Data is the world`s most valuable resource!
Data is not recent, but it is growing at an incredible rate. The increasing interactions between data, algorithms, and analytics of big data, connected data and individuals are opening enormous new prospects. Enterprises and even economies have now started developing products and services based on data-driven analogies. The ability to provide an agile environment to serve the data workload is critical with data powering so many innovative approaches, whether it be artificial intelligence, machine learning or deep learning. Data undoubtedly offers them the chance to enhance or redesign almost every part of their business model.
Engineers, researchers, and marketers of today could be the data scientists of tomorrow
According to data gathered by LinkedIn, Coursera and the World Economic Forum in the Future of Jobs Report 2020, it’s estimated that, by 2025, 85 million jobs may be displaced by a shift in the division of labor between humans and machines. Roles growing in demand include data analysts and scientists, AI and machine learning specialists, robotics engineers, software and application developers, and digital transformation specialists.
Top cross-cutting, specialized skills of the future
Data and AI skills are fast becoming essential digital skills required across all business disciplines.
Jobs of tomorrow
Roles growing in demand include data analysts and scientists, AI and machine learning specialists, robotics engineers, software and application developers, and digital transformation specialists.
Key roles in the Data Ecosystem
The lure of leveraging data for competitive edge is transforming organizations to become more data driven in their operations and decisions, it has resulted in an enormous job opportunity for various data related professions. The key data roles include Data Engineering, Data Analytics and Data Science.
Data Engineering entails managing data throughout its lifecycle and includes the tasks of designing, building, and maintaining data infrastructures. These data infrastructures can include databases – relational and NoSQL, Big Data repositories and processing engines – such as Hadoop and Spark, as well as data pipelines – for transforming and moving data between these data platforms.
Data Analytics involves finding the right data in these data systems, cleaning it for the purposes of the required analysis, and mining data to create reports and visualizations.
Data Scientists take Data Analytics even further by performing deeper analysis on the data and developing predictive models to solve more complex data problems.
A Deep Dive: Skills Needed for Data Professions
In terms of skills required to perform each of these roles, while there are some unique skills for each job role, there also some common skills that all data professionals need, however the level of proficiency required may vary.
IBM and Coursera hosted a very engaging webinar with 4 IBM Subject Matter Experts and discussed each of these roles in depth. The replay of the session can be found here:
Your burning Data career questions answered
We received over 300 questions before and during the webinar. Here are some of the most common questions received.
Q1. For someone with no work experience and a fresh graduate, which one is better Data Science or Data Analyst? Also, what advice would you give me to make my resume stand-out since I have no experience.
Both courses are good, but it would be great if you could start with one and then move onto the other one. In terms of making a start with no work-experience that’s a tough one. As a hiring manager, I tend to look for a portfolio in the form of:
1. School or side projects done during school
2. Work done on github with an emphasis on project summary, how clean the code in the notebooks is, what data modeling and visualization techniques have been applied
Basically, pick a passion project and create a portfolio based on it. Also, establish some presence by participating on relevant online forums, attend meetups, compete in hackathons, contribute to open source projects and submit proposals to trade journals and conferences. Finally, round out your resume with a diverse set of verifiable technical and non-technical skills.
Q2. I am mid-career with 10-15 years of experience looking to transition to a role in data. I have taken several online courses and earned badges. However, I am not being given option to pivot into DS roles due to lack of real-life experience. I also don’t want to start from scratch as a rookie. What advice do you have for me?
For these data roles in addition to technical skills and foundational mathematics you also need domain expertise. Since you’ve invested so many years in your career and have deep domain knowledge in your area, you should try to find jobs within related industries. That way you will not be starting from scratch and be able to leverage your existing skills.
Q3. To get a holistic view of Data science is the knowledge of Data engineering essential. Are Data engineers more technical as compared to Data scientist or Data analytics?
Data engineering is not essential to get a holistic view of Data Science. The key skill that you need as a Data Engineer is a good base knowledge of SQL and the ability to work with databases. You also need to have some basic foundational concepts and knowledge about how data systems work but otherwise the fields are independent. As a data scientist you’ll be working with data engineers and other stakeholders in the corporation, but you don’t necessarily need to have data engineering skills. The truth of the matter is that there’s always the opportunity to start in one role and evolve into another by expanding your knowledge and gaining experiences as you go along.
Q4. I’ve been learning python and have a decent grasp of the basics, but I haven’t tried using any libraries/packages. I also only have basic excel skills. What should I learn next to be able to start applying to data?
You should start learning the basic data science libraries like NumPy and Pandas and try to complete a whole data science pipeline. Load a data set, process the data, do some summary statistics, visualize it and then create a machine learning model. You should also learn to use a Jupyter notebook.
Q5. When it comes to data, there’s so many languages and disciplines to learn such as Python, SQL, R, RPA. Do you suggest learning a little bit of everything or specializing in one or two languages?
SQL is mandatory. Once you’ve mastered SQL, pick either Python or R and see which one you are more comfortable with and stick with it. Then, even if you need to use a different programming language, making the transition will be much simpler as the constructs are basically the same, the nuances and syntax may be different. Master one language and learn how to apply it.
Q6. How are the courses on Coursera going to help me get ready for an entry level job as a Data Engineer?
We’ve designed the program to prepare you for the entry level role in data engineering by not only have teaching you theory but also by applying the concepts learned in hands-on labs and projects. Every course in the Data Engineering program (and Data Analytics and Data Science) have several hands-on labs, projects and provide exposure to many data sets. So, by the end of these programs you will have access to a vast variety of data sets and several tools that data engineers use and apply. The projects leverage real databases and have you practicing with RDBMSes, Data Warehouses, NoSQL,big data, Hadoop, Spark etc.
Q7. Will Coursera help me get a job after I have completed a professional certificate? How can I get a job at IBM?
All Professional Certificate completers get access to several career support resources to help them reach their career objectives. They also get access to the Professional Certificate community for peer support and the ability to network with alumni who have successfully made a career change.
Q8. Which courses will help me prepare for the 3 data roles discussed – Data Engineer, Data Analyst and Data Scientist?
There are numerous data courses available in English, Spanish, Arabic, Russian and Brazilian Portuguese. The programs aligned with the 3 job roles discussed in this article are:
Source: ibm.com
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