Do you know what is a “Unicorn Employee”? Well, in today’s times, that is someone who is multi-talented, works hard, and is ready to go the extra mile. While it is quite difficult to become a unicorn employee, you can become one in Data Science by understanding and learning at least the basics of all the important Data Science skills.
Data Science is an increasingly competitive field these days with more and more people upskilling and increasing their experience. In such a scenario, it is important to learn a variety of new tools to remain relevant in the market.
What is Data Science?
In modern times, Data Science resembles the act of solving puzzles. Just as in a complex jigsaw puzzle, the goal is to piece together various elements of data to unveil the complete picture. As more data accumulates, patterns emerge, with each piece of information serving as a clue. These patterns can unveil intriguing insights across a spectrum of subjects, from the spread of diseases to human behavior on the internet. Utilizing numbers and statistics to unveil the hidden stories within the data is akin to crafting a narrative in the digital sphere.
Top 10 Data Science Skills to Learn in 2024
Discover the top 10 data science skills to master in 2024, essential for achieving unicorn status in the competitive Data Science job market! These skills, including Statistical Analysis, Programming (Python and R), Machine Learning, AutoML, and Cloud Services, are crucial for staying ahead in this rapidly evolving field. By honing these technical and non-technical abilities, you can position yourself as a valuable asset, driving innovation and growth in the dynamic world of Data Science.
Technical Skills
A Data Scientist creates predictive models and performs custom analysis on the data according to company requirements. This process has various steps including data extraction, exploration, visualization, etc. that require knowledge of various tools and skills. So let’s see the technical skills that a Data Scientist must have to be successful.
1. Statistical Skills
As a Data Scientist, your primary job is to collect, analyze, and interpret large amounts of data and produce actionable insights for a company. So obviously Statistical Skills are a big part of the job description!!! That means you should be familiar with at least the basics of Statistical Analysis including statistical tests, distributions, linear regression, probability theory, maximum likelihood estimators, etc. And that’s not enough! While it is important to understand which statistical techniques are a valid approach for a given data problem, it is even more important to understand which ones aren’t. Also, many analytical tools are immensely helpful in Statistical Analysis as a Data Scientist. The most popular of these are SAS, Hadoop, Spark, Hive, Pig, etc. So you must have a thorough knowledge of them.
2. Programming Languages
You will be doing application development, data management, application testing, etc. as a Data Scientist. Therefore programming Skills are a must-have tool in your toolbox! In general, Python
and R are the most commonly used languages for this purpose. Python is used because of its capacity for statistical analysis and its easy readability. Python also has rich libraries and various packages for Machine Learning, data visualization, data analysis, etc. that make it suited for data science. R is also another popular programming language for Data Science. It makes problem-solving very easy with the help of packages like Ggplot2, Esquisse, etc. While R is still very popular in academic circles, Python is becoming more and more famous in the Data Science industry.
3. Machine Learning
Machine Learning is all the rage in Data Science these days! It enables machines to learn a task from experience without programming them specifically. This is done by training the machines using various machine learning models using the data and different algorithms. So you need to be familiar with Supervised and Unsupervised Machine Learning algorithms like Linear Regression, Logistic Regression, K-means Clustering, Decision Tree, K Nearest Neighbor, etc. Luckily, most of the Machine Learning algorithms can be implemented using R or Python libraries (mentioned above!) so you don’t need to be an expert on them. What you need expertise on is the ability to understand which algorithm is required based on the type of data you have and the task you are trying to automate.
4. AutoML
For those who are not experts in the mysterious world of Machine Learning, Automated Machine Learning is godsent! It allows the application of Machine Learning solutions much easier for ML non-experts and may even be able to easily handle the complex scenarios in training ML models. So a tool like AutoML which can be used to train high-quality custom machine learning models while having minimal machine learning expertise will surely gain prominence. It can easily deliver the right amount of customization without a detailed understanding of the complex workflow of Machine Learning. However, AutoML is not a silver bullet and it can require some additional parameters that can only be set with some measure of expertise. (So you will have to learn some Machine Learning!)
5. Deep Learning
Deep Learning is a subset of Machine Learning that is normally used for more complex applications like Image Recognition, Natural Language Processing, etc. Hence, it is not necessary to know for more routine and basic Data Science applications that involve structured or tabular data. So it is commonly believed among Data Scientists that you don’t need to learn Deep Learning unless you want to go deep into Data Science! But that has changed in 2024! Complex applications of Deep Learning like Image Recognition, Natural Language Processing, etc. are becoming more and more popular even in normal Machine Learning applications. Therefore you must know at least the basics of Deep Learning if you want to become a Data Scientist. Even if you don’t need to use Deep Learning now, this will ensure that you find it much easier when you have to use it in the future!
6. Cloud Services
Why is a basic understanding of Cloud Services important you wonder? Well, more and more companies are moving their databases to the cloud with time. This could be a move to the public, private or hybrid cloud with the most popular contenders being Amazon Web Services and Microsoft Azure. Most companies are also moving big data and analytics applications on the cloud and so Data Scientist needs to understand these cloud services a little more deeply so that they can perform data analytics effectively. So you should learn a little about deploying your models and code to the cloud. This is a skill that many Data scientists don’t possess and so it will make you stand apart from the crowd as most companies move towards moving their databases to the cloud.
7. SQL
Even though NoSQL and Hadoop are a big part of Data Science these days, SQL is still the boss! So don’t think it is not important to know SQL in these times. You should be able to write and execute complex queries in SQL that will help in carrying out analytical functions and changing the database as required. You need to be proficient in SQL as a Data Scientist that you can access the data easily as well as work on it SQL can give you deep insights into a database depending on your query. It also has concise commands that can help you to save time and lessen the amount of programming you need to perform for difficult queries. So learn SQL as it will help you in understanding relational databases and add another feather to your cap as a Data Scientist.
Non-Technical Skills
Having Technical Skills is very important, but it’s not everything! A Data Scientist should also have various Non-Technical that allow them to work efficiently and be an all-rounder in the industry. So let’s see the Non-tech skills that a Data Scientist must have to be successful.
8. Data Intuition
Don’t underestimate the power of Data Intuition! It is the primary non-technical skill that sets a Data Scientist apart from a Data Analyst. Data Intuition involves finding patterns in the data where there are none! This is almost like finding the needle in the haystack which is the actual potential in the huge unexplored pile of data. Data Intuition is not a skill that you can be easily taught. Rather it comes from experience and continued practice. And this, in turn, makes you much more efficient and valuable in your role as a Data Scientist.
9. Business Acumen
Want to become a Data Scientist but never thought business knowledge is important? Well, it is important! Business Acumen is a must-have skill if. To become a good Data Scientist, you need to know your industry inside and out. You need to have a solid understanding of what business problems your company is trying to solve so that you can work towards solving them by leveraging data in new and different ways. To be able to do this, you also need to understand how the problem can be solved and how its solution can impact the business. This is why you need to know about how businesses operate so that you can correctly use your knowledge and efforts.
10. Communication Skills
You must be great at Communication Skills as well to become an expert Data Scientist! That’s because while you understand the data better than anyone else, you need to translate your data findings into quantified insights for a non-technical team to aide in the decision making. This can also involve data storytelling! So you should be able to present your data in a storytelling format with concrete results and values so that other people can understand what you are saying. That’s because eventually, the data analysis is less important than the actionable insights that can be obtained from the data which will, in turn, lead to business growth.
Conclusion
Becoming a unicorn in Data Science demands a blend of hard technical skills and soft interpersonal skills. In 2024, the top 10 skills include Statistical Analysis, Programming (Python and R), Machine Learning, AutoML, Deep Learning basics, Cloud Services, and SQL. Non-technical skills like Data Intuition, Business Acumen, and Communication are equally vital. Gaining these abilities will generally help data scientists succeed in a quickly changing industry, driving innovation and expansion.
