Data Science for beginners in this free online course
Data Science is over complicated by the fact of all the fields you need to learn. In this article you will learn what you need to get started to create data science projects with valuable actionable insights. This will introduce you to the best Data Science course online that is free.
Why learn Data Science?
Maybe you know why you want to learn Data Science, but here are few reasons you can add for your motivation.
Data Science is one of the tech jobs in the highest demand (source). Even better, the supply of data scientists is not big enough to keep up with it.
Knowing how to utilize data gives an edge. The most successful companies have all achieved with the power of data.
Excellent salary often starting at $100,000 in the U.S. The demand for Data Scientists is significant, which gives you an excellent starting salary.
Great flexibility as a Data Scientist. The need to work with data in every industry gives you both flexibility to work as a free lancer, but also in what field you enjoy the most.
Data Science is an interdisciplinary field, but you don’t need to be an expert in all of them. The fact that Data Scientist work in teams means you don’t need to master every aspect.
Best of all, it it not that difficult to become good enough to create Data Science project that give valuable actionable insights, such that your client can make data-driven decisions. You will learn that.
What you need to learn as Data Scientist
You are most likely a bit overwhelmed about all the skills you need as a Data Scientist, right?
Statistics. This includes methods for evaluating, interpreting, displaying, and making decisions based on data, which is key aspect of Data Science.
Math. Linear algebra is often listed as a needed skill as a Data Scientist, as it is the foundation of many models used in Data Science.
Data Analysis. The ability to analyse data, which is the one of the core aspects of a Data Scientist.
Programming. This includes to read and prepare data to create models, and apply the model to bring the actual value as a Data Scientist. You can say that programming connects it all together.
Machine Learning. The models are used to make predictions based on previous data, this creates value to make data-driven decitions.
Data Visualization. Data is difficult to understand, data visualization does three things, helps you understand quality of data, find patterns in data, and present findings.
Data Wrangling. Data needs to be prepared to be used, this is often overlooked by beginners, but has high impact on the accuracy of the models.
Communication. Often considered a soft-skill, and you would think that results that migh be obvious from your work are used by the business. Reality is often a bit different, you need to be a good communicator to convince the big bosses.
Collaboration. Also a soft-skill, but Data Scientist often work in teams and across diferent departments in an organization. You will need to understand the needs of other departments and area, to make valuable projects.
How scary is that? You can take a full education in each field. How can you master all that then?
Luckily, you don’t need to be specialist in any of them, you need some basics to manage it.
Pitfalls when learning Data Science
You now know that you don’t need to be an expert in most of the things.
Here are a few pitfalls while learning Data Science, which will make the journey longer or fail.
Focus too much on learning tech skills. This is common and most online courses focus on the long list of technical skills you need, a long list of programming languages, frameworks, and tools. They are very tangible and easy to sell as needed skills, but the technical skills will not teach you want you need.
Not understand the significant of correct data. Most beginners find some data and make projects. This is not how Data Scientists work, they start with a problem and need to find the data to make the project.
Solving the wrong problems. This is similar, when the data cannot bring the results that are needed, beginners focus on what problems they can solve. A Data Scientist should focus on what the problem is before exploring data. Nail the problem before starting.
Specialist learning. If you are good at one thing you tend to focus on that and becomes expert in that. Reality is, you need to learn the required level of all the skills needed. There are need for specialist, but before you can become a valuable specialist, you need to understand the full picture.
Not understand the power of domain knowledge. Most beginners think that Data Science is a one-size-fits-all kind of skill set. Reality is, that the best way to be hired is to have specialized domain knowledge. If you understand a field in-depth, you will know what brings value. People are hired if they have specialized domain knowledge, even if their other skills are questionable as a Data Scientist.
How to learn Data Science
The key to success of learning Data Science is to focus on what brings you the most value and not focus too broad.
Learn Python. Python is easy to learn, has a simple syntax, and is the most widely used programming language in the scientific community. It is a general purpose, which makes it easy to collaborate and it can be used for most things in Data Science.
Wait with R, Scala, Julia, and specialized languages. As a starting Data Scientist, you don’t need to know it all, many will never use any specialized language in their career, but if you do, you can learn it when needed.
Work in Notebooks with pandas. Learning how to work with data pandas and Jupyter notebooks will be sufficient. Later, you might need to learn big-data frameworks like Databricks, which are very similar in nature, hence the specifics your future employer uses will be easy to transition to.
Understand problem. The key skill a Data Scientist should master is understanding what problem you should solve and not what data can tell you. While data might be interesting to dive into, understanding what brings value and data-driven decisions are important to master.
Data Science Workflow. All learners, me including, tend to fall into details of what we are learning. To constantly keep the focus on the bigger picture is relevant. Therefore it is important to use the Data Science Workflow to ensure what you do adds value.
Get started with Data Science Course Online for free
With all that knowledge you now know how to get started.
Luckily, there is a free Data Science course online that you can follow for free, designed as you know is the easiest way to get the skills.
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