The Best Online Data Science Tutorials to Boost Your Career Growth.

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Data Science is fast becoming a popular career choice in the 21st century.

For those who want to change the direction of their career, or for those who are already immersed in the industry, data-driven science is an ever-changing field which provides plenty of opportunities to put specialised skills to good use.

A distinct advantage of data science as a career choice is that there's a significant amount of education available through online resources. This provides both convenience because professional development can fit around an already committed schedule, and efficiency because you can practice as much as you need until those newfound skills become commonplace.

If you're considering data science as a career, or you're already practicing and you want to increase your knowledge, here are some online data science courses that will boost your career growth and potential as a data scientist.


The Data Scientist's Toolbox.

This four-week course can be completed in between one and four hours a week for four weeks and is a comprehensive introduction to the key elements of data science. Covering both conceptual and practical components, you'll learn how to turn data into useable knowledge and receive an introduction to the tools that will help you become a skilled programmer.

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R Programming.

R Programming is at the crux of data science and this course will teach you how to use R as a practical tool for data analysis to help you get ahead in your career. The course includes installing and configuring software for use in high-level statistical programming projects and covers detailed concepts which will help you implement them.

The course also includes practical modules in R Programming which includes the following:

  • Reading data in R
  • Accessing R packages
  • Writing functions in R
  • Debugging 

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Getting and Cleaning Data.

There's little point in learning how to handle data if you don't have any to work with in the first place!

The Getting and Cleaning Data course provides a complete overview of everything you need to know to collect, clean and share data. The four-week course includes the most effective ways to retrieve data from the web from databases and API's and will discuss the best methods to do so. Once you have your data, cleaning or tidying it so it can be used effectively is the next step. The course gives a comprehensive overview of everything you need to know for a complete data set and pays particular attention to the following areas: 

  • Raw data
  • Processing instructions
  • Processed data.

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Exploratory Data Analysis.

Before you can start formally modelling your data, you must implement exploratory techniques to summarise your data. Exploratory data analysis will help you develop potentially convoluted statistical models as well as refining the possible ideas about the bigger picture that can be identified within the data.

This course covers basic principles as well as detailed plotting systems in R. Not only that, the Exploratory Data Analysis course also includes lessons in some popular statistical techniques which help turn data into visual images that have an impact on the intended audience. 

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Reproducible Research.

Reproducible research is standard practice within data science and it means that your data and findings are published so that others can quantify claims from the data and software code. Not only does this mean that others can look at the findings of your research, but reproducible research also allows them to build upon the information to further enhance your findings.

As a data scientist, reproducible research is on the rise as analysis of data becomes more sophisticated and involves large samples of data. Understanding key concepts in reproducible research will propel your career so this course will increase both your employability and your potential.


Statistical inference.

Statistical inference covers central concepts and techniques required to form judgements about the boundaries of a population based on random data sampling.

Inference is an area that can often leave data scientists overwhelmed as there are so many process choices and options available. This course gives a comprehensive overview which will help the statistician by giving them a hands-on method of achieving results when analysing data. 

Statistical inference can be performed by statistical modelling, randomisation of analyses, and a number of data-orientated strategies.

Inference can also be performed alongside many design-based theories (including Bayesian and frequentists), and complexities which include unobserved confounding and biases.

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Regression models.

Regression modelling is a statistical process which allows the practitioner to estimate the association between a range of variables progressing sequentially.

If you're serious about your career in data science, learning how to analyse regression models is crucial to your education. 

This course includes the following:

  • Regression analysis
  • Regression models used in inference
  • Least squares
  • Residual and variability investigation.
  • Scatterplot smoothing.

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Practical Machine Learning.

As a data scientist, it's essential that you have a thorough understanding of practical machine learning. It's one of the most common tasks that data scientists carry out and this course covers a complete overview of the fundamental concepts, practical skills, techniques required, and how to implement them all.

Laying a complete foundation to practical machine learning, you'll start at the very beginning in theories that include training and tests sets as well as error rates and over lifting. Not just conceptual, the course will also cover learning methods which are both model-based and algorithmic such as regression, classification trees, and random forests.

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Developing Data Products.

The crux of what any data scientist is trying to achieve is to create an output from statistical analysis. This course gives a complete overview on how to produce data products using Shiny, R packages, and visual imagery. Data products make potentially complicated analysis tasks simple and increase their performance of algorithms or inferences. The Developing Data Products course will give a fundamental overview on how you can produce a data product to give vital data analysis information to a large following.

Regardless of your area of interest or specialisation in data science, these popular courses will cover the basic concepts and practical skills you need to excel in your career! 

Đã đăng 2 tháng 7, 2017


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