We offer instructor-led workshops, bootcamps and custom training programs for you to learn 21st century skills. We believe in learning by doing. Learning experiences we offer cover the theory, tools and applied practices you'll need throughout the entire value creation lifecycle, from asking the relevant questions to making predictions and visualizing results. Training labs are designed to cover real world use cases, and applied knowledge of the data science and engineering practices and tools. When you learn from the practitioners and industry leaders, you can be confident that what you’ll learn will be relevant, complete, and practical.

Programs are designed to increase your proficiency in extracting insight and value from data. Learn to combine fundamentals, practices and tools from statistics, machine learning, computer science, data visualization and the social sciences to make sense of data for intelligent predictions. You will apply scientific methods to data challenges in different industries and, ultimately, prepare yourself for the real world experience.

What is Data Science?

[Understanding the DNA of Data Science]

This course will help you develop the strategic understanding of Data Science and the applied knowledge you need to seize the strategic, economic, social and business potential of data. Introduce yourself to the basics of Data Science and leave armed with in depth understanding of extracting insights, predictions and value from data.

It is an introductory course where the various aspects of data science are introduced and covered at a high level. This course takes you through the elements of the field of Data Science. It covers the history, philosophy and implementation of the data science method and life cycle and would be very useful to those wishing to gain exposure for the first time to this rapidly expanding area.

Anyone interested in Data Science. Especially for people who are wanting to learn more about what is involved in becoming a data scientist.

Undergraduate degree in maths, the sciences, computer science or a business major with a quantitative element.

Data Science for Business

[Data Science as Value Amplifier for CXOs]

The future belongs to the companies and people that turn data into meaningful insights, prediction and products. No matter your industry, or the type of organization you work for, your world is driven by data. To deal with this data driven world, leaders must be equipped to use data as a strategic resource. By attending this course you will be able to contribute in the design of data strategy, ignite initiatives leveraging data as a strategic asset and also equip yourself to lead an analytics team to success.

This course will help you address questions like do I really need Data Science in my organization –how will it help the business? What are the benefits of having a data science capability, and the risks of not having one? What can business expect as outcomes, and plus we will explore several use cases to show real value and impact of Data Science.

C-suite executives, senior technologists, architects and business people working with data scientists, managing Data Science oriented projects, or investing in Data Science ventures.

Desire to learn, curiosity, enthusiasm and willingness to make the difference.

Getting Started With R and Data Analysis

[Learn R for Effective Data Analysis]

Are you tasked with analyzing data, or are you about to be? Have you been using proprietary data analysis tools and would like to explore open source? If so, you are in for a treat as we introduce you to the most popular and rapidly growing open source statistical package R.

In this course you will learn how to use R for effective data analysis, how to install and configure software necessary for a statistical programming environment, learn and practice R programming language concepts, common and useful R commands. You will learn to use R for reading data, writing functions, making informative graphs, and applying basic statistical methods. Course is designed for people who are just starting with R as well as for data analysts who are switching to R from other software, such as SAS or SPSS.

The course is aimed at business professionals, data analysts, technologists, journalists, software developers, academics and students who already have some basic competence in using statistics but wish to begin using R for the first time.

Basic knowledge of statistics and programming languages.

Introduction to Machine Learning

[Algorithms that learn from Data and Examples]

This course will take you on a whistle stop tour of all the highlights of machine learning. It will demystify what can be seen as a somewhat daunting and impenetrable subject area. It will put the entire field into perspective and break it down into easily digestible chunks. See what it can do for your organization. You need never be afraid to enter the machine learning waters again.

Taking this course will give you a high level overview of the field of machine learning and how it differs from human learning. You will gain understanding of how the field is structured, the fundamental skills needed to perform machine learning successfully, and current ‘hot’ topics. A strong emphasis will be placed on illustrative examples and applications to trigger thinking about what machine learning can do for you.

Anyone interested in learning what is machine learning.

While it can’t hurt, no prior machine learning, programming, or mathematical background is required.

Introducing Python For Data Science

[Python for Beginners]

Attention all those who have heard about the language Python and would like to learn more. This gentle introduction will guide you through getting started with this increasingly commonplace language in the world of Data Science as well as couple of practical applications over the course of the day.

Nowadays Python is probably the programming language of choice (besides R) for data scientists for prototyping, visualization, and running data analyses on small and medium sized data sets. In this course you will learn basics of everything that is needed to use Python for Data Science, from the language itself, to numerical computing or plotting.

Developers, programmers, data analyst, teachers, students and any one interested in using Python for scientific data analysis.

Knowledge of some programming language and familiarity with linear algebra and statistics. (optional but preferred)

Statistics 101

[The Science of Decisions]

This course will introduce you to the basics of statistics. Have distant memories of learning statistics at secondary school? Remember what a normal distribution looks like? Recall all of this useful information plus how statistics is being applied in the world of Data Science to give meaningful results to businesses, government and NGOs alike.

Statistics 101 is a comprehensive introduction to fundamental concepts in statistics. Comprehensive means that this course provides a solid foundation if you are planning to pursue more advanced courses in Data Science.

An excellent refresher for those who have studied statistics before and great introduction to statistics for new learners.

An undergraduate degree in the physical or social sciences or a business major where some statistical analysis was required to draw conclusions from data sets.

Machine Learning Basics with R

[Classification and Regression Methods]

This course aims to provide an understanding of some fundamentals of Machine Learning based on classification methods, through the use of the R programming platform. It describes the different types of learning and the two main categories of their applications: classification and regression. With a focus on the former, it takes a close look at three of the most typical techniques and how they apply on datasets akin to those encountered in the real world.

Our goal is to give you the basic skills that you need to understand supervised machine learning algorithms and interpret their output, which is important for solving a range of Data Science problems.

Anyone interested in learning what is machine learning and applying supervised machine learning methods and R to solve data problems. Ideal for people interested in pursuing career in Data Science.

Knowledge of R programming language and familiarity with linear algebra. Basic familiarity with statistics and probability theory is recommended.

Introduction to Data Science Using R

[Ideal course for anyone interested in learning fundamentals of Data Science and R]

Data Science is now recognized as a highly-critical growth area with impact across many sectors including science, government, finance, health care, manufacturing, advertising, retail, and others. Companies are searching for data scientists. This specialized field demands multiple skills not easy to obtain through conventional curricula.

Introduction to Data Science using R lives up to its name. It highlights basic principles of Data Science and focuses on developing the understanding and the capabilities you need to fully appreciate the insights data can provide us today.

This course is for anyone interested in understanding what Data Science is and wants to learn how to use R for data analysis and modelling. Ideal for technology and business professionals, analysts, journalists, and students.

An undergraduate level of mathematics with some elementary statistics is required. Familiarity with basic programming languages and environments is desirable as some of the course exercises will involve scripting in R.

Get Started in Machine Learning

[Basic building blocks of practical Machine Learning]

Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen. Though it has been an area of active research for over 50 years, machine learning is currently undergoing a renaissance driven by Moore’s law and the rise of big data. This course is designed to help you learn basic principles needed to understand and apply machine learning models and methods.

This is a practical course that uses hands-on examples to step through real-world application of machine learning. This will enable you to understand the basic concepts, become confident in applying the tools and techniques, and provide a firm foundation from which to explore more advanced methods.

You are interested in Machine Learning. You have read a book or taken an online course and now want to know more and learn how to apply Machine Learning to solve real problems. Well-suited to machine learning beginners or those with some experience.

Basic understanding of calculus, statistics, probability theory, linear algebra. This will be refreshed but not in detail. Basic knowledge of python. All lab sessions will be done using IPython notebooks and Scikit-learn.

Introduction to Data Visualization

[Storytelling with Data]

As the quantity and availability of data increases, the ability to communicate the truths underlying the flood of information becomes more valuable. Data visualization provides a way to make information digestible and coherent.

Because data visualization has roots in both hard and soft sciences, this course will be equal parts strategy and practice. It will provide a foundation understanding of the topics as well as an introduction to the industry standard JavaScript library, D3. This course will look at how raw data can be turned into stories. If you ever find yourself needing to communicate something to someone using data, this course is for you. Whether you’re an analyst crunching numbers, a data scientist needing to communicate in a data-driven way, or a journalist responsible for data analysis, this course will give you the tools to tell stories with data.

This course is for technology & business professionals, analysts, journalists, or anyone interested in understanding what data visualization is and how to create their own data visualizations using D3.

Some familiarity with HTML, CSS and JavaScript.

Basics of Python for Data Science

[Learning the basics of the language for scientific data analysis]

Python is becoming the de facto superglue language for modern scientific computing. Python for Data Science course is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. Course will help you build a strong foundation which will enable you to work and develop scientific and machine learning models much more rapidly.

This course is a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. Learn parts of the Python language and libraries you’ll need to effectively solve a broad set of Data Science problems.

Anyone interested in learning Python for Data Science.

Knowledge of some programming language (does not have to be Python) and familiarity with linear algebra (optional but preferred). This is not a computer science class, understanding of basic programming concepts (like looping, recursion, pointers, etc.) are presupposed.

Data Science Foundation Bootcamp

[The foundation upon which more can be built]

Build foundation level competence in exploring, manipulating, analyzing, interpreting and visualizing data. Theory and hands-on labs emphasize on developing your knowledge of Data Science concepts, practices, models, life-cycle, visualization practices, platforms and modelling languages required for you to become advanced beginner in the area of Data Science.

Learning outcomes will focus on model design and development, data ingestion and cleaning, machine learning fundamentals, generally applied algorithms, R (for statistics and machine learning), data visualization and most importantly how to communicate and operationalize results and insights.

Anyone interested in learning Data Science. Especially if you want to learn Data Science to strengthen your existing career prospects, or planning to embark on a new career.

Basic knowledge of statistics, any programming/scripting language, and databases.

Cluster Analysis With R

[Identifying homogeneous groups of objects called Clusters]

This course presents a broad overview of Cluster Analysis with R, a form of unsupervised learning that is used for exploratory data analysis, data summation, ordination, and even predictive modelling. The course will provide an in depth review of both clustering theory and application across a large spectrum of disciplines and applied settings, from drug discovery to management science.

Clustering topics, such as issues with data types, measures of similarity, and clustering algorithms and their taxonomy, will be additionally explored in the form of a hands-on lab with the use of the R programming language. Students should have at least passing familiarity with the following topics: probability theory, statistics, computational complexity, matrix algebra, graph theory, programming in R.

This course is intended for those who are currently working as data analysts, programmers, market researchers with limited exposure to clustering techniques and algorithms as well as those looking to move into the field.

Students should have at least passing familiarity with the following topics: probability theory, statistics, matrix algebra, and programming in R.

Fundamentals of Machine Learning

[Learning from Data]

Machine Learning enables computational systems to adaptively improve their performance with experience accumulated from the observed data. This advanced level course in machine learning will take you through the theoretical and applied foundations of the subject with the rigor expected of an advanced level course.

Topics covered will include machine learning theory, paradigms (supervised, unsupervised, reinforcement, active and online), and techniques (models and methods). Labs are developed to practically learn how to use the R programming language and packages for applying the main concepts and techniques of machine learning.

This course is intended for those who have applied machine learning in their current job roles and would like to take this a little further with academic rigor as well as some hands on practice using R.

Basic knowledge of calculus and linear algebra to understand equations involving vectors and matrices. Knowledge of probability theory to understand what a probability density is. Programming proficiency in R.

Advanced Machine Learning

[Bases Expansions and Kernel Methods]

This course systematically tackles a wide range of statistical machine learning methods. It clearly builds upon the relationship that advanced methods have with simpler concepts like linear regression and kernel density estimation to make us understand the working of powerful techniques like support vector machines and neural networks. The idea is that students should not only be able to work with these approaches, but also have an understanding of why they work.

After attending this course you will be able to use R to apply a number of the most common and powerful statistical machine learning techniques, and appreciate the trade-offs involved in choosing particular techniques for particular problems.

This course is for data scientists, data analysts, or anyone interested in understanding the advanced machine learning methods, how they fit together and how they can be applied using R

Programming proficiency in R. Basic knowledge of calculus and linear algebra to understand simple equations involving vectors and matrices. Enough knowledge of probability theory to understand what probability density is.

Directed Graphical Models

[Graphical Models = Statistics x Graph Theory x Computer Science]

Directed Graphical Models (DGMs) are perhaps the most exciting and powerful tool of contemporary Data Science. They are networks of statistical models that provide an intuitively understandable graphical representation of the systems they model.

In this course, we look at what graphical models are, how they can perform the tasks like root cause analysis/causal diagnostics, decision support and automation, and how they can be constructed either from expert knowledge or data. Focus will be on applying these techniques to case studies selected to highlight the different applications of graphical models.

This course is for data scientists, technology professionals, systems engineers, data analysts, or anyone interested in understanding the use and value of DGMs within their field.

Programming proficiency in R. Basic knowledge of calculus and linear algebra to understand simple equations involving vectors and matrices. Enough knowledge of probability theory to understand what probability density is.

Hadoop as an Enterprise Data Platform

[Hadoop is a Game Changer]

With the growing adoption of Apache Hadoop as the platform of choice for data analytics across various industries, the need for professionals with expertise in large scale data management is increasing rapidly. Hadoop is a key component of the next – generation data architecture, providing a massively scalable distributed storage and processing platform.

There has never been a better time to get Hadoop training. This course will take you through an overview of the Hadoop ecosystem focusing on the business case for using Hadoop as an enterprise data platform. It is designed for business and technology professionals who require a deeper understanding of the technologies behind Hadoop and the business case for implementation.

This course is for technology professionals, business professionals, architects, or anyone interested in understanding the Hadoop and learning how Apache Hadoop addresses the limitations of traditional architectures and powers massive scale data analytics.

No prior Hadoop experience is required.

Hadoop for Solution Architects

[Store, manage, and deliver value from fast, massive data sets]

With the emergence of Hadoop, CIOs are rethinking their enterprise data architecture. It is a challenge to design a successful data architecture by selecting the right combination of people, processes, and technology. To overcome this challenge we need to build an effective roadmap that is driven by business objectives, enables stakeholders with better decision making capabilities and helps your business achieve desired goals.

Gain a clear perspective on architectures, techniques, tools, and frameworks you need to use data successfully. In this course participant’s work together to understand a given problem, define scope, create an architecture to address the problem, and brainstorm solutions. This course is designed for Solution Architects who have an understanding of Big Data technologies and are working on creating Architectures for new Hadoop solutions.

Solution Architects, Technology Professionals and IT Consultants.

Good understanding of system architecture, Hadoop and big data technologies.

Hadoop for Data Scientists

[Hive and Pig Hands-On Training]

All organizations face the challenge of how to predict from data, and the most successful are those that handle and exploit it effectively. More and more organizations therefore want Data Scientists who have the skills to deal with large scale data sets. Hive makes Hadoop accessible to users who already know SQL. Pig is similar to popular scripting languages for expressing data analysis programs. This course will teach you how to query Hadoop data with filters, joins, user-defined functions, and more.

Learn the fundamentals of data transformation, ingestion, and processing with Hadoop tools. Learning outcomes will cover joining multiple data sets, analyzing disparate data with Pig, organizing data into tables, performing transformations, and simplifying complex queries.

This course is designed for Data Scientists and Analysts with a basic understanding of Hadoop and MapReduce who need to extract and process data stored on Hadoop.

Practical experience of SQL and basic UNIX or Linux commands. Basic understanding of Hadoop and MapReduce programming.

Apache Mahout for Machine Learning

[Machine learning for building scalable intelligent applications]

The need for machine learning techniques like clustering, collaborative filtering, and categorization has never been greater, be it for finding commonalities among large groups of people or automatically tagging large volumes of social media data. This course introduces the scalable machine learning algorithms implemented on Hadoop and is designed for Data Scientists who are looking to use Mahout for implementing machine learning and building intelligent applications.

This course will introduce you to the fundamentals of the machine learning methods supported by Mahout i.e. collaborative filtering, clustering and classification and overview of recommendation platform. Hands-on insight on how to write different machine learning algorithms to be used in the Hadoop environment and choose the best one suiting the task in hand.

Data Scientists, Programmers and Software Engineers who wish to learn more about machine learning and Apache Mahout.

Practical experience of Hadoop and MapReduce programming. Basic knowledge of calculus, linear algebra and statistics.

Customized Data Science Training

[For organizations to ensure that data creates value calls for a reskilling effort]

Corporate Data Science training is for your people to take the next step to learn more about statistics, model development, Data Science lifecycle, machine learning, data management, data visualization and other decision making skills and concepts.

Courses can be tailored for your business and learning needs. We will work with you to design the curriculum and labs that best address your analytical goals and business requirements. This approach ensures that Data Science training is focused on your issues, business challenges, strategies, and opportunities.

Corporate training is a flexible and cost-effective option that allows you to train as many employees as you need from a single team or across departments to everyone in your organization (all types of organizations business, commercial, government and non-government).

Post training coaching and advisory services to help you deliver more value and results.

Whether you are actively working on amplifying the impact of data or considering how Data Science can support you in delivering game changing results, Persontyle has what you need to get started.

Book 2 hours free workshop to develop Data Science training solution specifically designed for your people and your business.

Let us create a customised Data Science training solution for your organization.

Set up a 30 minute conversation to discuss your Data Science needs