What’s trending: Big Data vs Machine Learning vs Deep Learning?

If you’re new to Analytics, you might encounter too many topics to explore in this particular field starting from Reports, Dashboards, Business Intelligence to Data Visualization to Data Analytics, Big Data to AI, Machine Learning, Deep Learning. The list is incredibly overwhelming for a newbie to begin his/her journey.

I really wanted to rank and check which one is currently trending relative to each topic among these five buzzwords: “Business Intelligence”, “Data Analytics”, “Big Data”, “Machine Learning”, “Deep Learning”.

I made use of my favorite Google Trends tool for my reference purpose. I’m interested to assess based on the worldwide data for last 5 years using “Google” search engine queries as the prime source.

Analytics Trends 1
Analytics Trends 1

I inferred the following from the above user-searched data:

  1. Big Data stayed at the top of the users’ mind for quite long time since 2012. However, Machine Learning is soaring higher from 2015, and it could potentially overtake Big Data in a year as the “hottest” skill-set to have for any aspiring Analytics professional.
  2. Deep Learning is an emerging space! It would eventually gain more momentum in 1 year from now. It’s essential to gain the knowledge of Machine Learning concepts prior to learning about Deep Learning.
  3. Needless to say, Data Analytics field is also growing moderately. For beginners, this could be the best area to begin your journey.
  4. BI space is starting to lose out its focus among the users thanks to self-service BI portals (and automation of building reports/dashboards), Advanced Analytics.

 

I happened to see few additional interesting insights when I drilled it down at the industry-wise.

  1. Data analytics is still the hot topic for Internet & Telecom
  2. Big data for Health, Government, Finance, Sports, Travel to name a few
  3. BI for Business & Industrial
  4. Machine Learning for Science

 

Users interest by Region says that China is keen on Machine Learning field and Japan on Deep Learning. Overall, Big Data still spread all over the world as the hot-topic for time being. Based on the above graphs, it’s quite evident that Machine Learning would turn out to be the top-most skill set for any Analytics professional to have at his/her kitty.

You can go through this Forbes article to understand the differences between Machine Learning and Deep Learning at a high level.

Pls let me know what you think would be the hottest topic of interest in the Analytics spectrum.

Google Introduces Natural Language Queries In The Docs “Explore” Tool

Google Docs Explore feature was first introduced last year, and it aids us while drafting documents, spreadsheets, slides to a good extent. In this article, I’d like to share few stuffs around how to use Google docs “Explore” feature on a spreadsheet. It uses machine learning algorithm to understand the natural language based text queries and delivers the outcome instantly!

Isn’t it cool if you could just use natural language texting to understand the top, bottom and basic statistics in a matter of few seconds?! Needless to say, it also recommends us to refine the queries further 🙂

For illustration purpose, here’s a simple example:

I’ve created a sample data depicting the students’ scorecard. It comprises of student names, subjects and the corresponding scores. If you click on the headers and navigate to Explore option at the bottom-right corner of the Google Spreadsheet screen, it would pop-up and recommend us the queries that we might be interested in.

Google Doc Explore 1
Google Doc Explore 1

Some of the questions can be:

a) Top Score,

b) Least Score or Bottom Score,

c) Unique Subjects

d) Average of Score by Subject

e) Histogram of Score

f) Bar Chart of Average Score by Subject

You can start exploring the available fields using this way. I believe it can be a handy tool if you use Google spreadsheet for your projects. It also allows us to customize the generated formula as per below screenshot. The syntax looks pretty much like an SQL programming.

Google Doc Explore 2
Google Doc Explore 2

You can also make use of this feature as part of your exploratory data analyses. And here’s another classic example for you:

If you’re a teacher/head of the school, you would be surprised to see the below distribution Average Score vs. Subject. The tool provides us the insight that Physics has got the lowest score among other subjects. This is just the starting point to your analysis to go deeper into the data, and uncover the patterns. One of the key decisions from this inference could be to refine your education methodologies for ‘Physics’ subject.

Google Docs Explore 3
Google Docs Explore 3

Go ahead and explore the Google’s Explore feature and let me know how it helps you!

Credits:

Article’s cover page: http://alicekeeler.com/2016/09/30/google-docs-not-research-tool-explore/

Top Data Science Skills By Job Role

Do you have a fair idea about data science? I hope, yes. How about its skill-sets?

For newbies, Data Science is the field which is intersecting Statistics, Mathematics, Programming/Technology and Business. Using a combination of all these components, insights, models can be drawn using the data. It was a term coined in 2001 by a Professor of Statistics, William S. Cleveland at Purdue University.

I read at least 5 articles from the web today to understand the nuances of this role. Little did I know prior to my research that there are lot many things attached to the buzzword “Data Science”.

Broadly speaking, the skill-sets required to be a Data Scientist (as the way many companies call the folks who work in Data Science domain) fall under the following skills viz,

  1. Business
  2. Technology
  3. Math & Modelling
  4. Programming
  5. Statistics

We can even sub-classify them into further such as Programming – R, SAS, Python, SQL, NOSQL to name a few.

Business2Community features an article written by Bob Hayes. He came up with a questionnaire listing 25 data science skills, captured the responses, analyzed and ranked the top 10 skills from the results based on the proficiency level as “Intermediate” criterion.

25 Skills in the Data Science by AnalyticsWeek and BusinessBroadway
25 Skills in the Data Science by AnalyticsWeek and BusinessBroadway

Top 10 Data Science skills in general are:

  1. S – Communication (87% possess skill)
  2. T – Managing Structured data (75%)
  3. M – Math (71%)
  4. B – Project management (71%)
  5. S – Data Mining and Viz Tools (71%)
  6. S – Science/Scientific Method (65%)
  7. S – Data Management (65%)
  8. B – Product design and development (59%)
  9. S – Statistics and statistical modeling (59%)
  10. B – Business development (53%)

It’s interesting to note the fact that Communication stands first when compared to other skills! Catering either to internal or external customers, the data scientists talk to business functions such as Marketing, HR, Operations, Finance etc.I think it makes sense because what’s the point if one works hard at developing a model but not conveying the results as per the business needs. Guess what? “Data Presentation” has become one of the top 10 skills in 2016 published by LinkedIn.

Bob also charted out the top skill sets by job role level. This one is another interesting perspective.

 

Top Data Science Skills by Job Role from Business Broadway
Top Data Science Skills by Job Role from Business Broadway

Researcher can focus more on Statistics; Business Manager on communication, project management; Developer mostly on the programming aspects and so on.

With that said, it’s very tough to focus on learning all the skill-sets of a typical data scientist at a single stretch. Depends on who you want to become, the above list would be beneficial for you. Hence, you can prioritize and narrow down to the list and start learning one at a time! If you’re already good at Statistical concepts, try learning how to program the techniques using “R” programming language. This way, I think one can steadily adapt to the data science skill-sets.

Please remember that there’s no one size fits all approach! If your buddy is good at programming because of his formal educational background being from a software discipline and moving faster on a learning curve, that’s perfectly okay for you to keep up at your pace depending on your comfort level. At the core of data science, you can be really good at one skill-set and know the basics & become eventually to an intermediate level at another skill-sets.

My focus will be on statistics to begin with. What is yours right now?