# Tag: Beginners (113)

**An Introduction to AI, updated**- Oct 28, 2020.

We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.**Ain’t No Such a Thing as a Citizen Data Scientist**- Oct 26, 2020.

With learn-it-quick courses on data science popping up nearly a dime a dozen, more people are obtaining the sense they can dive into professional work with minimal qualifications and scant experience or practice. While the notion of a 'Citizen Scientist' is intended to simply support a broader appreciation of science and the scientific process to more people, the 'Citizen Data Scientist' is being inappropriately seen as a fast track to a new career.**How to ace the data science coding challenge**- Oct 15, 2020.

Preparing to interview for a Data Scientist position takes preparation and practice, and then it could all boil down to a final review of your skills. Based on personal experience, these tips on how to approach such a review will help you excel in the coding challenge project for your next interview.**Machine Learning from Scratch: Free Online Textbook**- Sep 22, 2020.

If you are looking for a machine learning starter that gets right to the core of the concepts and the implementation, then this new free textbook will help you dive in to ML engineering with ease. By focusing on the basics of the underlying algorithms, you will be quickly up and running with code you construct yourself.**An Introduction to NLP and 5 Tips for Raising Your Game**- Sep 11, 2020.

This article is a collection of things the author would like to have known when they started out in NLP. Perhaps it will be useful for you.**The Most Important Data Science Project**- Sep 4, 2020.

What is the project every data scientist must do?**Getting Started with Feature Selection**- Aug 25, 2020.

For machine learning, more data is always better. What about more features of data? Not necessarily. This beginners' guide with code examples for selecting the most useful features from your data will jump start you toward developing the most effective and efficient learning models.**How Do Neural Networks Learn?**- Aug 17, 2020.

With neural networks being so popular today in AI and machine learning development, they can still look like a black box in terms of how they learn to make predictions. To understand what is going on deep in these networks, we must consider how neural networks perform optimization.**5 Different Ways to Load Data in Python**- Aug 13, 2020.

Data is the bread and butter of a Data Scientist, so knowing many approaches to loading data for analysis is crucial. Here, five Python techniques to bring in your data are reviewed with code examples for you to follow.**Introduction to Statistics for Data Science**- Aug 12, 2020.

Statistics is foundational for Data Science and a crucial skill to master for any practitioner. This advanced introduction reviews with examples the fundamental concepts of inferential statistics by illustrating the differences between Point Estimators and Confidence Intervals Estimates.**First Steps of a Data Science Project**- Jul 29, 2020.

Many data science projects are launched with good intentions, but fail to deliver because the correct process is not understood. To achieve good performance and results in this work, the first steps must include clearly defining goals and outcomes, collecting data, and preparing and exploring the data. This is all about solving problems, which requires a systematic process.**Easy Guide To Data Preprocessing In Python**- Jul 24, 2020.

Preprocessing data for machine learning models is a core general skill for any Data Scientist or Machine Learning Engineer. Follow this guide using Pandas and Scikit-learn to improve your techniques and make sure your data leads to the best possible outcome.**Understanding Time Series with R**- Jul 9, 2020.

Analyzing time series is such a useful resource for essentially any business, data scientists entering the field should bring with them a solid foundation in the technique. Here, we decompose the logical components of a time series using R to better understand how each plays a role in this type of analysis.**A Layman’s Guide to Data Science. Part 3: Data Science Workflow**- Jul 6, 2020.

Learn and appreciate the typical workflow for a data science project, including data preparation (extraction, cleaning, and understanding), analysis (modeling), reflection (finding new paths), and communication of the results to others.**Getting Started with TensorFlow 2**- Jul 2, 2020.

Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.**The 8 Basic Statistics Concepts for Data Science**- Jun 24, 2020.

Understanding the fundamentals of statistics is a core capability for becoming a Data Scientist. Review these essential ideas that will be pervasive in your work and raise your expertise in the field.**A Classification Project in Machine Learning: a gentle step-by-step guide**- Jun 17, 2020.

Classification is a core technique in the fields of data science and machine learning that is used to predict the categories to which data should belong. Follow this learning guide that demonstrates how to consider multiple classification models to predict data scrapped from the web.**Beginners Learning Path for Machine Learning**- May 5, 2020.

So, you are interested in machine learning? Here is your complete learning path to start your career in the field.**A Layman’s Guide to Data Science. Part 2: How to Build a Data Project**- Apr 2, 2020.

As Part 2 in a Guide to Data Science, we outline the steps to build your first Data Science project, including how to ask good questions to understand the data first, how to prepare the data, how to develop an MVP, reiterate to build a good product, and, finally, present your project.**A Beginner’s Guide to Data Integration Approaches in Business Intelligence**- Mar 18, 2020.

An integrated BI system has a trickle-down effect on all business processes, especially reporting and analytics. Find out how integration can help you leverage the power of BI.**Python Pandas For Data Discovery in 7 Simple Steps**- Mar 10, 2020.

Just getting started with Python's Pandas library for data analysis? Or, ready for a quick refresher? These 7 steps will help you become familiar with its core features so you can begin exploring your data in no time.**Decision Tree Intuition: From Concept to Application**- Feb 27, 2020.

While the use of Decision Trees in machine learning has been around for awhile, the technique remains powerful and popular. This guide first provides an introductory understanding of the method and then shows you how to construct a decision tree, calculate important analysis parameters, and plot the resulting tree.**How to learn data science on your own: a practical guide**- Feb 11, 2020.

While much focus today is on the rise in working from home and the challenges experienced, not as much is said about learning from home. For those lone wolfs studying Data Science in a self-directed way, a range of issues can get in the way of your goal. Learn about these common problems to prepare to focus yourself all the way to your educational goals.**Intro to Machine Learning and AI based on high school knowledge**- Feb 5, 2020.

Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.**Idiot’s Guide to Precision, Recall, and Confusion Matrix**- Jan 13, 2020.

Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough.**3 common data science career transitions, and how to make them happen**- Jan 6, 2020.

Breaking into a career in Data Science can depend on where you start. See if you fit into one of these three categories of "newbies," and then find out how to make your professional transition into the field.**Beginner’s Guide to K-Nearest Neighbors in R: from Zero to Hero**- Jan 3, 2020.

This post presents a pipeline of building a KNN model in R with various measurement metrics.**How To “Ultralearn” Data Science: removing distractions and finding focus, Part 2**- Dec 17, 2019.

This second part in a series about how to "ultralearn" data science will guide you through several techniques to remove those distractions -- because your focus needs more focus.**How To “Ultralearn” Data Science, Part 1**- Dec 13, 2019.

What is "ultralearning" and how can you follow the strategy to become an expert of data science? Start with this first part in a series that will guide you through this self-motivated methodology to help you efficiently master difficult skills.**Advice for New and Junior Data Scientists**- Nov 22, 2019.

If you are a new Data Scientist early in your professional journey, and you’re a bit confused and lost, then follow this advice to figure out how to best contribute to your company.**Beginners Guide to the Three Types of Machine Learning**- Nov 13, 2019.

The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.**Data Cleaning and Preprocessing for Beginners**- Nov 7, 2019.

Careful preprocessing of data for your machine learning project is crucial. This overview describes the process of data cleaning and dealing with noise and missing data.**Designing Your Neural Networks**- Nov 4, 2019.

Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture.**Introduction to Natural Language Processing (NLP)**- Oct 25, 2019.

Have you ever wondered how your personal assistant (e.g: Siri) is built? Do you want to build your own? Perfect! Let’s talk about Natural Language Processing.**5 Tips for Novice Freelance Data Scientists**- Oct 18, 2019.

If you want to launch your data science skills into freelance work, then check out these important tips to help you kick start your next adventure in data.**How to Become a (Good) Data Scientist – Beginner Guide**- Oct 16, 2019.

A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.**Introduction to Artificial Neural Networks**- Oct 8, 2019.

In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.**Know Your Data: Part 2**- Oct 8, 2019.

To build an effective learning model, it is must to understand the quality issues exist in data & how to detect and deal with it. In general, data quality issues are categories in four major sets.**Sentiment and Emotion Analysis for Beginners: Types and Challenges**- Oct 1, 2019.

There are three types of emotion AI, and their combinations. In this article, I’ll briefly go through these three types and the challenges of their real-life applications.**Know Your Data: Part 1**- Sep 30, 2019.

This article will introduce the different type of data sets, data object and attributes.**KDnuggets™ News 19:n36, Sep 25: The Hidden Risk of AI and Big Data; The 5 Sampling Algorithms every Data Scientist needs to know**- Sep 25, 2019.

Learn about unexpected risk of AI applied to Big Data; Study 5 Sampling Algorithms every Data Scientist needs to know; Read how one data scientist copes with his boring days of deploying machine learning; 5 beginner-friendly steps to learn ML with Python; and more.**5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python**- Sep 19, 2019.

“I want to learn machine learning and artificial intelligence, where do I start?” Here.**An Easy Introduction to Machine Learning Recommender Systems**- Sep 4, 2019.

Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.**[video] Introduction to Generative Adversarial Networks (for beginners and advanced Data Scientists)**- Aug 5, 2019.

Generative Adversarial Networks are driving important new technologies in deep learning methods. With so much to learn, these two videos will help you jump into your exploration with GANs and the mathematics behind the modelling.**Getting Started With Data Science**- Aug 5, 2019.

Over the past many months, I’ve received hundreds of messages from people asking me how they could get started with Data Science. Therefore, I thought it would be useful to write down a framework for those wanting to get started with Data Science.**Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree**- Aug 2, 2019.

This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon.**Exploring Python Basics.**- Jul 29, 2019.

This free ebook is a great resource for data science beginners, providing a good introduction into Python, coding with Raspberry Pi, and using Python to building predictive models.**AI in the Family: how to teach machine learning to your kids**- May 28, 2019.

AI is all the rage with today’s programmers, but what about the next generation? Machine learning can be introduced to young ones just now learning about code, and you can help spark their interest.**A complete guide to K-means clustering algorithm**- May 16, 2019.

Clustering - including K-means clustering - is an unsupervised learning technique used for data classification. We provide several examples to help further explain how it works.**Advice for New Data Scientists**- Apr 8, 2019.

We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.**A Beginner’s Guide to Linear Regression in Python with Scikit-Learn**- Mar 29, 2019.

What linear regression is and how it can be implemented for both two variables and multiple variables using Scikit-Learn, which is one of the most popular machine learning libraries for Python.**Neural Networks with Numpy for Absolute Beginners: Introduction**- Mar 5, 2019.

In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.**Python Data Science for Beginners**- Feb 20, 2019.

Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.**Decision Trees — An Intuitive Introduction**- Feb 14, 2019.

An extensive introduction including a look at decision tree classification, data distribution, decision tree regression, decision tree learning, information gain, and more.**Is Domain Knowledge a Hurdle to Start a Career in Data?**- Feb 8, 2019.

How would I decide which domain to choose, while starting my career in data? Is it an obstacle?**Exploring Python Basics**- Jan 31, 2019.

This free eBook is a great resource for any beginner, providing a good introduction into Python, a look at the basics of learning a programming language and explores modelling and predictions.**The cold start problem: how to build your machine learning portfolio**- Jan 4, 2019.

This post outlines what makes a good machine leaning portfolio, with useful examples to help you begin to understand the type of project that gets noticed by big companies.**An Introduction to AI**- Nov 21, 2018.

We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more.**New Book: Linear Algebra – what you need for Machine Learning and Data Science now**- Oct 24, 2018.

From machine learning and data science to engineering and finance, linear algebra is an important prerequisite for the careers of today and of the future. Learn the math you need with this book.**Apache Spark Introduction for Beginners**- Oct 18, 2018.

An extensive introduction to Apache Spark, including a look at the evolution of the product, use cases, architecture, ecosystem components, core concepts and more.**Machine learning — Is the emperor wearing clothes?**- Oct 12, 2018.

We take a look at the core concepts of Machine Learning, including the data, algorithm and optimization needed to get you started, with links to additional resources to help enhance your knowledge.**How To Learn Data Science If You’re Broke**- Oct 9, 2018.

A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more.**Introduction to Deep Learning**- Sep 28, 2018.

I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.**Free resources to learn Natural Language Processing**- Sep 18, 2018.

An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.**Hadoop for Beginners**- Sep 12, 2018.

An introduction to Hadoop, a framework that enables you to store and process large data sets in parallel and distributed fashion.**Linear Regression In Real Life**- Aug 28, 2018.

A helpful guide to Linear Regression, using an example of a friends road trip to Las Vegas to highlight how it can be used in a real life situation.**KDnuggets™ News 18:n24, Jun 20: Data Lakes – The evolution of data processing; Text Generation with RNNs in 4 Lines of Code**- Jun 20, 2018.

How to spot a beginner Data Scientist; How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning; Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray; Cartoon: FIFA World Cup Football and Machine Learning**Natural Language Processing Nuggets: Getting Started with NLP**- Jun 19, 2018.

Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.**How to spot a beginner Data Scientist**- Jun 15, 2018.

When beginning life as a data scientist, there are some clear signs that give it away...**A Beginner’s Guide to the Data Science Pipeline**- May 29, 2018.

On one end was a pipe with an entrance and at the other end an exit. The pipe was also labeled with five distinct letters: "O.S.E.M.N."**Top SAS Courses Online**- May 11, 2018.

High quality SAS training for beginners is out there and I’ll help you find it.**Introductory Data Concepts: Fantastic Video Tutorials from Ronald van Loon**- Jan 8, 2018.

Check out these introductory data videos from noted expert and influencer Ronald van Loon.**Getting Started with Machine Learning in One Hour!**- Nov 1, 2017.

Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.**Top 6 errors novice machine learning engineers make**- Oct 30, 2017.

What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.**Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning**- Oct 28, 2017.

This is a short post for beginners learning neural networks, covering several essential neural networks concepts.**Top 10 Machine Learning Algorithms for Beginners**- Oct 20, 2017.

A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.

**Introduction to Neural Networks, Advantages and Applications**- Jul 25, 2017.

Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.**Getting Started with Python for Data Analysis**- Jul 5, 2017.

A guide for beginners to Python for getting started with data analysis.

**Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2**- Jul 1, 2017.

Here are deep learning examples and demos you can just download and run, including Spotify Artist Search using Speech APIs, Symbolic AI Speech Recognition, and Algorithmia API Photo Colorizer.**Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners**- Jun 26, 2017.

Here are deep learning demos and examples you can just download and run. No Math. No Theory. No Books.**Introduction to Correlation**- Feb 22, 2017.

Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.**The Gentlest Introduction to Tensorflow – Part 4**- Feb 22, 2017.

This post is the fourth entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner, and focuses on logistic regression for classifying the digits of 0-9.**The Gentlest Introduction to Tensorflow – Part 3**- Feb 21, 2017.

This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.**Data Science Basics: Power Laws and Distributions**- Dec 21, 2016.

Power laws and other relationships between observable phenomena may not seem like they are of any interest to data science, at least not to newcomers to the field, but this post provides an overview and suggests how they may be.**Data Science Basics: What Types of Patterns Can Be Mined From Data?**- Dec 14, 2016.

Why do we mine data? This post is an overview of the types of patterns that can be gleaned from data mining, and some real world examples of said patterns.**Introduction to Machine Learning for Developers**- Nov 28, 2016.

Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.**Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed**- Nov 25, 2016.

Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.**Data Science and Big Data, Explained**- Nov 14, 2016.

This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.**How to Rank 10% in Your First Kaggle Competition**- Nov 9, 2016.

This post presents a pathway to achieving success in Kaggle competitions as a beginner. The path generalizes beyond competitions, however. Read on for insight into succeeding while approaching any data science project.**Data Science Basics: An Introduction to Ensemble Learners**- Nov 8, 2016.

New to classifiers and a bit uncertain of what ensemble learners are, or how different ones work? This post examines 3 of the most popular ensemble methods in an approach designed for newcomers.**Data Science 101: How to get good at R**- Nov 1, 2016.

Everybody talks about R programming, how to learn, how to be good at it. But in this article, Ari Lamstein tells us his story about why and how he started with R along with how to publish, market and monetise R projects.**Learn Data Science for Excellence and not just for the Exams**- Oct 31, 2016.

Are you currently pursuing your masters in Data Science? Overwhelmed with Buzzwords and Information? Don’t know where and how to start your study? Then start with this article and a starter kit provided, but learn it for excellence and not just for the exams.**A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18!**- Oct 20, 2016.

This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.**Data Science Basics: Data Mining vs. Statistics**- Sep 28, 2016.

As a beginner I was confused at the relationship between data mining and statistics. This is my attempt to help straighten out this connection for others who may now be in my old shoes.**Data Science Basics: 3 Insights for Beginners**- Sep 22, 2016.

For data science beginners, 3 elementary issues are given overview treatment: supervised vs. unsupervised learning, decision tree pruning, and training vs. testing datasets.**Machine Learning in a Year: From Total Noob to Effective Practitioner**- Sep 19, 2016.

Read how the author went from self-described total machine learning noob to being able to effectively use machine learning effectively on real world projects at work... all within a year.**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2**- Sep 8, 2016.

This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.**A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1**- Sep 6, 2016.

Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.**The Gentlest Introduction to Tensorflow – Part 2**- Aug 19, 2016.

Check out the second and final part of this introductory tutorial to TensorFlow.**The Gentlest Introduction to Tensorflow – Part 1**- Aug 17, 2016.

In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.**A Beginner’s Guide to Neural Networks with R!**- Aug 11, 2016.

In this article we will learn how Neural Networks work and how to implement them with the R programming language! We will see how we can easily create Neural Networks with R and even visualize them. Basic understanding of R is necessary to understand this article.**KDnuggets™ News 16:n29, Aug 10: Data Science for Beginners: Fantastic series; Automating Data Science Contest Winners**- Aug 10, 2016.

Data Science for Beginners: Fantastic Introductory Video; Contest 2nd Place: Automating Data Science; Contest Winner: Winning the AutoML Challenge with Auto-sklearn; Reinforcement Learning and the Internet of Things.**Getting Started with Data Science – R**- Aug 3, 2016.

A great introductory post from DataRobot on getting started with data science in R, including cleaning data and performing predictive modeling.**Data Science for Beginners: Fantastic Introductory Video Series from Microsoft**- Aug 3, 2016.

The remaining videos in Microsoft's Data Science for Beginners video series are available now. Have a look at what they offer.**KDnuggets™ News 16:n28, Aug 3: Data Science Stats 101; Understanding NoSQL Databases; Core of Data Science**- Aug 3, 2016.

Data Science Statistics 101; 7 Steps to Understanding NoSQL Databases; The Core of Data Science; Data Science for Beginners 2: Is your data ready?**Getting Started with Data Science – Python**- Aug 1, 2016.

A great introductory post from DataRobot on getting started with data science in the Python ecosystem, including cleaning data and performing predictive modeling.**Data Science Statistics 101**- Jul 28, 2016.

Statistics can often be the most intimidating aspect of data science for aspiring data scientists to learn. Gain some personal perspective from someone who has traveled the path.**Data Science for Beginners 2: Is your data ready?**- Jul 28, 2016.

This second video and write-up in the Data Science for Beginners series discusses what is required of your data before it can be useful.**Data Science for Beginners 1: The 5 questions data science answers**- Jul 26, 2016.

A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.**A Pocket Guide to Data Science**- Apr 11, 2016.

A pocket guide overview of how to get started doing data science, with a focus on the practical, and with concrete steps to take to get moving right away.**Introduction to Random Forests® for Beginners – free ebook**- Mar 6, 2014.

Random Forests is of the most powerful and successful machine learning techniques. This free ebook will help beginners to leverage the power of Random Forests.**Data Mining for Beginners Boot Camp, Salford video series**- Jan 29, 2014.

This series shows how to easily apply SPM software suite to your predictive modeling projects, using a modern banking application as an example. This series is at the beginner level, and is perfect for first-time users or for those who need a refresher course in model building and data analysis.**Top KDnuggets tweets, Jan 27-28: Dilbert takes on #BigData Analysis and Salaries; Free Tutorial, Data Analytics for Beginners**- Jan 29, 2014.

Dilbert takes on #BigData Analysis and Salaries of Top Performers. Hilarious! ; Free Tutorial - Data Analytics for Beginners: Part 1 - Installing R and RStudio; Part 2: Data Cleaning; A New Science of Cities emerges from Mobile Phone Data Analysis.