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Learning a recent skill can be daunting, especially if you’ve spent most of your time searching for the right course, university degree or boot camp. Before you even get to the point of spending a penny, take advantage of the free resources available first. Feel it, see if you like it, and experience most of the content online for free before you’re ready to get certified.
To combine: freeCodeCamp
When many people are thinking about getting into data science and what programming language they should learn – many people naturally turn to Python. And there is a reason for this. It is considered one of the best programming languages to learn and has been number one for some time. It contains a variety of libraries and frameworks and uses readable code.
To combine: StatQuest
Many bootcamps sometimes don’t cover certain elements that are very critical in the world of data science – statistics is one of them. From my own experience, I entered the world of data science with little or no understanding of the statistical side because my course never offered it. I found myself having to go back to learn a lot of things again – the right way!
Josh Starmer from StatQuest was on this journey and made statistics fun and simple to learn. Statistics is critical in data science and critical for your career development. It allows you to better understand what data analytics is and why it is critical in the entire data analysis process when creating solutions.
To combine: 3Blue1Brown
There’s nothing wrong with diving a little deeper when it comes to learning about the statistical/mathematical side of data science. I say this because it will only benefit you in your studies and career in data science. 3Blue1Brown is a YouTube channel presenting mathematics in an animated form.
To combine: DataCamp
As a data scientist, you’ll be working with a lot of data (obvious, right?). But when working with data, you need to remember that a lot of the data you provide will be unstructured and you will need to spend time cleaning it. This is one of the first steps in the data analysis process and is critical.
In this Data Camp YouTube video, you will learn the importance and different techniques for achieving spotless and consistent data. This live training will teach you the types of data cleansing challenges you will encounter.
To combine: Kris Naik
Machine learning is hugely popular right now, and it’s only going to get even bigger. Within data science, it is critical to understand the intricacies of machine learning – which is why I will recommend Krish Naik.
The linked video is a 6-hour overview of machine learning. I don’t expect you to cover it in one sitting, but in this 6-hour video you will learn about various aspects of machine learning, from linear regression to clustering algorithms. As you learn them, you’ll start to understand why understanding statistics is critical in data science – everything will start to make sense.
To combine: Learn easily
When working with data, your only task will not be to learn how to spotless it and obtain results for your decision-making process. As part of your role as a data analyst, you will be responsible for transforming results into data visualizations. This is intended to present your data in other forms, as well as to meet the needs of non-technical stakeholders.
Once you master these 6 aspects of data analytics, you’ll have a wealth of knowledge and skills to continue your studies in more unique sectors such as deep learning or natural language processing.
Nisha Arya is a data scientist, freelance technical writer, and editor and community manager of KDnuggets. She is particularly interested in providing career advice or tutorials in data analytics and theory-based knowledge in data analytics. Nisha covers a wide range of topics and wants to explore the different ways in which artificial intelligence can impact the longevity of human life. Nisha is an avid learner and strives to expand her technical knowledge and writing skills while helping others.