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Big Data in the current world of business is gaining a lot of buzz as it describes the large volume of both structured and unstructured data that is essential for any company to drive success. Big data is typically larger, more complex datasets, particularly from new data sources. These are so abundant in volume and impossible to manage by traditional data processing software. However, this massive amount of data today can be leveraged to address business issues, that wouldn’t have been able to tackle before. Thus, here are the reasons why should businesses need to hire Big Data talent in 2023.  

Improving Cybersecurity

The recent data breaches across the globe are a major concern for companies to thwart themselves from these threats. And keeping sensitive business data secured against malware and hacking is one of the biggest challenges for modern businesses. Fortunately, big data here comes into play, making cybersecurity better. It can store a huge volume of data and assist analysts to scrutinize, observe, and identify anomalies within a network. The tools of big data analytics can be utilized to spot cybersecurity threats, including malware or ransomware attacks, compromised and weak devices, and malicious insider programs. With these capabilities, big data analytics seems the most promising approach to improve cybersecurity.  

Data Literacy  

In the modern business landscape, almost every business is dealing with an enormous amount of data and managing it is a crucial task, indicating a critical skills gap. However, many organizations now are thinking of data literacy as a spectrum of related skills. And nourishing data literacy skills in each employee will be well worth for them. In a recent study, over half of potential employers, with 59 percent, ranked job experience or an interview requiring a candidate to demonstrate their abilities as top indicators of a person’s data literacy. Conversely, just 34 percent of companies provide data literacy training to their current employees, reports found.  

Spurring Better Decision Making

As businesses have lots of operations to perform and drive success, they need to take well managed and analyzed information to make informed and managed decisions. Earlier, organizations were lacking such methods, but the evolution of big data has provided them the direction and analyzed information that can be used to make better decisions. Big Data also assists enterprises in decision making for better and enhanced customer engagement through real-time data; increased efficiency of business operations; and no extra investment with enhanced capacity.  

Solving Business Issues

Analysis of data is typically essential to pinpoint what is happening in an organization, and what’s going wrong and why. For instance, UPS, the leading package shipping company, has deployed big data to not just boost profits, but to become more efficient and environmentally friendly. The company spends a staggering US$1 billion each year on the technology. Considering reports, by leveraging the On-Road Integrated Optimization and Navigation, or Orion, system, UPS is able to craft optimal routes that lessen distance, time, and fuel.

Big Data in the current world of business is gaining a lot of buzz as it describes the large volume of both structured and unstructured data that is essential for any company to drive success. Big data is typically larger, more complex datasets, particularly from new data sources. These are so abundant in volume and impossible to manage by traditional data processing software. However, this massive amount of data today can be leveraged to address business issues, that wouldn’t have been able to tackle before. Thus, here are the reasons why should businesses need to hire Big Data talent in chúng tôi recent data breaches across the globe are a major concern for companies to thwart themselves from these threats. And keeping sensitive business data secured against malware and hacking is one of the biggest challenges for modern businesses. Fortunately, big data here comes into play, making cybersecurity better. It can store a huge volume of data and assist analysts to scrutinize, observe, and identify anomalies within a network. The tools of big data analytics can be utilized to spot cybersecurity threats, including malware or ransomware attacks, compromised and weak devices, and malicious insider programs. With these capabilities, big data analytics seems the most promising approach to improve chúng tôi the modern business landscape, almost every business is dealing with an enormous amount of data and managing it is a crucial task, indicating a critical skills gap. However, many organizations now are thinking of data literacy as a spectrum of related skills. And nourishing data literacy skills in each employee will be well worth for them. In a recent study, over half of potential employers, with 59 percent, ranked job experience or an interview requiring a candidate to demonstrate their abilities as top indicators of a person’s data literacy. Conversely, just 34 percent of companies provide data literacy training to their current employees, reports chúng tôi businesses have lots of operations to perform and drive success, they need to take well managed and analyzed information to make informed and managed decisions. Earlier, organizations were lacking such methods, but the evolution of big data has provided them the direction and analyzed information that can be used to make better decisions. Big Data also assists enterprises in decision making for better and enhanced customer engagement through real-time data; increased efficiency of business operations; and no extra investment with enhanced capacity.Analysis of data is typically essential to pinpoint what is happening in an organization, and what’s going wrong and why. For instance, UPS, the leading package shipping company, has deployed big data to not just boost profits, but to become more efficient and environmentally friendly. The company spends a staggering US$1 billion each year on the technology. Considering reports, by leveraging the On-Road Integrated Optimization and Navigation, or Orion, system, UPS is able to craft optimal routes that lessen distance, time, and fuel. So, as the number of people trained to assess data will not grow according to the demand companies raise, this will create a challenge for them to hire experts. And as more and more companies become aware of the benefits of gleaning and analyzing data, demand for big data expertise will continue to rise.

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Why Businesses Should Invest In Employee Training

Employee training is beneficial for business.

Training attracts good employees and weeds out the bad.

Tips with employee training.

In many organizations, human resources are labeled as the most valuable asset. Yet, you’ll occasionally hear a few snickers from skeptics.

Opponents of the employees-as-company-assets mindset believe that more than a label, managers need to treat employees in more meaningful ways to really show value for the gem they have in their hands. This is why managers need to look after their employees’ training and development not only to increase the latter’s productivity at work but also to engage them in ways that acknowledge them as more than just a number in the organization.

Here are five reasons why investing in employee training programs is good for your business. Most HR software providers offer employee training, or integreations with training companies.

1. It helps attract and retain great talent.

By training your staff through employee development programs, you might overcome major hurdles in the hiring process and employee retention that many organizations experience. For one, employees regard training initiatives as one way of improving their craft based on the premise that such programs are educational in nature.

By sponsoring sales training programs, for instance, your salespeople can gain new or additional insights to improve their selling skills as well as develop their self-confidence and positive attitude at work. Needless to say, your reputation as a good employer that cares about your employees’ professional development will also be enhanced. As you help your employees further their skills and achieve personal growth, you create a goldmine of talent, which can give your business a market edge over competitors.

2. It can identify which employees are worthy of promotion.

Trained employees can form your pool of candidates for possible future promotion as they have developed certain levels of competence over time. With this pool, you need not look further for qualified candidates for managerial or executive posts in your organization, which, incidentally, are best given to someone promoted from within the organization.

As these employees are highly familiar with the business operations and organizational structure, you can be certain that they understand and are capable of complementing the goals of the company.

3. It can increase employee engagement.

Training your employees is a great way to take their minds off of their usual work for a short period of time. Employees who are not given opportunities to participate in other productive activities, such as those offered in training courses, are prone to become less motivated and happy at work.

Your employees are also likely to be more loyal to your company, seeing that you are willing to expend resources for their attendance at conferences or enrollment in specialized courses. The bottom line is that your willingness to invest in your employees’ training and development will likewise inspire them to invest as much hard work as they can into your business.

4. It translates into savings for the company.

The most effective training programs are those that empower employees to become multiskilled, extending their skill set across various areas.

It then becomes easier for companies to tap employees with diversified skills in performing a variety of functions or in transitioning them to other related roles within the organization. In return, employees feel empowered having expanded roles and responsibilities in your organization.

5. It helps shape the future of your organization.

As you make employee training and development programs part of your organization, you will find the need to update your offerings continuously. You have to think ahead about how you should be designing or refining your training methods over the long term to make them more responsive to employees’ needs, interests and goals.

You also need to make sure that your organization is abreast with the current trends in the industry and make an assessment whether they warrant a change in your business culture or brand of customer service, in which case, a new training framework should also be put in place.

Employee training and development should be a shared responsibility among employees, managers and the organization. When planned and implemented correctly and consistently, the benefits gained can spur considerable growth at both individual and organizational levels.

Tips and ideas for employee training

Regardless of the industry, it is critical that newly hired employees be properly trained. Without proper training, there may be an increase in staff turnover, low productivity and job dissatisfaction. Employee training requires attention to detail, organization and paying attention to the trainee’s abilities. Some of the things you can do to ensure adequate training may include:

Clearly outlined training manuals. All trainees should be provided with a well-organized manual. The manual should include a preview of the contents and then be broken down into individual topics that can be easily digested. The structure should be in a clear and logical order, and the information provided needs to be step by step and summarized with brief key points.

Getting to know new employees. It’s important that you take the time to have a conversation with new employees – get to know the person. Ask about their interests and family; this will help to ease their nerves as well as encourage them to be a part of your team.

Giving employees time to catch up. Keep in mind that training means the person is new to your company, so regardless of their experience, new employees need time to get acquainted with your business. Add responsibilities gradually, continue to offer additional training, even after the training period is done, and don’t be too hard them when mistakes occur.

Getting feedback about the training process. For a training program to be successful, it must always be in development. Once trainees have completed their training program, ask them for feedback, including if they have suggestions on ways to improve the training program. Remember to ask if the training covered everything. If employees respond that it didn’t ask what was missed. Was the training program too slow? Too fast? Was the structure satisfactory?

Data Science Vs Big Data: Key Differences

Data Science vs BigData: The key difference is in areas of focus, data size, tools, technologies used, and applications

Data Science and Big data are two interrelated concepts that have gained significant importance in recent years. Data science vs Big data is a trending topic. In the data analytics field, both play a vital role in leveraging data for decision-making, innovation, and gaining a competitive edge in today’s data-driven world.

The growth trend in the data segment of the industry suggests that data science and Big data analytics are the future. Data Science and Big data are two related but distinct concepts in the data analytics field. Data Science focuses on the application of statistical and machine learning techniques to extract insights from data and solve complex problems. It encompasses data acquisition, cleaning, exploration, and interpretation. Whereas, Big data refers to large, complex datasets that exceed the capacity of traditional data processing methods. Applications are in real-time processing and analysis fields like fraud detection, sentiment analysis, internet traffic analysis, etc.

Let’s delve into the key differences between Data Science and Big Data: Key Concept and Characteristics

Data Science is a multidisciplinary field combining scientific methods, algorithms, and systems for extracting valuable insights from structured and unstructured data. It emphasizes the use of data as the primary resource for analysis, decision-making, etc. To do so, they employ statistical techniques and ML algorithms. These data analysis techniques aim to solve real-world problems.

Scope and Methodology

Data science includes statistical analysis, ML, data visualization, and exploratory data analysis. These are employed to understand the patterns of data, make predictions and solve problems.

In big data, large datasets are handled using technologies and infrastructure. It involves distributed storage and processing frameworks like Hadoop and Spark. To manage vast volumes and high velocities of data, it enables parallel processing, scalability, etc.

Objectives

The primary goal of data science is to gain insights, extract valuable knowledge, and solve complex problems using data.

The main objective of big data is to store, process and analyze massive volumes of data efficiently.

Applications

Data Science is extensively used in business intelligence to analyze customer behavior, market trends, and sales data. In healthcare, it plays a crucial role in analyzing patient data for diagnosing diseases and treatment outcome prediction. It also aids in clinical decision support, personalized medicine, and identifying patterns for disease outbreaks. Data science is utilized in financial institutions for fraud detection, risk modeling, algorithm trading, and making informed investment decisions. They are applied to analyze the human language that enables applications like chatbots, voice assistants, and machine translation.

Big data analyze customer preference, behavior, and purchasing patterns to improve product recommendation, inventory management, pricing strategies, and personalized marketing campaigns. It handles massive amounts of data generated by IoT devices such as wearables and sensors. These technologies are employed to analyze social media data including user interactions, sentiment analysis, and trending topics.

Advantages

Data science helps organizations to make informed decisions by extracting meaningful insights from data. This is done through statistical analysis, ML techniques, and data visualization techniques. The wide range of applications including in finance, healthcare, business, etc. Efficient data management and analysis in data science offer significant cost savings.

Data science requires skilled professionals in the field. Due to the need for preprocessing and data cleaning, this technique is time-consuming and needs more resources. Since it deals with sensitive data, ethical concerns may be a problem.

Big data need skill and expertise in the field. Security and privacy are a concern when handling sensitive data. It can sometimes be expensive due to the need for specialized infrastructure and software.

Tools

Data science uses tools like Apche Hadoop, DataRovit, Tableau, QlikView, Microsoft HD Insights, TensorFlow, Jupyter Notebooks to effectively handle and analyze huge data.

Should I Become A Data Scientist (Or A Business Analyst)?

Introduction

One of the common queries I come across repeatedly on several forums is “Should I become a data scientist (or an analyst)?” The query takes various forms and factors, but here is a common real-life anecdote:

“I have been doing Sales for multiple BFSI giants for last 3 years, but I have stopped enjoying my role. After reading about Business Analytics and machine learning, my interest in this area has grown. Should I make a switch and learn data science? If so, How do I do this?

When I reflect back on how I took the decision, I realized – I happened to be lucky! The decision was relatively easier for me. Why? I knew the industries/roles, I would not enjoy – these included roles in Sales, roles in Physical engineering, and a few others. I was open to roles in data science in retail banks and investment banks and luckily ended up with Capital One.

Today, after spending ~8 years in the industry, it is far easier for me to guide and mentor people on whether Analytics is the right role for them or not. So, I thought, I’ll try and put my thoughts in a framework and share it with the audience of this blog. The aim of this post is to help those people who are sitting on the fence and thinking about which job/role is right for them. So, if you are someone deliberating a move in data science or are wondering whether you are a right fit with this industry, here is a neat framework that might help.

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Framework

I have put a framework in the form of a very simple test. This test is based on the attributes every analyst should possess. You should score yourself against each of the questions (out of the score mentioned after the question) and then add your scores. A good analyst should score more than 70 and anyone scoring below 50 should seriously re-consider a decision to be a data scientist.

Test Questions:

Do you love number crunching and logical problem solving – i.e. puzzles, probabilities, and statistics? (score out of 20)

By love I don’t mean like, I don’t mean you don’t mind numbers – I mean, do you have an obsession with numbers! Do you love doing guess-estimates at any time of the day – I have done those estimates while I am taking a shower, while I am driving, while I am watching a movie, or even when I am swimming (and lost my count of laps)! I know my friend Tavish does these calculations in his mind too – while he is driving or while he is playing badminton. If you want me to space out of a discussion, just ask me a really hard logical problem!

Key:

5 – dread mathematics & statistics, but can face to some extent

10 – Comfortable with mathematics and statistics, but need calculators and excel to work on problems. Don’t mind attempting puzzles

15 – Love crunching numbers and solving logical puzzles anywhere

20 – Can’t live without number crunching and logical puzzles – an obsession!

Do you enjoy working/handling unstructured problems? (score out of 20)

An analyst will inevitably be tested against unstructured and amorphous business problems. And it is how you solve these unstructured problems, that decides how good or bad an analyst you are. My first project in my first role stated: “In last few months, we have seen a high increase in high-risk customers of type X. You need to come up with a data-based strategy to measure, control, and improve this situation.“

Even the business did not have a clear definition of these customers. Can you handle this kind of ambiguity and provide a direction on your own? Do you enjoy these situations or you would rather be comfortable in a more defined role?

Key:

5 – Have tried these problems in past – but not my cup of tea!

10 – A score of 10 would mean, you like solving these problems once in a while (say 3 – 6 months)

15+ – You prefer unstructured problems over-structured. You don’t enjoy someone else structuring problems for you.

Do you enjoy deep research and can spend hours slicing and dicing data? (score out of 20)

Going back to the first project I faced, it took me 3 months to understand the business, have multiple discussions with stakeholders, brings them on the same page, and then mine the data to bring out solutions. You need an outlook of a researcher to be a good business analyst. When was the last time you spent hours and hours immersed in solving a problem? Can you do that again and again?

Key:

5 – You want a change every few hours. You can’t work on a single problem for the entire day

10 – You can work on a research problem – but need some side work to help you out of boredom

15 – You feel the side work is distracting you from making progress on the key problem you are working on. Would be happy if they are taken away

20 – Can’t stand distractions

Do you enjoy building and presenting evidence-based stories? (score out of 20)

A data scientist needs to be a fluid presenter. What is the use of all the hard work, if he is not able to influence his stakeholders? Communicating with data and presenting stories backed by data is one of the most important elements in the life of a data scientist. Imagine being part of companies like Google and Amazon – you have all the data you need (probably more than that) for the domain you are working on, but you need to convert it into a meaningful story, present it to the stakeholders and influence them to take the right decision!

Key:

5 – You struggle to communicate my mathematical thoughts to the audience

10 – You can manage telling stories with a lot of practice. Can’t think of doing this on the fly!

15+ – Any time, anywhere!

Do you always find yourself questioning people’s assumptions and are always curious to know ‘Why”? (score out of 10)

This is probably the best part and the most fun part! Here is a quote a read somewhere on Linkedin: Arguing with an Engineer is a lot like wrestling in the mud with a pig: After a few hours, you realize the pig likes it. Similarly, asking why comes naturally to a good data scientist. Some of the best data scientists would stop anyone and ask for a rationale if they are not clear – Why did you ask this question? What was your thought process? Why do you assume so? are just a few examples of these questions!

Key:

5 – You only ask questions when they are critical to be asked

8+ – You can’t stand the anxiety of not understanding something! Jumping to ask questions!

Do you enjoy problem-solving and thrive on intellectual challenges? (score out of 10)

Analysts require a knack for problem-solving. Most of the problems businesses would face would be unique to them and it would take a smart solver to solve them. Solutions, which work for one organization may not work for another – you need to be someone who quickly develops a deep understanding of a problem and then come out with innovative ways to solve these problems

Key:

3 – You don’t mind thinking about solving problems – but you struggle.

6 – You can solve problems at times

9 / 10 – You just love the process of intellectual thinking

End Notes:

What is my score? I would score somewhere between 80 – 85 on this test. It is your turn now. Do take the test and let me know, how much do you score? Also, do let me know if you think the test was helpful or otherwise.

Did you like this framework? We at Analytics Vidhya follow an analytical approach to problem-solving. If you want to become a data scientist with this analytical mindset, check out the Certified AI & ML BlackBelt Plus Program which offers 100+ hours of live-course, 100+hours of self-paced video, 18+ real-life projects, and the most important – 1:1 mentorship. The course is carefully crafted by experts so that you can become an industry-ready professional! 

Now that you know that you can / can not become a data scientist, you might be asking “How do I become a data scientist?”. Here’s the Roadmap – 

Related

Mathworks: A Pioneer In Mathematical Computing For Disruptive Big Data Technologies

MathWorks is the leading developer of mathematical computing software MATLAB and Simulink. MATLAB, the language of technical computing, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design for multidomain dynamic and embedded systems.

Engineers and scientists worldwide rely on these product families to accelerate the pace of discovery, innovation, and development in automotive, aerospace, electronics, financial services, biotech-pharmaceutical, and other industries. MATLAB and Simulink are also fundamental teaching and research tools used in global universities and learning institutions. Founded in 1984, MathWorks employs more than 3500 people in 15 countries, with headquarters in Natick, Massachusetts, USA.

Driving Innovation with Real-Time Data and Insights

MATLAB Integrates Workflows

Major engineering and scientific challenges require broad coordination across teams to take ideas to implementation. Every handoff along the way adds errors and delays. MATLAB helps automate the entire path from research to production.

MATLAB Is Trusted

Engineers and scientists trust MATLAB to send a spacecraft to Pluto, match transplant patients with organ donors, or just compile a report for management. This trust is built on impeccable numerics stemming from the strong roots of MATLAB in the numerical analysis research community. A team of MathWorks engineers continuously verifies quality by running millions of tests on the MATLAB code base every day.

MATLAB Is Designed for Engineers and Scientists

Everything about MATLAB is designed specifically for engineers and scientists:

•  Function names and signatures are familiar and memorable.

•  The desktop environment is tuned for iterative engineering and scientific workflows.

•  Documentation is written for engineers and scientists, not computer scientists.

An Experienced Leader

Sunil Motwani is the Industry Director at MathWorks India office and manages sales for commercial customers in the country. He has been at MathWorks since 2008 from the time it started operations in India. Prior to joining MathWorks, Sunil worked at Hewlett Packard & Agilent Technologies managing sales of test instruments for various industry segments in India including Aerospace & Defense, Communications, Semiconductor, Automotive and Industrial Automation.

Sunil has over 25 years of experience in sales of technology products across various regions within India having been based at Mumbai, Delhi, Hyderabad & Bangalore during this time. He holds a Bachelor’s Degree in Electronics Engineering from Visvesvaraya National Institute of Technology (VNIT), Nagpur and a Post-Graduate Diploma in Software Technology from National Centre of Software Technology (NCST), Mumbai.

Sunil has been responsible for building market expansion strategies, partnerships, domain expertise and engineering capabilities across the MathWorks’ operations in India. He sees a huge potential emerging in the market for engineering data analytics that drives applications like predictive/prescriptive analytics, fleet analytics, autonomous systems (automated driving, UAVs, Robotics), IoT etc. “Data science platforms make it possible for the engineering teams that develop and maintain the equipment to leverage their wealth of knowledge about how the equipment should operate. This idea of empowering the engineers, or domain expert, is often more appealing than hiring data scientists who have little knowledge of how the equipment operates”, he says.

Addressing Data Science Challenges

Sunil feels the significant challenge for the company has been helping customers get data from the source into the hands of end users, which is a common barrier for engineers who need data to formulate requirements for new products, troubleshoot field problems, and come up with new technologies. With more and more streaming data, the industry is faced with a data science challenge.  We need to ensure that the speed of data analysis is keeping pace with data intake and, equally important, provide the capability to zoom into and extract insight from stored data throughout the engineering community.

To address this new challenge, one often looks for those who have computer science skills, knowledge of statistics, and domain expertise relevant to their specific engineering problems. However, domain expertise is often overlooked, yet it is essential for making judgment calls during the development of an analytic model. “Instead of searching for elusive data scientists, we’re now working with engineering teams by helping their engineers to do data science with a flexible tool environment like MATLAB, which enables engineers to become data scientists,” he added.

Future Industry Perspectives

Sunil foresees there are three key trends to track when it comes to growth in big data analytics, AI, machine learning and deep learning.

Impacts of predictive analytics systems on industries like manufacturing and medical devices

•  It’s well-known at this point that data analytics technologies can bring significant business benefits in areas such as predictive maintenance.  However, the system architecture for such applications remains an open question. Customers are hesitant to share their data with vendors, logging all of the data from a machine is often impossible given the volume of data created, and responses to events may be needed in milliseconds – much too short of a time to wait for a response from an Internet server.  All of these will drive innovation at “the edge”, or on the equipment itself.

•  Medical Devices: Predictive analytics systems will allow for more informed and personal relationships between patients and physicians and more effective diagnoses at point-of-care. It is quite possible that predictive analytics will also drive the progress of both preventative and therapeutic care with the data collected from wearables and shared on personal devices.

Machine Learning and Deep Learning

•  As it becomes easier and easier to apply machine learning techniques, more products and services will incorporate machine learning models. Embedded systems, typically used for controls and diagnostics, will incorporate machine learning models that can detect previously unobservable phenomena. In 2023, we’ll continue to see machine learning models being incorporated in new places, especially in edge nodes and embedded processors.

•  While deep learning continues to look promising, there is still a lot of design and tuning necessary to train a useful deep network. Techniques such as automated hyperparameter tuning appear well-positioned to reduce this work, which should ramp-up the pace of adoption of deep learning.

Domain Experts Take on Data Science

6 Key Points You Should Focus On For Your Next Data Science Interview

Overview

Preparing for your next data science interview? You need to ensure you’re covering your basics

Here are 6 key points we’ve taken from our data science interview experience that you should focus on

Introduction

You’ve finally done it! You have landed an interview for a data science role. Now, a day before your interview, you’re not sure what to study. The day is almost here but there is so much to cover!

Sound familiar?

Interviews can be daunting – I completely get that. Add in Data Science, and you’ve got yourself a nerve-racking cocktail. Data Science professionals need to combine their technical skills with their soft skills. It’s a tough landscape to navigate.

Landing the interview is great – but cracking it? That’s where things get really interesting. What should you study? What should you leave out? Is there any cheat code you can apply and simply plug and play it in during the interview?

If you are in a similar situation – you’ve come to the right place!

There is nothing quite like real-world hands-on industry experience. And trust me, that will hold you in high regard in your data science interview. If you’re looking for something similar, the Ascend Pro program is made for you!  It combines data science knowledge with practical industry experience by industry leaders and experts – a one-in-a-lifetime opportunity to prepare yourself for your dream data science role.

1. Be Thorough with your Data Science Resume

The absolute basics of any interview, and especially a data science one. You should be able to explain everything listed on your resume. Anything that you could possibly reference, you should be able to speak about it.

If you’ve listed an NLP project for example, and are unable to explain the details – that’s a MAJOR red flag for the interviewer.

Use the day before the interview to edit and revise your resume. Cut details that are not required and add new ones if required. Think about each experience and project that you list – does it add something relevant?

That means your experience with a marketing firm as a non-technical person might not be very relevant for a data science role. You should consider keeping details like that off your resume. Mentioning it will just give the interviewer a sense that you are not clear about what you want from the job.

Also, think of how you will go about explaining your work experience. Your account should depict your skills and how they led to progress. Consider the following statements:

“Used LSTM’s to predict the company’s stock prices.”

“Used LSTM’s to predict the company’s stock prices with 40% more accuracy than the historical average.”

Doesn’t the second statement sound way more impressive than the first?

Make sure to make your achievements are measurable and quantifiable. This will leave a better impression on the minds of your data science interviewer(s).

I recommend reading our guide to building an effective Data Science Resume. It mentions the 4 key aspects that will make or break your data science application.

2. Study up on your Data Science Projects

Much like the other details on your resume, deciding what projects to talk about in your interview is also crucial. If there are any projects irrelevant to the role you’ve applied to, adding it in anyway isn’t a great practice. This just shows your interviewer that you cannot prioritize well.

Shortlist 3 to 4 projects which showcase your best work and prepare yourself to talk about them. These projects could be from your current organization, internships, from some coursework or even independent projects using datasets from Analytics Vidhya or Kaggle. Also, keep in mind that these projects should be relevant to your job profile.

I keep reiterating this because it is THAT important.

Let me give you my own example. I had listed a research project on my resume which I had done two years back. In hindsight, I should have left it off since it had nothing relevant to the internship role I was interviewing for – a data analytics intern.

As I went on explaining what I did in this project, I made the mistake of mentioning the term ‘cubic splines’. The interviewer immediately wanted me to elaborate on cubic splines and I realized that I had dug myself into a hole. And no, I did not get the internship.

There’s a lesson there for all of you data science enthusiasts! If you are looking for projects, refer to our list of 24 Ultimate Data Science Projects to boost your Knowledge and Skills.

3. Practice Solving Puzzles – A Key Data Science Skill

Puzzles are a fairly popular way of evaluating a candidate’s quick thinking and analytical acumen. You need to be logical, creative and good with numbers to solve puzzles.

Many organizations use puzzles for testing their candidates on their problem-solving skills. They want to know about your thought process and how you approach a problem.

I cannot give you a complete guide to solving each puzzle, but I do have a few tips for you to proceed towards puzzle-solving:

Approach the problem slowly and understand all the details. Ask for any assumptions if they are not explicitly mentioned

These are meant to showcase your thought process. So make sure to walk your interviewer through your solution while you think

Do not stick with an approach for too long. Take cues from your interviewer and modify your approach accordingly

Realize that it is okay if you were not able to completely solve the puzzle. Different puzzles have different levels of difficulty and not all of them are meant to be solved in one sitting

Try solving the puzzles in our list of 20 Hard Data Science Interview Puzzles that every analyst should solve at least once.

4. Prepare to Face Case Studies for Data Science Roles

Organizations use case studies as a means of evaluating candidates on how they approach real-life problems. Case studies are the closest thing to the problems that you would be encountering in your role later on. I have seen freshers struggle the most with this part of the data science interview process.

The tricky aspect of a case study is that it might not be directly related to data science. For example, I got a case study around how to predict the number of black cars in Delhi NCR right now. It’s a tricky one – but if you have a structured mindset – you’ll knock it out of the park!

Approaching a case study can appear hard since there is no fixed formula to solve them. But you can use the below points to guide yourself through them:

Ask a lot of questions. Whatever questions pop in your head, ask away! It will help you uncover a lot of details that you will require for the solution

Structure the problem. This could be organizing all available data into a table. Structuring might unveil some hidden patterns in the data

Practice! Try case studies from different domains like retail, healthcare, business, etc. The more you practice, the easier a new problem will feel

Remember what is important is good brainstorming and a great discussion. The goal is not to reach a fixed or pre-defined solution, but rather to find a path to it and show your thought process

Have a look at some of the case studies on Analytics Vidhya (practice each of them and you’ll be interview-ready in a jiffy):

5. Research the Job Profile and the Organization

Researching the job profile has obvious benefits. You would be able to streamline your preparation based on what is required from the role.

Sometimes, employers may even ask candidates a question or use a keyword to make sure they read the job description carefully:

“What technologies do we work with?”

“What are you expecting from this role?”

“Can you tell us the latest project our data science team open-sourced?”

These questions will be dreadful if you didn’t read up on the company and the role.

I highly recommend spending some time reading about the company’s mission, vision and core values. Find out about their key achievements. Try and find the data science set up that they have and what kind of projects they work on. If possible, find out about the hierarchy of the organization and how the data science team fits into it.

Studying the organization and its structure will help you frame better questions for your interviewers. This shows your enthusiasm and curiosity towards the organization and leaves your interviewers impressed.

6. Review Confusing Data Science Terms

Are there any data science terms that have bamboozled you before? I’m sure there are a few – this is true for even experienced data scientists.

A few confusing terms or concepts that I encourage you to read up on a day before your interview:

Type I and Type II errors

Precision and Recall

False Positive Rate and True Negative Rate

Business metrics v STatistical metric

Model deployment

I frequently have to look up the difference between these terms and I am sure most of you do as well. These can stump you if asked in an interview. You know the answer, but the slight differences just aren’t coming to you.

Make sure to revise such terms a day before the interview. Refer to our glossary of common machine learning and data science terms for a quick idea around these concepts.

End Notes

Telephonic Screening

Assignments

On-site interview, which has several rounds like technical, case studies, puzzles, guesstimate, and more.

The ‘Ace Data Science Interviews‘ course covers all of these rounds in detail. The course also has a rich collection of Interview Questions along with many helpful tips and tricks. This could significantly increase your chances of acing your next Data Science Interview. So make sure to check it out!

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