Investigating Data Scientists, their Skills and Team Makeup

A new survey of 490 data professionals from small to large companies, conducted by AnalyticsWeek in partnership with Business Over Broadway, provides a look into the field of data science. Download the free Executive Summary of the report, Optimizing your Data Science Teams.

Our world of Big Data requires that businesses, to outpace their competitors, optimize the use of their data. Understanding data is about extracting insights form the data to answer questions that will help executives drive their business forward. Do we invest in products or services to improve customer loyalty? Would we get greater ROI by hiring more staff or invest in new equipment?

Getting insights from data is no simple task, often requiring data science experts with a variety of different skills. Many pundits have offered their take on what it takes to be a successful data scientist. Required skills include expertise in business, technology and statistics. In an interesting study published by O’Reilly, researchers (Harlan D. Harris, Sean Patrick Murphy and Marck Vaisman) surveyed several hundred practitioners, asking them about their proficiency in 22 different data skills. Confirming the pundits’ list of skills, these researchers found that data skills fell into five broad areas: Business, ML / Big Data, Math / OR, Programming and Statistics.

Data Skills Survey and Sample

We invited data professionals from a variety of sources, including AnalyticsWeek community members and social media (e.g., Twitter and LinkedIn), to complete a short survey, asking them about their proficiency across different data skills, education, job roles, team members, satisfaction with their work outcomes and more.  We received 490 completed survey responses. Most of the respondents were from North America (68%), worked for B2B companies (79%) with less than 1000 employees (53%) in the IT, Financial Services, Education/Science, Consulting and Healthcare & Medicine (68%). Males accounted for 75% of the sample. A majority of the respondents held 4-year (30%), Master’s (49%) or PhD (18%) degrees.

Data Science Skills

Figure 1. Proficiency levels across 25 data skills. Click image to enlarge.

Data science is an umbrella term, under which different skills fall. We identified 25 data skills that make up the field of data science. They fall into five broad areas: 1) Business, 2) Technology, 3) Programming, 4) Math & Modeling and 5) Statistics. Respondents were asked to indicate their level of proficiency for each of 25 different skills, using a scale from 0 (Don’t know) to (Expert).

Proficiency levels varied widely across the different skills (see Figure 1). The respondents reported a high degree of competency in such areas as Communications, Structured data, Data mining, Science/Scientific Method and Math. The respondents indicated a low degree of competency in such areas as Systems Administration, Front- and Back-end programming, NLP, Big and distributed data and Cloud Management.

Job Roles

Figure 2. Job roles of data professionals.

Respondents were asked to indicate which of four options best described themselves and the work they do (e.g., job role). Over half indicated their primary job role was a Researcher, followed by Business Management, Creative and Developer (see Figure 2.).

Most of the respondents identified with only one primary job role (49%). About 32% indicated they had two job roles. About 14% indicated they had three job roles and 4% indicated they had all four job roles.

Figure 3. Satisfaction with outcome of analytics projects by job role
Figure 3. Satisfaction with outcome of analytics projects by job role

Looking at data professionals who selected only one job role, we examined their satisfaction with the outcomes of their analytics projects (see Figure 3.). The results showed that data professionals who identify as Researchers reported significantly higher levels of satisfaction with the work they do compared to data professionals who are Business Management or Developers.

Data Scientists are not Created Equal

Figure 4. Proficiency in Data Science Skills by Job Roles. Click image to enlarge.

What does it mean to be a data scientist? After all, there are many different skills that fall under the umbrella of data science. The professionals’ job role was logically related to their proficiency in different skills (see Figure 4.). I examined differences of data professionals who indicated they had only one primary job role. Data professionals in Business Management roles had the strongest business skills of all data professionals; Developers were the strongest in Technology and Programming skills; Researchers were the strongest in Statistics and Math & Modeling skills. The Creative types didn’t excel at any one skill but appeared to have a decent level of proficiency across all skill areas.

Data Science is a Team Sport: The Need for Complementary Skills

Figure 5. Effect of teammate’s expertise on satisfaction with analytics work outcomes. Click image to enlarge.

The results of the survey showed that data professionals tend to work together to solve problems. Seventy-six percent of the respondents said they work with at least one other person on projects that involve analytics.

To better understand how teams work together, we looked at how a data professional’s expertise impacts their teammate. We asked respondents how satisfied they were with the outcomes of their analytics projects. Additionally, we asked data professionals if their teammates were experts in any of the five data skill areas.

Results showed that Business Management professionals were more satisfied with the outcome of their work when they had quantitative-minded experts on their team (e.g., Math & Modeling and Statistics) compared to when they did not (see Figure 5.). Additionally, Researchers were more satisfied with their work outcome when they were paired with experts in Business and Math & Modeling. Developers were more satisfied with their work outcomes when paired with an expert in business. Creatives’ satisfaction with their work product is not impacted by the presence of other experts.

Summary and Implications

Solving problems with data requires expertise across different skill areas: 1) Business, 2) Technology, 3) Programming, 4) Math & Modeling and 5) Statistics.

Different types of data professionals (as defined by their role) are proficient in different areas. Not surprisingly, data professionals in Business Management” roles are the most proficient in business skills. Researchers are the most proficient in Math & Modeling and Statistics skills. Developers are the most proficient in Technology and Programming. The Creative types have some proficiency in all skill areas but are not the best in any single one skill area.

It appears that a team approach is an an effective way of approaching your data science projects. Solving problems using data (e.g., a data-driven approach) involves three major tasks: 1) identifying the right questions, 2) getting access to the right data and 3) analyzing the data to provide the answers. Each major task requires expertise in the different skills, often requiring a team approach. Different data professionals bring their unique and complementary skills to bear on each of the three phases of data intensive projects.

Finally, these preliminary findings are interesting and have important implications for business in helping chief data officers and hiring managers better understand their data science capabilities. Chief data/analytics officers need to focus on both data skills of their professionals as well as team composition. Additionally, recruiters need to effectively market to and recruit data professionals who have the right skills to fill specific roles. Getting feedback from data professionals can help organizations identify and close any talent gaps and improve how they manage their data science teams.

Download the free Executive Summary of the report, Optimizing your Data Science Teams.

To learn more about the DS3 Enterprise version, click here.


Apr 02, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.


Q:What is statistical power?
A: * sensitivity of a binary hypothesis test
* Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
* Ability of a test to detect an effect, if the effect actually exists
* Power=P(reject H0|H1istrue)
* As power increases, chances of Type II error (false negative) decrease
* Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: ‘how many times do I need to flip a coin to conclude it is biased?’
* Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis



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@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

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Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

Social Media Analytics – What to Measure for Success?

If you are using social media for business or promotional purposes, then you should know how to measure it. However, when getting on to measure social media, it is not just for the sake of having some metrics, but to measure the effectiveness of the social media campaigns and further streamline it for better results. With proper analytics, you will be able to understand what was successful, what wasn’t, what your target audience expects, and how you can improve.

Two types of measurements

We may find that there are two major social media measurements to consider as:

  • Ongoing analytics – Tracking activities over time and monitoring performance.
  • Campaign-centric analytics – Getting analytics related to each event or campaign to assess its success.

Ongoing analytics will help to track the pulse of your social media communications regarding your business and brand in general. Once you set up the elements of brand tracking, you may just let is run by default and frequently fetch the data to see how it is going.

On the other hand, the campaign-specific metrics will help to understand the actual impact of your targeted content, which may be different from campaign to campaign. An ideal social media analytics program will take both of these measurements in a fine balance.

Monitoring social media analytics

Monitoring your social media analytics may surely distinguish between success and failure of your social media activities. In this excerpt, we are trying to outline what all things you need to monitor on social media and what tools to be used to do proper analysis and reporting or social media campaigns.

Social media analytics compass

It is almost impossible to monitor and measure everything related to social media on every channel at a time. So, we need to determine what is essential for your business and how to do monitoring of it well. For a better understanding of beginners, we will discuss the most critical generic areas of the so-called social media compass, i.e., the most important analytical measures.

  1. Size of the target audience

Many are confused about the matter whether the size of your audience really matters? Of course, it does matter if you are trying to promote a brand or service and building a relevant audience. It is essential to continuously build a relevant audience if you want to take your message to the right people on time.

Your audience will grow gradually through organic methods as well as on investing in paid ads or so. In fact, there is nothing wrong if you plan to invest in audience building tactics if you have a scope of converting the audience into business overtime. You should compare your rate of audience growth over a week or month with that of your competitors. Along with building the audience rate, also keep track of the unfollowers too on the go.

  1. Audience profile

As you slowly grow your audience, it is also essential to ensure that you are building the right type of audience, especially when you are paying for it, deciding whether you are making a worthy investment. Say, for example, if you try to build it through Twitter, this platform will allow you to access reports stating what types of profiles like marketers, entrepreneurs, or musicians, etc. are a part of your audience group.

You can do the same on Facebook also by setting up an ad which is targeted to a specific category of your Facebook target audience. This is also possible on Instagram to get some real Instagram likes. Say, for example; you can do this filtering for a specific interest of people, and see how many of your followers fall in that category to plan relevant campaigns for them. With a smart approach to it, you may perform this profile analysis across all social media platforms. You may use a traditional approach like surveys to most advanced premium tools offered by social media platforms to accomplish this.

  1. Reach and engagement

The campaigners also should monitor the social reach of your content and also see how much actually pay keen attention to it, even if not responding. Lack of responses doesn’t necessarily be non-interest. Engagement is another key aspect of monitoring as some of them with keen interest may engage with your content. If you find no engagement at all, then it may be either be the wrong content or the wrong audience you hit. You may typically split your audience under the following categories.

  • Lurkers – those who simply watch your content, but not interacting.
  • Influencers – They are connected to a large audience and can make an influence among them.
  • Engagers – People who are largely active in your target community and people will start recognizing their names.
  1. Traffic

The primary objective of your social media campaigns is to bring traffic back to your website or product pages. For some promoters, traffic is just enough. Say for example, for a site; they get paid for ads based on the volume of traffic. For the rest, the traffic needs to be converted into sales to meet their objective.

  1. Content analysis

As we have seen, creating content and sharing it through social media is an expensive and work-intensive affair. So, on a regular basis, you need to do content analysis as well to see if your efforts are getting recognized or not. You have to check whether:

  • Whether your images, videos, and text updates work the best?
  • Whether the content you share is in fine balance with the right mix or too much focused-on promotion?
  • Do you have enough engagement on the questions?
  • What changes are there on the social media platforms and what changes it demands from you?
  1. Sentiment analysis

The sentimental analysis covers the negative, positive, or neutral mentions on your brand. The latest social media tools are more focused on measuring the sentiments of the target audience over your brand through their social mentions about you. Even though these tools are not 100% accurate, it can surely be a good indicator of where you go wrong and what to correct.

These are some functional pointers included in social media analytics, which is a far wider specialty. However, it could be a good starting point if you master over these to streamline your social media campaigns to meet your online objectives.

Source: Social Media Analytics – What to Measure for Success? by thomassujain