Data Science and Artificial Intelligence are two of the most talked about technology trends in the tech world, creating innumerable job opportunities for professionals across all the industries and sectors. Looking at the unprecedented speed in which these technologies are moving forward, acquiring Data Science skills and Artificial Intelligence skills are necessary, when it comes to building a career in Data Analytics.
“How to become a Data Scientist?” is probably the most asked question by the aspirants. Everyone wishes to know the skills needed to become a successful Data Scientist and an AI professional.
In practice, Artificial Intelligence is usually amalgamated with Data Science. Hence, discussing the key skills to become a Data Scientist will cover the areas of Artificial Intelligence too.
This blog will cover the following topics:
- Skills Required to Become a Data Scientist
- Hard Skills
- Programming: Python & R
- Data Visualization: Tableau and Microsoft
- Statistics
- Mathematics
- Database
- Soft Skills
- Business Acumen
- Communication Skill
Skills Required to Become a Data Scientist
Broadly speaking, one can segregate the essential skills to become a data scientist, into two major categories:
- Hard Skills
- Soft Skills
A Data Scientist is not merely a person sitting behind the computer who codes all day but is a highly critical professional who has to make crucial business decisions on the basis of reports curated carefully with the help of technologies. Hence striking a perfect balance between hard and soft skills is mandatory to become a successful data scientist.
Let’s uncover the top data science skills in detail.
Hard Skills
Extracting the useful insights from a bulk of raw data is the crux of Data Science. Now data extraction, exploration, slicing, visualization, etc., are some of the steps of the entire lengthy process which is carried-out using various tools and technologies. Basically, a Data Scientist has to have the following hard skills:
Programming: Python & R
Application development, testing, data management, etc., are some of the core jobs performed by Data Scientists on a daily basis. Thus, a level of proficiency in programming is expected from the Data Scientists in all the organizations. Among all the programming languages, R and Python are the most popular in Data Analytics.
Both are statistical programming languages and are used in organizations across all industries. R Programming has rich libraries and extensive APIs that make writing codes easier and allows the data scientists to perform operations faster than Excel.
Though R Programming is a well-known technology among AI professionals, nowadays is outperformed by Python. However, learning R Programming will definitely give a better understanding of Python as it is comparatively newer than R.
Like R Programming, Python is also a multi-paradigm language, but it is better suited to writing codes for machine learning programs than R Programming. Being a multi-purpose technology, Python has gained momentum among Data Scientists because of its flexibility and agility. Therefore mastering at least one of two is a must-have to become a data scientist.
Does the name of programming give you a fright? Simpliv’s Python Tutorial will surely make your journey to become a data scientist a lot easier.
Data Visualization: Tableau and Microsoft
The data extracted from the databases are still in their raw form unless they are modeled and curated to represent in a particular fashion. Analyzing the complex data sets being the core job of data scientists, learning and getting a hands-on experience in data visualization tools is another important data science skill.
Data
visualization is all about taking large and complex data sets and compileing
them and representing them in a pictorial manner. These pictorial
representations are curated and modified with a sole motive of makeing
the information interpretable and help make critical business decisions faster.
Data visualization tools like Tableau, PowerBI, Qlikview, D3.js, etc., accumulate the complex data sets and compile them to represent in interactive graphs, charts and trends that facilitate data scientists with eye-opening insights and help take actions quicker.
As per Gartner’s Magic Quadrant 2020 for Analytics and Business Intelligence Tools, Microsoft and Tableau are the industry leaders, while MicroStrategy and TIBCO Software are giving the technologies giving tough competition to the market leaders during the period of research.
Statistics
It is not only technologies that a data scientist should master, but also the concepts and methodologies that bring out the valuable insights. Behind all the tools are the logics and concepts that are used for performing operations and a sound knowledge of statistical methods will help data scientists utilize the information in a better way.
Methods like Mean, Mode, Median, Variance, Kurtosis, ANOVA, Quartile, Regression, Correlation, Logistic Regression, K-nearest neighbors, Linear Discriminant Analysis are widely used statistical concepts in data science.
For instance, if a data scientist wants to test an application for all the possible inputs and compare the outcomes with the ideal result, she would plot a graph with all the outcomes pointed as dots and will compare it with the ideal situation using a straight line. This methodology is nothing but Linear Regression which a very important concept in Statistics.
Mathematics
A data Scientist cannot master Statistics unless she is good with numbers. In many of the areas, Statistics and Mathematics are used simultaneously, but there are fields of practice which are exclusive to Mathematics.
Analytics is about performing mathematical operations on numbers to get the statistics. Mathematics is inseparable from Data Science, as without calculations and applying mathematical operators on numbers, no inferences can be made. Mathematical tools like Discrete Mathematics, Calculus, Linear Algebra, Probability, etc., are some of the very important data science skills to be mentioned in one’s resume.
Imagine if you want to know how Google searches web pages and how it ranks billions of pages in its results, you may have to understand logarithms, binary search, recurrence equations and a lot more that will help you understand What algorithm does Google work on and How.
Database
All the raw data that are given to a data scientist are stored in databases, which can be accessed using various codes as and when required. Also, it is not just a single database that a data scientist works on, but multiple relational databases and big data marts that store petabytes of data waiting to get fetched.
Knowledge of databases and query languages is necessary for a Data Scientist to discern how to extract the right data in the right quantity, and in a manner that helps her create statistical models and perform various operations.
There are plenty of relational database technologies in market like MySQL, Amazon Redshift, BigQuery, PostgreSQL, etc., which are pretty good choices as per the size of data.
If the data is non-relational then some of the choices available are MongoDB, Apache Cassandra, Apache HBase, etc.
As part of Query Languages, SQL is the mostly used database query language.
Gartner predicts that by 2022, 75% of all the databases will be deployed or migrated to Cloud, with only 5% remaining as on-premise databases.
Soft Skills
As mentioned earlier in this blog, most of the tech firms lag in the race of becoming the number one because of their lack in focus on Soft Skills. In addition to possessing hard core skills, a data scientist must also be a critical thinker and should be able to multi-task. Let’s know what Soft Skills one should have to become a data scientist.
Business Acumen
A Data Scientist will not be able to solve complex queries if he does not understand the real-life problem behind it. Knowledge of business, ability to brainstorm, ability to do a root-cause analysis, and ability to infer conclusions from the present cases are some of the unspoken expectations from a data scientist.
It is loosely considered that a data scientist must be only proficient in technology; however, with the changing corporate dynamics, a strong business acumen is one of the must-haves to become a data scientist.
How can you upskill your business acumen?
Ask yourself these questions as part of the roadmap to enhance your knowledge of business:
- What is your organization into?
- Which industry does your organization belong to?
- What is the position of your organization in the market?
- Who are the competitors, and what are their strengths and weaknesses?
- What are the core processes of the organization?
- What are the different teams involved in performing the core operations?
- Where are the substantial gaps?
- What can be done to bridge those gaps?
Once you are well-acquainted with your organization and the industry, you should look for opportunities to exploit to gain better results. You should not limit yourself within the walls of KPIs but should try to look beyond and fill the existing gaps.
Communication Skill
It goes without saying that knowledge is incomplete unless you know how to convey it. With companies being multi-cultural platforms bringing in skills from various geographical regions, communication becomes one of the toughest challenges of all.
Data Scientists are the individuals who need to coordinate various teams at once and impart the knowledge they want to implement. Hence, without having outstanding communication skill, doing justice to the roles and responsibilities is nearly impossible.
Moreover, most companies nowadays are focusing on larger geography for possible customers which mandates the data scientist to be able to communicate information in multiple languages. Hence, understanding the customers, communicating to them, and listening to their issues will help build a rapport and enhance the communication in turn.
Conclusion
All in all, we can say that the Data Scientist is a technically sound professional and a critical business decision-maker. He should be sound technically in terms of programming, database, statistics, mathematics, data visualization, and should also be a critical thinker and should have good communication skills.
As per a report by McKinsey in 2017, Automation is supposed to bring big shifts in coming 15 years to the world of work, as AI and Robotics change or replace some jobs, while others will be created. Millions of people worldwide may need to switch occupations and upgrade skills.
Hence it is quite clear that freshly graduates or professionals who wish to make it big in the tech world need to upskill and advanced education methods can certainly get you there in no time. Learn more about it in this riveting blog!
Data Science is the most desirable profession and getting a hands-on experience and certification will surely help one land on high-paying job in absolutely no time.
Master Data Science and Machine Learning and take your career to the next orbit today!
Let us know in your comments which skill you would like to build first. Do you think we should include any other skill as well in the above list? Let us know in the comments section.
Don’t forget to share this information with someone who is on the path to becoming an efficient Data Science Professional.
