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eLearning Bundle - Data Science Programming Essentials Online Course

eLearning Bundle - Data Science Programming Essentials

by Certstaffix® Training

Length: 4 Course(s)      Price: $1000/person (US Dollars)      Bulk Pricing: 6+ Contact Us      Access Length: 6 Months      Category: Data Science

This eLearning bundle consists of these 4 courses: Introduction to Python Training, Advanced Python Training, Learning Python for Data Science and Learn R programming.
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  Online Data Science Classes: Learn at Your Own Pace

We will enroll you into a demo (Windows 10) eLearning session. Login information will be emailed to you.

Prefer a self-paced learning solution to fit your own schedule? Certstaffix® Training offers eLearning courses bundled together:

  • Learn at your own pace - Start and stop as it is convenient for you. Pick up where you left off.
  • Lecture utilizing video and recorded screen shots
  • 6 month subscription length
  • Any software needed for training is provided in an online lab environment.

  Detailed Training Topics

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Introduction to Python Training - 41 hours and 30 minutes

Designed for students with little to no experience with programming languages, this training course provides an introduction to Python, a high-level programming language which is quite user friendly. To begin, we'll explain how Python works, along with its benefits and uses, and how to get started with writing a simple script. From there, you'll learn how to work with strings and sequences, perform math operations and conditional processing, and collect user input and output results. You'll also learn how to write to and read from files, write functions, handle exceptions, and work with dates and times. This training course is taught in Python 3. The differences between Python 2 and Python 3 are noted where they exist.

Advanced Python Training - 30 hours and 52 minutes

In this Python training course, students already familiar with Python programming will learn advanced Python techniques. This advanced Python course is taught using Python 3; however, differences between Python 2 and Python 3 are noted.

Learning Python for Data Science - 3 hours and 39 minutes

Python is an open-source community-supported, general-purpose programming language that, over the years, has also become one of the bastions of data science. Thanks to its flexibility and vast popularity that data analysis, visualization, and machine learning can be easily carried out with Python. This course will help you learn the tools necessary to perform data science.

In this course you will learn all the necessary libraries that make data analytics with Python a joy. You will get into hands-on data analysis and machine learning by coding in Python. You will also learn the Numpy library used for numerical and scientific computation. You will also employ useful libraries for visualization, Matplotlib and Seaborn, to provide insights into data. Further you will learn various steps involved in building an end-to-end machine learning solution. The ease of use and efficiency of these tools will help you learn these topics very quickly. The video course is prepared with applications in mind. You will explore coding on real-life datasets, and implement your knowledge on projects.

By the end of this course, you'll have embarked on a journey from data cleaning and preparation to creating summary tables, from visualization to machine learning and prediction. This video course will prepare you to the world of data science.

Learn R programming - 1 hour and 29 minutes

R is a high-level statistical language and is widely used among statisticians and data miners to develop statistical applications. This solution-based video will be your guide, taking you through different programming aspects with R.

Beginning with the basics of R programming, this video provides step-by-step resources and time-saving methods to help you solve programming problems efficiently. Starting with the installation of R, each recipe addresses a specific problem with a discussion that explains the solution and offers insight into how it works.

You will learn to work with powerful R tools and techniques. You’ll be able to boost your productivity with the most popular R packages and tackle data structures such as matrices, lists, and factors. You’ll see how to create vectors, handle variables, and perform other core functions. You’ll be able to tackle issues with data input/output and will learn to work with strings and dates.

Moving forward, we’ll look into more advanced concepts such as metaprogramming with R and functional programming. Finally, you’ll learn to tackle issues while working with databases and data manipulation.
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