Imagine it is the year 2030. The resume you have built since 2020 seems perfect. Well, it seemed perfect back then, is it perfect now after 10 years? How can we make our resume futureproof? The answer is simple – by learning about the skills that future recruiters will look for.
The work system will undergo a heavy change in the next decade. We are on the verge of reskilling emergency. According to the annual meeting report of the World Economic Forum, we will need to reskill 1 billion+ worker by 2030. Hence, many youngsters are wondering which skill will be most prominent after industrial revolution 4.0. The future is data-driven decision-making. The upcoming workplace will adopt a data-fueled culture.
Data-driven decision making: The growth-fuel to future
Data-driven decision-making is the process of making organizational decisions based on actual data analytics. There are 2 types of data- quantitative and qualitative. Quantitative data deals with figures, metrics, numbers, stats, etc. Qualitative data deals with observations, social media trends, images, videos, etc. Marketers need to take into consideration both of these to make the best decision.
Google was one of the first organizations to make data-driven decision-making a reality. Google created a People Analytic Department to make better HR decisions using data. They did employee surveys, performance reviews, manager interviews. The result revealed – teams with better managers actually performed better and were happier. Therefore google thought what will make managers a ‘good manager’? Google created the “Great Managers Award” where in-house employees nominated managers they thought made a difference and wrote down the examples that made them great.
Google took these data and established 8 great manager behaviors and 3 reasons why managers struggle. Since then they look upon these qualities before hiring managers and improved trainee programs promising to meet the qualities.
Another great example is Southwest Airlines’ great deal offering which goes perfectly with what customers were looking for. How do they understand customers’ motives? It is quite simple. They track users’ website behavior and actions. They analyze data and offer a personalized deal to each of them. Customers find the deals to be matching their requirements in budget and opt to take them!
A beginner should start learning data analysis with these topics-
- Structured Query Language (SQL)
- BI tools
- R or Python-Statistical Programming
- Data Visualization
- Microsoft Excel
- Presentation Skills
- Machine Learning
Structured Query Language –
SQL is a domain-specific language used for programming and managing relational databases. In simple words, the place where all the data is kept is called the database. The language that is used to communicate with the database is SQL. It is often called the extended version of MS Excel. You can manage a bigger database with SQL than Excel.
SQL is regularly needed for data administrators, developers, data analysts looking to find out queries of organizational needs. If you’re one of those ‘big Data’ junkies, SQL is the pathway to it.
BI tools –
BI (Business Intelligence) is a set of processes, architectures, and technologies that convert raw data into meaningful information. BI transforms data into actionable intelligence and knowledge. This is the reason, organizations are prone towards BI before taking any major product and sales campaign. Business intelligence covers a wide array of topics.
Universities abroad offer a 4-year Business intelligence degree which shapes an undergrad perfectly for the near future. The major includes data science, computer science, business administration, statistics, economics, and related fields. Bangladesh needs this practice to enable undergrads to be ready for global professions.
If not a formal degree, anyone can grasp the basic command of BI by learning Data mining, data warehousing, cloud data services, etc.
R & Python –
Anything excel can’t do – R or python can do it 10x better! They are powerful statistical languages that are marked as an industrial standard. If you truly want to become a data scientist you need to master at least one of these two along with SQL.
Now the question arises- which one is better? Well, both of them are perfect as long they result in inaccurate data. R suits better for statistical learning and ad-hoc analysis. On the other hand, Python is better for machine learning with the flexibility of website integration and production use.
Data visualization –
Data visualization is graphically representing information and data. It is effective in communicating complex data easily with the audience. It makes the long digits visually fun and appealing. So the popularity of data visuals among marketers is growing tremendously. Storytelling with great visuals makes any marketing great marketing, because of its power to easily make a place in the human brain.
Microsoft Excel (and Power BI), Tableau, Zoho Analytics, Datawrapper are some of the best tools to get started with great visuals!
Microsoft Excel –
Probably the best tool for quickly analyzing the small-scale data. While a programming language like R or Python is better suited for large data sets, organizations rely on Excel for lighter data comprehension. It is easier to learn and a must-have skill for any company anywhere in the world!
Presentation skill –
Talent is useless if it remains confined. And expressing talent properly is associated with great presentation skills. It is effective in binding the audience with the topic. Presenting well requires intense practice with a few dos and don’ts. For those who are naturally not eloquent, public speaking can be really nerve-wracking. There are many crash courses available online for getting a better grip over it.
If you are nervous too about presentation and public speaking, 10 Minute School to the rescue! Catch up with Ayman Sadiq sharing some amazing tips and tricks in this free course from 10 Minute School and seize the day.
Machine learning –
One of the most interesting parts of data science. Machine learning is the branch where artificial intelligence merges with computer science to imitate the way the human brain learns focusing on data and algorithms. To learn machine learning from scratch start solving projects. Here are some fun projects to get started with it. To understand algorithms, start with simple projects such as decision trees, K-nearest neighbors, or logistic regression.
Real-life examples of machine learning – image recognition and speech recognition (we use them frequently for verification purposes), medical diagnosis, predictive analytics, etc.
The skills needed to be successful in today’s world market vary subsequently from those of past and future. New Roles are coming into existence while many roles are vanishing. We have to keep track of the latest trend to align with new financing models, newer business practices, and fast-paced metrics to ensure people have the right set of skills to fulfill their potential.