. The lessons you learned in college may be outdated. This is why all data science experts must continuously keep in touch with the news. One must attend conferences where experts will be talking about a lot of new stuff.
2. Advertise Yourself
There is nothing gained by being quiet. Advertising yourself is a good way to land a good job. You must make sure to mention all your transferable skills in your resume. Be aggressive with job consultants and bid only for the best jobs. Even if one is not adequately qualified for the job, one must apply for it. If there are jobs that you don’t feel are good enough, say so. Tell recruiters you are overqualified for the job. It will work eventually.
3. Focus On End-User Benefit
Companies are looking to solve problems. They don’t care if a model is perfect if it doesn’t solve their problems. Candidates must focus on the end-user benefit. When talking about a project, tell interviewers about the benefits it gave to the one who used it. It is not just about the accuracy of models and the cleanliness of data. It is all about how people will feel when they see the outcome.
4. Convey Your Strengths
Not every data science expert works on the same projects. They specialize in different areas. One must identify their strong areas and convey this during the interview. Candidates must be able to tell the hiring manager what benefits they will bring to the team. They must also say which roles they are best suited for. Whatever it is that a person excels in, it must be conveyed properly.
Wrapping Up
Knowing what to expect and reading the tips to ace a data science interview is all good. But ultimately, it is your expertise in the subject and your presence of mind to answer positively that will bring success.
More Information:
How to Become an Ethical Data Scientist in India?
Data Science Engineer Vs Machine Learning Engineer
Benefits and scope of data science and data analytics courses in india
Executive Post Graduate Programme In Data Science from IIIT BANGALORE/UPGRAD
Master Of Science In Computer Science from LIVERPOOL BUSINESS SCHOOL/UPGRAD
