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35 Key Data Scientist Qualifications And Skills – Whitepaper

35 Key Data Scientist Qualifications And Skills - A Whitepaper

The fact that big data science is one of the highest paid professional areas to get into, means you need a long list of data scientist qualifications and skills.

No matter if you live in the USA, Canada, UK, Australia, India or somewhere else, the minimum set of required skills (such as technical knowledge, software, math, and statistics) is not enough to truly succeed and to get above average median base salary – $110,000 per year.

You need a broad range of behavioral characteristics, traits, qualifications, knowledge, certificates, and understandings to be a professional who is able to bring accurate data insights for decision-making in an organization.

In this Whitepaper:

  • 35 key qualifications and skills needed for a data scientist
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Qualitative vs quantitative data – Infographic 

qualitative vs quantitative data - infographic 1

If you are involved in statistics, marketing or data science, it is essential to know what is the difference between qualitative and quantitative data and analysis.

They are different approaches to understanding data and to perform analysis of information.

Our post defines, explains, compares, and gives examples of qualitative data versus quantitative data, variables, and analysis. The infographic summarizes the information in a brief manner.

In this infographic:

  • Qualitative vs quantitative data: definition, examples, characteristics, contrast, similarities, and differences.
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Key machine learning areas you need to know – Infographic

How to learn machine learning - infographic

Learning ML involves understanding maths, statistics, programming languages (such as Python and R), algorithms and etc.

Nowadays, there are many easy, fast, affordable or free ways to learn Machine Learning for beginners and advanced learners. You can find online courses, books, interactive lessons, websites, and places to gain and share knowledge.

In the post:

  • What is Machine Learning?
  • Best ways to learn: Python, Probability, Algorithms, Calculus, Linear Algebra, and ML as a whole.

In the infographic:

  • List of the key machine learning areas you need to know
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Supervised vs unsupervised iearning – Infographic

Supervised vs Unsupervised Learning - infographic

When it comes to fundamentals of data science, we should know what is the difference between supervised and unsupervised learning in machine learning and in data mining as a whole.

It is not only about to know when to use the one or the other.

Unsupervised and supervised learning algorithms, techniques, and models give us a better understanding of the entire data mining world.

We will compare and explain the contrast between the two learning methods.

In this infographic:

  • Unsupervised vs supervised learning: examples, comparison, similarities, differences.
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5 Best machine learning algorithms for classification – Infographic

Machine Learning Classification Algorithms - infographic

It can be tricky to decide which is the best machine learning algorithm for classification among the huge variety of different choices and types you have.

Still, there are machine learning classification algorithms that work better in a particular problem or situation than others.

You might need algorithms for: text classification, opinion mining and sentiment classification, spam detection, fraud detection, customer segmentation or for image classification.

The right choice depends on your data sets and the goals you want to achieve.

In this infographic:

  • List of the most popular and proven machine learning classifiers.
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Simple linear regression example – Infographic

Simple Linear Regression Example Infographic

Many of simple linear regression examples (problems and solutions) from the real life can be given to help you understand the core meaning.

From a marketing or statistical research to data analysis, linear regression model have an important role in the business.

As the simple linear regression equation explains a correlation between 2 variables (one independent and one dependent variable), it is a basis for many analyses and predictions.

Apart from the business, LR is used in many other areas such as analyzing data sets in statistics, biology or machine learning projects and etc.

In this infographic:

  • Simple linear regression example
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Qualitative data analysis methods – Infographic

Qualitative Data Analysis Methods - infographic

As data is an invaluable source of business insight, the knowing what are the various qualitative data analysis methods and techniques has a crucial importance.

There are different approaches, types of statistical methods, strategies, and ways to analyze qualitative data.

Choosing the best way and technique depends on the nature of your data and the goals set in qualitative research.

In the post:

  • What is qualitative data analysis? Definition and explanation.
  • Techniques and methods used in qualitative data analysis: list and explanation.

In this infographic:

  • What is qualitative data analysis?
  • List of techniques and methods used in qualitative data analysis.
Download the infographic See the post

Interval Scale: definition, examples, characteristics, and comparison with ratio data – Infographic

Interval Data Infographic

In the world of data management, statistics or marketing research, there are so many things you can do with interval data and the interval scale.

In fact, together with ratio data, interval data is the basis of the power that statistical analysis can show.

Both interval and ratio scales represent the highest level of data measurement and help a wide range of statistical manipulations and transformations that the other types of data measurement scales cannot support.

In this infographic:

  • What is interval data?
  • Definition, meaning, and key characteristics.
  • Examples of interval data.
  • Interval vs Ratio data.
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Discrete vs continuous data – Infographic (comparison chart)

Discrete vs continuous data - comparison chart

Statistics and data management sciences require a deep understanding of what is the difference between discrete and continuous data set and variables.

The similarity is that both of them are the two types of quantitative data also called numerical data.

However, in practice, many data mining and statistical decisions depend on whether the basic data is discrete or continuous.

In this infographic:

  • What is discrete data? Definition and examples.
  • What is continuous data? Definition and examples.
  • Discrete vs continuous data: diferences with comparison chart
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Nominal data vs ordinal data – Infographic (comparison chart)

Nominal data vs Ordinal data - Comparison Chart

When working with data sciences, we need to understand what is the difference between ordinal and nominal data, as this information helps us choose how to use the data in the right way.

A data scientist decides how to determine what types of data analysis to apply based on whether the data set is nominal or ordinal.

In this infographic:

  • What is nominal data and what is ordinal data?
  • Definition and examples
  • Nominal vs ordinal data: key differences with a comparison chart
Download the infographic See the post