Isn’t it good to think about our career path once in few months? Trying to implement what the life has taught being proactive or say anticipate what is going to come in next 5–10Years. I may be overconfident that I can sustain my career with just digital marketing but it’s not going to bring exponential growth from X to 10X. I believe in the sentence for long time > ”Data is new oil”. And thankfully have moved from a traditional marketer to a data-driven specialist whether from performance-based marketing perspective or bringing tools for scalable execution and analysis.

But the New Problem!

Until now it is about how a human can use the data but in few years how we going to feed the machine to use the data. This is an observation if we see the industry trend, the job title related with data are getting sexier and more unique. I am forecasting with the innovation of automated digital ads(an example), the amount of work which marketers need to put to create ads is decreasing. So what as a marketer need to do is going to drastically different than what we used to do.

Some cases where currently AI extensively used in marketing are:

  1. Personalization of product merchandising in ecommerce (Evergage.com)(merchandiser can use this understanding to better merchandise the catalogue instead of playing with his own assumptions)
  2. App Predictions of user abandonment, loyalty, returns and profits ( localytics app marketing predictions) (app marketers need to understand how this works And better utilise automation of push notifications and lifecycle marketing )
  3. Attribution modelling (Windsor.ai) (analyst just need to make sure all the touch points and allow the system to predict where to spend and decrease)
  4. Using tools like Boomtrain, brands can send out customized email newsletters based on previous interactions recipients have had with content.
  5. Advertising: core field. With innovation of automated dynamic ads(facebook product feed) , automated bidding(facebook, Google, doubleclick), automated Budget Optimisation(doubleclick, smartly). The amount of marketers need to do will be reduced. Marketers need to be ready for this and learn how this automations works to better drive them

some more use cases : https://www.linkedin.com/pulse/15-applications-artificial-intelligence-marketing-robert-allen/

And the Idea!

So I need to learn and master any of the things which going to change the business drastically how they execute things. Or even we can use that to improve the other sectors in the society. Before data was just data analytics, now the data scope is expanded or packaged as Data scientist, Machine learning specialist/developer, AI specialist/engineer/developer. So I thought of collecting what companies are sharing in their description, just to get some patterns (at the end of the article is the consolidated info)

Than the idea, the execution here is tough because the topic is very broad and complex than I thought of. A silly way I did is collected all the description of many job listings and created a word cloud to see whats the repetitive word in those description (Below image)

It may not be exactly correct but it gave some sense to start and ask questions about those words. Next what I did is grouping those words in sticky notes and ask questions.

This data is big topic but the infographic from NUS explained well enough on what is my part. The infographic explained where developers, or database engineers or advanced marketers or data analyst(or says data scientist) play their role.

Knowing which part is our role can help us to eliminate the clutter and provide a better focus

According to the part where next generation advanced Marketers or data engineers sits is after the data extraction until Data visualisation. So the stages are

  1. Use scripting languages like python or R to extract the Raw data
  2. Use libraries/packages can be used to clean the data, process and transform the data
  3. Use algorithm to identify the pattern. Basically answering your business questions whether it can insights or recommendations, etc.
  4. Finally, send the data to visualisation tools. Or using basic visualization is available on python and R.

So what knowledges we need?

1. Extraction:

  1. Scripting Languages: R or Python (The mostly used are this both) or spark
  2. Scripting Environments: R or Python. Matlab become very old
  3. API calls to extract from platforms like facebook, Google, other ad networks
  4. Knowledge on connecting different systems
  5. Know what to extract.

2. Cleaning, Processing, And Transforming

Environment or Tools for processing :

*tools to process and analyse very complex data.

3. Framework : This are the packages for python according to the consolidated Job info.

Mlib or Mllib are said to be the libraries of Spark.

4. Algorithms:

*NLP plays important role on identifying the dense or size of each text and can be used to use cases as like sentiment analysis, trending topics, social monitoring, text recommendation what we get on messaging.

NLP toolkits (CoreNLP, OpenNLP, NLTK, Gensim, LingPipe, Mallet, etc.)

*Text pre-processing and normalization techniques, such as tokenization, POS tagging and parsing and how they work at a low level.

5. Statistical Modelling: (Modelling use algorithms)

6. Presenting that data:

On Additional The processed or the raw can be stored in Hadoop

7. Action Plans I planned

  1. Data science course by Microsoft : https://www.edx.org/microsoft-professional-program-data-science
  2. Google machine learning coursehttps://cloud.google.com/training/data-ml

8. Some links

Back to the story again….

This both were interesting to me comparing others as the latter focus it’s teaching on marketing use cases which will be easy for me(as am a marketer) to understand concept. The former is because their course material starts with excel and the some of add one which can be used to do some predictions and modelling. And then the deeper lessons focused on R or python.

The intention of sharing this article was to get feedbacks about the understanding I had so far and if there is other ideas from you how we can move forward with Data science and machine learning technologies. Whats your thought?

Initially published in Medium and Linkedin

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The Job Descriptions collected:

TIA:

In one of singapore government Job listing :

Toookitaki:

A hospital:

AI developer

Singa, Caffe, etc.)

Silent eight:

What will gain extra points::

Others :

Perx: Senior Data Scientist Lead

From Twitter JD:

Bonus points:

UBER:

What you’ll do

What you’ll need

FROM ALTITUDE LABS:

Job Requirements

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