Skip to 0 minutes and 1 second SPEAKER: Data science getting you down? Destress by learning the ins and outs of data science in just two weeks. Follow along with expert Radhika Subramanian as you discover the fundamentals of data science. This course will help you conceptualise effective data science techniques, explore data science case studies, and implement what you’ve learned in your own project. Choose the pace and the place where you learn. Step into your future with data science projects and applications from Packt and FutureLearn.
Weekly study2 hours
Data Science Projects and Applications
Learn practical skills for a career in data analysis or data science
Data science allows organisations to understand and interpret swathes of data and gain insights that allow for smarter decision making, driven by data.
This two-week course provides an overview of the key concepts in data science, from understanding regression to using K-means clustering.
Discover new data analysis techniques through engaging data science projects
Whether you’re beginning a career in data science or want to understand your organisation’s data better, this course will strengthen your knowledge of data analysis tools and techniques.
You’ll complete practical projects that will demonstrate real-world applications of data science. This will allow you to assess different data science scenarios and choose the best approach, grounded in a broad knowledge of data analysis methodology.
Explore data visualisation tools and methods
You’ll delve into different types of data analysis and explore how to create effective data visualisation using MySQL. Using case studies as a starting point, you’ll discover step-by-step data visualisation processes, from gathering the data to displaying the output.
With this knowledge, you’ll be able to solve problem statements with data visualisation and be able to apply this knowledge to your own work.
Develop your data manipulation and interpretation skills
Having explored data analysis methods, you’ll then learn to evaluate and interpret this data in a meaningful way.
With a solid understanding of how to collect, review, and evaluate data, you’ll be able to explain your findings and the supporting methodology.
By the end of this course, you’ll understand applied data science methods and concepts. You’ll have gained insights into a variety of analytical approaches to data and be able to use these to interpret data effectively.
This video course will help you to learn the important concepts and theories of applied data science that you need to know to store, manipulate, and visualize huge chunks of data.
The course starts with an introduction to applied data science and a tutorial on how to set up a Jupyter notebook. You’ll then go on to understand linear regression using Boston data. As you advance, you’ll discover data visualization techniques and explore time series and data evaluation. You’ll also get to grips with extended data analysis with the help of a temperature analysis activity. Toward the end, you’ll be introduced to k-means clustering and gain a solid understanding of decision trees.
Learning on this course
On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.
What will you achieve?
By the end of the course, you‘ll be able to...
- Classify effective data science techniques
- Explore relevant data science case studies
- Practice your learnings on your own projects
Who is the course for?
This course is designed for anyone interested in learning about data science, particularly those looking to begin a career in data science of analytics. It would also be suitable for those wanting to better understand their organisation’s data and how to use and interpret it effectively.
What software or tools do you need?
You’ll need to download and install the Anaconda software to your Windows, MacOS, or Linux system. We’ll show you how to do this on the course.
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