7 In-Demand Data Analyst Skills to Get You Hired in 2023

Once you have the necessary abilities, switching to a career in data analytics can lead to solid employment in a well-paying sector. Data analysts and scientists are in more demand each year than there are qualified candidates to fill those positions. 

The US Bureau of Labor Statistics projects that there will be 23% more job vacancies between 2021 and 2031, a rate much greater than the country's predicted average job growth of 5%.

But which abilities are most in demand in the data world?

The seven skills now in demand in data science are some of the ones that millions of global learners search for most frequently. Start by honing these abilities to prepare for a new profession in data analysis, which is experiencing rapid growth.

Let's examine them in more detail, along with the steps you may take to learn them.

7 In-Demand Data Analyst Skills


SQL (Structured Query Language) is a programming language for managing and manipulating relational databases. It inserts, updates, retrieves and deletes data from a database. SQL is widely used for managing large amounts of data and is supported by most relational database management systems. SQL commands include SELECT, INSERT, UPDATE, and DELETE.

Statistical Programming

Statistical programming uses programming languages, software, and tools to perform statistical analysis and modelling. Commonly used statistical programming languages include R, SAS, and Python. These languages provide a wide range of libraries and packages for statistical analysis, including data visualisation, descriptive statistics, inferential statistics, and machine learning.

Statistical programming is commonly used in data science, bioinformatics, and finance to analyse and interpret large amounts of data. It allows for the automation of complex analysis, the ability to handle large datasets, and the ability to reproduce results.

Statistical programming also allows for data integration from various sources, such as databases and flat files, and the ability to output results in multiple formats, such as tables and plots. It enables data scientists and researchers to extract valuable insights from data and make data-driven decisions.

Probability and statistics

Probability and statistics are related fields in mathematics that deal with data collection, analysis, interpretation, presentation, and organisation.

Probability is the branch of mathematics that studies the chance or likelihood of an event occurring. It deals with random variables, probability distributions, and their properties. Probability is used to make predictions and estimate the possibility of future events, such as the outcome of a coin toss or the probability of a stock market crash.

On the other hand, statistics is the branch of mathematics that deals with data collection, analysis, interpretation, presentation, and organisation. It includes descriptive statistics, which deals with summarising and describing data, and inferential statistics, which deals with making inferences and predictions based on a sample of data.

Both probability and statistics are used in many areas of study, such as finance, biology, engineering, economics, etc. They are used to make decisions and predictions based on data and to conclude population parameters based on sample data.

Machine learning

Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computers to learn from data without being explicitly programmed. It allows computers to learn from past experiences and improve their performance on a specific task through training and without human intervention.

There are different types of machine learning, each with its own set of techniques and algorithms. Some of the main types of machine learning include:

  • Supervised learning: A model is trained on a labelled dataset, where the correct output is provided for each input. Common examples include image classification and linear regression.
  • Unsupervised learning: In unsupervised learning, a model is trained on an unlabeled dataset, where the correct output is not provided. Common examples include clustering and dimensionality reduction.
  • Reinforcement learning: In reinforcement learning, an agent learns to make decisions by interacting with its environment and receiving feedback through rewards or penalties.
  • Semi-supervised learning: In semi-supervised knowledge, the model is trained on a dataset that contains a small amount of labelled data and a large amount of unlabeled data.

Machine learning is used in many industries and applications, including natural language processing, computer vision, speech recognition, finance, healthcare, and self-driving cars.

Data Management

Data management is collecting, storing, maintaining, and efficiently utilising data. It involves several tasks, such as data integration, data quality control, data security, data warehousing, and data governance.

Data integration combines data from different sources and makes it available for use. This can involve cleaning, transforming, and merging data from other formats and structures.

Data quality control is the process of ensuring that the data is accurate, consistent, and complete. This includes tasks such as data validation, data profiling, and data reconciliation.

Data security protects data from unauthorised access, disclosure, disruption, modification, or destruction. This includes tasks such as encryption, firewalls, and access controls.

Data warehousing is collecting and storing large amounts of data from various sources for reporting and analysis. This includes tasks such as data modelling, data extraction, data transformation, and data loading.

Data governance establishes policies, procedures, and standards for managing data throughout its lifecycle. This includes data stewardship, lineage, cataloguing and metadata management, and privacy and regulatory compliance.

Overall, data management is a critical aspect of any organisation, as it enables organisations to make data-driven decisions, improve efficiency and productivity, and comply with legal and regulatory requirements.

Statistical VIsualisation

Statistical visualisation, also known as data visualisation, creates graphical representations of data to help understand, explore, and communicate complex information. Statistical visualisation aims to make it easy to see patterns, trends, and outliers in the data that might not be immediately obvious from looking at raw numbers.

There are many different types of statistical visualisations, including:

  • Bar charts: used to compare values across various categories.
  • Line charts: used to show changes in data over time.
  • Scatter plots: used to establish the relationship between two variables.
  • Histograms: used to indicate the distribution of a single variable.
  • Heat maps: used to establish the relationship between two variables and the density of the data in different areas.
  • Pie charts are used to indicate the proportion of different categories.
  • Box plots: used to indicate the distribution of a variable, including the median, quartiles, and outliers.

Statistical visualisation is commonly used in business, economics, science, and medicine to understand, explore and communicate data effectively. It can be done using various software and programming languages such as R, Python, SAS and Tableau.


Econometrics is the application of statistical and mathematical methods to economic data. It estimates and makes inferences about financial relationships, tests economic theories, and predicts future economic events.

Econometrics involves the use of statistical techniques to model and analyse economic data. This includes linear and non-linear regression techniques, time series analysis, and maximum likelihood estimation.

There are two main branches of econometrics:

  • Cross-sectional econometrics, which deals with the analysis of data collected at a single point in time, and
  • Time-series econometrics deals with the study of data collected over time.

Econometric models and techniques are widely used in policy-making, forecasting and risk management, and business decisions. Econometrics is used in many areas of economics, including macroeconomics, microeconomics, and finance. It is also used in other fields, such as marketing, political science, and sociology, to analyse data and make predictions. The use of econometrics software such as EViews, R, SAS, and STATA daily in econometrics research and analysis.

Tips for Learning Data Analysis Skills

  • Start with the basics: Learn the fundamental concepts of statistics and programming, such as probability, statistics, and the basics of a programming language like Python or R.
  • Practice, practice, practice: The more you practice, the better you will become. Look for datasets online and practice analysing them using different techniques.
  • Learn from experts: Take online courses or tutorials from experts in the field, or read books on data analysis written by experienced practitioners.
  • Work on projects: Apply what you have learned by working on real-world projects. This will give you a chance to practice your skills and gain experience.
  • Learn from others: Join online communities, such as forums and social media groups, to learn from others and share your knowledge.
  • Learn to visualise data: Data visualisation is an integral part of data analysis, so be sure to use visualisation tools and techniques to present your findings effectively.
  • Keep yourself updated: Data analysis is an ever-evolving field. Stay current by reading articles, blog posts, and industry publications to stay updated with the latest trends and developments.
  • Seek feedback: Share your work with others and ask for feedback; it will help you improve and refine your skills.
  • Be curious and stay motivated: Always question and try to understand the underlying mechanisms of the data, and stay motivated to continue learning and growing.
  • Never stop learning, and seek new opportunities to expand your knowledge in data analysis and related fields.

Becoming proficient in data analysis takes time and effort, but with dedication and practice, you can gain the skills and knowledge needed to become an experienced data analyst.

How to include Data  Analyst Skills on your resume

  • Highlight your technical skills: List the specific tools and programming languages you have experience with, such as Python, R, SQL, Excel, and Tableau.
  • Include relevant coursework: If you have taken any courses or certifications related to data analysis, mention them on your resume.
  • Showcase your experience: Include any relevant work experience in data analysis, such as internships or previous jobs where you have applied data analysis skills.
  • Use specific examples: Use concrete examples to demonstrate your skills and experience, such as describing a project you worked on and the specific data analysis techniques you used.
  • Use industry-specific language: Use the language and terminology specific to the industry you are applying; this will help demonstrate your understanding of the field and make your resume stand out.
  • Highlight any projects you have worked on: Mention any projects you have worked on, such as data analysis or data visualisation projects, and describe the skills and techniques you used to complete them.
  • Emphasise your achievements: Highlight your accomplishments, such as increasing revenue or reducing costs through your data analysis work.
  • Show your results: Demonstrate the impact of your data analysis work by including metrics such as ROI or other relevant performance indicators.
  • Include any relevant soft skills: Highlight any soft skills you have that are relevant to data analysis, such as attention to detail, problem-solving, and communication skills.
  • Keep your resume up to date: Regularly review and update it to ensure that it accurately reflects your current skills and experience.
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