Introduction to Data Science: Everything You Need to Know

What is Data Science?

Simple Answer: Data science is the process of extracting meaning from data. If we can understand what the data is telling us, then we can better recognize problems and begin to suggest logical solutions.

Slightly More Complex Answer: Data science is a multidisciplinary field that combines statistics, mathematics, computer science, and business knowledge to extract insights from data.

Why are so many businesses investing in Data Science?

Data science is a relatively new field, and businesses are investing in it because they see the potential for growth and innovation. Data science allows businesses to make better decisions by analyzing large amounts of data, and it can also help them find new opportunities and optimize their operations.

Data science can help businesses solve a variety of problems, including:

  • Analyzing customer behavior to understand buying patterns and preferences
  • Developing marketing strategies based on customer data
  • Optimizing website design and content to improve conversions
  • Preventing fraud
  • Performing risk assessments
  • Forecasting sales and revenue trends
  • Spotting areas of improvement in business operations

What is “Big Data”?

Big data is a term used to describe the large volume of data that organizations face. This data is often complex and unstructured, making it difficult to manage using traditional database management tools. The big data phenomenon has been driven by the growth of online consumer activity, the proliferation of mobile devices, and the rise of social media.

Can big data really help businesses make better decisions? 

Yes, big data can help businesses make better decisions. By analyzing large data sets, businesses can identify patterns and trends that would otherwise be difficult to detect. This information can help businesses make more informed decisions about product development, marketing, and other strategic initiatives. For example, big data can help businesses identify patterns in customer behavior that can be used to improve marketing campaigns or product development. Additionally, big data can help businesses optimize operations by identifying areas where improvements can be made.

What are the different types of Data Science? 

Here are some of the common data science disciplines:

  • Machine learning: Machine learning is a type of data science that focuses on developing algorithms that can learn from data and improve over time.
  • Deep learning: Deep learning is a type of machine learning that uses algorithms to learn from data in a way that mimics the workings of the human brain.
  • Data mining: Data mining is a type of data science that focuses on extracting valuable information from large data sets.
  • Statistical analysis: Statistical analysis is a type of data science that uses statistical methods to analyze data and make predictions.
  • Data visualization: Data visualization is a type of data science that focuses on creating visual representations of data.

What are the best traits and skills required to make a good data scientist?

Some of the best traits and skills required to make a good data scientist include:

  • Analytical skills: Data scientists need to be able to analyze data and identify patterns.
  • Problem-solving skills: Data scientists need to be able to identify problems and suggest solutions.
  • Communication skills: Data scientists need to be able to communicate

Does Data Science require being able to code? 

No, data science does not require being able to code. However, coding can be helpful for data scientists who want to automate tasks or perform complex analyses. Additionally, many data scientists use coding languages such as Python or R to develop statistical models or data visualizations.

What is the recommended education path for those wishing to pursue Data Science?

There is no one specific education path for those wishing to become data scientists. Many data science job postings are seeking candidates with at least a bachelor’s degree in a field such as mathematics, statistics, computer science, or engineering. Additionally, many job postings for data scientist positions are looking for individuals with advanced degrees such as master’s degrees or PhDs.

Is a College Degree required to work in Data Science?

There is no one-size-fits-all answer to this question, as it depends on your specific qualifications and experience. However, in general, a degree is not always required to work in data science. Many data scientists have degrees, but there are also many who do not. What is more important than your degree is your experience and expertise in data analysis and modeling. If you have strong skills in these areas, you may be able to find a job in data science even if you do not have a formal degree.

Explore Data Science Certification Options

If you’re wanting to get into the data science world, but you are reluctant to commit to long and expensive degree programs, then you may want to consider what certification routes may exist. There are many data science platforms now in existance. With a little research you can identify which of these platforms offer certification programs, and then begin to explore the certification path for those platforms of most interest to you.

Consider Online Learning Options

In the new world of online learning there are many individuals with a tremendous depth of expertise and experience who offer courses on a variety of different platforms in data science related subjects. You can consider any of the following online learning platforms including:, LinkedIn Learning,, or

Learn The Basics Before Deepdiving

It’s perhaps best to invest some time on a more generic overview of Data Science prior to going super deep on any one particular platform. For around $12 you can probably find a pretty decent Data Science Introduction Course on Udemy. Once you have an understanding of what data science is and what it entails, the next step is to zoom in on what particular technology you’d like to learn about and then consider taking additional online training courses specific to your desired certification path.

Consider Market Potential Before Investing Time

Some data science platforms are relatively new, and although they may offer extremely facinating and useful technology, their may not yet be a huge job market for all platforms. Before investing time on any technology consider how much market potential exist.

If your just starting off in data science, then you will want to strike a balance between:

  • Obtainable Skills – meaning not so complex that it’s too big of a jump from your current technical skill level. For example: if you don’t have a development background, then consider focusing on solutions that can be implemented with No Code/Low Code. Meaning you won’t have to learn a programing language to succeed within the niche. This will allow you to learn the concepts without also learning a whole new language.
  • Market Demand/Potential for the Product– Ideally focus on learning solutions offered by well established software brands with high market share in their perspective niches.
  • Supply & Demand of existing specialized workforce – Much of what we are referring to as “market potential” relates to the supply and demand of labor/workers-available to implement a high value solution within an organization. There is definately some truth in the old saying: “if it was easy everyone would do it!” If everyone has a skill, then that skill probably holds little value to potential employers since the ‘supply’ of the skill is high. If you are wondering how to determine if a product certification is in high demand, then simply go to Indeed or LinkedIn and search for jobs related to the skill/certification you are desiring. For example: if you search “Certified Salesforce” you will find a high number of open jobs, because there has been a long standing shortage of labor in this market.

So to somewhat recap – you want to:

  1. Find obtainable skills based on your current experience and knowledge
  2. The skills are for a product that is well known, in high demand and provides high degree of value to the companies adopting it.
  3. There is a shortage of labor to implement the product within the companies that desire to adopt the offered solution.

Find Just The Right Level Of Complexity! You want it to be just complicated enough that not everyone can do it (or they at least think they can’t), but not so complicated that it’s unobtainable. Find a niche you love that falls into these guidelines and there is a high chance that you will land a job with a certification. Which leads us to our next point…

Pursue Data Science Certifications

As discussed above, some data science certifications will offer greater potential than others. Data Science is still a relatively new field, which also means the industry is still trying to figure out what it wants from qualified candidates. Because of this, some certs will become more popular and in-demand than others. Try to stay ahead of the curve by persuing certs that offer the greatest potential. Generally speaking- if a certification is easy to get, then it may not be worth anything.

Expect to put some work into obtaining your certification. Don’t get discouraged if you don’t pass a certification exam the first time. It’s like the Neo building jump scene from The Matrix – “No one makes the first jump”. Although this thankfully isn’t always true as you can pass a certification on the first try, it’s also true that many individuals who are new to a subject may have to retake a certification exam. Be encouraged to know that the high level of work required to obtain a certification will keep the riffraff out! This means your hard work will pay off, and your certification will actually be valuable when you get it!

What are the best Data Science Platforms and Certifications for 2022 and beyond?

Some of the most popular data science certifications include:

  • Microsoft Certified: Azure Data Scientist Associate – An entry-level certification for those with experience using Azure to build and train machine learning models, as well as deploy them.
  • Google Data Analytics Certificate – The Google Data Analytics Certificate is a course offered through Google that teaches students how to use data analytics to improve their business. No relevant experience required. Data analysts prepare, process, and analyze data to help inform business decisions. They create visualizations to share their findings with stakeholders and provide recommendations driven by data. Students who complete the course receive a certificate from Google.
  • AWS Certified Data Analytics – Specialty – The AWS Certified Data Analytics – Specialty is a certification that demonstrates proficiency in data analytics. It covers topics such as data acquisition, transformation, modeling, and visualization. The certification is aimed at professionals who work with big data and want to learn how to use the AWS platform to analyze that data.
  • AWS Data Scientist Learning Path – The AWS Data Scientist Learning Path is an online training program designed to help you become a data scientist. The program covers a variety of topics, including data analysis, machine learning, and big data. The learning path also includes a number of hands-on exercises that will help you apply what you learn in the program to real-world scenarios.
  • Tableau CRM – Salesforce Einstein Analytics Certification – The Tableau CRM – Salesforce Einstein Analytics Certification is a certification that is offered by Tableau and Salesforce. It is a certification that is aimed at professionals who want to be able to use Tableau and Salesforce Einstein Analytics to create data-driven insights. The certification consists of a set of exams that test the professional’s ability to use these tools to create data-driven insights.
  • MathWorks MATLAB Certification – MATLAB is a software for mathematical analysis, data visualization, and machine learning. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. 
  • Alteryx Designer – Alteryx Designer is a data science tool that enables you to perform data preparation, blending, and advanced analytics. It provides a visual interface for drag-and-drop data manipulation, as well as tools for writing code. Alteryx Designer is used by data scientists and business analysts to solve problems such as predictive modeling, market segmentation, and forecasting.
  • RapidMiner Studio – RapidMiner Studio is a data science tool that enables you to build, test, and deploy machine learning models. It provides an interface for drag-and-drop data manipulation, as well as tools for writing code. RapidMiner Studio is used by data scientists to solve problems such as predictive modeling, market segmentation, and forecasting.
  • Dataiku – Dataiku is a data science platform that enables you to perform data analysis and machine learning on your data. It has a user-friendly interface and includes a variety of tools to help you work with your data. You can use Dataiku to explore your data, build models, and deploy your models into production.


Whether you are just starting out your career or you are looking to make a career pivot into the field of data science, it’s my hope that this article offers some encouragement to you. Data Science is often featured as this overly complex futuristic thing from Sci-Fi Movies. While it’s true that the depths of data science can be extensive, it’s also true that as technology advances that new tools are being developed to make field of Data Science more approachable than ever! You no longer need an advanced mathematics degree to jump into the data science arena and add some serious value to a companies decision making abilities.

If you are interested in Data Science Careers, then please comment below to let us know what platforms you have focused on and what steps you have taken along your own learning journey.

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