Data Collection, Processing, and Analysis - eLearning Bundle Course

Learning Data Analysis with R - 6 hrs 7 min

R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

This video delivers viewers the ability to conduct data analysis in practical contexts with R, using core language packages and tools. The end goal is to provide analysts and data scientists a comprehensive learning course on how to manipulate and analyse small and large sets of data with R. It will introduce how CRAN works and will demonstrate why viewers should use them.

You will start with the most basic importing techniques, to downloading compressed data from the web and learn of more advanced ways to handle even the most difficult datasets to import. Next, you will move on to create static plots, while the second will show how to plot spatial data on interactive web platforms such as Google Maps and Open Street maps. Finally, you will learn to implement your learning with real-world examples of data analysis.

This video will lay the foundations for deeper applications of data analysis, and pave the way for advanced data science.

• Import and export data in various formats in R
• Perform advanced statistical data analysis
• Visualize your data on Google or Open Street maps
• Enhance your data analysis skills and learn to handle even the most complex datasets
• Handle vector and raster data in R

Learning Path: Statistics and Data Mining for Data Science - 5 hrs 51 min

Data science is an ever-evolving field, with an exponentially growing popularity. It includes techniques and theories based on the fields of statistics, computer science, and most importantly machine learning, databases, and visualization. If you wish to enter the world of statistics and data mining, then look no further because this practical video course will walk you through the basics as well as the advanced concepts in a step-by-step manner.

The highlights of this Learning Path include learning when to use different statistical techniques, how to set up different analyses, and how to interpret the results and applying statistical and data mining techniques to analyze and interpret results using CHAID, linear regression, and neural networks.

This Learning Path begins with explaining the steps to analyse data and identify which summary statistics are relevant to the type of data you are summarizing. You will then learn several procedures, such as how to run and interpret frequencies and how to create various graphs. You will also be introduced to the idea of inferential statistics, probability, and hypothesis testing.

Next, you will learn how to perform and interpret the results of basic statistical analyses such as chi-square, independent and paired sample t-tests, one-way ANOVA, post-hoc tests, and bivariate correlations and graphical displays such as clustered bar charts, error bar charts, and scatter plots. You will then learn how to use different statistical techniques, set up different analyses, and interpret the results.

Moving ahead, this Learning Path shows the comparing and contrasting between statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. Next, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling. Finally, you will explore segmentation modeling to learn the art of cluster analysis and will work with association modelling to perform market basket analysis.

By the end of this Learning Path, you will gain a firm knowledge on data analysis, data mining, and statistical analysis and be able to implement these powerful techniques on your data with ease.

• Identify the basics of analyzing data
• Identify the importance of summarizing individual variables
• Use inferential statistics and perform the Chi-Square test
• Define correlations
• Differentiate between the various types of predictive models
• Utilize linear regression and explore the results of a decision tree
• Perform cluster analysis and work with neural networks

Data Science: Mathematical Methods - 50 min

Advanced business computing applications such as Artificial Intelligence (AI) and Machine Learning (ML) require advanced mathematics and statistics. While relatively few financial professionals need to be experts in these disciplines, it is important for all those who use financial technology tools to understand the mathematical and statistical concepts and methods that underlie them. Doing so can help financial professionals use technology more effectively, as well as help them to identify appropriate applications for different tools and to be aware of those tools’ limitations.

This course provides an overview of the core mathematical and statistical methods that are important to contemporary data science.

• Define functions and list their uses in computing
• Identify and define key mathematical methods, including linear algebra and calculus, and recall their role in computing applications
• Recognize and define important statistical measures and methods and identify their role in advanced business computing applications

Additional Features

• Inline Activities
• Post-Assessment
• Subtitles (Closed Caption): American English

Quantitative Trading: Data and Machine Learning (ML) - 1 hr

Analysts developing data models face a number of key data challenges, including biases - such as confirmation and availability biases - bad data, and model inaccuracies. One key type of data model, known as machine learning, allows the user to query the model for answers to simple questions.

This course provides an overview of major pitfalls in developing data models and discusses the importance of Machine Learning (ML) in detail.

• Recognize the importance of alternative data, including big data and expert data
• Recall how biases, bad data, and model inaccuracies can all affect the handling of data
• Identify the key features of both Supervised Machine Learning (S-ML) and Unsupervised Machine Learning (U-ML)
• Recognize how dimension reduction reduces the dimension of a data set and how data clustering groups large amounts of multi-dimensional data

Additional Features

• Inline Activities
• Post-Assessment
• Subtitles (Closed Caption): American English

Essential Career Skills Pack:

Productivity and Time Management - 30 minutes

It seems that there is never enough time in the day. But, since we all get the same 24 hours, why is it that some people achieve so much more with their time than others? This course will explain how to plan and prioritize tasks, so that we can make the most of the limited time we have. By using the time-management techniques in this course, you can improve your ability to function more effectively – even when time is tight and pressures are high. So, by the end of the course you will have the knowledge, skills and confidence to be an effective manager of your time.

Basic Digital Skills - 13 minutes

With the rise of digital transformation and technology, having a basic digital literacy is essential for all types of jobs, regardless of the industry. To stay competitive and be successful in the workplace, enhancing your digital skills should be a top priority.

4 Ways to Boost Creativity - 30 minutes

The digital economy is opening up ways for everyone to be creative. It doesn’t just mean being artistic – it’s more about ideas, solutions, alternatives, incremental improvements. Peter Quarry and Eve Ash discuss ways that mental capacity can be developed, perspectives changed, group power leveraged and making things actually happen.

The 11 Essential Career Soft Skills - 1 hour 10 minutes

Soft Skills are the traits, characteristics, habits, and skills needed to survive and thrive in the modern work world. Soft skills aren't usually taught in school, but you will learn them all here in this course. Are you someone that other people in your organization and industry like to work with, collaborate with and partner with? Are you seen as a valuable asset to any new project that comes along?

This soft skills training course will teach you how to develop the skills that can make the difference between a lackluster career that tops out at middle management versus one that lands you in the executive suite. Or to wherever you define career success. So many soft skills seem like common sense at first glance, but they are not commonly applied by most workers. This soft skills training course will give you an edge over your competitors. It will also make your job, your career and your life more rewarding and enjoyable.