Top 10 Recession-Proof Data Science Skills to Master in 2024
Yachana Sharma
08 January 2024
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Explore the in-demand Data Science skills to master in 2024, Data Science tools and the relevant courses from Careervira’s partners to learn these skills and tools.
How Will Data Science Skills Be In Demand Even In Recession?
Important Data Scientist Skills and Responsibilities
Careervira: Helping You Make Faster and Better Decisions
Explore the in-demand Data Science skills to master in 2024, Data Science tools and the relevant courses from Careervira’s partners to learn these skills and tools.
Description
As per the International Monetary Fund (IMF), a recession might well be around the corner. This might explain why multiple companies, like Amazon, Twitter, Cisco, Intel, Meta, etc., are laying off employees. Such a prediction raises concern about what skills or job profiles will help one weather the changing markets. While there is no guarantee that any job is recession-proof, there are some skills that experts feel are indispensable to companies.
As per Emsi Burning Glass and Harvard Business Review (HBR), during this recession period, companies are focusing more on skills rather than degrees. They feel this recession tide has changed the perspective of companies, with many preferring to retain employees possessing hard-core skills. According to HBR, data science skills are one such qualification that will continue to be in great demand.
Data analysis and data science-related skills are in constant demand as data has become irreplaceable today. According to a report, the global data science market will expand at a Compound Annual Growth Rate (CAGR) of 27.7% by 2026.
With the advancement of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and cloud computing, the demand for different data science skills is here to stay. This article lists the top 10 recession-proof Data Science skills to master in 2024.
What is Data Science?
Data science is the branch of information technology that studies and deals with the massive volume of data generated by businesses and customers. These studies focus on analyzing the data generated by apps, services, customers, and other sources to extract valuable and actionable insights. Such meaningful data helps business executives and top management make expert business decisions. Data science is increasingly being employed across different industries like finance, tech, healthcare, marketing, etc.
By integrating machine learning algorithms (ML), data science can build predictive models and analyze future requirements. To build actionable insights from granular data generated from multiple sources, data science often requires mathematics, statistics, coding and programming skills, cutting-edge analytics, and AI. One study predicts that the data science market will touch $79.7 billion by 2030, growing at a CAGR of 33.6% from 2021 to 2030.
Source: Google search trend for “Data Science”
Data science is the branch of information technology that studies and deals with the massive volume of data generated by businesses and customers. These studies focus on analyzing the data generated by apps, services, customers, and other sources to extract valuable and actionable insights. Such meaningful data helps business executives and top management make expert business decisions. Data science is increasingly being employed across different industries like finance, tech, healthcare, marketing, etc.
By integrating machine learning algorithms (ML), data science can build predictive models and analyze future requirements. To build actionable insights from granular data generated from multiple sources, data science often requires mathematics, statistics, coding and programming skills, cutting-edge analytics, and AI. One study predicts that the data science market will touch $79.7 billion by 2030, growing at a CAGR of 33.6% from 2021 to 2030.
Source: Google search trend for “Data Science”
How Will Data Science Skills Be In Demand Even In Recession?
Since data analysis and data-driven science are required by companies to understand the daily market and predict the future, data science jobs are expected to be immune to market volatility.
Basic Data Science Skills
Most companies require basic graduation or post-graduation (with specialization in Computer Science, Mathematics, or Statistics) for data science roles. For job roles like data scientists or principal data analysts, a doctorate or Master's degree is mandatory. On a fundamental level, the technical Data Science skills to learn are:
Programming: It is essential to have proficiency in programming languages Python, C/C++, R, Perl, SQL, and Java. Python is the most common programming language that helps in data analysis, data wrangling, data cleansing, and various other data science roles. Other programming languages create efficient AI algorithms and create or manage large datasets. Careervira specializes in curating top programming language courses for users on Python, R, C, Java, etc., programming skills required for Data Scientists.
Statistical analysis: It is the science and art of collecting and analyzing data for extracting trends and patterns for actionable insights. It also involves numerical analysis and removes bias. Such statistical analysis also includes probability and distribution techniques. Conclusions drawn from statistical analysis help in better decision-making and in predicting future demands based on past trends. Use Careervira’s platform to learn about the numerous statistics courses available online, like Statistical Fundamentals, Probability and Statistics with Python, Statistics with R Specialization, etc.
Mathematics: Calculus and linear algebra are essential tools in data science. The calculus principles and formulas taught in high school come in handy in analyzing and extracting meaningful data from a pile of data. Use Careervira to curate numerous mathematical courses like Mathematics Fundamentals, Mathematics for Machine Learning Fundamentals, etc., as per your requirements for learning Data Science maths skills.
Data visualization: This is another skill that helps in communicating and translating data from a visual context using tools like charts, graphs, different forms of bars, and other visual aids. Visualizing data helps provide a clear picture of what the data is conveying. Careervira is a top site that curates numerous data visualization courses, such as the Data Visualization Nano Degree Program, Data visualization in R, etc., matching the unique individual requirements of users.
Data wrangling: It is another essential skill and methodology wherein data science professionals have to remove errors and merge complex data sets so that they become accessible and easy to analyze. In this massive data expansion era, storing and managing large clusters of data in an organized manner is essential. Careervira’s comprehensive curation enlists numerous data-wrangling courses like SQL data wrangling in Oracle, Data Science Wrangling, etc., for users to choose from.
Knowledge of various analytics and visualization tools: Tools like Power BI, Tableau, Excel, MongoDB, SAS Visual Analytics, Zoho Analytics, Airtable, etc., make it quick and easy to analyze and understand massive datasets. In this cut-throat competitive market, having proficiency in using analytics software can make landing a data science job easier. Careervira is a top educational learning site that curates the best courses on data analysis tools like Hadoop, SAS, Hive, etc.
Machine Learning (ML) and Deep Learning (DL): ML and DL are subsets of AI that help machines to learn by themselves. These models feed on data to understand various objects and situations and develop on their own to become better at analyzing or predicting circumstances and entities. Careervira curates numerous ML courses like IBM Machine Learning Professional Certificate, Machine Learning Fundamentals with Python, etc, making it easy for learners and professionals.
Since data analysis and data-driven science are required by companies to understand the daily market and predict the future, data science jobs are expected to be immune to market volatility.
Basic Data Science Skills
Most companies require basic graduation or post-graduation (with specialization in Computer Science, Mathematics, or Statistics) for data science roles. For job roles like data scientists or principal data analysts, a doctorate or Master's degree is mandatory. On a fundamental level, the technical Data Science skills to learn are:
Programming: It is essential to have proficiency in programming languages Python, C/C++, R, Perl, SQL, and Java. Python is the most common programming language that helps in data analysis, data wrangling, data cleansing, and various other data science roles. Other programming languages create efficient AI algorithms and create or manage large datasets. Careervira specializes in curating top programming language courses for users on Python, R, C, Java, etc., programming skills required for Data Scientists.
Statistical analysis: It is the science and art of collecting and analyzing data for extracting trends and patterns for actionable insights. It also involves numerical analysis and removes bias. Such statistical analysis also includes probability and distribution techniques. Conclusions drawn from statistical analysis help in better decision-making and in predicting future demands based on past trends. Use Careervira’s platform to learn about the numerous statistics courses available online, like Statistical Fundamentals, Probability and Statistics with Python, Statistics with R Specialization, etc.
Mathematics: Calculus and linear algebra are essential tools in data science. The calculus principles and formulas taught in high school come in handy in analyzing and extracting meaningful data from a pile of data. Use Careervira to curate numerous mathematical courses like Mathematics Fundamentals, Mathematics for Machine Learning Fundamentals, etc., as per your requirements for learning Data Science maths skills.
Data visualization: This is another skill that helps in communicating and translating data from a visual context using tools like charts, graphs, different forms of bars, and other visual aids. Visualizing data helps provide a clear picture of what the data is conveying. Careervira is a top site that curates numerous data visualization courses, such as the Data Visualization Nano Degree Program, Data visualization in R, etc., matching the unique individual requirements of users.
Data wrangling: It is another essential skill and methodology wherein data science professionals have to remove errors and merge complex data sets so that they become accessible and easy to analyze. In this massive data expansion era, storing and managing large clusters of data in an organized manner is essential. Careervira’s comprehensive curation enlists numerous data-wrangling courses like SQL data wrangling in Oracle, Data Science Wrangling, etc., for users to choose from.
Knowledge of various analytics and visualization tools: Tools like Power BI, Tableau, Excel, MongoDB, SAS Visual Analytics, Zoho Analytics, Airtable, etc., make it quick and easy to analyze and understand massive datasets. In this cut-throat competitive market, having proficiency in using analytics software can make landing a data science job easier. Careervira is a top educational learning site that curates the best courses on data analysis tools like Hadoop, SAS, Hive, etc.
Machine Learning (ML) and Deep Learning (DL): ML and DL are subsets of AI that help machines to learn by themselves. These models feed on data to understand various objects and situations and develop on their own to become better at analyzing or predicting circumstances and entities. Careervira curates numerous ML courses like IBM Machine Learning Professional Certificate, Machine Learning Fundamentals with Python, etc, making it easy for learners and professionals.
Important Data Scientist Skills and Responsibilities
Let us now understand the most important Data Scientist skills and responsibilities:
Data Analysis
To extract granular insights from large datasets in order to help business executives make strategic decisions for the organization, some data science professionals perform data analytics. Data analysis and pulling insights from data require tools, programming skills, statistical techniques, and other proficiencies like mathematics. Data analysis often involves cleaning, changing, wrangling, and processing raw data to extract relevant information and actionable insights to help the business understand the market trend and make informed decisions.
According to a research report, the globaldata analytics market will grow at a CAGR of 29.9% by 2030 and will be valued at $329.8 billion. The report predicts that data analysts will remain in demand through the recession.
Data analysts are responsible for preparing the data that enables business executives to make prompt and sound business decisions. Using various tools and programming languages, they determine the latest market trends and can even predict future market demands. The average annual salary of a data analyst is $5,932.
But surprisingly, there is an acute shortage of professionals with superior proficiency in data analytics skills or data science tools. Learning programming languages like Python, R, etc., and tools like Zoho Analytics, Google Analytics, etc., is a sure way to make your career in data science and be recession-proof.
Today almost all companies prefer using automated tools and techniques to perform data analysis and data science tasks. These tools help to extract valuable insights from granular data sets generated by users or organizations. Data analytics tools often enable integration of other services and Application Programming Interfaces (APIs). Since these tools perform tasks with lots of automation, it renders quick and error-free data analysis. SAS, Spark, Hive, Rare, Apache Spark, Pig, etc., are popular data analytical tools that data scientists use for various data-driven analyses and finding patterns for business prosperity.
Numerous companies provide courses and certifications to teach valuable data science skills. The average salary of a SAS-based data analyst is $101,916, while that of a Hadoop analyst is $75,000, and that of an Apache Spark data analyst is $116,000.
Source: Google search trend for “Data analysis tools”
Tableau's market share is rising to 3.96% this year. Power BI's market share rises to 16.17% this year. According to a report, the global Hadoop market will touch $842.25 billion by 2030. Careervira is a top online course curating site that provides an exhaustive list of courses on data analysis tools like Hadoop, SAS, Hive, etc. Taking such courses can give you an in-depth understanding of data analysis tools.
Data Management
Data management professionals are also in demand in the market because they are responsible for handling and managing all data generated by organizations. Data management is the practice and approach of ingesting, organizing, storing, maintaining, and, categorizing the massive data produced and compiled by an organization and its consumers. Effective data management plays a significant role in deploying IT systems and other analytical tools that run on data for analysis and predicting trends. Properly organized data drives operational decision-making and strategic planning by corporate executives, business managers, and other end users.
Data management professionals are responsible for ensuring that the data generated by corporate systems and their consumers remains available all the time and is accurate, and accessible by different departments involved in data analysis and ML operations. On average, top data management professionals can earn $217,885 annually. A data manager in the US has an average base salary of $85,078.
According to the Grand View Research report, in 2021, the global enterprise-grade data management market was $82.25 billion and will grow at a CAGR of 14% from 2022 to 2030. We can see massive growth in this job role in the years to come. But the number of skilled professionals in this vertical is limited. Since today's businesses generate massive data, most of the unstructured data remains poorly managed and hence unused. Through data management skills, aspirants can help organize, assess, and manage all existing data to make it easy for decision-makers and top business executives to make effective decisions.
Source: Google search trend for “Data management”
Careervira is a top site that curates the best online data management courses. Such courses can help aspirants achieve a clear understanding of the various concepts related to data management. By opting for the enterprise data management courses one can even learn data management skills at their own pace. The global data management community also offers industry-level certifications on data management that aspirants can opt for. So, if you want to make your career recession-proof, opt for these data management skill-development courses.
Programming Skills
Technology is emerging as the backbone for almost every sector - financial or e-commerce. Behind the success of these technologies is the art of programming. A good and efficient programmer is always in demand, be it in a recession or pandemic. Any investment aspirants make for developing their programming skills in terms of money, time, and effort will help them reap the rewards throughout their careers. In data science, especially in analysis, visualization, and machine learning algorithm development, companies need robust programmers who can develop techniques and applications to analyze market trends, customer patterns, and other supply-chain demand. According to Report Linker, the global coding and programming market will grow at a CAGR of 8.20% from 2022 to 2027.
Programmers must acquire proficiency in computer languages like C, C++, Python, Java, R, Matlab, etc. For data science jobs, Python and R, are two popular and essential languages. Along with programming languages, one should have a command over various Integrated Development Environments (IDEs) like PyCharm, Eclipse, Visual Studio, NetBeans, IntelliJ IDEA, etc. Apart from programming languages, aspirants must also have proficiency in version control systems like GitHub (free) and Perforce (premium). Again, a proper understanding of programming paradigms like Object Oriented Programming (OOPs), and other concepts like database systems, API integrations, data structures, containers, etc., enhances your value in the job market.
ML is another popular domain, which combines data science and artificial intelligence, which is also one of the significant data science skills. This domain uses filtered data for training algorithms so that machines can learn on their own and then can automatically extract valuable insights from data. ML engineers are in much demand by companies which consider their skills to be top class. This subset or sub-branch of AI often needs to work with various forms of data (structured, semi-structured, and unstructured). Projects like face recognition, object identification in images, pattern recognition, analyzing market trends automatically, and analyzing user behaviour leverage ML.
According to the Fortune Business Insight report, in 2021, the global ML market was $15.44 billion and will grow to $209.91 billion by 2029. The demand for ML roles is rising tremendously. ML requires skills like programming, mathematics, statistics, algorithm development, and AI. Since it uses a massive amount of data for training the models, a basic idea of Big Data can make you stand out from the rest of your peers. According to Glassdoor, the average annual salary of an ML engineer is $136,431.
Source: Google search trend for “Machine Learning”
The rate at which businesses collect data is incredible. Despite the availability of numerous abundant solutions such as analytical tools, BI solutions, spreadsheets, and automated visualization solutions and libraries, over the recent decades, businesses are still struggling to take advantage of the prospects hidden in enterprise and customer-generated data. Communicating actionable insight from data to the business decision-makers and stakeholders is a skill. This skill is essential for data analysts and BI professionals. According to Gartner's prediction, data storytelling will dominate business intelligence by 2025.
Storytelling using data is an influential method of communicating information tailored to a particular set of audiences (non-technical mostly) with a compelling narrative. As humans, we are hard-wired to narrate stories to understand information. Thus, we hold this natural instinct to understand complex things through storytelling. Data storytelling merges three skills: data analysis, storytelling, and data visualization using libraries and tools like Tableau or Google Charts. According to ZipRecruiter, the average annual salary of a data storyteller is $84,243.
Source: Google search trend for “Data Storytelling”
If you want a recession-proof skill in 2023, learn storytelling through data. You can hone this skill by opting for courses available online. Storytelling With Data and Data Visualization course which can be found on Careervira, is an excellent choice for beginners.
Statistics
Statistics and statistical analysis are two of the most important data science skills. Through statistical models, Business Intelligence (BI) professionals and data analysts can identify data trends extracted from Big Data. Through statistical techniques like linear regression, resampling, classification, finding the median, etc., enterprises can easily extract meaningful insights from data. Numerous core data analytics methods rely on time series, regression analysis and factor analysis. According to Report Ocean, the Statistical Analysis Software market size will reach $61620.0 million in 2028. Statisticians who can analyze data based on statistics to extract actionable insight are sure to get jobs even during a recession. A statistician in data analysis receives a salary of $93,453 annually.
Source: Google search trend for “Statistics for data science”
Some other skills a Data Scientist needs which are in demand are leadership and managerial skills. Every company is on the lookout for managers and leaders possessing top-notch data analysis and industry-standard skills, as well as people skills. The combination of influential skills, persuasive skills, and managerial skills alongside data analytics skills makes an employee indispensable. Hence, learning leadership and managerial skills is a wise choice to secure your career in this recession. According to some research reports, the average annual salary of data science managers ranges from $134,866 to $165,891.
Popular spreadsheet tools like MS. Excel or Google Spreadsheets can achieve data manipulation, data handling, and data visualization. By investing time in MS. Excel one can carry out various data-related processing, statistical operations, and chart generation with much ease. Spreadsheet software like MS. Excel features formulas, functions, and shortcuts ease various tasks in data science. Because of their easy-to-use interface, anyone with the minimum computer skills can master it effectively. MS. Excel is beneficial because one can store and filter data, perform calculations, analyze different forms of structured data, visualize data through distinct forms of charts, print reports easily, automate calculations and various operations, transform raw data to clean data, etc.
According to the Regional Research Reports, in 2021, the global Spreadsheets Software market size was valued at $1 million and will touch $2-$3 million by 2030, progressing at a CAGR of 8.6% from 2022 to 2030. All these facts show the increase in the use of spreadsheets at the corporate level. So, learning to use spreadsheets like MS. Excel will give aspirants a beneficial edge in competitive job interviews. Mastering data science skills through MS. Excel can help protect your career in this recession. According to ZipRecruiter, the average annual salary of a data analyst with knowledge of Excel is $55,700.
SQL is another well-known database management query language and is the most learnt data science skill. Often many computer science degrees and college syllabi cover this subject. It helps data science professionals create, update, and manage structured data generated by business apps and systems. All the modern big data tools, like Hadoop, Apache Spark, etc., employ SQL to bolster relational database approaches and process structured data. According to JC market research report, the global SQL market demand will reach $29,145.1 million in 2031. An SQL developer gets an annual average salary of $79,543. The demand for SQL developers is always high and hence, it is a recession-proof skill for 2023.
Let us now understand the most important Data Scientist skills and responsibilities:
Data Analysis
To extract granular insights from large datasets in order to help business executives make strategic decisions for the organization, some data science professionals perform data analytics. Data analysis and pulling insights from data require tools, programming skills, statistical techniques, and other proficiencies like mathematics. Data analysis often involves cleaning, changing, wrangling, and processing raw data to extract relevant information and actionable insights to help the business understand the market trend and make informed decisions.
According to a research report, the globaldata analytics market will grow at a CAGR of 29.9% by 2030 and will be valued at $329.8 billion. The report predicts that data analysts will remain in demand through the recession.
Data analysts are responsible for preparing the data that enables business executives to make prompt and sound business decisions. Using various tools and programming languages, they determine the latest market trends and can even predict future market demands. The average annual salary of a data analyst is $5,932.
But surprisingly, there is an acute shortage of professionals with superior proficiency in data analytics skills or data science tools. Learning programming languages like Python, R, etc., and tools like Zoho Analytics, Google Analytics, etc., is a sure way to make your career in data science and be recession-proof.
Today almost all companies prefer using automated tools and techniques to perform data analysis and data science tasks. These tools help to extract valuable insights from granular data sets generated by users or organizations. Data analytics tools often enable integration of other services and Application Programming Interfaces (APIs). Since these tools perform tasks with lots of automation, it renders quick and error-free data analysis. SAS, Spark, Hive, Rare, Apache Spark, Pig, etc., are popular data analytical tools that data scientists use for various data-driven analyses and finding patterns for business prosperity.
Numerous companies provide courses and certifications to teach valuable data science skills. The average salary of a SAS-based data analyst is $101,916, while that of a Hadoop analyst is $75,000, and that of an Apache Spark data analyst is $116,000.
Source: Google search trend for “Data analysis tools”
Tableau's market share is rising to 3.96% this year. Power BI's market share rises to 16.17% this year. According to a report, the global Hadoop market will touch $842.25 billion by 2030. Careervira is a top online course curating site that provides an exhaustive list of courses on data analysis tools like Hadoop, SAS, Hive, etc. Taking such courses can give you an in-depth understanding of data analysis tools.
Data Management
Data management professionals are also in demand in the market because they are responsible for handling and managing all data generated by organizations. Data management is the practice and approach of ingesting, organizing, storing, maintaining, and, categorizing the massive data produced and compiled by an organization and its consumers. Effective data management plays a significant role in deploying IT systems and other analytical tools that run on data for analysis and predicting trends. Properly organized data drives operational decision-making and strategic planning by corporate executives, business managers, and other end users.
Data management professionals are responsible for ensuring that the data generated by corporate systems and their consumers remains available all the time and is accurate, and accessible by different departments involved in data analysis and ML operations. On average, top data management professionals can earn $217,885 annually. A data manager in the US has an average base salary of $85,078.
According to the Grand View Research report, in 2021, the global enterprise-grade data management market was $82.25 billion and will grow at a CAGR of 14% from 2022 to 2030. We can see massive growth in this job role in the years to come. But the number of skilled professionals in this vertical is limited. Since today's businesses generate massive data, most of the unstructured data remains poorly managed and hence unused. Through data management skills, aspirants can help organize, assess, and manage all existing data to make it easy for decision-makers and top business executives to make effective decisions.
Source: Google search trend for “Data management”
Careervira is a top site that curates the best online data management courses. Such courses can help aspirants achieve a clear understanding of the various concepts related to data management. By opting for the enterprise data management courses one can even learn data management skills at their own pace. The global data management community also offers industry-level certifications on data management that aspirants can opt for. So, if you want to make your career recession-proof, opt for these data management skill-development courses.
Programming Skills
Technology is emerging as the backbone for almost every sector - financial or e-commerce. Behind the success of these technologies is the art of programming. A good and efficient programmer is always in demand, be it in a recession or pandemic. Any investment aspirants make for developing their programming skills in terms of money, time, and effort will help them reap the rewards throughout their careers. In data science, especially in analysis, visualization, and machine learning algorithm development, companies need robust programmers who can develop techniques and applications to analyze market trends, customer patterns, and other supply-chain demand. According to Report Linker, the global coding and programming market will grow at a CAGR of 8.20% from 2022 to 2027.
Programmers must acquire proficiency in computer languages like C, C++, Python, Java, R, Matlab, etc. For data science jobs, Python and R, are two popular and essential languages. Along with programming languages, one should have a command over various Integrated Development Environments (IDEs) like PyCharm, Eclipse, Visual Studio, NetBeans, IntelliJ IDEA, etc. Apart from programming languages, aspirants must also have proficiency in version control systems like GitHub (free) and Perforce (premium). Again, a proper understanding of programming paradigms like Object Oriented Programming (OOPs), and other concepts like database systems, API integrations, data structures, containers, etc., enhances your value in the job market.
ML is another popular domain, which combines data science and artificial intelligence, which is also one of the significant data science skills. This domain uses filtered data for training algorithms so that machines can learn on their own and then can automatically extract valuable insights from data. ML engineers are in much demand by companies which consider their skills to be top class. This subset or sub-branch of AI often needs to work with various forms of data (structured, semi-structured, and unstructured). Projects like face recognition, object identification in images, pattern recognition, analyzing market trends automatically, and analyzing user behaviour leverage ML.
According to the Fortune Business Insight report, in 2021, the global ML market was $15.44 billion and will grow to $209.91 billion by 2029. The demand for ML roles is rising tremendously. ML requires skills like programming, mathematics, statistics, algorithm development, and AI. Since it uses a massive amount of data for training the models, a basic idea of Big Data can make you stand out from the rest of your peers. According to Glassdoor, the average annual salary of an ML engineer is $136,431.
Source: Google search trend for “Machine Learning”
The rate at which businesses collect data is incredible. Despite the availability of numerous abundant solutions such as analytical tools, BI solutions, spreadsheets, and automated visualization solutions and libraries, over the recent decades, businesses are still struggling to take advantage of the prospects hidden in enterprise and customer-generated data. Communicating actionable insight from data to the business decision-makers and stakeholders is a skill. This skill is essential for data analysts and BI professionals. According to Gartner's prediction, data storytelling will dominate business intelligence by 2025.
Storytelling using data is an influential method of communicating information tailored to a particular set of audiences (non-technical mostly) with a compelling narrative. As humans, we are hard-wired to narrate stories to understand information. Thus, we hold this natural instinct to understand complex things through storytelling. Data storytelling merges three skills: data analysis, storytelling, and data visualization using libraries and tools like Tableau or Google Charts. According to ZipRecruiter, the average annual salary of a data storyteller is $84,243.
Source: Google search trend for “Data Storytelling”
If you want a recession-proof skill in 2023, learn storytelling through data. You can hone this skill by opting for courses available online. Storytelling With Data and Data Visualization course which can be found on Careervira, is an excellent choice for beginners.
Statistics
Statistics and statistical analysis are two of the most important data science skills. Through statistical models, Business Intelligence (BI) professionals and data analysts can identify data trends extracted from Big Data. Through statistical techniques like linear regression, resampling, classification, finding the median, etc., enterprises can easily extract meaningful insights from data. Numerous core data analytics methods rely on time series, regression analysis and factor analysis. According to Report Ocean, the Statistical Analysis Software market size will reach $61620.0 million in 2028. Statisticians who can analyze data based on statistics to extract actionable insight are sure to get jobs even during a recession. A statistician in data analysis receives a salary of $93,453 annually.
Source: Google search trend for “Statistics for data science”
Some other skills a Data Scientist needs which are in demand are leadership and managerial skills. Every company is on the lookout for managers and leaders possessing top-notch data analysis and industry-standard skills, as well as people skills. The combination of influential skills, persuasive skills, and managerial skills alongside data analytics skills makes an employee indispensable. Hence, learning leadership and managerial skills is a wise choice to secure your career in this recession. According to some research reports, the average annual salary of data science managers ranges from $134,866 to $165,891.
Popular spreadsheet tools like MS. Excel or Google Spreadsheets can achieve data manipulation, data handling, and data visualization. By investing time in MS. Excel one can carry out various data-related processing, statistical operations, and chart generation with much ease. Spreadsheet software like MS. Excel features formulas, functions, and shortcuts ease various tasks in data science. Because of their easy-to-use interface, anyone with the minimum computer skills can master it effectively. MS. Excel is beneficial because one can store and filter data, perform calculations, analyze different forms of structured data, visualize data through distinct forms of charts, print reports easily, automate calculations and various operations, transform raw data to clean data, etc.
According to the Regional Research Reports, in 2021, the global Spreadsheets Software market size was valued at $1 million and will touch $2-$3 million by 2030, progressing at a CAGR of 8.6% from 2022 to 2030. All these facts show the increase in the use of spreadsheets at the corporate level. So, learning to use spreadsheets like MS. Excel will give aspirants a beneficial edge in competitive job interviews. Mastering data science skills through MS. Excel can help protect your career in this recession. According to ZipRecruiter, the average annual salary of a data analyst with knowledge of Excel is $55,700.
SQL is another well-known database management query language and is the most learnt data science skill. Often many computer science degrees and college syllabi cover this subject. It helps data science professionals create, update, and manage structured data generated by business apps and systems. All the modern big data tools, like Hadoop, Apache Spark, etc., employ SQL to bolster relational database approaches and process structured data. According to JC market research report, the global SQL market demand will reach $29,145.1 million in 2031. An SQL developer gets an annual average salary of $79,543. The demand for SQL developers is always high and hence, it is a recession-proof skill for 2023.
Careervira: Helping You Make Faster and Better Decisions
Data science, AI-related skills and leadership skills are highly sought by companies across all industries. Considering that a recession may be round the corner, one should improve the possibilities of getting employed. Skills like machine learning, deep learning, data analytics, AI, leadership, etc., are still in their infancy thus their demand is not going to fade even during the recession.
If you have completed your graduation and want to shift to a recession-proof job profile, having some of these most in demand data science skills (at least) will surely ensure success. Careervira is an excellent online platform that can help secure your job even in a global recession. We provide excellent industry-standard certification courses to help you prepare for a robust career with the help of in-depth skills. Staying ahead of the predicted recession is now easy with Careervira!
Data science, AI-related skills and leadership skills are highly sought by companies across all industries. Considering that a recession may be round the corner, one should improve the possibilities of getting employed. Skills like machine learning, deep learning, data analytics, AI, leadership, etc., are still in their infancy thus their demand is not going to fade even during the recession.
If you have completed your graduation and want to shift to a recession-proof job profile, having some of these most in demand data science skills (at least) will surely ensure success. Careervira is an excellent online platform that can help secure your job even in a global recession. We provide excellent industry-standard certification courses to help you prepare for a robust career with the help of in-depth skills. Staying ahead of the predicted recession is now easy with Careervira!
