**The Importance of Statistics and Machine Learning in Interviews**

Statistics is a crucial aspect of many fields, including data science and machine learning. In interviews, statistics plays a significant role in evaluating a candidate's understanding and application of statistical concepts. A strong grasp of statistics can make or break an interview, as it demonstrates a candidate's ability to analyze complex data and draw meaningful conclusions.

In the author's experience, they noticed that statistics is a very strong aspect of many interviews. The author recalls attending four to five interviews where statistics was a significant focus. In each of these interviews, the interviewer asked scenario-based questions that required the candidate to demonstrate their understanding of statistical concepts and techniques. These questions were not just basic random variables but also covered more advanced topics such as feature engineering and algorithm selection.

The author notes that having a strong foundation in statistics can give an individual a competitive edge in job markets. In today's data-driven world, companies are looking for individuals who can analyze complex data and draw meaningful insights. A strong grasp of statistics can help candidates stand out from the competition and increase their chances of landing a job.

**The Importance of Machine Learning**

Machine learning is another critical aspect of many fields, including data science. In interviews, machine learning plays a significant role in evaluating a candidate's understanding and application of machine learning concepts. A strong grasp of machine learning can demonstrate a candidate's ability to develop predictive models and analyze complex data sets.

In the author's experience, they noticed that machine learning was not as prominent in their previous interviews as statistics. However, when they were introduced to a project involving image classification, their interest in machine learning increased. They began exploring different algorithms and techniques for image classification, including BGG16. This experience highlighted the importance of machine learning in real-world applications.

**Advice for Aspiring Data Scientists**

For individuals looking to transition into careers in data science and machine learning, the author offers several pieces of advice. Firstly, they emphasize the importance of starting early. The author notes that it took them two years to gain sufficient knowledge and experience to pursue a career in data science. This highlights the need for patience and persistence when pursuing a career in this field.

The author also suggests that individuals should focus on acquiring end-to-end understanding of the data science pipeline, from acquiring data to deploying a model. Companies are looking for candidates who can demonstrate this complete understanding, and having two or three projects under one's belt can be sufficient to showcase this ability.

**The Value of Experience**

Experience is essential in any field, including data science. The author notes that their experience as a technical support engineer helped them gain valuable skills that they later applied to data science. This highlights the importance of taking on new challenges and gaining experience in different areas.

In the author's experience, having seven years of experience has been beneficial in navigating the field of data science. However, they note that it took time and effort to gain this level of expertise. They emphasize the need for continuous learning and professional development in order to stay up-to-date with the latest developments in the field.

**The Role of Deep Learning**

Deep learning is a subset of machine learning that involves using neural networks to analyze complex data sets. In interviews, deep learning often comes under scrutiny, as it requires a strong understanding of mathematical concepts and algorithms.

The author notes that their experience with image classification projects highlighted the importance of deep learning in real-world applications. However, they also note that deep learning is not always the best approach for every problem. It's essential to have a deep understanding of different algorithms and techniques before selecting one for a project.

**Suggestions for Aspiring Data Scientists**

For individuals looking to transition into careers in data science and machine learning, the author offers several suggestions. Firstly, they emphasize the importance of acquiring knowledge in statistics and machine learning. These subjects are critical components of any data scientist's toolkit.

The author suggests that individuals should focus on building a strong foundation in statistical concepts and techniques. This includes understanding random variables, feature engineering, and algorithm selection. Additionally, having experience with machine learning algorithms and deep learning techniques can be beneficial in landing a job.

**Conclusion**

In conclusion, statistics and machine learning are critical aspects of many fields, including data science. A strong grasp of these subjects can demonstrate an individual's ability to analyze complex data and draw meaningful conclusions. The author offers several pieces of advice for individuals looking to transition into careers in data science and machine learning, including the importance of acquiring knowledge in statistics and machine learning, building a strong foundation in statistical concepts and techniques, and gaining experience through projects and real-world applications.

**Interview Experience**

The author's interview experiences highlighted the significance of statistics and machine learning in evaluating candidates. In their previous interviews, statistics was a prominent focus, with scenario-based questions that required candidates to demonstrate their understanding of statistical concepts and techniques. However, when they were introduced to a project involving image classification, their interest in machine learning increased.

In this interview, the author notes that there were no deep learning-focused questions, but rather a focus on data science and ML. This highlights the importance of being prepared for different types of interviews and having a broad understanding of statistical concepts and techniques.

**The Importance of Continuous Learning**

Continuous learning is essential in any field, including data science. The author notes that their experience as a technical support engineer helped them gain valuable skills that they later applied to data science. This highlights the importance of taking on new challenges and gaining experience in different areas.

In the author's experience, having seven years of experience has been beneficial in navigating the field of data science. However, they note that it took time and effort to gain this level of expertise. They emphasize the need for continuous learning and professional development in order to stay up-to-date with the latest developments in the field.

"WEBVTTKind: captionsLanguage: enhi friends uh today we have saurabh sinha one of our learners and students who have successfully transitioned to a career in data science just to give you some background saurabh graduated with mca in 2013 and he has close to seven years of experience at siemens and sarnar corporation most recently at cerner corporation he has spent about just over five years and most recently he has been a senior technical support engineer and uh he is now joining he's now transitioning from that role to an assistant manager in data science business unit of concentrix so concentrix is a publicly traded business services company based out of fremont in california and again the most important aspect here is the successful transition of saurabh from uh from a senior again he has seven years of experience which is which is considerable and he's moving from an experienced senior technical support engineer role to an assistant manager in a data science business unit and the transition is what we want to focus a lot on first of all congratulations saurabh on behalf of all of our team i've been through your first case study i've seen i've seen the journey of the effort you've put in the struggle that you've put in and that's that's a hats off to that whole effort and most importantly that persistence let's let's go into it so the first question that i had was many people with your sort of background might be little little afraid to make a transition from their role to a data science role right so again we have some students very similar to you who have this question in their mind that hey i am in a technical support engineering role can i really make a transition to data science rule so could you walk us through your learning journey on how you made the most of the curriculum assignments mentorship all of that in making this successful transition sure first first of all i would like to thank you like thanks overall applied ai team because whatever like what the kind of support which i'm getting from applied it like if i'm getting stuck in any assignment like i'm continuously like keep on asking questions and they are like yeah okay you can do like this even in case studied i was getting some suggestion and these are the best thing like these are the building blocks like i developed my concept uh because of these changes like i was aware like whatever the techniques that i have to do but they have told me no you have to do like this there is other techniques also so i have applied so that is the main thing from where i learned a lot on like for these machine learning things one of the associate he he was the data analyst in my company at that point doctor he suggested me about the applied ai course and at that time i was again confused because i thought now i will put my whole effort in order to learn all these techniques but i was like in market lots of other courses were there but i thought okay i will check with uh like every opinion like who is currently working in that particular domain so one of my friend he suggested me okay apply the ai like just go through the videos that there are lots of online free videos of applied uh ai and just go through those video and then decide whether you you should go for this course i went through the video which like from the initial video where you discuss just the basic thing like what are the data science life cycle and other roles like lots of thing was there so i i immediately like i started my journey in 2018 uh uh to learn all these machine learning loads but later on in 2019 in the month of june or july i've joined applied ai course i came from support background so there were no coding experience and after understanding the algorithm i'm trying to implement those algorithms so that was one of the best thing which was happened at that point of time and i was able to make a code i am able to execute it and in case i am getting any uh kind of confusion like i was keep on like i have asked a number like lots of questions with the applied that's perfectly okay we are happy here to help you and we see that you put in so much effort right and whenever you are stuck it's also our obligation to help you because you're putting in the effort that's the most important part yeah and uh it was like every time i'm getting like within 24 like whatever you told like in your video within 24 hours you will get the answer so every time with sometime within an hour i'm getting answer from them like what type of confusion i was having so i started and parallely i was doing some uh assignments so uh in first six months uh for me it was like one or two month of break also because i stopped because of some personal work i've stopped at that particular time but again i started from 2020 january because i completed overall course content which was mandatory at that point till december i was completed all the course content but uh later on like lots of additional videos on lots of the state of the art techniques so those thing i left at that particular of time time and later i was just going through all those video which was like time series and according to my assignment also like if i need those kind of concept while doing my assignments so then i was covering those things cool so in first six months it was like i have done with three assignments and three or four assignment and course content was almost i covered the neutral network also in first six months but later on january february uh i started like with the other assignment i completed rest of the 15 but like my first name was to complete first 15 assignment so that i will get like i will work with apply the item in some kind of self-case study and real those would be some real kind of case study so i i was like trying to do like okay first i have to complete my first 15 assign i went then i will get chance to work on some real case study and meanwhile what i was doing in my company because i was having good number of experience also so i was keep on in touch with the data analysts and data scientists in my company very good and like i was trying to understand like what type of problem they are trying to solve so there was one problem which they were trying to solve which was running because of our team so i was most familiar to those kind of business problem because those business problem but like they are trying to solve but it was internal project and i was involved like okay sharing ideas what actually is happening so i was like once or twice i had conversation meeting with those business analysts and the data analyst team and trying to convey like how actually we are working so uh at that time i was getting the idea on that project also that was trying to you're trying to understand the concepts you learned applied a course in the context of problems that your teams or the teams that you knew in data science were also working in that way you get a much more real world context of everything that you're learning yes and whatever like in your each and every case study you are like like this is the business problem and how we are have to convert it into the machine learning problem so those understanding like i was getting those kind of okay such teaches us like this so even i have to get some problem like which is currently because in my organization lots of data was there but the thing was that the people who are not thinking like okay where we can use like in our team because we we are from support teams so i was put on like my lead and everyone i was saying okay we can do like that and that was the thing like from where i started having meeting with data and listing because my lead was also supporting and we were having like a communication with the data analyst or the business analyst team just to get the idea how they are trying to cover so see this way i like even my business understand like how we are trying to solve the business problem those understanding also developed and at the same time with the help of applied ai team i'm learning all the algorithms and how these things are working and how we can convert those business problem to a machine learning problem so those understanding was because of this applied ai course only it's very important for somebody like you with about seven years of experience especially for experienced professionals right whatever whatever domain they're in whether they're in technical support or sales whatever domain it is it's very important for them to connect the concepts the new concepts that they're learning to the business problems that they or their teams encounter because that is that is the expected maturity from somebody with experience right right cool yes cool coming to your interview experience at uh uh at uh at concentrix could you walk us through how many rounds were there uh what what was the focus of each round how much depth was being expected how much rigor was being expected from from somebody like you as you move into a assistant manager for a data science business unit so i'm just trying to understand what was the expectation from the company in each of the interviews and what level of depth and breadth were they expecting from you first round was because everyone is working from home so first round was uh that python test so 25 questions were there so they were asked they asked me to solve those within the 30 minutes so i i gave like it completed within 30 minutes and i got selected in the first round after because i cleared that python code test uh like syntax was there and apart from python there were a few computer reason questions and in python code like it was object like how object is created and how if it would call classes like what is going to call like those kind of those sort of questions was there all these were multiple choice questions right yeah yeah that was coding questions because you can't quote 25 programs in 30 minutes right that is right but uh after that uh they called me hr called me hr team and they told me yeah you are clear with the first round and then uh this schedule second round for me in second round uh like it was about 30 to 45 minutes of round and at that in that round they started they wanted to know like all the recent projects which i worked on so i've told them a machine learning problem each and every like whatever from where you are getting the data after getting data what are the sort of thing missing value analysis what are the techniques what are different techniques for the missing value analysis and after that like lots of question in statistics also because statistic and i have like in my past four or five interview i noticed that statistics is a very strong like everyone should be very good in statistics also and that too whatever question like few scenario based question like whether they have understanding like where to apply which kind of a statistics technique and apart from that they are going to basic random variables like they wanted to know like whether i am clear with all the basic concepts also like those things mathematics yeah right and later on when they came to like their work few questions on feature engineering also and after that they comes to algorithm part and which algorithm you have used which algorithm like you are comfortable so ah it was random for us any questions more from a deep learning perspective for this specific role uh deep learning because i i told initially when i was introducing to project they told me okay please describe some reason to project at that time i told i recently have done image classification problem and where i'm trying to implement i implemented bgg16 and i am going through other algorithm so they told okay you have you worked on computer vision project and after that like uh in my last four or five interview also there no one goes that much deep in deep learning section most of the question now complete they are asking on machine learning very rarely or just for the understanding whether people have understanding or not uh but in this interview in in this interview there were no questions questions would also depend on the exact role the type of work that the team does probably in the interviews that you attended probably they were not too deep learning focus they're more data science and ml focused that could be the case cool cool so another question i had for you is uh what suggestions would you give to somebody in your work experience right so somebody let's say probably somebody slightly younger to you or somebody even with your sort of experience seven to ten years experience or somebody with two to five years experience from a technical support engineering type of roles what suggestions would you give to those learners to successfully transition to careers in data science and machine learning uh for me like it was it is almost seven years now uh it is six point eight six year eight months so basically i would i wanted to convey a message for the people who are like experienced and who's who are working in some kind of domain where there is no not much exposure to coding and all that so how i started see it took almost two years more than two years because i started my journey at 2018 and after that lots of ups and downs was it then finally i joined applied ai course in the month of july last year even i believe two or three project is enough to get into the data science field like people are wondering how many projects we can show like it is two or one or two project because once they will start even any recruiter will start they would ask question from uh like overall question like end to end question from one project only if you are even for me it was more than two project but they wanted to dig deep like in one project only not in all the projects so two or three project is enough so companies are looking for end-to-end understanding from acquiring data to being able to deploy a model using it yeah and yeah that is that is mandatory for people like me like seven or eight years of experience that is mandatory like like we should know that much of thing yes yes yes sounds good sarah thank you very much for taking the time in to share your detailed journey and also to share your interview experiences and also suggestions for people like you who are seven or slightly senior senior roles with with with about seven years of experience i'm sure a lot of learners both at applied air course and outside of it will surely benefit from the key learnings that you have had in your own journey uh over the last uh over the last couple of years so thank you very much on behalf of the whole team at applied a course all of our students and all of our mentors i'll surely share your feedback about our with our mentors who have guided you in case studies and answered your queries i'll surely share that feedback and thank you very much on behalf of everyone thank you sir thank you and lastly i want to again thank you whole team whole team of apple idi because uh they're like effort what kind of effort they are putting day and night and that makes like the learners like hours like we are the student and our things like easier like they are just even i'm getting reply at 2 30 or 3 o'clock in night also so that was the one of the great like best thing supporting was fabulous because they provided me all the like within 24 hours and that too sometime within one hours and like that so i'm very very very much thankful to a whole team of apple idea sure thank you very much youhi friends uh today we have saurabh sinha one of our learners and students who have successfully transitioned to a career in data science just to give you some background saurabh graduated with mca in 2013 and he has close to seven years of experience at siemens and sarnar corporation most recently at cerner corporation he has spent about just over five years and most recently he has been a senior technical support engineer and uh he is now joining he's now transitioning from that role to an assistant manager in data science business unit of concentrix so concentrix is a publicly traded business services company based out of fremont in california and again the most important aspect here is the successful transition of saurabh from uh from a senior again he has seven years of experience which is which is considerable and he's moving from an experienced senior technical support engineer role to an assistant manager in a data science business unit and the transition is what we want to focus a lot on first of all congratulations saurabh on behalf of all of our team i've been through your first case study i've seen i've seen the journey of the effort you've put in the struggle that you've put in and that's that's a hats off to that whole effort and most importantly that persistence let's let's go into it so the first question that i had was many people with your sort of background might be little little afraid to make a transition from their role to a data science role right so again we have some students very similar to you who have this question in their mind that hey i am in a technical support engineering role can i really make a transition to data science rule so could you walk us through your learning journey on how you made the most of the curriculum assignments mentorship all of that in making this successful transition sure first first of all i would like to thank you like thanks overall applied ai team because whatever like what the kind of support which i'm getting from applied it like if i'm getting stuck in any assignment like i'm continuously like keep on asking questions and they are like yeah okay you can do like this even in case studied i was getting some suggestion and these are the best thing like these are the building blocks like i developed my concept uh because of these changes like i was aware like whatever the techniques that i have to do but they have told me no you have to do like this there is other techniques also so i have applied so that is the main thing from where i learned a lot on like for these machine learning things one of the associate he he was the data analyst in my company at that point doctor he suggested me about the applied ai course and at that time i was again confused because i thought now i will put my whole effort in order to learn all these techniques but i was like in market lots of other courses were there but i thought okay i will check with uh like every opinion like who is currently working in that particular domain so one of my friend he suggested me okay apply the ai like just go through the videos that there are lots of online free videos of applied uh ai and just go through those video and then decide whether you you should go for this course i went through the video which like from the initial video where you discuss just the basic thing like what are the data science life cycle and other roles like lots of thing was there so i i immediately like i started my journey in 2018 uh uh to learn all these machine learning loads but later on in 2019 in the month of june or july i've joined applied ai course i came from support background so there were no coding experience and after understanding the algorithm i'm trying to implement those algorithms so that was one of the best thing which was happened at that point of time and i was able to make a code i am able to execute it and in case i am getting any uh kind of confusion like i was keep on like i have asked a number like lots of questions with the applied that's perfectly okay we are happy here to help you and we see that you put in so much effort right and whenever you are stuck it's also our obligation to help you because you're putting in the effort that's the most important part yeah and uh it was like every time i'm getting like within 24 like whatever you told like in your video within 24 hours you will get the answer so every time with sometime within an hour i'm getting answer from them like what type of confusion i was having so i started and parallely i was doing some uh assignments so uh in first six months uh for me it was like one or two month of break also because i stopped because of some personal work i've stopped at that particular time but again i started from 2020 january because i completed overall course content which was mandatory at that point till december i was completed all the course content but uh later on like lots of additional videos on lots of the state of the art techniques so those thing i left at that particular of time time and later i was just going through all those video which was like time series and according to my assignment also like if i need those kind of concept while doing my assignments so then i was covering those things cool so in first six months it was like i have done with three assignments and three or four assignment and course content was almost i covered the neutral network also in first six months but later on january february uh i started like with the other assignment i completed rest of the 15 but like my first name was to complete first 15 assignment so that i will get like i will work with apply the item in some kind of self-case study and real those would be some real kind of case study so i i was like trying to do like okay first i have to complete my first 15 assign i went then i will get chance to work on some real case study and meanwhile what i was doing in my company because i was having good number of experience also so i was keep on in touch with the data analysts and data scientists in my company very good and like i was trying to understand like what type of problem they are trying to solve so there was one problem which they were trying to solve which was running because of our team so i was most familiar to those kind of business problem because those business problem but like they are trying to solve but it was internal project and i was involved like okay sharing ideas what actually is happening so i was like once or twice i had conversation meeting with those business analysts and the data analyst team and trying to convey like how actually we are working so uh at that time i was getting the idea on that project also that was trying to you're trying to understand the concepts you learned applied a course in the context of problems that your teams or the teams that you knew in data science were also working in that way you get a much more real world context of everything that you're learning yes and whatever like in your each and every case study you are like like this is the business problem and how we are have to convert it into the machine learning problem so those understanding like i was getting those kind of okay such teaches us like this so even i have to get some problem like which is currently because in my organization lots of data was there but the thing was that the people who are not thinking like okay where we can use like in our team because we we are from support teams so i was put on like my lead and everyone i was saying okay we can do like that and that was the thing like from where i started having meeting with data and listing because my lead was also supporting and we were having like a communication with the data analyst or the business analyst team just to get the idea how they are trying to cover so see this way i like even my business understand like how we are trying to solve the business problem those understanding also developed and at the same time with the help of applied ai team i'm learning all the algorithms and how these things are working and how we can convert those business problem to a machine learning problem so those understanding was because of this applied ai course only it's very important for somebody like you with about seven years of experience especially for experienced professionals right whatever whatever domain they're in whether they're in technical support or sales whatever domain it is it's very important for them to connect the concepts the new concepts that they're learning to the business problems that they or their teams encounter because that is that is the expected maturity from somebody with experience right right cool yes cool coming to your interview experience at uh uh at uh at concentrix could you walk us through how many rounds were there uh what what was the focus of each round how much depth was being expected how much rigor was being expected from from somebody like you as you move into a assistant manager for a data science business unit so i'm just trying to understand what was the expectation from the company in each of the interviews and what level of depth and breadth were they expecting from you first round was because everyone is working from home so first round was uh that python test so 25 questions were there so they were asked they asked me to solve those within the 30 minutes so i i gave like it completed within 30 minutes and i got selected in the first round after because i cleared that python code test uh like syntax was there and apart from python there were a few computer reason questions and in python code like it was object like how object is created and how if it would call classes like what is going to call like those kind of those sort of questions was there all these were multiple choice questions right yeah yeah that was coding questions because you can't quote 25 programs in 30 minutes right that is right but uh after that uh they called me hr called me hr team and they told me yeah you are clear with the first round and then uh this schedule second round for me in second round uh like it was about 30 to 45 minutes of round and at that in that round they started they wanted to know like all the recent projects which i worked on so i've told them a machine learning problem each and every like whatever from where you are getting the data after getting data what are the sort of thing missing value analysis what are the techniques what are different techniques for the missing value analysis and after that like lots of question in statistics also because statistic and i have like in my past four or five interview i noticed that statistics is a very strong like everyone should be very good in statistics also and that too whatever question like few scenario based question like whether they have understanding like where to apply which kind of a statistics technique and apart from that they are going to basic random variables like they wanted to know like whether i am clear with all the basic concepts also like those things mathematics yeah right and later on when they came to like their work few questions on feature engineering also and after that they comes to algorithm part and which algorithm you have used which algorithm like you are comfortable so ah it was random for us any questions more from a deep learning perspective for this specific role uh deep learning because i i told initially when i was introducing to project they told me okay please describe some reason to project at that time i told i recently have done image classification problem and where i'm trying to implement i implemented bgg16 and i am going through other algorithm so they told okay you have you worked on computer vision project and after that like uh in my last four or five interview also there no one goes that much deep in deep learning section most of the question now complete they are asking on machine learning very rarely or just for the understanding whether people have understanding or not uh but in this interview in in this interview there were no questions questions would also depend on the exact role the type of work that the team does probably in the interviews that you attended probably they were not too deep learning focus they're more data science and ml focused that could be the case cool cool so another question i had for you is uh what suggestions would you give to somebody in your work experience right so somebody let's say probably somebody slightly younger to you or somebody even with your sort of experience seven to ten years experience or somebody with two to five years experience from a technical support engineering type of roles what suggestions would you give to those learners to successfully transition to careers in data science and machine learning uh for me like it was it is almost seven years now uh it is six point eight six year eight months so basically i would i wanted to convey a message for the people who are like experienced and who's who are working in some kind of domain where there is no not much exposure to coding and all that so how i started see it took almost two years more than two years because i started my journey at 2018 and after that lots of ups and downs was it then finally i joined applied ai course in the month of july last year even i believe two or three project is enough to get into the data science field like people are wondering how many projects we can show like it is two or one or two project because once they will start even any recruiter will start they would ask question from uh like overall question like end to end question from one project only if you are even for me it was more than two project but they wanted to dig deep like in one project only not in all the projects so two or three project is enough so companies are looking for end-to-end understanding from acquiring data to being able to deploy a model using it yeah and yeah that is that is mandatory for people like me like seven or eight years of experience that is mandatory like like we should know that much of thing yes yes yes sounds good sarah thank you very much for taking the time in to share your detailed journey and also to share your interview experiences and also suggestions for people like you who are seven or slightly senior senior roles with with with about seven years of experience i'm sure a lot of learners both at applied air course and outside of it will surely benefit from the key learnings that you have had in your own journey uh over the last uh over the last couple of years so thank you very much on behalf of the whole team at applied a course all of our students and all of our mentors i'll surely share your feedback about our with our mentors who have guided you in case studies and answered your queries i'll surely share that feedback and thank you very much on behalf of everyone thank you sir thank you and lastly i want to again thank you whole team whole team of apple idi because uh they're like effort what kind of effort they are putting day and night and that makes like the learners like hours like we are the student and our things like easier like they are just even i'm getting reply at 2 30 or 3 o'clock in night also so that was the one of the great like best thing supporting was fabulous because they provided me all the like within 24 hours and that too sometime within one hours and like that so i'm very very very much thankful to a whole team of apple idea sure thank you very much you\n"