10 Skills That Make You a Rockstar Data Scientist - Complete Guide for 2023
Scope of the Article
→ In this article, we will discuss 10 skills that make a person a rockstar data scientist…
→ Firstly, we will be discussing what data science is, who are data scientists, and the Skills required for data scientists.
→ Data science is one of the most hotly debated topics in the IT field due to the enormous amount of data being generated in today's world.
→ Next, we will be discussing the future trends of data scientists, recent trends in the data science domain, etc.
Now, let us get into the topic and make things interesting!
Introduction
Data science is that which combines the proper set of tools and can solve problems regarding data. Data science is an interdisciplinary field that applies scientific methods, procedures, algorithms, and systems to extract or infer knowledge and insights from noisy, unstructured data, and to apply knowledge from data. Data mining, machine learning, big data, computational statistics, and analytics are all connected to data science.
Data science is one of the most vehemently debated issues in the IT field because of the vast numbers of data being produced nowadays. Data science is now being used by organizations to grow their operations and increase customer satisfaction as it continues to gain popularity.
Who is a Data Scientist?
Data wrangling, data segregation, collecting, and analysis are just a few of the tasks performed by qualified and competent data scientists. Their extensive understanding of statistical methods and mathematical formulas is distinctive and exceptional.
Data scientists are sometimes referred to as analytical professionals who use their knowledge to gather important insights that help the firm thrive and expand or survive in competitive markets.
Now, let us discuss the top 10 skills required for a data scientist or rockstar in data science.
Skills required to become a Data Scientist
Programming Skills
Data visualization
Machine Learning and Deep Learning
Probability and Statistics
Data Wrangling
Database Management Systems
Cloud Computing
Microsoft Excel
Communication Skills
Teamwork
Let us discuss each of those in detail, why and how it's useful for a person to become a data scientist,
Programming Skills and Software
Programming skills are very important to become a data scientist, mainly programming languages like R programming, python programming, SQL query writing, Java programming, Matlab, and Tensorflow any language can be used in which we are comfortable. In general programming language can be chosen according to the problem existing.
Programming languages are important in the same way knowing the software and packages in it also plays a major role. This is required for building any applications, or software.
Data visualization
Data Visualization is defined as representing data that is gathered in the form of tables, graphs, and pie charts. Because they will be presenting data charts to managers and stakeholders, data scientists should be excellent visualizers.
The ability to generate a narrative from the facts and produce an extensive presentation is provided by visualizations. The ability to understand and learn more about the data being considered, as well as its susceptibility, requires the ability to visualize data. When presented visually, the real worth of data is known and comprehended.
Machine Learning and Deep Learning
Data science is divided into machine learning and deep learning. They are contemporary technical applications that streamline corporate procedures and demonstrate how effectively human thought can be represented by computer systems.
Many businesses have incorporated various applications of deep learning and machine learning into their operational procedures. Data scientists should therefore be familiar with various machine learning and deep learning applications if they are looking for employment in the present business environment.
Machine learning makes it possible for computers to learn a task from experience without having to be explicitly programmed. Both supervised and unsupervised machine learning methods must be acquainted with you. Since the majority of machine learning methods are implemented using Python and R libraries, you don't need to be an expert.
These are important skills for data scientists because you will need the expertise to understand which algorithm should be applied based on the type of data you have and the task you are trying to automate.
A branch of machine learning called Deep Learning is typically applied to more complicated applications. Deep Learning is the route to go if you want to understand data science's more intricate complexity.
It is increasingly required to understand at least the fundamentals of Deep Learning to become a data scientist because complicated Deep Learning applications, such as Image Recognition, Natural Language Processing, etc., are becoming popular even in traditional Machine Learning applications.
Probability and Statistics
Organizations frequently integrate data-driven methodologies into their processes, from predictive analysis to AI-driven apps. A data scientist with a strong background in probability and statistics can help the process by contributing their expert knowledge and abilities.
Many prospective data scientists also enroll in specialized certificate programs to sharpen their analytical abilities. In simple words, we can say that probability and statistics are connected.
Data Wrangling
Data scientists frequently work with raw, unstructured data. To achieve efficiency and timeliness, it is crucial to have a solid understanding of the data-wrangling process. Data wrangling is the process of preparing raw data for analysis by data scientists by cleaning and arranging it into the required format or structure, according to experts. Every company goes through the process of data wrangling, which is crucial. It is, therefore, more significant than other data science procedures.
Give business and data analysts a very accurate representation of actionable facts on time. Reduce the time needed for processing, responding, and gathering and organizing chaotic data before using it. Allowing data scientists to concentrate more on data analysis than data cleaning
Database Management Systems
Large amounts of data must be managed and processed by a data scientist. A collection of programs used for database management can modify, index, and alter databases. You can define, retrieve, and manage database data using database management in data science. You can also manage the data, the format, the field names, the record and file structures, etc
A database is a software program that displays the filtered data as a table, schema, or other entity. Since database management makes up the majority of data scientists' work, having a fundamental understanding of the subject facilitates quicker and more effective job performance. Additionally, numerous certification programs aid aspirants in learning the fundamentals of database administration.
Cloud Computing
the method of automating, bringing about efficiency, and organizing data and information through the use of information technology (IT) infrastructures such as apps, servers, data storage systems, and development tools.
It is crucial to have a basic understanding of cloud services because many businesses are currently migrating their data to the cloud. These changes might involve using Microsoft Azure, Amazon Web Services, or other competitors' private or public clouds. The majority of businesses are also migrating sophisticated analytics and data applications to the cloud.
To execute data analytics efficiently, a data scientist must have cloud expertise.
Microsoft Excel
The best data editor for tables and virtual platforms for advanced data analytics is Microsoft Excel. You can save as many versions and make as many changes as you want. In comparison to other programs, Microsoft Excel makes it comparatively simple to manipulate data. You may even hunt up the necessary data among several records using Microsoft Excel.
Communication Skills
You need to be a master communicator if you want to become a data scientist. It is your responsibility as a data scientist to comprehend data more thoroughly than anybody else and to interpret your conclusions so that the non-technical team can use them to make wise judgments. Your ability to communicate will help you present the data effectively and efficiently as well as communicate your conclusions and outcomes.
Teamwork
Data scientists can't do their jobs alone. You'll have to collaborate with business executives to develop strategies, with designers and product managers to make better products, with marketers to launch more effective campaigns, and with client and server software developers to build data pipelines and streamline workflow.
Developing use cases in collaboration with your team members can help you understand the business objectives and the data needed to address issues. You will need to understand the best strategy for handling the use cases, the information required to resolve the issue, and how to interpret and communicate the solution so that everyone can understand it.
How can these skills help data scientists in their work?
Probability and Statistics skills can be used in Data Science for the:
More exploration and understanding of the data.
For the identification of the two variables underlying dependencies or relationships
Future trend prediction based on the previous data trend.
Data motive or patterns determination
Uncovering Data anomalies
Machine Learning skills can be used in Data Science for the:
Detection and management of risk and fraud.
health care( genetics, genomics and image analysis are some major fields of data science).
Planning of the airline routes.
Performing filtering of the spam automatically.
System of voice and facial recognition.
Improvement in the field of interactive voice response (IVR).
Process of translation and recognition of comprehensive language and documents.
DevOps skills can be used in Data Science for the:
Data cluster management, scaling, provisioning, and configuration.
Information infrastructure Management
Script creation for the various environments foundation provisioning and configuration automation.
Excel skills can be used in Data Science for the:
Allow creation of ranges and also naming of them.
It allows the data to be merged, trimmed and filtered.
It allows for designing pivot charts and tables.
Changing the reference among mixed, absolute and relative and deleting duplicate values from the record.
Look-ups performed among large records such as thousands of records.
Data Visualization skills can be used in Data Science for the:
Plotting of data for powerful insights.
Finding out the relationship among unknown variables.
Visualization of the areas that require improvement or attention.
Facts identification that affects the behavior of customers.
Understanding the proper position of the products.
Trends from social media, websites, connections and news displays.
Management of reporting of clients, the performance of employees and mapping of quarter sales and so on.
Database Management skills can be used in Data Science for the:
Management, retrieval and definition of the data in the database.
Rules definition for testing, validating and writing the data.
Multi-user environment support for parallel data accessing and manipulation
Why are Data Science skills becoming popular?
Nowadays, data-driven and decision-making goals have become the most important and focused goals of companies. It is a global phenomenon. According to the report of International Data Corporation (IDC 2021), the use of data worldwide will increase from 61% to 175 zettabytes.
At this time, there is a great increase in the field of data science. The reason behind it is that it allows organizations to process the data in an efficient format and also allows them to interpret the data.
Some of the reports of The Data Science Skills Survey conducted by the By AIM and Great Learning:
Common skills looked at by recruiters
According to the data provided by the survey of the AIM and Great Learning, 84.4% of professionals consider machine learning as one of the most important skills required for the process of hiring.
According to AIM and Great Learning, 84.3% of respondents to the survey (means 4 out of 5) responded that the top skill to be considered in the candidates at the time of recruitment of the data scientist is Machine Learning.
Statistics (78.9%) and Communication (72.8%) proficiency follow this. Communication skill is considered more important than programming language skill(70.0%) by some recruiters. Data Wrangling and Preprocessing skills are considered at the recruitment time is the response provided by 62.5% of respondents and according to 55.6 %, Data Visualisation skill is considered by the recruiter at the time of data scientist recruitment.
Recruiters consider machine learning as the top skill thought provided by the 92.3% (9 in 10) professionals having experience of more than 10 years, in comparison to the 81.9% of respondents having experience of fewer than 3 years.
According to 4 in 5 IT professionals, recruiters provide importance to the consideration of critical skills like Machine Learning (84.3%), Programming Knowledge (81.4%), Communication (81.4%) and Statistics (81.4%). And According to 9 out of 10 (90.0%) BFSI and Pharma & Healthcare professionals, recruiters provide importance to the consideration of Statistics skills.
And the same respondents from the BFSI sector said that Machine Learning is one of the core skills that recruiters seek.
In Pharma & Healthcare, there is the highest(60.0%) share of professionals who said that the recruiters prioritize domain knowledge. In Pharma & Healthcare Presentation skills(70.0%) are considered more prioritized and Retail, E-commerce, and CPG (73.7%) in comparison to other industries.
Basic skills needed for a data science career
According to the data science survey conducted by the AIM and Great Learning, 87.8% of respondents claimed that a programming language such as R, SQL, or Python is one of the most basic skills required for kickstarting a data science career. Following this, the responses claim that statistics knowledge (80.6 %) and understanding of ML (75.6 %) are required.
And all 100 % of respondents having experience of above 10 years claim that the statistical programming coding ability is one of the most basic skills required to kick start a career in data science. Following the basic Machine Learning and statistics knowledge at 80.8 %. And 83.3 % (5 out of 6) respondents that are professionals in Data Science and have the experience below 3 years responded that statistics knowledge is a must. And the professionals having experience of 3 to 6 years (77.4 %) claimed that the skills of data wrangling and preprocessing are more prioritized in comparison to other skills.
And apart from these if we talk about the industry, 94.7% (9 in 10) of respondents in the E-Commerce, Retail, and CPG responded that the most basic skill for starting a Data Science career is Machine Learning concept knowledge.
And the Statistics skills demand is highest among the professionals of BFSI (86.7 %) and in Pharma and Healthcare (70.0 %) demand for the skills like Data Visualization is highest. In long and short, every industry agrees on the thought that programming language knowledge is one of the most basic skills needed for initializing a Data Science career.
With recent advances in data science like AutoML, AI as a Service, and Predictive Analytics, how is the role of data scientists changing?
The skills used by data scientists for their work will change, with more and more increments in the importance of AI and coding skills. With the parallel of this business, mindedness is also required much more.
If we talk about the past, data scientists are less focused on coding instead of that they have more focused on modeling and statistics. And this migration among the data scientists is due to the increment in the data complexity.
Data sets are taking larger growth and becoming more desperate. Apart from that, the tools used by data scientists for data analysis are also becoming more sophisticated. Due to the growth in the size of the datasets, there is an increment in the requirement for data scientists to have good coding skills.
Nowadays, being a data scientist is considered among the world's most secure jobs. At the time a lot of cybersecurity was also required to be added to it. In the cybersecurity field, data scientists are likely to face growing demand.
As digital transformation reliance is growing rapidly in the world, the requirement of the protection of data from cyber security threats and hackers will also become more prioritized.
For helping companies in data protection, it is required that the data scientist will be familiar with the techniques and tools used for the cyber security
Nowadays an era is coming in where data science is becoming a team sport. So now it is no longer focused on the model building but instead of that, it is more about what has been done by you with the model once you have the model.
And the real challenge becomes how the model is operationalized and how those models are taken by you and the scaling of the model at that strength which makes them actionable across the organization.
Future of Data Scientists
Data scientists are now being frequently hired for automating company processes and activities. Although it's possible that automation could largely replace data scientists in the future, it's more likely that the field of data science will be greatly enhanced by artificial intelligence (AI) and other forms of automation. In many cases, data scientists will still be needed to interpret and oversee the results of automated processes. Additionally, low-code or no-code platforms are expected to grow and adopt much more widely than we can currently imagine
What are some of the best books and online courses to learn Data Science?
Best Books
Data Science from Scratch: First Principles with Python
Designing Data-Intensive Applications
Data Science For Dummies
Big Data: A Revolution That Will Transform How We Live, Work, and Think
Storytelling with Data: A Data Visualization Guide for Business Professionals
Practical Statistics for Data Scientists: 50 Essential Concepts
Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Head First Statistics: A Brain-Friendly Guide
R for data science: Import, Tidy, Transform, Visualize, And Model Data
Best Online Courses
Data Science Specialization — JHU @ Coursera.
Introduction to Data Science — Metis.
Applied Data Science with Python Specialization — UMich @ Coursera.
Data Science MicroMasters — UC San Diego @ edX.
Dataquest.
Statistics and Data Science MicroMasters — MIT @ edX.
Conclusion
- Firstly we had seen what is data science, Data science is one of the most vehemently debated issues in the IT field because of the vast numbers of data being produced nowadays.
2. Next, we saw why data scientists and how data scientists are useful. Data scientists are referred to as analytical professionals who use their knowledge to gather important insights that help the firm thrive and expand or survive in competitive markets.
3. Then, we had seen the 10 skills required to become a data scientist as Programming Skills, Data visualization, Machine Learning, Deep Learning, Probability and Statistics, Data Wrangling, Database Management Systems, Cloud Computing, Microsoft Excel, Communication Skills, and Teamwork.
4. At last, we saw the recent trends and future of data scientists in the coming years.
However, these skills are not mandatory to become a data scientist but are required to be unique and a rockstar in data science. Hope you got it and know the things required to become a rockstar in data science.