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How to choose the right data science / analytics / big data training?

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Over the last 2 years, this is the most common query I receive from our readers:

Which data science / analytics training should I go for?

The query comes in varied shapes and size, but the inherent question is still the same.

Analytics and Big Data Salary Report

 

I can empathize with people facing these questions – the number of tools, analytical techniques under application and trainings provider, all have increased many-fold in last few years. If the trends and projections are to be believed, this is probably just the start of a growth phase.

Let’s take an example, as a person switching from software industry, do you learn SAS or do you learn R? Or should you learn Big Data tools and techniques? How about machine learning? Data Visualization tools? Even if you zero in on one of these, the next question which arises is where and how to undergo these trainings?

best big data science trainings

I am sure most of the person in this situation feel like the person in the image above. This is where a framework can help you.

 

Framework to choose right analytics training:

I aim to provide a framework to you to decide:

  • Which tool to learn?
  • Which techniques to focus on?
  • How to learn?
  • Where to learn?

You can apply it at various stages of your analytics career to find out what should you be learning next.

 

Overview of the framework:

The answer to first 2 questions in this framework are in form of levels or steps. You start from level 0 and move one step at a time. So if you are a complete fresher start from Level 0 of tools and level 0 of techniques. But, if you are a fresher with statistics background, start with Level 1 of tools (assuming you know Excel) and Level 1 of techniques (move to level 2 if you know predictive modeling)

Once you have finalized the tools and techniques to learn, move on to step 3 and step 4 of the process.

 

Step 1: Which tool to learn?

Level 0: Excel.

If you don’t know excel, you should learn it first. You should be able to play with Pivot tables, do simple data manipulations and apply lookups in Excel.

Level 1: SAS / R / Python

This is going to be your work horse. You can choose any of these languages. For a more detailed comparison, have a look at this article.

Level 2: QlikView / Tableau / D3.js

You should add up your repository with one of the visualization tools.

Level 3: Big Data tools

This in itself can be multiple levels – start with Hadoop stack – HDFS, HBase, Pig, Hive, Spark

Level 4: NoSQL Databases

Again, you can read an overview of NoSQL databases here and start by learning the most popular one – MongoDB.

Exception 1: If you come from MIS / reporting background, you can start from learning visualization tools like QlikView and Tableau (Level 2) and then go to Level 1

Exception 2: If you come from software engineering / web development and know one of the 2 languages – Java or Python, you can start from Big Data tools as well (level 3)

 

Step 2: Which techniques should you be learning?

Now that you know, which tool would you want to learn, let us look at the techniques to learn. Again the structure is similar

Level 0: Basics of statistics – Descriptive and Inferential statistics

Level 1: Basic predictive modeling – ANOVA, Regression, Decision trees, Time Series

Level 2: All other remaining machine learning techniques except Neural nets

Level 3: Neural nets and deep learning

 

Step 3: How should you learn?

How should you learn is dependent on 2 factors:

  • Resources you can spend on learning; and
  • Your self learning motivation.

This image explains the selection:

how_to_learn2

On one extreme, you have option to join open courses – where you spend low (almost zero) resources, but need high self learning motivation. On the other hand, you have courses run by big universities like Stanford / MIT / North Western, where you will need to spend money and will get help and mentor-ship from experts over longer duration. You can choose the style of your learning depending on where you fit in.

Please note that irrespective of which method and blend you choose, you will need to aid these trainings by hands on projects and practice. No resources or trainings can cover that for you. Here are a few examples of these projects.

For people relying completely on self learning, our learning paths can be of great help. There is one for Python, SAS, Weka and Qlikview each and several more under development.

 

Step 4: Where to learn?

Now that you know, what to learn and how to learn, you can shortlist various options available. You should talk to people who have undergone that training / course and gather some reviews. You can also use our training listing page and apply filters to shortlist the trainings available for various tools and techniques. We have more than 300 trainings listed here and are in process of adding more trainings and courses.

 

End Notes:

So, there you go! You should have a way to find out your way through this data science course juggle. Hope you find this framework immensely useful. I have tried to put a framework to the most common query I get from our audience. The idea is to enable you to make the right decision to the extent possible. If you think, you are in a situation which doesn’t get addressed by the framework above, please feel free to ask those questions through comments / discussion portal.

P.S. These are my views. A lot of these recommendations are based on my experience and what I think is the right choice. As you can expect, some of these questions don’t have a right or wrong answer. They are subjective in nature. So, if you have a different opinion about something I have mentioned, please feel free to let me know.

If you like what you just read & want to continue your analytics learning, subscribe to our emailsfollow us on twitter or like our facebook page.

25 Comments

  • Suravi Kalita says:

    Nicely written article.

  • Darshana says:

    Dear Kunal,

    Thanks a lot for sharing very interesting insights about choosing the right program for analytics and big data. 🙂

    Regards,

    Darshana

  • Ruthger says:

    Hi Kunal,

    Very nice and clear article!

    What I actually missed were basic Unix Shell programming skills. It can be extremely useful to know how to use commands like grep, awk and sed etc to perform essential data cleaning and pre-processing of the data before bringing these data as for ex. a .csv file into Excel or R.

    Could you expand a bit on what you feel are the advantages of QlikView / Tableau / D3.js beyond for example making the graphics in R?

    All the best!

    Ruthger

    • Kunal Jain says:

      There are 2 advantages where I think a data visualization tool can come very handy:

      1. Understanding and exploration of Huge Data – For example, while working on Avazu CTR Kaggle problem, we were working on 7GB data with anonymized columns. It was becoming time consuming to load this data in R and perform exploratory analysis. With QlikView, we could load the entire data in less than 5 minutes and then perform exploratory analysis very quickly. What helps is quick slice and dice and drill throughs available. So you can quickly identify high and low value population and segregate them in your modeling in R

      2. The second application is in finally delivering your insights to the customers. Once your analysis is complete, you can use story-telling feature of these visualization tools to present your findings. You can bookmark the graphs and access them quickly on the go. If people want to explore additional information – it is typically far more easier to do so rather than opening RStudio and then writing / running the codes.

      Hope this helps you answer the question.

      Regards,
      Kunal

  • Amit says:

    Nice article,

    People, please do not attend Great Lakes program in Chennai, it’s waste of money.

    poorly conducted and highly rated program, not fit for people with experience, this is not a value for money proposition.

    It’s highly theoretical and no exposure to Big Data and Hadoop without which no one in Industry would be ready to take you in for Sr position.

    Please refrain from joining the course in Chennai. I part of second batch which was started last year.
    Capstone project will be flop, industry tie up’s are just marketing they have no written approval from companies like HCL, Accenture, IBM, Cognizent etc.
    Please take your informed decision by evaluating and comparing various programs and institutes, this is my feedback for the institute.

  • Prasenjit says:

    Hi Kunal,

    As always your articles have been very much informative and the posts related to Learning Path on SAS, R, Python, Weka were really helpful for analytics newbies trying to get into/switch/shift from other knowledge driven industries.

    Just to add few more pointers on the usage of tools like SPSS, Stata and Matlab would be icing on the cake for a data science professional for interpreting ANOVA tables, solving linear regressions and multivariate analysis.
    There is a coursera level1 course starting(23 March) in Applied Regression Analysis as a part of which they are providing free access to Stata software …link –> https://www.coursera.org/course/appliedregression

    Some more pointers on the “Where to learn” would be for a “not-so-reputed” institute Calcutta Business School who have tie ups with SAS institute to nurture students in Analytics and Data Science stream with concepts of Data Mining, Machine Learning, Big Data and exposure to tools like SPSS, R, Matlab,Big Data(apache stack), Tableau and SAS(Base, EGBS, Eminer, Content Categorizer Studio, DI, VA). I have sent you a message couple of days back in Linkedin with the course content attached for your perusal.

    Many thanks for all of your efforts and you have been doing a fabulous job in making things easier for Analytics professional who needs a little help and push to succeed.
    Would request you and your team to publish a Learning Path on
    1) Big Data concepts with references to Hortonworks/Cloudera platform or base Apache Stack.
    2) Fraud, Risk, AML related analytics case study

    Kind Regards,
    Prasenjit

  • Pankaj says:

    Hi Kunal,

    Nice article! Gives a good direction to anyone looking to jump in this field! Thanks 🙂

    Regards,
    Pankaj

  • vaibhav kumar says:

    Hi Kunal,

    first of all thank you for clearing our doubts and providing useful information.
    I did my graduation last year and currently pursuing certification course in business analytics.
    I do have interest in this domain and wanted to pursue masters in business analytics(MBA).
    Is a good step???
    and please do suggest me college apart from iim’s. as i came across galgotia’s univesity in noida ,started offering mba in analytics in 2013, chandigarh university,empi college,BML Munjal university (establishedlast year) and niit university (rajasthan).
    PLEASE DO SUGGEST ME COLLEGES ,AS I’M VERY MUCH CONFUSED.

  • anil says:

    Hi Kunal Sir,

    From past few months i read all your threads and now i am a big fan of yours sir and desperately motivated for Data analytics.
    I am a software professional having 3 years experince in ETL process, data analysis and ETL testing and very good in oracle SQL/PLSQL and oracle SQL analytical functions.
    I had also enrolled BIGDATA HADOOP developer course from SimpliLearn, but i want become a data analyst or data sceince.
    I am very confused to found the right way to jump into ocean of Data Analytics,therefore requesting you to please suggest the right way to get into data analytics feild,

    Thanks,
    Anil Kumar

  • anil kumar says:

    Hi Kunal Sir,

    From past few months i read all your threads and now i am a big fan of yours sir and desperately motivated for Data analytics.
    I am a software professional having 3 years experince in ETL process, data analysis and ETL testing and very good in oracle SQL/PLSQL and oracle SQL analytical functions.
    I had also enrolled BIGDATA HADOOP developer course from SimpliLearn, but i want become a data analyst or data sceince.
    I am very confsed to found the right way to jump into SEA of Data Analytics,therefore requesting you to please suggest the way to get into data analytics feild,

    Thanks,
    Anil Kumar

  • Pankaj Singh says:

    Thank you Kunal, This is really great article. it help me a lot.

    Regards,
    Pankaj

  • rajshekar targar says:

    why learn both qlikview and SAS. both are BI tools.can’t we learn anyone of them?

  • aniruddha says:

    Hi,
    I am currently doing mba financial services from a tier 2 B school in Mumbai;posses a BE (IT) background with experience in pl/sql programming in a MNC.I am interested in Credit Risk analytics like modeller or model appreciation etc.Currently I am learning R;but I doubt whether R is used in this segment or not.Would it be better to learn SAS.

  • krishna satish says:

    Hi,

    I am working as a Sr.Software Developer in a MNC . i work under Mainframe Technology and i have a total of 4+ years of experience. i am planning to learn new technology/concept/platform to grow up in my career. i am just aware of EXCEL but not to the level(LEVEL 0) you mentioned above). Hence could you please guide me on my framework. your response would be much appreciated

  • karthik v says:

    it’s really superb sir ….
    thank you very much

  • Pankaj Kumar says:

    Hi Kunal,

    I have been selected for Pgpba program at Great lakes…and also at Praxis Business School, Kolkata…praxis PGPBA is a full time program with internship & placement… Currently I am working in IBM .I have 3 yrs of experience in Market research..Please guide me which of two is a better option…what kind of opportunities I could get after PGPBA from great lakes…Please reply.

  • Himank says:

    Hi Kunal

    I have done B.E. in computer science and working on Qlikview for ETL as well as for Data Visualization from past 3 Years. I m keen too learn something else also n researching for any analytical course or something good that will help my career.
    It will be greatful if you suggest whats best for me.

    Thanks in advance
    Himank

  • Rayaan says:

    Hi Kunal,

    Thanks for the insights in this article, However I’ll ask you a more specific case about myself.
    I’m in to data warehousing (Informatica) and RDBMS (Oracle & TD) with 7 yrs of experience.
    with sudden outburst of requirements in BigData industry I’ll like to make a shift to these technologies.
    My interest lies in NOSQL Dbs & in the field of data science. Could you please chalk out the plan for me.
    Also let me know how much of Java is required to be able to functions as BIG DATA analyst or to effectively perform my activities.

    Thanks in advance
    Rayaan

  • Kamal T says:

    I first encountered the learning paths but then I was confused which one to start with. This article is a way to decide that (and much more). This is exactly what a beginner would need! Bookmarked

  • Vikram says:

    Hi Kunal,
    Nice Article . I am a Mechanical Engineer working as an Operational Engineer in a MNC. I have experience of working with Qlik View. But I don’t have experience of working with SQL or R or SAS. Is it possible to learn SQL or SAS without prior programming knowledge of programming. And is experience in SAS and SQL necessary to work in Business Analytics.

    Please guide

  • Vijay says:

    Hi Kunal, I’m an engineering graduate with 13 years of experience in corporate sales. I’m looking at changing my career into analytics one due to my interests for that field and it’s prospective opening … I have fine few coursera courses in R programming and machine learning, etc… Pls suggest me a path to help me land in this industry as currently bi don’t have any experience in this field … Suggestions pls, will doing a certification programs offered by ISB, IIMB, etc will help … Looking forward to your advice in how I should place myself, steps, etc … Looking forward to your valuable feedback

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