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You are reaping the benefits of data science every day even if you don’t notice it.
Google uses it to know you better and deliver more personalized search results. Facebook is collecting mounds of information on you and mining it for advertisers with every message, Like, and share. Amazon uses it to make online shopping a little bit more personalized.
McKinsey calls data science as the next frontier for innovation. Harvard Business Review dubbed it the sexiest job of the 21st Century. From one to the other, there is still a huge shortage of manpower. So, if you are thinking about becoming a data scientist, now would be a good time to start.
From appreciating data visualization to understanding machine learning, there’s a lot of ground to cover. The five Udemy courses below will not give you all the skillsets. But they will gently ease you into the complex world of data science.
Discover: The easiest trapdoor into the world of data science.
Data science is a huge magnificent beast. On the extreme end of the spectrum, a data scientist has core machine learning or programming skills and can tame “Big Data“. Data visualization is a tiny but important subset of this larger skillset. It is important because a data scientist has to weave a story from the tangled mess of big data. The data scientist has to make it easy for the decision makers.
Even without data science, visualization can stand alone for communicating information clearly. Think of your average pie-chart or the at-a-glance impact of an excellent infographic. This introductory (and free) course can be the first step for understanding any data. You just need to have a basic understanding about web and design terminologies.
Combine this with the more career oriented Want to be a Data Scientist? free course on Udemy which should answer a few more questions for you.
Discover: How to use tap the power of Excel for data visualization.
Microsoft Excel is one of the least expensive everyday pieces of software you can use to practice your first data visualization project. Data scientists use it to gather and analyze data. Learning the different ways to present data is one big step towards mastering Excel and its enormous utility. It also has an interface most of us are already familiar with.
The course uses 5 different types of data and the right chart type to explain them. Two case studies should further solidify the concepts. Start with Excel 2007 or any version above it. Some familiarity with VBA and Excel functions is desirable.
Discover: How the simple syntax of SQL can help you do some real data analysis without any technical background.
Learning a bit of SQL could be one of the best career decisions you make. If you deal with data of any kind in your day job, Structured Query Language (SQL) will help you talk to that data and draw answers efficiently. SQL is a simple “query language” that helps anyone extract data from different databases, and then combine them together to create reports.
The 35-lecture course can be finished over the course of a weekend. Arm yourself with real world SQL queries and then tweak them for your own specific purposes. The ease of the course should help you whet your appetite for the rigors of data science skills that lie ahead. Even if you stop right here, the ability to extract data and manipulate it to your ends should be a positive notch on your CV.
Discover: How to perform all steps in a complex data science project.
Things get serious with this 21-hour course which provides a good introduction to the data science field and tools like SQL, SSIS, Gretl, and Tableau. The entire course is divided into learning modules (pathways) which you can follow as independent units, mix them up, or learn them all in one go. Look through the preview of the course structure explained in Lecture 5 to plan your attack.
The learning pathways are spread out over 200 lectures. They cover data visualization and data mining, statistical modeling, data preparation, and finally end with presentation skills. The course starts with data visualization which is always the most fun part of data science rather than data preparation which is the most ponderous.
The A-Z course balances a broader view of the entire field with the nitty gritty you need to get the handle on a complex data science project.
Discover: How to apply Tableau to real-life data analytics exercises.
Kirill Eremenko follows the above course with comprehensive exercises on Tableau. Tableau is some of the more widely used data analysis software and a step up from Microsoft Excel. One of the cons is that, unlike Excel, it can be cost prohibitive. There’s a free version of Tableau for personal use, but it has a limited feature set. If you are interested in data science as a career, the visually intuitive tool is a good start before the heavy lifting down the road.
The 6-hour course helps you learn the latest version of Tableau from scratch. Go from installation to connecting the software to external datasets, to creating visualizations and a story from the data. I haven’t taken the course but it is an updated version to Kirill’s well-reviewed and popular course on Tableau 9.
Start to Think Like a Data Scientist
This is just the beginning. These five data science classes will give you a solid background in data science fundamentals before you decide to move a step closer to big data processing tool like R programming, Python, Hadoop, Spar, Panda, Dremel, and others.
Udemy has a buffet of other vital data science courses. Some of them are:
- Become a Hadoop Developer
- Taming Big Data with MapReduce and Hadoop – Hands On!
- Data Science and Machine Learning with Python – Hands On!
- Learning Python for Data Analysis and Visualization
- Taming Big Data with Apache Spark and Python
- SAS Programming for Beginners
The biggest takeaway from your first effort will be the answer to the golden question – do you love data science?
How does it look from here? Do you think data science is hot or hyped? Does it have a place in your present job function?