Data has unanimously become the single most valuable resource for any organization. The more you know , the better can you do whatever it is that you do. As such, the demand for Data Analysts, people who could make sense of all that data, grew exponentially in the last decade. From the most basic levels of operational architectures to monitoring thousands of users – trying to find that one tiny detail that might transform their experiences, Data Analysts allowed decision makers to actually see what their decisions are doing. To plan ahead . To think strategies decades in advance.
But then, came the AI boom and nothing remained the same.
Far surpassing the capabilities of the human brain , machine learning based artificial intelligence solutions are now able to do the work that a team of Data scientists could do in a week , in less than an hour. The only catch here is that building such a smart software takes time. The bigger companies have already moved onto AI but the younger ones are still in the process of building it. Data Analysts have to accept the reality – Their role IS going to change. There is no stopping it.
The good thing is , due to the still unstable nature of the Data Industry Ecosystem, now is the best time to move up the ladder. You might have to work really hard picking up new skills on the way , but combined with your pre-existing knowledge of data , you can add value to a business that is irreplaceable.
The following is a list of career paths you can take if you already have knowledge of playing around with data :
1. Data Explorer
A Data Explorer is expected to be able to identify and connect to new data sources, merge and prepare the data, and build production-ready data pipelines. The purpose of the products you’ll be helping to build is for them to run in production, and so you’ll be obsessed with automation and reproducibility. You’ll be the local expert on the details of the data – when a new data source is added, you’ll know what fields it contains and which new features you might be able to engineer from it. You will also have your eyes open to new open data sources that you might be able to use to enrich your internal data. And although a good portion of feature engineering will be done by the Data Modeler, you will be in charge of engineering features like KPIs, which require your deep familiarity with the business implications of the data. You’ll still need to be familiar with machine learning algorithms, and you’ll probably need to have a firm grasp on data architecture concepts, such as distributed computation.
Good Fit For : Analysts with skills in : SQL, SaaS, Excel, Data Visualization
Things You Need To Learn : Business Intelligence , Data Cleaning, Machine Learning , Automation , Distributed Computing
- Excel Guide by Trello’s Founder Joel Spolsky – You suck at Excel
- Online course on Basics of Data Cleaning by the European Data Portal
- Udacity Course on Python
- Udacity Course on Machine Learning
2. Data Modeler
A Data Modeler is in charge of building predictive models and generating either a product or a service from those models, and then implementing them. You will create checks and metrics for monitoring these models, because there will be a huge amount of them in production! You will be a master of machine learning models and the frameworks used to validate their quality. You will apply your creativity in feature engineering: using abstract mathematical techniques to select and combine the right variables and use them in the right model. This will often require you to reduce the number of variables from an enormous number down to something more manageable. In short, you will be the go-to person on your team for all thing math, stats, and algorithms – and also for knowing how to use different types of data in the many models available to you at your fingertips.
Good Fit For : Statisticians, Computer Scientists, Financial analysts, and, Mathematicians;
Things You Need To Learn : Python , R , Data Visualization, Data Modelling , Machine Learning
- Andrew Ng’s Course on Machine Learning
- Anand Rajaraman and Jeffrey Ullman’s book – Mining of Massive Datasets
- Oxford professor Nando de Freitas’s Deep learning lecture series on YouTube
- Python Machine Learning: a practical guide around scikit-learn
3. Data & Analytics Product Owner
A Data & Analytics Product Owner is a Jack of all trades. There are many paths that lead to this role. You might already have been a Data Explorer or a Data Modeler; you might lead an analytics team, or you might come from outside the analytics team altogether. No matter what their background, these Product Owners have established that they have a good, well-rounded expertise in the world of data and machine learning, coupled with complementary skills in management and communication. Their main job is supporting Data Modelers and Data Explorers by gathering requirements, prioritizing tasks, and making sure the products and services being built are working for the end users within and beyond your organization. They have to be able to explain data and analytics products and have deep knowledge of the user profiles. They are the bridge between the data team and those who rely on the data team.
Product Owners are expected to apply user experience (UX) and design thinking concepts to data products and services that will no longer be used only by technical users but instead by the broader organization and even users and customers outside the organization. They are the person the organization relies on to ensure value comes out of all the data and analysis..
Good Fit For : Analytics Managers, Senior Analysts, Product Managers with Data Science exposure
Things You Need To Learn : Product & Team Management, Scrum , Everything else needed by Data Modelers and Explorers.
- All of the resources for Data Modeler and Explorer
- Agile Manifesto – For those new to management
- The Elements of SCRUM – By Chris Sims and Hillary Louis Johnson
The world is rapidly changing with new factors and trends coming into account all the time. A good career requires hard work , constantly. You have to train yourself regularly to keep on top of the game. The above three are great paths for you to steer your career towards. But if not , don’t forget, there is a whole world waiting for you.