Check out these 13 essential skills to become a successful data scientist. Data Scientist needs to master a range of technical and non-technical skills to succeed in the workplace.
Data Scientist have High-Level Education
Virtually all data scientists boast higher education qualifications.
In fact, 46 percent of them hold PhDs, whereas 88% have a Master’s degree.
Despite the existing exceptions, having a strong academic background is necessary for developing the level of knowledge needed to become a data scientist.
If you desire to become a data scientist, you can consider earning a Bachelor’s degree in Statistics, Physical Sciences, Social Sciences, and Computer Science.
Some of the common areas of study include mathematics and statistics (32 percent), and is followed closely by computer science (19 percent) and engineering (16 percent).
A Bachelor’s degree in any of these courses will provide you with the right skills required for processing and analyzing big data.
However, a degree is not enough to make you a data scientist.
Many data scientists hold a PhD or Master’s degree, and they take online courses to help them acquire specialized skills such as how to utilize Big Data or Hadoop querying.
Hence, you can take up a Master’s degree in Astrophysics, Mathematics, Data Science, or other related areas.
Skills gathered while studying for your degree will assist you in transitioning to data science
Aside from learning in the classroom, consider practicing what you learn by doing something useful like exploring data analysis, creating a blog, or developing an application to help you in acquiring more knowledge.
Python Coding – Essential for Data Scientist
This is among the most commonly used coding languages needed in data science tasks, alongside others like C/C++, Perl, or Java.
About 40% of all the respondents interviewed by O’Reilly said that they utilize Python as their primary programming language.
Due to the versatility of this programming language, you can utilize it in completing virtually all the phases involved in undertaking data science roles.
Python can take a wide array of data formats as well as allow you to import SQL tables seamlessly into your code.
Furthermore, it enables you to develop datasets, and you can find almost any desired dataset you require on Google.
Comprehensive knowledge regarding any of these analytical tools, particularly for data science R is needed.
R is developed to satisfy data science requirements.
You can use it to address any issue you encounter while exploring data science.
Currently, about 43% of data scientists utilize R to overcome statistical hurdles.
However, learning R is not easy, primarily if you have already mastered a given programming language.
Since it’s still a valuable resource for those aspiring to be data scientists, try using the resources available on the Internet like Simplilearn’s Data Science Training with R Programming Language to kickstart your journey in learning R.
Even though this is not necessarily needed, it’s widely recommended in most cases.
Experience in Pig or Hive is also a plus for aspiring data scientists.
Also, learning about cloud tools like Amazon S3 can prove useful.
A study conducted on 3490 LinkedIn data science roles placed Apache Hadoop in the second position among the most vital skills that data scientists should have, with a rating of 49 percent.
Hadoop comes into play whenever you encounter a scenario whereby the amount of data in your possession surpasses your system’s memory or in cases where you require sending data to other servers.
You can leverage Hadoop in rapidly conveying data to different points on a given system.
Away from that, you can utilize it in data filtration, summarization, data sampling, and data exploration.
Even though Hadoop and NoSQL are significant aspects of data science, candidates are still expected to be in a position to write and carry out sophisticated SQL queries.
SQL entails a programming language that can assist you in carrying out operations such as extract, delete, and add data from a given database.
The language can also assist you in undertaking analytical roles and transforming database structures.
As a data scientist, you have to be SQL proficient, primarily because structured query language is developed for helping you in accessing, communicating, and working on data.
The language provides you with valuable insights when you utilize it in querying a database.
It features brief commands that allow you to minimize the volume of programming and time required to carry out complex queries.
Learning structured query language will not only assist you in better understanding relational databases but also improving your profile as a qualified data scientist.
Apache Spark is quickly growing into the most well-known big data technology across the globe.
Although it is similar to Hadoop, the difference between the two lies in the fact that Spark has a higher speed.
The reason behind this disparity is owed to the fact that Hadoop writes and reads to disk, which slows it down, whereas Spark stores all its computations in the memory.
Apache Spark is developed primarily for data science, primarily to assist in running its sophisticated algorithm faster.
It aids in the dissemination of data processing, especially when you encounter a significant volume of data, thus, allowing you to save time.
Apache Spark also helps data scientists in dealing with complicated unstructured data sets.
You can use this big data technology on a cluster or single machine.
Apache Spark helps data scientists in preventing data loss.
The power of this technology rests in its platform and speed, which makes the carrying out of data science tasks easy.
Using Apache Spark, you can perform analytics from data intake to computation distribution.
AI, Machine Learning & Data Scientist
Many data scientists lack proficiency in machine learning fields and methods.
They include adversarial learning, reinforcement learning, neural networks, and many others.
In case your desire is to be different from the other data scientists in the field, you have to be conversant with machine learning methods such as logistic regression, decisions trees, machine learning, etc.
Such skills will assist you in dealing with various data science issues that are based on forecasts of leading organizational outcomes.
Data science calls for the application of these skills in various machine learning areas.
In one of its many surveys, Kaggle found out that a small portion of data experts are proficient in sophisticated machine learning skills like unsupervised machine learning, computer vision, natural language processing (NLP), survival analysis, adversarial learning, recommendation engine, outlier detection, survival analysis, time series, and supervised machine learning.
Data science mainly deals with massive volumes of data sets.
The corporate world is known for generating a considerable amount of data regularly.
This data calls for translation into a specific format that can be comprehended easily.
This is because people are wired to understand images in various forms, such as graphs and charts compared to raw data.
As a proficient data scientist, you must be in a position to visualize data using data visualization tools like Tableau, ggplot, and Matplottlib.
Such tools will assist you in converting sophisticated results drawn from your projects into an understandable format.
The truth is that most people do not know what p values or serial correlation entails.
You have to demonstrate to them visually what such terminologies stand for in your outcomes.
Data visualization provides organizations the chance to deal with data directly.
As such, it allows them to gather the necessary insights to assist them not only in acting on emerging business opportunities but also staying ahead of their competitors.
Mastering Unstructured Data as a data scientist
It’s crucial for a data scientist to be proficient in working with unstructured data.
Unstructured data entails undefined content.
In this case, some of the examples include audio, video feeds, customer reviews, videos, social media posts, and blog posts, among many others.
Since unstructured data involves heavy texts put together, sorting it can be a daunting task.
Many people refer to unstructured data as “dark analytics” due to its complex nature.
Dealing with unstructured data allows you to derive insights that can prove useful in the decision-making process.
As a qualified data scientist, you ought to have the ability to manipulate and comprehend unstructured data drawn from various platforms.
Of late, you might have spotted this phrase almost everywhere, primarily because it’s associated with data scientists.
In his guest blog, Frank Lo talked about what it means and some of the other vital “soft skills” a few months back.
Curiosity can be described as the thirst for additional knowledge.
Data scientists should ask questions regarding data, as they spend almost 80% of their valuable time gathering and preparing data.
The reason is that data science is an area that is quickly evolving, and as a data scientist, you have to keep learning to remain relevant in the field.
This situation calls for constant updating of your knowledge by reading relevant books and online content regarding the emerging data science trends.
Avoid being overwhelmed by the massive volumes of data available online.
Instead, try as much as possible to draw insights from the information you find.
Curiosity is among the skills you require to grow as a data scientist, as it will allow you to go through the large amounts of data in a bid to get the right answers and insights.
A good data scientist has an in-depth understanding of the particular industry in which he or she is working in, and knows the exact business issues that need to be addressed.
When it comes to data science, being in a position to identify the issues that need to be solved for the success of the business is essential, as well as finding new techniques for helping it use its data.
To accomplish this, you have to know how the issues you address can affect the business.
This explains the reason why you must understand how enterprises work in a bid to help you channel your attention where it is needed.
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Companies in need of a qualified data scientist look for people who can fluently translate their complicated findings to non-technical staff members, particularly sales and marketing departments.
Data scientists should allow businesses to make their own decisions by equipping them with quantified insights, as well as understanding what their non-technical teams require so that they can translate the data correctly.
Take a look at our most recent survey for additional details regarding the communication skills that quantitative experts should have in their line of work.
You also need to communicate a language that the company understands.
You also need to leverage data storytelling when communicating.
As a proficient data scientist, you should be familiar with ways of creating a storyline, particularly around the data you are working with so that you can make it understandable to everyone.
For example, presenting your data on a table may not be as useful as sharing the insights drawn from it through storytelling.
By using storytelling, you will be in a position to relay your findings appropriately to your employers.
During communication, direct your attention to values and results that are hidden in the data being analyzed.
The reason is that many enterprise owners are interested in how such factors can affect their business positively as opposed to what you assessed.
Try focusing on providing value and creating long-lasting associations through excellent communication.
Bear in mind that data scientists cannot accomplish the desired results by working alone.
As such, you have to work alongside company executives to come up with strategies; marketers to help you in launching better-converting campaigns; product managers and designers to help you create improved products, and server software developers to boost workflow and develop data pipelines.
You will have to work hand-in-hand with almost everybody in the organization, including customers themselves.
In essence, you will be required to collaborate with all your team members to come up with use cases for identifying the business objectives and data that will be needed in solving problems.
Away from that, you have to know the best approach for addressing the use cases, the data necessary for solving the issue and how to go about translating and presenting the results in an understandable manner.