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High Demand for Data Scientist profiles in LinkedIn

High Demand for Data Scientist profiles in LinkedIn

Currently, Data Science experts are the most sought candidates in the world. According to a research report published by DJ Metrics, the number of ‘Data Scientist’ profiles in LinkedIn has nearly doubled over the last few years. At present, there are more than 11,400 data scientists on the professional networking website, out of which, 52% have added the particular job description (read Data Scientist) during the period between 2012 and 2015.

About the Research

DJ Metrics have taken into account 60,200 LinkedIn profiles of professional experts, while 27,700 records of Educational data and 254,000 records of skills sets were also used to conduct an analysis. Additionally, they have analysed the database of 6200 companies that have provided employment to the Data Scientists. The names of the Companies were collected by analysing the profiles of the Data professionals, since they have listed the names of their employers.

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Great Career Opportunities

Great Career Opportunities

Researchers are forecasting that there will be a steady rise in the demand for trained Data Scientists, because of the increased adoption of Big Data and Business Intelligence by the leading global companies. High-end business organisations like Microsoft and Facebook are going through a continuous recruitment phase, as these companies had accelerated their hiring process by 151% and 39% respectively in 2014, as compared to what they had done in 2013.

According to the research report, about 65% of the total recruitments were carried out by the following industries:

  • Information Technology and Services, Internet and Computer Software Sector: 9%
  • Education: 3%
  • Banking and Finance: 2%
  • Marketing and Advertising: 2%

Big Data demands Bigger Skills

Big Data demands Bigger Skills

 DJ Metrics has analysed the database of 254,000 skills in order to figure out the growth in the number of skilful Data Science professionals. The results are significant, as apart from the general ‘power’ skills; namely, Data Analysis, Analytics and Data Mining, the top skills found among the vast number of profiles included R, Python, Machine Learning, MATLAB, JAVA, Statistics and SQL. Surprisingly, the Chief Data Scientists are found to have the least technical skills, as only 27% of the profiles had listed Python, while 26% listed R as their technical skill sets. On the other hand, 52% and 53% Junior Data Scientists have listed Python and R, respectively.

Top Recruiters

Top Recruiters

If you see the chart above, you will see that Microsoft and Facebook are the top recruiters over the given period. Surprisingly, Google has not made it to the top 10, although it has recruited quite a number of Data Science professionals. The reason may be that the Data Scientists at Google are called ‘Quantitative Analysts’, which is probably used by their employees while listing their designation on LinkedIn. Since, LinkedIn has researched about the general Data Scientists; they may not have detected the alternate titles.

Countries with highest Data Scientist population

Countries with highest Data Scientist population

Almost 55% of the total Data Scientists in the world are currently located in the United States of America (USA), which makes the top of the list. The second country with maximum numbers of Data Science professionals is United Kingdom (UK), while the third position is occupied by India.  

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Know The Answer To These Interview Questions To Get A Job As Data Analyst

List of Interview Questions for Data Analysts

With this Data analyst interview guide you will know what to expect in an interview round for a position of data analyst.

A good data analyst or scientist must be capable of drawing actionable insights from the data that a company generates. They must possess a good sense of what data they must collect and should have a solid process for carrying it out effectively using processes of data analysis and building predictive models.

A data analyst must possess a strong foundation in the following topics: operations research, statistics, machine learning along with some database skills, such as SQL or SAS in order to clean, retrieve and process the data from different sources. One can lead to this role from different pathways thus candidates can expect to be bombarded with questions relevant to statistics ort mathematics and even computer programming or engineering.

Data scientists are also often required to script programs using R or Python or Matlab and the role will typically not place emphasis on the programming skills or practices and the general software engineering skills which is necessary for working with production quality software.

Here is a list of common data analyst interview questions:

Operational questions:

  1.  Describe the steps that you follow when creating a design a data-driven model to manage a business problem. For example you may try and automatically classify customer support mails, by either sentiment or topic. Another task may be to predict a company’s employee churn.
  2. What models would you classify as simple models and which are the ones that are complex according to you? What are the comparative strengths and weaknesses of choosing a more complex model over a simplistic one?
  3. What are the possible ways in which you can combine models to create an ensemble model and what are the main advantages of doing this?
  4. Tell us about certain pre-processing steps that you may carry out on data before using them to train a model and describe the conditions under which they may be applied.

Role specific questions:

About basic ideas in probability, statistics and machine learning:

  1. Define what is confidence interval and why do you think it is useful?
  2. What is the main difference between correlation and independence?
  3. What is Bayes Theorem? What is conditional probability? What is its use in practice?
  4. When and how do you understand that you have collected ample data for building a model?
  5. Tell us the difference between classification and regression.

Hope this list of common data science interview questions will prepare you for a job at a reputable data analysis company. For more such data science news, tutorials and articles with emphasis on programming and analytics view our regular updates from DexLab Analytics.

 

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Quantitative Analysis 1 – Five Number Summary

To be a successful analyst or be a part of great analytics team, there are 3 important dimensions one would aspire to be or have. They are technical, business and tools. Hence, we would begin with one of the sub dimension of the technical skills, i.e. being quantified self or developing quantitative skills.

 Quantitative Analysis 1 – Five Number Summary

As per the Informs, the definition of Analytics shall be:

  Continue reading “Quantitative Analysis 1 – Five Number Summary”

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