What to expect in an Analytics Interview?

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Once I posted my first blog “How to Start a Career in Analytics for Free”, I was flooded with questions on how to prepare for analytics interviews. After giving a good amount of interviews in the last 6 months I could find a clear pattern in which they are conducted.

Although Analytics as a field has been present for a long time, people have recently started adopting it as a career. Hence, there is a lot of scattered information available on the net, relating to interview preparation, but no material complete in itself.

It is also impossible to prepare for each potential question, as Analytics is a vast field. However, the same questions can be bucketed into different categories and one can concentrate on specific categories depending on the work profile. Each company tests on these categories by holding different interview rounds which will also be covered subsequently.

Before jumping into categories of interview questions and different interview rounds, it is necessary to understand the flow of data analysis and associated designations.

FLOW OF DATA ANALYSIS

 
flow data analysis

    1. Define your Questions: One must begin with the right question(s). Questions should be measurable, clear and concise. Design your questions to either qualify or disqualify potential solutions to your specific problem or opportunity.
    2. Decide on the Objectives: It is impossible to do sound analysis without knowing what you wish to achieve. Too often an analysis is started without a clear idea of where it is going. The result is usually a lot of wasted time and an inadequate analysis. Avoid this by deciding on the objectives of the analysis before starting it.
    3. Data Collection: The way you collect data should relate to how you’re planning to analyze and use it.Essentially, collecting data means, putting a design for collecting information into operation. You’ve decided how you’re going to get information – whether by direct observation, interviews, surveys, experiments and testing, or other methods – and now you want to implement your plan. Recording and organizing data may take different forms, depending on the kind of information you’re collecting.
    4. Data Cleaning: Improving data quality is an essential step in data analysis. It includes converting data into a structured format, handling missing data, feature engineering and weeding out futile information. Analyzing bad quality data will result in erroneous conclusions unless steps are taken to validate and clean it.
    5. Analysis: Analysis involves examining information in ways that reveal the relationships, patterns and trends in it. That may mean subjecting it to statistical operations that can tell you not only what kinds of relationships seem to exist among variables, but also to what level you can trust the answers you’re getting.  The point, in terms of your evaluation, is to get an accurate assessment in order to better understand your work and its effects on those you’re concerned with, or in order to better understand the overall situation.
    6. Data Modeling: This involves building models that correlate the data with the business outcomes and then make suitable recommendations. This is where the unique expertise of an analyst becomes critical to business success—correlating the data and building models that predict business outcomes. Such an analyst must have a strong background in statistics and machine learning to build scientifically accurate models and avoid the traps of meaningless correlations and models that are so reliant on existing data that their future predictions are useless. But statistical background is not enough; analysts need to understand the business well enough that they will be able to recognize whether the results of the mathematical models are meaningful and relevant.
    7. Optimize and Repeat: The flow of data analysis is a continuous and repeatable process. Each stage should be monitored and optimized accordingly for better results.

DESIGNATIONS

designations
      • Data Architect:-
        Large enterprises generate huge amounts of data from various different sources. The Data Architect is someone who can understand all the sources of data and work out a plan for integrating, centralizing and maintaining all the data. He must be able to understand how the data relates to the current operations and the effects that any future process changes will have on the use of data in the organization. He needs to be able to have an end-to-end vision, and to see how a logical design will translate into one or more physical Databases, and how the Data will flow through the successive stages involved.
        This may include things like designing relational databases, developing strategies for data acquisitions, archive recovery, and implementation of a database, cleaning and maintaining the database by removing and deleting old data etc.
      • Data Engineer:-
        Data engineers are hard core engineers who know the internals of database softwares. He compiles and installs database systems, writes complex queries, scales it to multiple machines, ensures backups and puts disaster recovery systems in place. He usually has a deep knowledge and expertise in one or more different database softwares (SQL / NoSQL).
      • Data Analyst/Business Analyst:-
        The primary task of a data analyst/business analyst is compilation and analysis of numerical information. They usually have a computer science and business degree. They get analytical insights out of all the data which an organization can have (Database soft wares or just excel sheets) which makes sense for the organization and compile them into decent reports so that other non-technical folks can understand and decide their course of action.
        An analyst usually works to get analytical insights out of data and this job profile does not include working with statistics (usually) and has nothing to do with “Big Data” in particular.A decent mid-sized organization can have many analysts. For example – a sales analyst may look at total sales in the past quarter and figure out a proper sales strategy (where to sell and whom to sell to maximize profits). He will then communicate the report to the leadership.
      • Data Scientist:-
        “Data Scientist” is a very recent phenomenon. The overall mission of a scientist is same as an analyst but once the volume and velocity of data crosses a certain level, it requires really sophisticated skills to get those insights out.
        A “Data Scientist” usually has many overlapping skills – Database Engineering, handling Big Data systems, knowledge of statistical programming languages, business knowledge and knowledge of statistics / data mining.
        Whereas a traditional data analyst may look only at data from a single source a data scientist will most likely explore and examine data from multiple disparate sources. The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can solve a business problem. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.
      • Business Intelligence Engineer:
        These are the people who consume historical data from transactional databases, denormalize it, write huge gigantic SQL/Hive queries to flatten, reshape and aggregate the data, do some basic statistical analysis and then build fancy data visualizations using tools/programming to present/communicate the data effectively. The dashboards they build are often used by senior management and VPs. These people are not the strongest technical people, but they are jack of all trades and can fill in anyone’s shoes whenever required. This is also a very cross-functional role as you work with data engineers to get the data, data scientists to get statistical analysis done and with business analysts/managers to present the insights.
NOTE:-
      • The above mentioned designations are loosely defined in the analytics industry. Hence it is best to look at work profiles rather than designations as they can often be misleading.
      • Now that we have covered the flow of data analysis and associated designations, we can move towards the categories of interview questions and different interview rounds.

CATEGORIES OF INTERVIEW QUESTIONS

sas
(A) NON-TECHNICAL:-
      1. Basic Overview of Analytics Field: One should have a broad picture about what the analytics industry is all about. A knowledgeable candidate will always have an upper hand.
      2. Basic Domain Knowledge: Analytics can help solve problems for various industries like e-commerce, retail, banking, telecom, pharmaceutical, BFSI etc. If the role offered is industry specific, then it’s always a plus to have a basic domain knowledge.
      3. Communication and Presentation Skills: This is one of the most important skills. No matter how strong your other skills are, a company will not compromise on effective communication and presentation skills (both verbal and written) i.e. a candidate lacking such skills will rarely make it through.
      4. Energy and Passion: The first question of any one-to-one interview will always be “tell me about yourself and how you landed here?” Skills can be taught but passion sure cannot, a passionate candidate leaves a wonderful first impression. Matching this passion with energy reflects confidence of the candidate and becomes an instant hit with the interviewer.
      5. Logical Thinking: Logical thinking is the process in which one uses reasoning consistently to come to a conclusion. Problems or situations that involve logical thinking call for structure, relationships between facts and chains of reasoning that “make sense.
        ”Companies evaluate logical thinking on “• points” by the help of “-> points”:-
      • Structural Approach
      • Problem Solving Skills
      • Attention to Details
      • Ability to Handle Pressure
        -> Aptitude Test
        -> Puzzles
        -> Case-Studies
        -> Guess Estimates
(B) TECHNICAL:-
      1. Basic Statistics: Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data.
        Generally our knowledge of statistics is limited to what we learnt in high-school. Hence, it is important to brush up and learn more advanced concepts which can be done using innumerable free resources available online.
      2. Basic Mathematics: Mathematics is the science of numbers and their operations, interrelations, combinations, generalizations, abstractions of space configurations and their structure, measurement, transformations, and generalizations.
        If you’re mathematical skills are rusty, it is advisable to revisit the high school mathematics curriculum which in my opinion is quite comprehensive.
      3. Machine Learning: In general, machine learning is about learning to do better in future based on what was experienced in the past. The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance.
        For example, Facebook’s News Feed changes according to the user’s personal interactions with other users. If a user frequently tags a friend in photos, writes on hiswall or “likes” his links, the News Feed will show more of that friend’s activity in the user’s News Feed due to presumed closeness.
        Machine Learning is considered an advanced skill and definitely a must in highly technical roles.
        Other examples of machine learning problems include face detection, spam filtering, medical diagnosis, customer segmentation, fraud detection and weather prediction.
      4. Databases & Big Data Concepts: A database is a structured set of data held in a computer, especially one that is accessible in various ways whereas Big data is a buzzword used to describe a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software
        It is important to understand how data was traditionally stored and retrieved from databases and update oneself on how to handle such data, now that it has increased in volume exponentially.
      5. Basic Programming: In the most basic sense, programming means creating a set of instructions for completing some specific task.
        All programming languages are built on (more or less) the same basis. You can be asked to perform simple operation/algorithms using any programming language (of your choice) to check your basic programming skills.
      6. Statistical Tools: There are tools that help you in statistical analysis.
        This is a must skill to have and the most popular statistical tools in the market today are R, SAS and Python.
      7. Data Visualization: Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization
        In my opinion, this is not a necessary skill as it can be easily acquired but it definitely gives you an edge over other candidates.
      8. Related Projects/Competitions: The best way to check skills (both technical and non-technical) of a candidate is by asking him to explain his related projects/competitions.
        Hence it is very important to do a lot of projects and also participate in competitions (Kaggle, Data-Hackathons etc.) which display your ability to practically apply your skills. A candidate will never be selected just on the basis of his theoretical knowledge.
      9. Resume: I cannot stress enough on the fact that this is probably the most vital and often neglected (from the candidate’s side) part of the interview process. This is a powerful document that summarizes your entire professional life in at most a couple of pages.
        I have been in interviews where all the technical questions were asked only from my resume and if we think about it, this should not sound strange. One is called for an interview on the basis of their resume (as the company finds your mentioned work profile, skills and projects appealing). Hence many a times they limit their questions to the points mentioned in your resume only.
NOTE:-
      • Preparation for freshers vs experienced candidates:-
      1. Freshers are generally tested more on their non-technical skills and taught technical skills on board, though knowledge of technical skills do provide them an edge. (Some companies also test freshers on knowledge of their respective undergraduate discipline)
      2. Experienced candidates (having no technical skills) wanting to shift their career into analytics should also concentrate on non-technical skills and compete against freshers for entry level positions.
      3. Experienced candidates (having technical skills) but no related work experience should concentrate on both non-technical and technical skills when applying for desired work profiles.
      4. Experienced candidates (having technical skills) with related work experience should concentrate more on technical skills when applying for desired work profiles.
      • It is important to understand the work profile when applying for a certain designation. Preparation should then be work profile specific and not designation specific.

INTERVIEW ROUNDS

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      1. Aptitude: This round checks for quantitative, verbal and problem solving skills. This is not used to identify suitable candidates but rather remove the unsuitable ones.
      2. Group Discussion: In short, group discussions tests if you know the topic well, are able to present your point of view in a logical manner, are interested in understanding what others feel about the same subject and are able to conduct yourself with grace in a group situation.
        This (like the aptitude round) also acts as an elimination round.
      3. Personality Fit: Despite having apt skills, selection of a candidate might boil down to whether he will fit the company’s corporate culture or not.
        It is best to research about the company, its core values and processes beforehand.
      4. Logical: This round involves testing on puzzles, case-studies and guess estimates as discussed in “Categories of Interview Questions (Non-Technical)”
      5. Technical: This round involves testing on technical skills as discussed in “Categories of Interview Questions (Technical)”
      6. Coding: A coding round is used to assess ones basic programming skills.
      7. Resume Based: As discussed earlier, resume is a vital part of the interview process. A candidate should be thorough with each and every line written in their resume.
NOTE:-
      • Different companies have different “categories of interview questions” and different “interview rounds”. This blog, to my knowledge, covers each and every type of the same.

IMPORTANT POINTERS

      • People willing to start a career in analytics can read my first blog “how to start a career in analytics for free”
      • Both blogs are aimed towards freshers or professionals in early stages of their careers.
      • Both blogs are applicable for all types of organizations (small, mid-sized or large corporations)

END NOTES

As some of you would already know, I started from scratch a little more than a year ago. There are a lot of free (high quality) resources available online to kick start your career in analytics.
Currently, there is a huge demand for people with the right skills in the analytics industry and companies, for a change, are running after such candidates. You are not only in a position to choose between companies but also dictate your own salary. If one concentrates on acquiring the right skills then I am sure everything will fall into place.
As per me, after weighing my options, I have decided to move into “cardekho.com/gaadi.com” next month as a Junior Data Scientist.

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59 comments

    1. Hi Ankur,

      Thanks for your comments. I have purposely not mentioned any resources in the blog and everybody is encouraged to dig deep on the net for them. There are vast number of such resources available,one only needs to identify which resources best suits their need and proceed accordingly.

      I am broadly listing down some resources:-

      Non-Technical:-

      => Basic Overview of Analytics Field – Read related blogs and articles, become part of professional communities and attend free seminars, read beginner level analytics books
      => Basic Domain Knowledge – Online Reading
      => Logical Thinking
      (a) Aptitude – RS Aggarwal/Arun Sharma
      (b) Puzzles – Online Reading, Books – How Would You Move Mount Fuji?, Puzzles to Puzzle you, The Great Book of Puzzles and Teasers
      (c) Case-Studies – Online Reading, Case in Point, Vault’s Guide
      (d) Guess-Estimates – Online Reading

      Technical:-

      => Statistics – Text Books, MOOCs
      => Mathematics – High School Mathematics
      => Machine Learning – Text Books, MOOCs
      => Databases and Big Data Concepts – Text Books, MOOCs
      => Basic Programming – Text Books, MOOCs
      => Statistical Tools – MOOCs

      Like

  1. Hi Akshay,
    Thanks for the info.It is indeed very helpful.i am in the same situation as u were.I did my B.E in Electronics & working as SAP Security Consultant in since 2 years.Can you please let me know what is the starting salary i can expect after learning & applying data analytics as advised by you.I went through all the comments on both your blogs but couldnot find a clear answer to the same.Your response will be highly appreciated.

    Liked by 1 person

    1. Hi Ayesha,

      Thanks for writing in.

      It is very tough to estimate a starting salary because it depends on so many factors. Among these factors some might be in your hand (like amount of knowledge , projects that you have done, preparation for interviews, background, relevant skills etc.) and some might not be (like if the job vacancy is immediate or crucial, recession, is the company making profit etc.). Best way to get an estimate would be to check on Glassdoors.

      All I can say for sure is data analytics is offering competitive salaries. When I say competitive, it obviously means in comparison with all the other fields. The main reason is that amount of people with good knowledge of data science is less in comparison with the amount needed.

      I know this is not the answer that you were looking for but it would be unfair to make estimations. Finally I know people working as SAP consultants and I can say for sure that you can expect much better salaries in the field of data science (currently, provided you gain good knowledge)

      Hope this helps 🙂

      Like

  2. Hi Akshay, I must say your is one of the best blog !

    I need your suggestion as I have done my engineering in IT in 2012, I have been in small scale IT Sales and online bidding. This gave me exposure in minimal project management. now after two years of similar experience i am planning to do a course involving SAS,Rand excel. They are asking for 25000 for this online course. I want your insight on this as should this course be done in order to get a job in analytics ans will be able to end up woth job after this course.
    Really NEED HELP

    Like

    1. Hi Shivam,

      Thanks for liking the blog.

      Firstly, you can learn all tools online for free but you will need to put in a lot of effort. There are numerous courses from best universities, you can look them up on either edX or Coursera. As you’re not paying money it is very easy to drop out or probably not work as hard as you would for a course that you payed for. You need to be motivated and learn as much as you can from such courses. It will be best to practically apply your knowledge that you gained from such courses(projects/competitions).

      Secondly, SAS R and Excel are just tools that helps in data analysis. Its good to learn the tools but its a must to learn the subject of data science. So look for courses that blend the two. No company hires a SAS/R/Excel Programmer to my knowledge. People hired use these tools as in when needed.

      Hope this helps, good luck !

      Like

  3. hey. that was some insight !! Thank you !
    Can You tell me if i could switch my field from being a core industry mechanical engineer to business analyst career . I Don’t have a relevant experience of the field, but i am good with maths and analytical problem solving. I don’t have a computers background so i dont know about SQL but i have ample of working on Excel and SAP .
    Should i try for the field.??

    Like

    1. Hi Bhanu,

      If you really are interested then you can make a switch. Considering your skill set you should go for a less technical role i.e. BA. It will be a task to explain why would you want to shift from a core mechanical job to analytics and also prove that you’re competent.
      Once you make a switch, it will be pretty easy to shift to another analytics company.

      Good Luck 🙂

      Like

  4. Hey akshay, m a fresher from b.tech but wanna go for analyst profile. In ur blog what u mentioned about skills n all I believe I have them in me and for increasing my chances I did certification in sas basic , nw lookin for job openings but I don’t c any for fresher most of them are for experienced person, could you tell me what are the months when opening starts for freshers, or do you know any company which has opening now. My residing place is Bangalore . ll be gr8 help if u can suggest some.

    Like

    1. Hi Raghav,

      A somewhat similar question was asked on another blog of mine. I am pasting both the question and answer for your reference.

      Question :

      Hello Akshay,

      I’m a 2015 passout with BTech CS background. I’m looking for a job in analytics. I’ve a fair understanding of Data Science and Big Data technologies. I did a certification course in Big Data from Jigsaw Academy and also completed some MOOC. Also, I’ve done some projects and participated in some competitions. Since I’m a fresher I’m struggling to find a job in analytics industry. Do you have any suggestions? How can I approach Accenture Analytics? I’ve already registered in their career site. I would like to hear from you.

      Answer :

      Hi Mohammed,

      Your Differentiators:-
      1. CS Background – Plus
      2. Understanding of Data Science and Big Data Technologies – Plus
      3. Certification in Big Data from Jigsaw Academy and done MOOCs – Plus
      4. Participated in competitions and done projects – Big Plus

      Lets go over the reasons of why you can be struggling to find a job in the analytics industry:-
      1. ‘You haven’t covered the basics’. I have mentioned some basic skills that a company is looking for in freshers:-
      -> Structured Thinking
      -> Attention to Details
      -> Numerical Skills
      -> Problem Solving Capabilities
      -> Communication Skills
      -> Knowledge of Undergrad Discipline

      Once you have these basics covered is when those differentiators count. If your basics are not covered, your differentiators can very well back-fire.

      For Example: A candidate brags about his Kaggle Projects, MOOC Assignments, Data Hackathons so on and so forth. Given a simple case-study the same candidate falls flat on his face or the candidate just can not communicate his result. What impression would such a candidate leave on the interviewer?

      2. You aren’t applying to the companies in the right way or applying to too few of them:-
      (a) Search for all the companies that fall under your interest. There are a lot of small/mid-sized analytics companies that you will come across, once you dig in. Generally you just have to send your resume and cover letter to the hr and can expect your profile to be reviewed by them.
      (b) Large organizations generally don’t hire freshers that easily. The best chance is campus recruitments or mass off-campus drives. Your best bet is getting referrals because too many freshers apply on company portals and too few get a call.
      (c) Target companies that are currently hiring. No matter how good you are, a company will not hire you until it has vacancies. Follow these companies on Social Media, follow them on job portals and apply once they have vacancies.

      3. Either your resume isn’t appealing (and you do not get interview calls) or when you do get calls you don’t do well enough in the interview:-
      (a) You are a product and you market yourself through your resume. Make it short, crisp and irresistible.
      (b) There is a pattern of how analytics interviews are conducted. I am currently working on another blog about ‘what to expect in an analytics interview’ and that will help you in this aspect. This is a vast topic and sure deserves a whole blog !

      To your last question: As already mentioned before, Accenture Analytics does not hire freshers. They only hire candidates with Post Graduations and sometimes hire internally.

      Figure out where you are lacking and I am sure you’ll find a place in the analytics industry. Good Luck !

      ——————————————————————————–

      Hope this helps 🙂

      Like

  5. Hey buddy

    Nice article! Made for a great read.

    Long post ahead but I request you to bear with me.

    I wanted to ask for some advice. I’m new to the analytics world, got 7 months of experience but there is not so much real data science, more of SQL data pulling and creating performance reports.

    I’m going to switch jobs soon. In anticipation, so far I have:

    1. Gotten a Base SAS certification
    2. Studied essential basic statistics : ANOVA, Regression (Logistic, Multivariate and Linear), Corellation, hypothesis testing etc and applying them in SAS

    I want to learn more and become stronger at this before I apply for the shift. I want to learn:

    1. Decision trees
    2. Cluster analysis
    3. Dimensionality reduction
    4. Neural networks

    I want to do one more certification before I apply to strengthen both my knowledge and profile.

    The dilemma:

    Option 1—— Should I become a SAS certified Statistical Business Analyst? This certifies my knowledge of regression anova etc. I will then have to search, learn and practise the above 4 techniques on my own.

    OR

    Option 2—– Should I become a SAS certified predictive modeller using Enterprise Miner? All this is covered in that course BUT its a GUI tool where the SAS code is generated automatically as you click.

    I am quite seriously considering option 1.

    Like

    1. Hi RS,

      Thanks for writing in and apologies for replying so late. I think you might have made up your mind by now.

      Its great to see you focusing on multiple aspects of data science (Statistics/Maths, Machine Learning, Programming Tools).

      I think you should go with option 1 because the more you apply things the more you will learn. Option 2 seems more like a black box, generally for people who are more interested in the results rather than the process.

      You are in a good position to apply to companies and I wish you all the best. Keep me posted on your developments.

      Cheers !

      Like

  6. Hi sir – i have completed B.E computer science in 2010 and now iam working in testing field for past 6 yrs of exp. I want a change in my career. I heard about SAS and went thro abt SAS in net and saw ur blogs. Can you pls is it good that i can move to SAS by this time. If so from where i can start bcos iam not aware of what and where to gain knowledge for SAS. Please exlain me sir Thanks

    Like

    1. Hi Selvi,

      SAS has a high market share in the current industry but it is only a tool. To change your career be subject oriented rather than tool oriented.

      Learn the subject using SAS. As SAS is paid tool, there are limited free resources available, however, a lot of paid courses are available for it. Browse the net and I am not you’ll get many !

      Like

  7. Hello Sir, I’m 2015 passed out, was an intern for couple of months and then had to quit that and started preparing for aptitude . while I was surfing on the net to explore the options I came across analytic s , this field involves lots of problem solving and analysis which I like to do the most.

    My doubt is should I work and simultaneously learn courses to enhance my skill sets in data science??

    OR
    Should I quit the job which I’m not interested and concentrate more in to Analytic s related activities so that I can get more deeper at lesser time.

    kindly help me to come out of my confusion.

    Like

  8. Hi Nitish,

    Thanks for writing in.

    Do not quit your current job without even knowing if you really have an interest in analytics. Moreover, being jobless might make even make you unproductive. First try and read a lot of material (books, ebooks, articles, blogs, videos) about data science and if it interests you then plan on how would you go about improving your skills. Give time to it after office hours and on weekends and you will be good !

    Hope this helps !

    Like

  9. Hi akshay

    nice articles

    i am mohit pursuing MBA in business analytics .My graduation is in BBA, during my training sessions i worked on various IBM tools like

    SPSS statistics ,
    SPSS modeler,
    IBM info sphere big insight,
    Cognos BI,
    Cognos TM1.

    Also I have written 3 research papers .

    i wanted to ask for advise.

    will expertise on these tools is sufficient to apply in companies?
    or should i learn R or python statistical tools as well.

    because i have seen various vacancies where companies are also demanding for same. Also almost every company hire candidate with experience of 3-5 year.

    as i am fresher and comes from non technical background (BBA) in which sectors should i apply for job ?

    Like

    1. Hi Mohit,

      Apologies for the late reply. As I can see you have mostly worked on tools specific to IBM. I will advise that you start learning general tools like R and Python to increase your chances. Also, more importantly learn to apply the tools for analyzing data.

      Its always tough to break into this field but with some hard work and constant dedication I am sure one can do it. Once you’re experienced its relatively easier to shift jobs. About the sector try and research a little about it and you’ll find your answer. Hope this helps !

      Like

  10. great job !! am a 2014 pasout and have more than a year exp in operations ! I did a DS with R course , but i cant find many jobs on R compared to SAS . though my initial research suggested R as the booming language , i can hardly find any opening here in INDIA. Should i go for SAS and phyton as well ?? or should i back my skill set with big data .??

    Like

    1. Hi Arun,

      Cracking a data science/analytics jobs is not only about knowing a tool. I have mentioned about the skills that you may need for it in the blog. Try and incorporate them and things will turn out to be good.

      Like

  11. Hi mate;I am a 2008 baby batch pass out and having a business development /institutional sales work experience of 5.5 years now.I want to move into a technical field as an analyst.I saw r and sas are the popular tools.what kind of profiles can I get with my kind of experience?I am right now doing a course from jigsaw academy in r and sas.they are focussing on using r and sas in different sectors including cluster analysis;factor analysis;regression etc.what kind of profiles can I get? And how hard is it to break into this sector?I am getting 7 lacs per annum right now.what is the best way yo apply for jobs ?

    Like

  12. Hello Akshay,

    I have gone through both of your Blogs and the information provided in them is very helpful. Thank you for sharing the knowledge with everyone.
    I have few more doubts.
    I have done my Bachelors in Electrical and Electronic and Landed up in TCS through Campus selection.
    I have an experience of 3 years in .NET field, though not core programming. In fact, since my Bachelors was in a non-IT branch, I had some difficulty with programming and moreover I wasn’t that interested in core programming. But now I have developed a strong interest in Analytics and I have studying all the online material available to get into this field.
    I have shifted from India to US recently. Now I have to start looking for jobs here. But I am very confused on my resume composition. I have included my past experience. But now since I would be applying for Analyst jobs, I am not sure if they would consider my resume. Could you please provide any suggestions on my resume composition so that the chances are more for being selected.

    Thanks.

    Like

    1. Hi Arundhati,

      I am happy that you liked the blog.

      My knowledge about analytics trends is limited to India. Recruitment processes can vary a lot in different countries and hence I feel you should get in touch with people in US itself for better suggestions.

      Also, you’re previous experience may or may not be relevant depending on the kind of analytics jobs that you’re applying for. However you can mold your resume to highlight the analytical skills that you portrayed while working as a .NET developer. I have talked more about this in the blog and that should suffice.

      Hope this helps 🙂

      Like

  13. Hi Akshay.
    I just found your blog today and liked the posts very much.
    Thanks for writing this blog.
    I am a B.E graduate in Mechanical branch. I too am placed in Accenture right now. I am starting to get an interest in analytics domain. I do not have any required skills though.
    I would like to know if it is advisable to join accenture (still time is there for DOJ) and prepare for analytics simultaneously or if there are any other ways please suggest them.
    Thanks for your help.

    Like

    1. Hi Chandru,

      Thanks for liking the blog !

      Case 1: A job offer from for an analytics designation (however small-sized the company may be/however less it may pay), you should go for that
      Case 2: If no such offers as described above, then join Accenture and improve you skills. You’ll be able to switch into analytics (inside or outside Accenture) after some time.

      Best of Luck !

      Like

  14. Is it advisable to start a career in analytics as fresher from non IT background, or work in some other company and after getting some experience switch to analytics. As I will take classes for learning the concepts. But still is it a right choice by keeping job availability for freshers in this domain in mind. I want to enter this field but some hesitation because am a fresher from non IT background.

    Like

    1. Hi Dilip,

      First make sure that you have interest in this field. Once you’re sure about it develop the required skills through trainings/online courses/books/blogs etc. and apply for entry positions.

      A person from non IT background can definitely do well but you’ll need to put in some hard work. To operate statistical programming/data manipulation languages like R, SQL and SAS, you will need to know basic coding. Just work on it and you’ll be good !

      Like

  15. Hey Sir! I’m a second year computer engineering student after a bit surfing on internet for fields in computer I found out Data Science. And it looks quite impressive. To be honest I’m not very much into Developing or Testing I was looking for something different and looks like I found it but I’m not quite sure how to pursue it I mean how to start from the scratch. What are the things that I should look for to go into it. Can you please tell me a good source or any institute which can guide me throughout this. I’m very much looking forward to it and it also sounds like of my type. I really want to do something different and this thing seem to be. Can you suggest me any good institute in Mumbai?
    Thank you for your time!

    Like

    1. Hi Sameer,

      Thanks for writing in.

      It seems that you want to pursue analytics as a field because it looks different and interesting from outside. I would suggest that you read more about it and it even try to do some hands on problems. It might happen that this field may not appeal to you at all.

      Answering your question, I believe you can gain skills without paying money. Try doing free online courses before paying money to analytics institutes. As mentioned earlier, I have no knowledge about analytics institutes and its best that you get in touch with them for any assistance.

      Hope this helps !

      Like

  16. Hello Akshay,

    Thanks for writing such a really clean and comprehensive piece of write-up on Analytics. I would like to know what are the prospects of Analytics for an MBA(with specialization in Analytics) Also among the professionals you have listed, which one will be the best fit for me.

    Please reply.

    Regards,
    vikas

    Like

    1. Hi Vikas,

      Thanks for writing in and sorry for the delay in response.

      Prospects of getting into the analytics space after doing an MBA is quite good. Although you will be aligned more towards the business side rather than the technical side.

      Business Analyst profile would be the best suited role for you.

      Hope this helps,
      Cheers

      Like

  17. Hi Akshay,

    First of all, the blog was very informative and to the point. I went through both your blogs and they cleared a lot of things. Just a few questions though.

    1. I have bachelor’s degree in computer engg. Have been working in unix production support for more than 3years now. I want career shift and I am finding analytics as a very interesting field but I have no prior experience or training for the same. Are the technical and non technical skills mentioned in this blog enough for this switch considering my educational and work background?

    2. Also, does my prior experience hold any value once I try to switch to analytics?

    Thanks,
    Nikita.

    Like

    1. Hi Nikita,

      Thanks for writing in and sorry for the delay in response.

      Yes, the technical and non-technical skills mentioned in the blog are sufficient for you to switch into the analytics space.

      Yes your prior experience definitely holds value. You need to be able to showcase your work experience in such a way that reflects that you’ll be an asset even in the analytics space.

      Hope this helps, cheers !

      Like

  18. Hi Akhsay

    I fall in the same category and same situation as you were in 2 years back.
    I joined Accenture 5 months back and was trained in a stream which is not of my interest.I have been reading about analytics and its scope since about an year and i really believe that i can do well in this field.
    I am going through all the available resource.

    But i have my few doubts that how can we really migrate from one part of Accenture to another?

    Like

  19. Hi Akshay,

    I have 5 yrs of exp in iOS app development & now thinking because to shift my career to Analytics. Got through to IIIT-Bangalore for their Post Graduate Diploma in Data Analyst. Need to discuss about this transition of my career & after the course job prospect. I am making 9 lacs pa now & how much I could ask for after this Degree. This course is of 11 months & a good score will help to get a placement from the institute, as they have said. If you could provide me with your number or mail id it would be great for me. my mail id is synad9@gmail.com Thanking you for your nice blog which brings everyone here for a valuable discussion. Thanks a lot.

    Like

    1. Hi Sayan,

      Thanks for writing in.

      As mentioned previously, I have very little knowledge about paid courses offered by various universities.I would advise you to get in touch with the university/alumnis for more information on it.

      Like

  20. Hi Akshay,

    I Certified as Basev SAS, and Predictive Modeling.
    i want to prepare resume as fresher.
    i am from ststistical background.
    kindly suggest me some technical and non technical skill

    thanks

    Like

  21. Hello Akshay,
    I left a comment previously in one of your blog, unable to find it.
    I am an electrical and electronics engineering graduate. I have two years experience in Quality assurance.
    I really want to make this switch in analytics.

    I am going to take a Data science using SAS and R course from some institute starting this month.
    Is it possible for me to switch into analytics after this course?
    How difficult is it going to be to make this switch?
    please help me with this.Very tensed regarding my future.
    Would pe really grateful if you can suggest me a career path?

    mayanknl91@gmail.com

    Like

    1. Hi Mayank,

      Thanks for writing in.

      I am assuming that you have read this blog. So you are aware of the various skills required to excel in the field of analytics. Now I would suggest that you work on these skills using any medium. Once you do that you can easily switch. Hope this helps !

      Like

  22. Hi Akshay,
    currently, I,m pursuing masters in Economics. i have been taught statistics and Econometrics but I’m not that much good in non-technical skills.

    how can i improve my non-technical skills?

    Like

  23. Hi sir,
    I have completed BE in ECE stream (2016 passed out), I am interested in data analytics and I attended some counseling in data analytics training centre they are suggesting me to go with SAS and R, if I learn these two will I get job as fresher?

    Like

  24. Hi Akshay,
    Thanks a lot for the info. It really helped 🙂
    Can you specify which type of companies,as in, profiles(business/investment/banking/software etc) intake data analysts?
    Thanks in advance.

    Like

  25. Hi Akshay, came through your blog today. Thanks for the insight.
    I have recently completed my masters in applied mathematics and i am presently persuing ‘Data Science using SAS and R’ from ALabs. I wanted to know what more can i do to get a satisfactory package for the job in Data Analysis? Any preparatory advice from your side?

    Like

    1. Hi Anam,

      Thanks for writing in. You already seem to have a masters degree so I don’t think you can add any more educational value to your resume. You should have an edge over a lot of people already. It’s best that you showcase your knowledge in form of projects and Kaggle competitions now and keep applying for jobs simultaneously.

      Hope this works for you, cheers !

      Like

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