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LEARN THE SKILLS THAT ARE ACTUALLY IN DEMAND

Based upon input from hundreds of leaders, hiring managers, and recruiters in the field

DATA SCIENCE INFINITY will provide you unlimited access to everything you need to get ahead of the competition, and land a great role in this exciting industry.

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The foundational content is based on expert experience within leading Data Science organisations, as well as input from hundreds of Data Science leaders, hiring managers and recruiters within the field.

The programme ensures you grasp the core, foundational skills first, paving the way for infinite future development.

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No hiring manager is going to pay you just to be good at coding, or just to be good at maths, or just to know a lot of machine learning algorithms - but they will pay you (and they'll pay you well) to add tangible value to their business.

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Because of this, not only does the programme cover the technical skills required for success, there is also a heavy focus on the softer skill-set that will set you apart from the other candidates.

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From experience interviewing hundreds of Data Scientists at companies including Amazon, and Sony - we will cover techniques and inside knowledge around how to approach and succeed in Data Science interviews.

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learn the right way

With a focus on intuition, understanding, and project driven application

All content is taught with a dedicated focus on intuition, understanding, and application.

Nothing is complex if broken down into the key parts and explained with an eye on application.

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In the course, we have a mock client called ABC Grocery.  For every concept we learn about, we get a request from the client and we learn about how we can solve this particular problem - both the theory, and the hands on application.  

Theory on it's own isn't enough - Data Science is all about being able to add tangible value to a business, customer, or end-user.

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The tasks that we complete can be taken further and become part of an extremely interesting and powerful portfolio of projects covering:

• Regression

• Classification

• Clustering & Segmentation

• PCA

• Causal Impact Analysis

• Association Rule Mining

• A/B Testing

• ...and Deep Learning projects will be added very soon as well!

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GET UNLIMITED SUPPORT & GUIDANCE

To accelerate your learning journey & transition into the field

Learning in isolation can be hard! 

Trying to find the answer to an issue you're facing, or get guidance on a concept you don't quite understand can be frustrating & de-motivating. 

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Signing up to the DATA SCIENCE INFINITY Premium Plan means being part of a private community of equally invested peers, and direct access to dedicated guidance, support, and direction - not only regarding the programme, but to any part of your Data Science journey.

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>> If you don't understand a concept - the support is there.

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>> If you don't know how to approach a problem or task - the support is there.

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>> If you want help enhancing your Resume to make you stand out over other candidates - the support is there.

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>> If you want help preparing for an upcoming interview - the support is there.

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I am dedicated to helping you succeed!

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I created DATA SCIENCE INFINITY to help aspiring Data Scientists like you, get ahead of the pack.

I am dedicated to helping you become a great Data Scientist, and to land an amazing role in this exciting & future-proof field!

Hi,

I'm Andrew

Connect with me

hear my thoughts about what is required for data science!

Here I talk with Kate Strachnyi about what is required for Data Science in 2021

In this video I speak with Ken Jee about my career, learning, and interviewing!

I was invited to speak with Jon Krohn, on the SuperDataScience podcast

what is being said about data science infinity online?

COURSE OVERVIEW

01 - foundations for success

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Let's go!

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In this first section, we get you set up with everything you need.

We discuss what makes a GREAT Data Scientist - and to ensure that we're all starting on the right foot we take a good hard look at self-confidence and imposter syndrome!

foundations for success

• Welcome to DATA SCIENCE INFINITY!

• Tell me a bit about yourself - and what you want to achieve!

• Join the private group - and start getting *dedicated* support (Premium Plan Only)

• How to get the most out of the private group (Premium Plan Only)

• What makes a GREAT Data Scientist?

• Self Confidence & Imposter Syndrome

• Course Overview

02 - coding: sql + Python

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Coding is a big part of Data Science, and SQL & Python are the two most commonly listed programming languages listed on job descriptions - so they are essential to know.

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SQL

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For SQL - you will connect to the DATA SCIENCE INFINITY database hosted in the cloud on AWS - and together we'll learn the key concepts with a focus on discovering insights from data, and adding business value.

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Python

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For Python, you will learn concepts from based Python, as well as the popular packages Pandas, Numpy, and Matplotlib.  We learn, and then we apply these learnings in unique and interesting ways. 

We create a number guessing game, we write code to find all prime numbers between one and one million in a fraction of a second, we split an image into it's 3 colour components, and more. 

Learning with mini-projects helps you understand the concepts and makes learning a lot more enjoyable!

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SQL for Data Science

• Introduction to SQL for Data Science

• Connecting to the DATA SCIENCE INFINITY cloud database

• Introduction to SQL Workbench/J

• Troubleshooting: Database disconnecting frequently

• The SELECT statement (Practical)

• Applying selection conditions using the WHERE statement (Practical)

• Aggregation functions and the GROUP BY statement (Practical)

• Conditional rules using CASE WHEN (Practical)

• The use of WINDOW functions (Practical)

• Joining tables using JOIN (Practical)

• Stacking data using UNION and UNION ALL (Practical)

• Executing multiple queries using TEMP TABLES and CTE (Practical)

• Other useful TIPS & TRICKS! (Practical)

• SQL Interview Test 01 - Questions

• SQL Interview Test 01 - Solutions & Discussion

• SQL Interview Test 02 - Questions

• SQL Interview Test 02 - Solutions & Discussion

• SQL Interview Test 03 - Questions

• SQL Interview Test 03 - Solutions & Discussion

• 10 Common SQL Theory Questions (with answers)

Python for Data Science

Base Python

• Introduction to Python for Data Science

• Installing Anaconda (Practical)

• Introduction to Spyder (Practical)

• Introducing VARIABLES and DATA TYPES (Practical)

• Assigning our data to VARIABLES (Practical)

• A deeper look at working with STRINGS (Practical)

• A deeper look at working with NUMBERS (Practical)

• Introduction to DATA STRUCTURES (Practical)

• Data Structure 1: LISTS (Practical)

• Data Structure 2: TUPLES (Practical)

• Data Structure 3: SETS (Practical)

• Data Structure 4: DICTIONARIES (Practical)

• Adding smarts to our code using CONDITIONAL STATEMENTS (Practical)

• Going loopy with FOR LOOPS (Practical)

• Loop de Loop with WHILE LOOPS (Practical)

• Receiving information using the INPUT FUNCTION (Practical)

• ** MINI PROJECT ** Building a Number Guessing Game (Practical)

• Getting func'y with FUNCTIONS (Practical)

• ** MINI PROJECT ** Finding Prime Numbers (Practical)

• A note on using pop() with sets in Python

• Get to know the very useful LIST COMPREHENSION (Practical)

• Handling Exceptions with....EXCEPTION HANDLING (Practical)

• Where to from here...

Numpy for mathematical operations

• Introduction to Numpy

• Creating Numpy Arrays (Practical)

• Numpy Array Operations (Practical)

• Manipulating Numpy Arrays (Practical)

• ** MINI PROJECT ** Calculating Planet Volumes (Practical)

• Image Manipulation using Numpy (Get the data)

• ** MINI PROJECT ** Image Manipulation (Practical)

Pandas for data exploration & manipulation
 

• Introduction To Pandas

• Accessing & Downloading The Data

• Creating Pandas DataFrames & Importing Data (Practical)

• Exploring & Understanding DataFrame Data (Practical)

• Accessing Specific Columns In Our DataFrame (Practical)

• Adding & Dropping Columns In Our DataFrame (Practical)

• Adding Columns Using Map, Replace, And Apply (Practical)

• Sorting & Ranking Data (Practical)

• Selecting Rows & Columns using LOC & ILOC (Practical)

• Renaming Columns (Practical)

• Joining & Merging DataFrames (Practical)

• Aggregating Data Using GROUPBY (Practical)

• Pivoting A DataFrame (Practical)

• Dealing With Missing Values (Practical)

• Dealing With Duplicate Data (Practical)

• Creating Charts And Plots Using Pandas (Practical)

• Exporting Data (Practical)

Matplotlib for data viz

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• Introduction To Matplotlib

• Our First Plot (Practical)

• Formatting Our Plot: Features (Practical)

• Formatting Our Plot: Colours & Styles (Practical)

• Working With Subplots (Practical)

• Let's Grab Some Height & Weight Data To Use!

• Creating & Refining A Histogram (Practical)

• Creating & Refining A Scatter Plot (Practical)

• Enhancing Our Plots Using Visual Aids (Practical)

• Adding Text To Our Plots (Practical)

• Saving Plots (Practical)​

03 - LEARN STATISTICS, THE RIGHT WAY

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You'll often hear about all the math you need to know to become a Data Scientist.

Don't be scared away by this...  Yes, you do need to know some math, but you don't need to spend a year reading text books before you're allowed to progress.

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Quite the opposite...

Here we imagine being sportswear company Nike, as well as a head basketball coach in the NBA, and we learn the key statistical concepts you need to know for Data Science with a focus of intuition & application rather than just formulas!

statistics 101

• Statistics Section Overview

• The Different "Types" Of Data

• Quiz Time! Types of Data

• What Is A Distribution?

• Working With A "Normal Distribution"

• Key Types Of Distributions To Know...

• Quiz Time! Distributions

• The Central Limit Theorem

• Get Confident With Confidence Intervals

• Quiz Time! Dealing With Uncertainty

• Introduction to Hypothesis Testing

• Hypothesis Testing: One Sample T-Test

• Hypothesis Testing: Independent Samples T-Test

• Hypothesis Testing: Paired T-Test

• Hypothesis Testing: Chi Square Test For Independence

• P-Values: What They Area - And What They Are Not

• Quiz Time! Hypothesis Testing & P-Values

• Symbols and Notation

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04 - intro to key data science tools

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Let's understand, and get hands-on with the key tools that Data Science teams are using today - including tools such as Github & Jupyter Notebooks

Key data science tools

​Jupyter Notebook

• How to use Jupyter Notebook

Github

• Overview of Git & Github

• Getting Started - Branches, Pull Requests, and Merges! (Practical)

• Forking a Repository (Practical)

• Pushing and Pulling between your local PC and GitHub (Practical)

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05 - let's meet our client...

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For each concept or algorithm that we learn about in the course, we get a request from our mock client ABC Grocery 

They have provided us access to a sample of their data, and we'll be using this to showcase the value of Data Science & Machine Learning in a project based manner.

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We learn how we might solve each task - and then we get our hands dirty and take on the challenge!

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The tasks we complete can also be taken further and mean you can compile a really interesting portfolio of projects covering:

• AB Testing

• Regression

• Classification

• Clustering

• PCA

• Causal Impact Analysis

• Association Rule Mining

let's meet our client!

• Introducing ABC Grocery!

• Getting the Data

06 - a/b testing

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Here we introduce the very important and common concept of A/B Testing and we discuss our task from our client ABC Grocery.

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We then code up the appropriate statistical method to assess the impact of the test, and we follow that by creating code templates for several other key hypothesis tests that will come in extremely handy during your career as a Data Scientist!

a/b testing

• What is AB Testing?

• Our Task for ABC Grocery!

• Getting The Data

• Chi-Square Test for ABC Grocery (Practical)

• Let's code up a One Sample T-Test (Practical)

• Let's code up an Independent Samples T-Test (Practical)

• Let's code up a Paired Sample T-Test (Practical)

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07 - machine learning

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machine learning

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introduction

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• ​Introduction to Machine Learning

• Introduction to Machine Learning in Data Science

• Overview of Supervised Learning

• Overview of Unsupervised Learning

• Introducing scikit-learn for Machine Learning in Python

Preparing & Cleaning Data for ML

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• A Checklist for Data Cleaning & Preparation

• Dealing with Missing Values

• Dealing with Missing Values - Pandas (Practical)

• Dealing with Missing Values - SimpleImputer (Practical)

• Dealing with Missing Values - KNNImputer (Practical)

• Dealing with Categorical Variables

• Dealing with Categorical Variables - One Hot Encoding (Practical)

• Dealing with Outliers

• Dealing with Outliers (Practical)

• Feature Scaling for Machine Learning

• Feature Scaling for Machine Learning (Practical)

• Feature Selection in Machine Learning

• Feature Selection in Machine Learning - Getting the Sample Data

• Feature Selection in Machine Learning - Correlation Matrix (Practical)

• Feature Selection in Machine Learning - Univariate Testing (Practical)

• Feature Selection in Machine Learning - RFECV (Practical)

• Model Validation & Over-fitting

• Model Validation & Over-fitting (Practical)

• Quiz Time! ML Data Cleaning & Preparation

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Supervised Learning

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ML for Regression Tasks

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• Introduction to Machine Learning for Regression

• Our Regression Task for ABC Grocery

• Our Regression Task for ABC Grocery - Getting The Data

• Our Regression Task for ABC Grocery - Creating The Data

• Getting The Sample Data

Linear Regression

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• High Level Overview

• Basic Code Stencil (Practical)

• The Formula for a Straight Line (Advanced Theory)

• Finding the "best" line using Least Squares (Advanced Theory)

• Evaluating Model Fit using R-Squared (Advanced Theory)

• Multiple Input Variables (Advanced Theory)

• Adjusted R-Squared (Advanced Theory)

• Understanding P-Values (Advanced Theory)

• Advanced Code Template (Practical)

• Quiz Time! Linear Regression Concepts

Decision Trees for Regression

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• High Level Overview

• Basic Code Stencil (Practical)

• Splitting Criteria (Advanced Theory)

• Stopping Criteria (Advanced Theory)

• Evaluating Model Performance (Advanced Theory)

• Advanced Code Template (Practical)

• Quiz Time! Regression Tree Concepts

Random Forests for Regression

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• High Level Overview

• Basic Code Stencil (Practical)

• Feature Importance (Advanced Theory)

• Evaluating Model Performance (Advanced Theory)

• Advanced Code Template (Practical)

• Predicting The Missing Loyalty Scores (Practical)

• Quiz Time! Random Forest (Regression) Concepts

ML for Classification Tasks

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• Introduction to Machine Learning for Classification

• Our Task for ABC Grocery

• Getting the sample data

Logistic Regression

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• High Level Overview

• Basic Code Stencil (Practical)

• Probability, Odds, and log(Odds) (Advanced Theory)

• The Formula for a Sigmoid Curve (Advanced Theory)

• Maximum Likelihood Estimation (Advanced Theory)

• Evaluating Classification Accuracy (Advanced Theory)

• Advanced Evaluation Techniques (Advanced Theory)

• Changing the Classification Threshold (Advanced Theory)

• Advanced Code Template (Practical)

• Quiz Time! Logistic Regression Concepts

Decision Trees for Classification

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• High Level Overview

• Basic Code Stencil (Practical)

• Splitting Criteria (Advanced Theory)

• Stopping Criteria (Advanced Theory)

• Evaluating Classification Accuracy (Advanced Theory)

• Advanced Code Template (Practical)

• Quiz Time! Classification Tree Concepts

Random Forests for Classification

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• High Level Overview

• Basic Code Stencil (Practical)

• Feature Importance (Advanced Theory)

• Evaluating Classification Accuracy (Advanced Theory)

• Advanced Code Template (Practical)

• Quiz Time! Random Forest (Classification) Concepts

K-Nearest-Neighbours (KNN) for Classification

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• High Level Overview

• Basic Code Stencil (Practical)

• Measuring Distances In Multi-Dimensional Space (Advanced Theory)

• The Importance Of Feature Scaling (Advanced Theory)

• What Value For "K"? (Advanced Theory)

• Advanced Code Template (Practical)

• Quiz Time! KNN Concepts

Advanced applications of scikit-learn

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• Grid Search for Hyperparameter Tuning

• Grid Search for Hyperparameter Tuning (Practical)

• Pipelines - getting the sample data

• Automating workflows with Pipelines (Practical)

Unsupervised Learning

K-Means Clustering

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• High Level Overview

• Getting The Sample Data

• Basic Code Stencil (Practical)

• Measuring Distances In Multi-Dimensional Space (Advanced Theory)

• The Importance Of Feature Scaling (Advanced Theory)

• What Value For "K"? (Advanced Theory)

• Our Task For ABC Grocery

• Advanced Code Template (Practical)

• Quiz Time! K-Means Concepts

Principal Component Analysis (PCA)

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• High Level Overview

• Our Task For ABC Grocery

• Getting The Data

• Advanced Code Template (Practical)

• Quiz Time! PCA Concepts

Association Rule Learning

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• High Level Overview

• Our Task For ABC Grocery

• Getting The Data

• Advanced Code Template (Practical)

• Quiz Time! Association Rule Learning Concepts

Causal Impact Analysis

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• High Level Overview

• Our Task For ABC Grocery

• Advanced Code Template (Practical)

• Quiz Time! Causal Impact Analysis

Machine Learning Model Deployment

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• Overview of ML Model Deployment & The Important Considerations We Need To Make!

• Getting & Setting Up The Required Files

• Deploying Our Web-App Locally (Practical)

• Deploying Our Web-App Live (Practical)

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09 - From Learning to Earning: A Framework for Success!

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I've interviewed & screened hundreds of Data Science candidates at companies including Amazon & Sony.

I will show you:

• How to create a stand-out Resume that will appeal to both recruiters & hiring managers.

• How to build up a portfolio of projects that showcases the value you can add.

• How to go into Data Science interviews with confidence, covering:

  • Building rapport with your interviewer​

  • Effectively answering questions you don't know the answer to

  • Effectively answering questions about mistakes you've made in the past

  • Tips for take-home assignments

  • Tips for coding tests

  • Questions to ask (and not ask) your interviewer

  • How to deal with rejections

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Let me share my knowledge with you, and help you get interviews for the roles you want!

From Learning to Earning

• Section Introduction

• The 3 key areas for Learning to Earning in Data Science

• BRAND - Small changes to make your CV or Resume stand out

• BRAND - What hiring managers want to see from Projects & Portfolios

• BRAND - Small brand changes that can have a big impact overall

• APPLYING - Understanding the role

• APPLYING - Speaking to a Recruiter or HR

• APPLYING - An overview of Data Science Interviews

• INTERVIEWING - Keep the human connection in mind: Simple ways to build rapport

• INTERVIEWING - Effectively answering questions you don't know the answer to...

• INTERVIEWING - Effectively answering questions about mistakes you've made...

• INTERVIEWING - Tips for Take-Home Assignments

• INTERVIEWING - Tips for Coding Tests

• INTERVIEWING - Questions to ask (and not ask) your interviewer

• Let's talk about rejections...

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C.R.A.I.G - your system for tackling the hiring process!

• Systematically Approaching The Hiring Process

• How To Apply The C.R.A.I.G System - And Get Ahead Of The Competition

• Your C.R.A.I.G System Help-Sheet (Downloadable PDF)

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All the content plus the option of dedicated support, guidance, and direction for any part of your Data Science journey...

let's do this!

There are two DATA SCIENCE INFINITY tiers available for completing the Data Science Professional Certification, depending on your requirements & goals...

premium plan

• DSI Data Science Professional Certification

• Unlimited access to all current and future content

• Unlimited access to dedicated support & guidance in the private members community

• A professionally designed project portfolio

>> The Premium Plan is for students who want to complete their DSI Data Science Professional Certification with confidence, and move seamlessly towards an incredible role in the field.  Students get dedicated mentorship when learning AND personalised guidance & support during the interview process

upskill plan

• DSI Data Science Professional Certification

• Unlimited access to all current and future content

>> The Upskill Plan is for students who want to complete their DSI Data Science Professional Certification and future-proof their career

still not sure?

The MUST HAVE Skills For Success

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• A detailed learning pathway based upon conversations I’ve had with HUNDREDS of Data Science leaders, hiring managers & recruiters

Inside Knowledge On The DS Hiring Process

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• Get ahead of the competition, based upon my experience interviewing & screening HUNDREDS of candidates at companies including Amazon & Sony PlayStation

What makes a GREAT Data Scientist?​​​​​​​

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• What is it that differentiates good Data Scientists from the GREAT ones?  How can you be great too?

BONUS 1 Your copy of the C.R.A.I.G System

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• Straight from the DATA SCIENCE INFINITY curriculum.  C.R.A.I.G is my system for preparing for, and delivering answers in Data Science interviews.

BONUS 2 An exclusive DATA SCIENCE INFINITY joining discount​

FAQ

How long do I have access to the programme?

>> Access is unlimited. Learning Data Science is a journey not a destination!

Is the course self-paced?

>> Yes, content is pre-recorded - meaning that you can move at your own pace and revisit concepts again if you need!

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How long does the course take to complete?

>> It's a great question - although a tricky one to answer, as it depends on how much time you have to commit to it.  The great thing about DATA SCIENCE INFINITY is that you have unlimited access to the content meaning you can earn your certification in a time frame that suits you.  The average completion time is 12 weeks - but remember, more incredible content is, and will continue to be added over time so you can continue to grow and evolve as a Data Scientist!
 

Do I get access to any new content that is added?

>> Yes!  Signing up means getting access to the currently available content and all content that is added in the future!  You will evolve & grow with the programme - another aspect of DATA SCIENCE INFINITY that sets it apart from the competition!

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How do I get the certificate?

>> At the end of the content, you will be prompted to contact me regarding your certification.  We will discuss your progress and your results on the tests & quizzes.  After this, you will be awarded the DATA SCIENCE INFINITY Data Science Professional Certification - and I will provide you with your certificate!

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Are there any pre-requisite skills I need?

>> DATA SCIENCE INFINITY has been created in a way that is accessible to anyone.  We always start right from the beginning and then move onwards towards the more complex concepts.  Everything is taught with a focus on intuition and understanding - and with the Premium Plan you will also have my dedicated support if you ever want to discuss concepts in more detail!

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I have students who had zero technical experience prior to joining, and I have students who have a lot of experience in the field already.  Both groups have found great value in this programme.

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Is there a job guarantee from this programme?

>> No matter what they say, no programme can guarantee you land a job after completion.  It takes dedication, commitment and persistance from you as well.  In saying this, DATA SCIENCE INFINITY will put you in the best possible position to land a great role so as you are learning the skills & tools that hiring managers have told me they need & want - and you are learning how to navigate the hiring process and get ahead of the competition based upon my experience interviewing and screening hundreds of candidates at top tech companies.  I have seen what differentiates those who land the role from those you miss out time and time again, and this programme is all about getting on the right side of that hiring decision!  Take a look at some of the feedback on the site, and you'll see that it really does work!

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What if I'm unhappy with the programme?

>> I want you to be happy! If you are not satisfied within 30 days, you can request a full refund

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free mini-course!

Install Python, learn the basics, and get coding!

Manipulate & Analyse a Dataset using the popular Python package, Pandas!​

PROJECT 1 Coding

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• Write code that will find all Prime numbers up to one million in under a second!

PROJECT 2 Machine Learning

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• Build, Train, and Test a real Machine Learning model!