Why learn like everyone else?
There is a disconnect between what is being taught for Data Science - and the demands and requirements in the real world.
The content for DATA SCIENCE INFINITY is based on input from leaders, hiring managers, and recruiters within the field.
Focus your time learning and understanding the skills & concepts that hiring managers need & want.
Why struggle through interviews, and miss out on the roles you really want?
Learn what interviewers really want to see - based on experience interviewing & screening hundreds of candidates at companies including Amazon, and Sony PlayStation.
Why learn in isolation?
Get unlimited access to dedicated support & guidance on any part of your Data Science journey (Premium Plan only)
Why Data Science?
With ever-increasing volumes of data being generated and collected, companies from all industries are looking to hire Data Scientists to help them stay ahead of the competition.
It is widely known that Data Science was labelled "The Sexiest Job of the 21st Century" and that the salaries in the field are among some of the highest.
Salary insights company Payscale calculated that the median salary for a Data Scientist in the US is $96k.
As a result, the market has been flooded with aspiring Data Scientists - but due to the increased competition, many are struggling to land any role, let alone their dream role.
I've interviewed hundreds of Data Scientists at companies including Amazon & Sony PlayStation and I've seen countless candidates come up short. I want to show you what you need to know to move ahead of the competition and get the role you want.
"Whether you're at the very beginning of your journey, you've already started learning, or you're looking to transition from another field - DATA SCIENCE I N F I N I T Y will take you ahead of the pack, ensuring you can land a great role in this exciting field and make a tangible impact when you do!"
Learn the right content
DATA SCIENCE I N F I N I T Y will provide you unlimited access to everything you need to get ahead of the competition, and land a great role in this exciting industry.
The foundational content is based on expert experience within leading Data Science organisations, as well as input from hundreds of Data Science leaders and recruiters within the field.
The programme ensures you grasp the core, foundational skills first, paving the way for infinite future development.
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. 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.
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.
Evolve with the programme
The course content will continue to grow and evolve based on what the student community want to learn.
Once you have the fundamentals in place, let's look to broaden out your skill-set!
There is no certificate for this programme, the learning never ends!
Get dedicated support & guidance
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.
Signing up to the Premium Plan for DATA SCIENCE INFINITY 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.
If you don't understand a concept - the support is there.
If you don't know how to approach a problem or task - the support is there.
If you want help preparing for an upcoming interview - the support is there.
If you want guidance with a Data Science task in your role - the support is there.
I am dedicated to helping you succeed!
What knowledge will you gain?
From discussions with hundreds of aspiring Data Scientists, I have found the most common frustrations are; not knowing where to start, and not knowing how to move from learning, to landing a great role
I will help you with both!
The base content has been selectively chosen to cover the key foundational skills required to ensure you can successfully progress forward in your learning and career.
All content is taught with a dedicated focus on intuition & understanding. Nothing is complex if broken down into the key parts and explained with an eye on application.
DATA SCIENCE I N F I N I T Y does not focus solely on technical skills, instead blending these with the essential soft skills that will build you into a Data Scientist focused on impact & added value.
Extra content will be continuously added, based on the needs and wants of you, the student. This ensures that you can continue to develop and improve in the areas that excite you the most.
DATA SCIENCE I N F I N I T Y contains in-depth guidance and support around the creation of a stand-out CV/Resume, how to approach Data Science interviews, and how to build your presence online.
The base content for the course has been carefully curated, covering the intuition & application of the key concepts you need to know to become a great Data Scientist, including...
BONUS: coding prerequisites
If you already know SQL & Python (base, numpy, pandas, matplotlib), that's great...if not, we've got you covered with full introductory practical sections on both!
All learning is done with a focus on real world application.
Learning to code can be dry. Not here! We learn concepts then apply them in unique and interesting ways, including splitting an image into it's 3 colour components using nothing but numpy, and creating a function that can find all prime numbers between one and one million in a fraction of a second!
Foundations for success
Outlining what makes a GREAT Data Scientist.
Goal setting & ways to stay motivated in what can be an intimidating field!
Key foundations of Statistics
You'll often hear about all the maths you need to know to become a Data Scientist, but don't be scared by this...yes, you do need to know some maths, but you don't need to spend a year reading text books before you're allowed to progress.
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 formulas!
Intro to key Data Science tools
Let's get to know about the cloud providers, and why we would make use of their amazing infrastructure & tools!
Let's also ensure you are signed up to Github and that you understand and can undertake the key processes that Data Science teams are using today.
Let's meet ABC Grocery...
For the Machine Learning tutorials, we will be looking to solve & explore all sorts of business problems & tasks from our client ABC Grocery.
They've given us access to a sample of their data, and we'll be using this to showcase the value of Data Science & Machine Learning!
ML: Data Preparation & Cleaning
Garbage in...Garbage out...
Machine Learning models need the right data, in the right format...otherwise the results can be underwhelming & unexpected!
Let's discuss this, and create some code templates so you always have the right code available for your career in Data Science!
ML: Intuition & Application
In the course we cover the key algorithms & models required to ensure you can take on virtually any business problem.
For each algorithm, we start with a high level introduction and then we head into Python and code up a basic template for the model so you can get a feel for it.
From there, we are back to the theory - covering everything that goes on under the hood, ensuring you understand exactly what is going on, and can get the most out of it.
After that, we head back into Python and code up an advanced version - taking on our challenge from ABC Grocery!
ML: Deployment to a live website
A hot topic in the world of Data Science is the deployment of our ML models.
It isn't always neccessary, but at the very least, a Data Scientist should understand the basics.
We run through the theory around what this all means, and what needs to be considered.
From there, we will deploy one of our ML models onto a live website using Flask & Heroku!
Turning business problems into Data Science solutions
This is what separates great Data Scientists from the rest...
...in other words, a Data Science candidate that gets hired or promoted, or one that gets passed by.
So this is important...
Here I will share my framework of 13 key questions you should get answers to prior to and during any Data Science project.
How to create a stand-out Resume
You'll get all sorts of advice on this, but let me show you the simple changes you can make to stand out from other candidates.
I've interviewed & screened hundreds of Data Science candidates at companies including Amazon & Sony. I have also asked dozens of leading recruiters in the field what it is that makes a difference here.
Let me share this knowledge with you and help you get interviews for the roles you want!
How to succeed in Data Science interviews
I've interviewed & screened hundreds of Data Science candidates, and through this process I have learned exactly what can differentiate a stand out & successful candidate from the rest.
For this programme I have also asked key Data Science leaders in the field for insight on their hiring process - from coding tests to take-home assignments (as well as their favourite interview questions)
Let me share these insights with you and support you on you way to landing the role you really want!
How to build your online presence
This is more important than you may think. Having a strong online presence will open doors for you, now, and in the future.
It can be hard making traction initially, but let me help you get started!
Who is behind DATA SCIENCE I N F I N I T Y ?
"Hi, I'm Andrew.
I am dedicated to helping you become a great Data Scientist, and to land an amazing role in this exciting field!"
A little bit more about me...
"I'm Andrew Jones. I've spent over 13 years in Data Science at companies including Amazon & more recently Sony PlayStation where I developed and prototyped Machine Learning based features for the PlayStation 5.
I've interviewed & screened hundreds of Data Science candidates, and through this process have learned exactly what can differentiate a stand out & successful candidate from the rest.
In 2019 I authored "The Essential A.I. & Data Science Handbook for Recruitment" which is available on Amazon.
Throughout my career, I've had the opportunity to mentor fellow Data Scientists; from their entry into the field, to developing their technical & non-technical skill-sets, as well as providing guidance around preparing for, and being successful with promotions and interviews.
I'd love you to be part of the DATA SCIENCE I N F I N I T Y community so I can help you move successfully into this exciting industry, and support you as you develop into an incredible Data Scientist!
I'm dedicated to ensuring that your Data Science journey is a long-lasting, fruitful, enjoyable, and positive experience.
Please do connect with me on LinkedIn, and feel free to let me know any questions you have!"
Enroll now!
If you want to accelerate your Data Science learning journey, get ahead of the competition, and land an amazing role in this exciting field - then enroll now. I can't wait to work with you!
There are two plans available, depending on whether you want to be part of the private members group for the unlimited and dedicated support and guidance on your Data Science learning journey.
Please see below for more information, pricing, and payment plans.
Want more information ?
I'd love to discuss any questions you have around the programme. Please do send me an email at: [email protected] or send me a message on LinkedIn!
Course Curriculum
Please see below for the full course curriculum (221 tutorials)
Note: You may need to click the expand button at the bottom of the page to see all tutorials
- Welcome to DATA SCIENCE INFINITY! (1:17)
- Tell me a bit about yourself - and what you want to achieve!
- Join the private Slack channel - and start getting *dedicated* support & guidance (Premium Plan Only)
- How to get the most out of the private Slack channel (Premium Plan Only)
- What makes a GREAT Data Scientist? (3:04)
- Self Confidence & Imposter Syndrome (3:31)
- Course Overview (3:31)
- Introduction to SQL for Data Science (8:49)
- Connecting to the DATA SCIENCE INFINITY cloud database (PRACTICAL) (10:06)
- Introduction to SQL Workbench/J (6:58)
- Troubleshooting: Database disconnecting frequently
- The SELECT statement (PRACTICAL) (9:43)
- Applying selection conditions using the WHERE statement (PRACTICAL) (8:48)
- Aggregation functions and the GROUP BY statement (PRACTICAL) (10:11)
- Conditional rules using CASE WHEN (PRACTICAL) (9:12)
- The use of WINDOW functions (PRACTICAL) (11:38)
- Joining tables using JOIN (PRACTICAL) (19:23)
- Stacking data using UNION and UNION ALL (PRACTICAL) (4:34)
- Executing multiple queries using TEMP TABLES and CTE (9:50)
- Other useful TIPS & TRICKS! (PRACTICAL) (20:50)
- Introduction to Python for Data Science (8:56)
- Installing Anaconda (PRACTICAL) (7:21)
- Introduction to Spyder (PRACTICAL) (5:09)
- Introducing VARIABLES and DATA TYPES (PRACTICAL) (10:04)
- Assigning our data to VARIABLES (PRACTICAL) (4:50)
- A deeper look at working with STRINGS (PRACTICAL) (16:19)
- A deeper look at working with NUMBERS (PRACTICAL) (7:02)
- Introduction to DATA STRUCTURES (PRACTICAL) (1:06)
- Data Structure 1: LISTS (PRACTICAL) (17:41)
- Data Structure 2: TUPLES (PRACTICAL) (7:04)
- Data Structure 3: SETS (PRACTICAL) (10:57)
- Data Structure 4: DICTIONARIES (PRACTICAL) (11:28)
- Adding smarts to our code using CONDITIONAL STATEMENTS (PRACTICAL) (13:03)
- Going loopy with FOR LOOPS (PRACTICAL) (12:57)
- Loop de Loop with WHILE LOOPS (PRACTICAL) (5:59)
- Receiving information using the INPUT FUNCTION (PRACTICAL) (5:30)
- ** MINI PROJECT ** Building a Number Guessing Game (PRACTICAL) (13:28)
- Getting func'y with FUNCTIONS (PRACTICAL) (8:40)
- ** MINI PROJECT ** Finding Prime Numbers (PRACTICAL) (22:34)
- A note on using pop() with sets in Python
- Get to know the very useful LIST COMPREHENSION (PRACTICAL) (9:04)
- Handling Exceptions with....EXCEPTION HANDLING (PRACTICAL) (9:00)
- Where to from here... (2:15)
- Introduction to Numpy (3:24)
- Creating Numpy Arrays (PRACTICAL) (15:02)
- Numpy Array Operations (PRACTICAL) (11:46)
- Manipulating Numpy Arrays (PRACTICAL) (14:11)
- ** MINI PROJECT ** Calculating Planet Volumes (PRACTICAL) (10:31)
- ** MINI PROJECT ** Image Manipulation using Numpy (Get the data)
- ** MINI PROJECT ** Image Manipulation (PRACTICAL) (17:06)
- Introduction To Pandas (1:36)
- Accessing & Downloading The Data
- Creating Pandas DataFrames & Importing Data (PRACTICAL) (11:50)
- Exploring & Understanding DataFrame Data (PRACTICAL) (14:23)
- Accessing Specific Columns In Our DataFrame (PRACTICAL) (7:51)
- Adding & Dropping Columns In Our DataFrame (PRACTICAL) (9:34)
- Adding Columns Using Map, Replace, And Apply (PRACTICAL) (13:54)
- Sorting & Ranking Data (PRACTICAL) (12:40)
- Selecting Rows & Columns using LOC & ILOC (PRACTICAL) (19:35)
- Renaming Columns (PRACTICAL) (8:43)
- Joining & Merging DataFrames (PRACTICAL) (14:48)
- Aggregating Data Using GROUPBY (PRACTICAL) (18:58)
- Pivoting A DataFrame (PRACTICAL) (11:33)
- Dealing With Missing Values (PRACTICAL) (21:32)
- Dealing With Duplicate Data (PRACTICAL) (9:05)
- Creating Charts And Plots Using Pandas (PRACTICAL) (15:52)
- Exporting Data (PRACTICAL) (14:23)
- Introduction To Matplotlib (1:22)
- Our First Plot (PRACTICAL) (8:56)
- Formatting Our Plot: Features (PRACTICAL) (9:00)
- Formatting Our Plot: Colours & Styles (PRACTICAL) (13:52)
- Working With Subplots (PRACTICAL) (8:26)
- Let's Grab Some Height & Weight Data To Use!
- Creating & Refining A Histogram (PRACTICAL) (9:23)
- Creating & Refining A Scatter Plot (PRACTICAL) (8:44)
- Enhancing Our Plots Using Visual Aids (PRACTICAL) (8:03)
- Adding Text To Our Plots (PRACTICAL) (17:08)
- Saving Plots (PRACTICAL) (3:33)
- Statistics Section Overview (1:08)
- The Different "Types" Of Data (3:52)
- What Is A Distribution? (13:31)
- Working With A "Normal Distribution" (5:40)
- Types Of Distributions (7:27)
- The Central Limit Theorem (9:08)
- Get Confident With Confidence Intervals (10:09)
- Introduction to Hypothesis Testing (9:54)
- Hypothesis Testing: One Sample T-Test (9:13)
- Hypothesis Testing: Independent Samples T-Test (9:39)
- Hypothesis Testing: Paired T-Test (8:38)
- Hypothesis Testing: Chi Square Test For Independence (10:01)
- P-Values: What They Area - And What They Are Not (3:39)
- Symbols and Notation (4:11)
- A Checklist for Data Cleaning & Preparation (10:30)
- Dealing with Missing Values (THEORY) (8:41)
- Dealing with Missing Values - Pandas (PRACTICAL) (12:42)
- Dealing with Missing Values - SimpleImputer (PRACTICAL) (11:05)
- Dealing with Missing Values - KNNImputer (PRACTICAL) (11:49)
- Dealing with Categorical Variables (THEORY) (7:14)
- Dealing with Categorical Variables - One Hot Encoder (PRACTICAL) (10:50)
- Dealing with Outliers (THEORY) (9:25)
- Dealing with Outliers (PRACTICAL) (13:34)
- Feature Scaling for Machine Learning (THEORY) (7:17)
- Feature Scaling for Machine Learning (PRACTICAL) (8:18)
- Feature Selection in Machine Learning (THEORY) (10:59)
- Feature Selection in Machine Learning - Getting the Sample Data
- Feature Selection in Machine Learning - Correlation Matrix (PRACTICAL) (4:26)
- Feature Selection in Machine Learning - Univariate Testing (PRACTICAL) (17:52)
- Feature Selection in Machine Learning - RFECV (PRACTICAL) (13:48)
- Model Validation & Over-fitting (THEORY) (7:54)
- Model Validation & Over-fitting (PRACTICAL) (18:06)
- High Level Overview (4:53)
- Basic Code Stencil (PRACTICAL) (8:57)
- The Formula for a Straight Line (ADVANCED THEORY) (7:57)
- Finding the "best" line using Least Squares (ADVANCED THEORY) (12:57)
- Evaluating Model Fit using R-Squared (ADVANCED THEORY) (11:32)
- Multiple Input Variables (ADVANCED THEORY) (4:51)
- Adjusted R-Squared (ADVANCED THEORY) (5:40)
- Understanding P-Values (ADVANCED THEORY) (6:33)
- Advanced Code Template (PRACTICAL) (29:31)
- High Level Overview (9:01)
- Basic Code Stencil (PRACTICAL) (13:09)
- Probability, Odds, and log(Odds) (ADVANCED THEORY) (6:50)
- The Formula for a Sigmoid Curve (ADVANCED THEORY) (5:42)
- Maximum Likelihood Estimation (ADVANCED THEORY) (8:26)
- Evaluating Classification Accuracy (ADVANCED THEORY) (7:09)
- Advanced Evaluation Techniques (ADVANCED THEORY) (11:12)
- Changing the Classification Threshold (ADVANCED THEORY) (10:32)
- Advanced Code Template (PRACTICAL) (30:49)
- High Level Overview (7:58)
- Getting The Sample Data
- Basic Code Stencil (PRACTICAL) (13:02)
- Measuring Distances In Multi-Dimensional Space (ADVANCED THEORY) (5:58)
- The Importance Of Feature Scaling (ADVANCED THEORY) (5:17)
- What Value For "K"? (ADVANCED THEORY) (7:31)
- Our Task For ABC Grocery (2:23)
- Advanced Code Template (PRACTICAL) (26:18)
- Section Introduction (4:17)
- The 3 key areas for Learning to Earning in Data Science (5:00)
- BRAND - Small changes to make your CV or Resume stand out (8:27)
- BRAND - What hiring managers want to see from Projects & Portfolios (6:31)
- BRAND - Small changes that can have a big impact (3:16)
- APPLYING - Understanding the role (3:49)
- APPLYING - Speaking to a Recruiter or HR (3:24)
- APPLYING - An overview of Data Science Interviews (3:31)
- INTERVIEWING - Keep the human connection in mind: Simple ways to build rapport (2:38)
- INTERVIEWING - Effectively answering questions you don't know the answer to... (3:44)
- INTERVIEWING - Effectively answering questions about mistakes you've made... (2:33)
- INTERVIEWING - Tips for Take-Home Assignments (7:20)
- INTERVIEWING - Tips for Coding Tests (6:36)
- INTERVIEWING - Questions to ask (and not ask) your interviewer (2:09)
- Let's talk about rejections... (2:38)