Important Statistics Skills And Tips To Develop Them

Updated 13 December 2022

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Companies rely on data analysis and predictions to make informed and unbiased business decisions. Having strong statistical skills helps you generate critical business insights by organising and analysing large volumes of data. If you are looking to build a career as a statistical analyst, data analyst or data scientist, understanding relevant skills can help you complete your tasks efficiently and progress your career. In this article, we discuss what statistics skills are, list the top technical and non-technical statistical skills and also share tips on how to develop them.

What Are Statistics Skills?

Statistics skills are competencies and capabilities that help professionals use statistical methodologies to identify patterns and insights from large datasets. It also involves using mathematical models to predict outcomes. These skills help candidates review data critically to gather useful information that adds value to the organisation. Statistical skills are in demand across several industries, including health care, banking, insurance, education, economics and business.

Examples Of Technical Statistical Skills

The following technical skills can help statisticians, data analysts and data engineers build efficient analytical models and analyse data to generate crucial insights. Here are some popular statistical skills that are helpful in the workplace:

1. Data analysis

Data scientists and statisticians apply various statistical theories, techniques and concepts to solve data challenges in the workplace. Being familiar with basics in this field like probability, distribution curves, variance, standard deviation and other topics can help you collect, organise, analyse and present data efficiently. Additionally, these concepts help you understand the data you are working on, helping you find valuable insights that add business value to your organisation.

2. Multivariable calculus and linear algebra

Calculus and algebra help statisticians and data scientists solve machine learning problems. For example, professionals apply these two mathematical concepts for training artificial neural networks. These mathematical theories also help statisticians optimise fitting functions to match a model to a data set and simplify complicated analysis problems involving high-dimensional data. Having a clear understanding of advanced calculus and linear algebra can help you solve several data challenges as part of your role.

3. Predictive modelling

The ability to interpret data to make predictions and model different scenarios is a crucial tenet of statistics and data science. Predictive analysis looks for patterns in existing or new data sets to forecast future behaviour, events and results. Professionals apply predictive analytical skills in various industries, like equipment maintenance, customer analytics and medical diagnosis. The range of benefits and use cases makes predictive modelling a highly valued skill for statisticians and data engineers.

4. Machine learning and deep learning

With the increasing relevance of machine learning (ML) applications, employers value candidates with ML knowledge. As a statistician, your role might require training machine learning algorithms to identify patterns, insights and anomalies. Deep learning is an advanced technology that uses neural networks to create complex analytical models. Being familiar with various machine learning and deep learning algorithms can help you handle multiple challenges in the workplace efficiently. Here are some of the popular ML algorithms:

Random forests
Decision trees
K-nearest neighbour
K-means clustering
Linear regression
Logistic regression
Naive Bayes classifiers

5. Data visualisation

Statisticians use data storytelling to highlight results and insights as part of their role. Data visualisation helps statistical analysts convert these insights into easy-to-understand visual forms for business executives, clients and other stakeholders. Popular data visualisation techniques include bar graphs, line and pie charts, bubble charts, histograms, scatter plots and heat maps.

Related: Types Of Graphs And Charts

6. Model production and deployment

A considerable part of a statistician's role involves building and deploying statistical models. Statisticians require the ability to choose the correct algorithm for training data and then find meaningful clusters and patterns. Once they have identified a suitable model, they work with other team members like data engineers to deploy the model in a production environment. A sound understanding of model production and deployment helps statisticians complete their tasks efficiently.

7. Programming and coding

Being familiar with the basics of programming and writing code can help you create custom code snippets to meet your data analysis requirements. Most data scientists and statisticians use Python and Structured Query Language (SQL) for their daily tasks. R is another popular programming language among statisticians as it helps in handling various data science challenges like statistical computing. Besides these programming languages, you can also consider learning other common coding languages like C, C++, Java and Julia.

Examples Of Non-Technical Statistical Skills

Besides technical skills, statistical jobs require a variety of soft skills that help candidates perform well and collaborate efficiently with other team members. Here are the top soft skills that help statisticians and data scientists:

Attention to detail: Statistical roles involve scrutinising vast data sets to identify patterns and insights. Strong attention to detail can help you spot anomalies and derive insights from datasets.
Business knowledge: Having a complete understanding of the fundamental nature of a business helps candidates ask the right data analysis questions, identify new ways for the organisation to use the data and increase revenues using the insights.
Curiosity: The desire to continually learn is a crucial skill-set for analysts. Being curious helps statisticians learn and adopt new technologies and tools, helping them retain a competitive edge in the workplace.
Critical thinking: Statisticians assess and analyse data sets to derive valuable insights. Critical thinking skills can help you reach accurate and unbiased conclusions based on their analysis.
Communication: Though statisticians and data scientists work independently, they require precise and efficient workplace communication skills to share and present their findings to team members, managers and other stakeholders. The ability to communicate successfully also helps you explain how to use the generated insights to make sound business decisions.
Collaboration: Statisticians work as part of a larger team that includes other statisticians, data scientists, data engineers, programmers and other professionals. Collaborating and working harmoniously with others can help you advance your career and advance to managerial positions.
Organisation and multitasking: The role of a statistician or data analyst involves working with multiple data sets and projects simultaneously. Staying organised and having the ability to multitask efficiently without losing focus can help you complete tasks on time.
Problem-solving: Statisticians often analyse vast volumes of data to identify critical patterns and insights. Keen problem-solving skills can help you identify relevant datasets and build innovative solutions that aid the organisation reach various business objectives.

Tips To Improve Statistics Skills

If you are considering a career that involves statistical analysis, you can consider following these steps to develop and hone the skills for the role:

1. Set goals

The first step to developing any new skill is to set realistic and achievable goals. Start by identifying the list of skills you wish to acquire. Next, set a deadline to achieve each of these goals. It is a good idea to understand the job description and list the major skills that the role requires. Focus on one or two goals at a time to avoid overburdening yourself. Once you have acquired a particular skill, you can move on to the next one on your list.

2. Consider certifications or courses

An easy way to acquire a new technical skill is by learning it professionally. An online course or certification teaches you the basics and you might also get the opportunity to apply the skill with the help of practical coursework. Online certificates and courses also familiarise the latest tools and technologies in the industry. Adding the completed coursework to your resume can help to differentiate your profile from other candidates.

3. Practice daily

One of the best ways to improve your statistical skills is to practice them daily. Regular practice gives you the confidence to handle new challenges. Just like other technical skills, core statistical competencies like data analysis, data modelling and mathematics require repeated and consistent practice to attain mastery. Practicing daily helps improve your muscle memory, giving you the ability to approach bigger challenges in the workplace confidently. Dedicate one to two hours daily to practice and hone core statistical skills.

4. Be an active member of the online statistical community

Taking an active part in the online statistical community can help you further refine and build your skills in the subject. Become a member of online communities and open source projects where statisticians and statistical analysts share ideas. You can also improve your skills by participating in open-source projects and competitions where you compete against professionals in the industry. This helps you learn best practices and exchange ideas and solutions with more experienced professionals in the field.

5. Enlist the help of a mentor

A mentor can be a manager, team leader or a trusted colleague at work. Work with your mentor to identify your strengths and weaknesses. Being open to receiving feedback from your team members helps you identify skills that require improvement. Alternatively, you can also consider hiring an online tutor to provide one-on-one personalised training on specific areas or topics.

Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.