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Extra Crunch roundup: Digital health VC survey, edtech M&A, deep tech marketing, more

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I had my first telehealth consultation last year, and there’s a high probability that you did, too. Since the pandemic began, consumer adoption of remote healthcare has increased 300%.

Speaking as an unvaccinated urban dweller: I’d rather speak to a nurse or doctor via my laptop than try to remain physically distanced on a bus or hailed ride traveling to/from their office.

Even after things return to (rolls eyes) normal, if I thought there was a reliable way to receive high-quality healthcare in my living room, I’d choose it.

Clearly, I’m not alone: a May 2020 McKinsey study pegged yearly domestic telehealth revenue at $3 billion before the coronavirus, but estimated that “up to $250 billion of current U.S. healthcare spend could potentially be virtualized” after the pandemic abates.

That’s a staggering number, but in a category that includes startups focused on sexual health, women’s health, pediatrics, mental health, data management and testing, it’s clear to see why digital-health funding topped more than $10 billion in the first three quarters of 2020.

Drawing from The TechCrunch List, reporter Sarah Buhr interviewed eight active health tech VCs to learn more about the companies and industry verticals that have captured their interest in 2021:

  • Bryan Roberts and Bob Kocher, partners, Venrock
  • Nan Li, managing director, Obvious Ventures
  • Elizabeth Yin, general partner, Hustle Fund
  • Christina Farr, principal investor and health tech lead, OMERS Ventures
  • Ursheet Parikh, partner, Mayfield Ventures
  • Nnamdi Okike, co-founder and managing partner, 645 Ventures
  • Emily Melton, founder and managing partner, Threshold Ventures

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Since COVID-19 has renewed Washington’s focus on healthcare, many investors said they expect a friendly regulatory environment for telehealth in 2021. Additionally, healthcare providers are looking for ways to reduce costs and lower barriers for patients seeking behavioral support.

“Remote really does work,” said Elizabeth Yin, general partner at Hustle Fund.

We’ll cover digital health in more depth this year through additional surveys, vertical reporting, founder interviews and much more.

Thanks very much for reading Extra Crunch this week; I hope you have a relaxing weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist

8 VCs agree: Behavioral support and remote visits make digital health a strong bet for 2021

Woman having a medicine video conferencing with her doctor using digital tablet. Senior woman on a video call with a doctor using her tablet computer at home.

Image Credits: Luis Alvarez (opens in a new window) / Getty Images

Lessons from Top Hat’s acquisition spree

Image Credits: Bryce Durbin

In the last year, edtech startup Top Hat acquired three publishing companies: Fountainhead Press, Bludoor and Nelson HigherEd.

Natasha Mascarenhas interviewed CEO and founder Mike Silagadze to learn more about his content acquisition strategy, but her story also discussed “some rumblings of consolidation and exits in edtech land.”

How VCs invested in Asia and Europe in 2020

Last year, U.S.-based VCs invested an average of $428 million each day in domestic startups, with much of the benefits flowing to fintech companies.

This morning, Alex Wilhelm examined Q4 VC totals for Europe, which had its lowest deal count since Q1 2019, despite a record $14.3 billion in investments.

Asia’s VC industry, which saw $25.2 billion invested across 1,398 deals is seeing “a muted recovery,” says Alex.

“Falling seed volume, lots of big rounds. That’s 2020 VC around the world in a nutshell.”

Decrypted: With more SolarWinds fallout, Biden picks his cybersecurity team

Image Credits: Treedeo (opens in a new window) / Getty Images

In this week’s Decrypted, security reporter Zack Whittaker covered the latest news in the unfolding SolarWinds espionage campaign, now revealed to have impacted the U.S. Bureau of Labor Statistics and Malwarebytes.

In other news, the controversy regarding WhatsApp’s privacy policy change appears to be driving users to encrypted messaging app Signal, Zack reported. Facebook has put changes at WhatsApp on hold “until it could figure out how to explain the change without losing millions of users,” apparently.

Hot IPOs hang onto gains as investors keep betting on tech

A big IPO debut is a juicy topic for a few news cycles, but because there’s always another unicorn ready to break free from its corral and leap into the public markets, it doesn’t leave a lot of time to reflect.

Alex studied companies like Lemonade, Airbnb and Affirm to see how well these IPO pop stars have retained their value. Not only have most held steady, “many have actually run up the score in the ensuing weeks,” he found.

Dear Sophie: What are Biden’s immigration changes?

lone figure at entrance to maze hedge that has an American flag at the center

Image Credits: Bryce Durbin / TechCrunch

Dear Sophie:

I work in HR for a tech firm. I understand that Biden is rolling out a new immigration plan today.

What is your sense as to how the new administration will change business, corporate and startup founder immigration to the U.S.?

—Free in Fremont

Hello, Extra Crunch community!

Hello in Different Languages

Image Credits: atakan (opens in a new window) / Getty Images

I began my career as an avid TechCrunch reader and remained one even when I joined as a writer, when I left to work on other things and now that I’ve returned to focus on better serving our community.

I’ve been chatting with some of the folks in our community and I’d love to talk to you, too. Nothing fancy, just 5-10 minutes of your time to hear more about what you want to see from us and get some feedback on what we’ve been doing so far.

If you would be so kind as to take a minute or two to fill out this form, I’ll drop you a note and hopefully we can have a chat about the future of the Extra Crunch community before we formally roll out some of the ideas we’re cooking up.

Drew Olanoff
@yoda

In 2020, VCs invested $428m into US-based startups every day

Last year was a disaster across the board thanks to a global pandemic, economic uncertainty and widespread social and political upheaval.

But if you were involved in the private markets, however, 2020 had some very clear upside — VCs flowed $156.2 billion into U.S.-based startups, “or around $428 million for each day,” reports Alex Wilhelm.

“The huge sum of money, however, was itself dwarfed by the amount of liquidity that American startups generated, some $290.1 billion.”

Using data sourced from the National Venture Capital Association and PitchBook, Alex used Monday’s column to recap last year’s seed, early-stage and late-stage rounds.

How and when to build marketing teams at deep tech companies

Pole lifting rubber duck with hook in its head

Image Credits: Andy Roberts (opens in a new window) / Getty Images

Building a marketing team is one of the most opaque parts of spinning up a startup, but for a deep tech company, the stakes couldn’t be higher.

How can technical founders working on bleeding-edge technology find the right people to tell their story?

If you work at a post-revenue, early-stage deep tech startup (or know someone who does), this post explains when to hire a team, whether they’ll need prior industry experience, and how to source and evaluate talent.

Bustle CEO Bryan Goldberg explains his plans for taking the company public

Bustle Digital Group CEO Bryan Goldberg

Bustle Digital Group CEO Bryan Goldberg. Image Credits: Bustle Digital Group

Senior Writer Anthony Ha interviewed Bustle Digital Group CEO Bryan Goldberg to get his thoughts on the state of digital media.

Their conversation covered a lot of ground, but the biggest news it contained focuses on Goldberg’s short-term plans.

“Where do I want to see the company in three years? I want to see three things: I want to be public, I want to see us driving a lot of profits and I want it to be a lot bigger, because we’ve consolidated a lot of other publications,” he said.

It may not be as glamorous as D2C, but beauty tech is big money

Directly Above Shot Of Razors On Green Background

Image Credits: Laia Divols Escude/EyeEm (opens in a new window) / Getty Images

The U.S. Federal Trade Commission is not a huge fan of personal-care D2C brands merging with traditional consumer product companies.

This month, razor startup Billie and Proctor & Gamble announced they were calling off their planned merger after the FTC filed suit.

For similar reasons, Edgewell Personal Care dropped its plans last year to buy Harry’s for $1.37 billion.

In a harsher regulatory environment, “the path to profitability has become a more important part of the startup story versus growth at all costs,” it seems.

Twilio CEO says wisdom lies with your developers

SAN FRANCISCO, CA – SEPTEMBER 12: Founder and CEO of Twilio Jeff Lawson speaks onstage during TechCrunch Disrupt SF 2016 at Pier 48 on September 12, 2016 in San Francisco, California. Image Credits: Steve Jennings/Getty Images for TechCrunch

Companies that build their own tools “tend to win the hearts, minds and wallets of their customers,” according to Twilio CEO Jeff Lawson.

In an interview with enterprise reporter Ron Miller for his new book, “Ask Your Developer,” Lawson says founders should use developer teams as a sounding board when making build-versus-buy decisions.

“Lawson’s basic philosophy in the book is that if you can build it, you should,” says Ron.

Source: https://techcrunch.com/2021/01/22/extra-crunch-roundup-digital-health-vc-survey-edtech-ma-deep-tech-marketing-more/

AI

Digital ID Verification Service IDnow Acquires identity Trust Management AG, a Global Provider of ID Software from Germany

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IDnow, a provider of identity verification-as-a-service solutions, will be acquiring identity Trust Management, a global provider of digital and offline ID verification software from Germany.

IDnow confirmed that it would continue to maintain identity Trust Management’s Düsseldorf location and will retain its employees as well.

The acquisition of Identity Trust Management should help IDnow with further expanding into new verticals while offering its services to a larger and potentially more diverse client base in Germany and other areas.

The combined product portfolio will aim to provide comprehensive ID verification methods, ranging from automated to human-assisted and from being purely online to point-of-sale. All these ID verification methods will be accessible through the IDnow platform.

Identity Trust Management has established its operations in Germany’s identity industry during the past 10 years, with a solid reputation and portfolio of clients focused on telecommunications and insurance services.

Andreas Bodczek, CEO at IDnow, stated:

“Identity Trust Management AG has built an impressive company both in terms of product portfolio and client relationships. We have known the leadership team for years and have established a partnership rooted in deep loyalty and mutual understanding. We are excited to welcome identity Trust Management AG’s talented team to the IDnow family and look forward to combining the strengths of both companies to create a unified, market-leading brand.”

Uwe Stelzig, CEO at identity Trust Management AG, remarked:

“This combination unites the power of IDnow’s innovative technology with identity Trust Management AG’s diverse set of capabilities to create a differentiated identity verification platform. Together, we will be well-positioned to achieve our joint vision of providing clients with a unique, one-stop solution for identity verification.”

This is reportedly IDnow’s second acquisition in just the past 6 months following that of Wirecard Communication Services in September of last year.

As covered in December 2020, the European Investment Bank (EIB) had decided to provide €15 million of growth funding to Germany-based identity verification platform, IDnow. Founded in 2014, IDnow covers a wide range of use cases both in regulated sectors in Europe and for completely new digital business models worldwide.

The platform allows the identity flow to be adapted to different regional, legal, and business requirements on a per-use case basis.

As explained by the IDnow team:

“IDnow uses Artificial Intelligence to check all security features on ID documents and can therefore reliably identify forged documents. Potentially, the identities of more than 7 billion customers from 193 different countries can be verified in real-time. In addition to safety, the focus is also on an uncomplicated application for the customer. Achieving five out of five stars on the Trustpilot customer rating portal, IDnow technology is particularly user-friendly.”

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Source: https://www.crowdfundinsider.com/2021/03/172910-digital-id-verification-service-idnow-acquires-identity-trust-management-ag-a-global-provider-of-id-software-from-germany/

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AI

How Machine Learning is Being Applied to Software Development

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When Elon Musk proposed the idea of autonomous vehicles, everyone assumed it to be a hypothetical dream and never took it seriously. However, the same vehicles are now on the roads, being one of the top-selling cars in the United States.

The applications of artificial intelligence and machine learning are visible in all areas, from Google Photos in your smartphone to Amazon’s Alexa at your home, and software development is no exception. AI has already changed the way iOS and Android app developers work.

Machine learning can enhance the way a traditional software development cycle works. It allows a computer to learn and improve from the experiences without the need for programming. The sole purpose of AI and ML is to allow computers to learn automatically.

Moreover, being a software developer, you might need to specify minute details to let your computer know what it has to do. Developing software integrated with machine learning can help you make a significant difference in your developing experience.

Machine Intelligence is the last invention that humanity will ever need to make!

When it comes to how machine learning and AI help developers, only the sky’s the limit. Taking it even broader, AI has always transformed every industry it has ever entered. Here’s a quick rundown of stats that convey the same:

As the figures stated, artificial intelligence and machine learning are surely transforming the world, and the development industry is no exception. Let’s have a look at how it can help you write flawless code, deploy, and rectify bugs.

AI and ML in Development – How Does This Benefit Software Developers?

Whether you’re a person working as an android app developer or someone who writes codes for a living, you might have wondered what AI has in it for you. Here’s how developers can harness the capabilities of machine learning and AI:

1. Controlled Deployment of Code

AI and machine learning technologies help in enhancing the efficiency of code deployment activities required in development. In the development spectrum, the deployment mechanisms include a development phase where you need to upgrade your programs and applications to a newer version.

However, if you fail to execute the process properly, you need to face several risks including corruption of the software or application. With the help of AI, you can easily prevent such vulnerabilities and upgrade your code with ease.

2. Bugs and Error Identification

With the advancements in Artificial intelligence, the coding experience is getting even better and improved. It allows developers to easily spot bugs in their code and fix them instantly. They don’t have to read their code, again and again, to find potential flaws in their code anymore.

Several machine learning algorithms can automatically test your software and suggest changes.

AI-powered testing tools are certainly saving a plethora of time to developers and help them deliver their projects faster.

3. Secure Data Storage

With the ever-growing transfer of data from numerous networks, cybersecurity experts often find it complex and overwhelming to monitor every activity going on in the network. Due to this, there might be a threat or breach that may go away unnoticed, without producing any alerts.

However, with the capabilities of artificial intelligence, you can avoid issues such as delayed warnings and get notified about bugs in your code as soon as possible. These tools gradually lessen the time it takes a company to get notified about a breach.

4. Strategic Decision Making and Prototyping 

It’s a habit for a developer to go through a hefty and endless list of what needs to be included in a project or code they’re making. However, technological solutions driven by machine learning and AI are capable of analyzing and evaluating the performance of existing applications.æ

With the help of this technology, both business leaders and engineers can work on a solution that cuts down the risk and maximizes the impact. By using natural language and visual interfaces, technical domain experts can develop technologies faster.

5. Skill Enhancement

To keep evolving with the upcoming technology, you need to evolve with the advancement in technology. For the freshers and young developers, AI-based tools help them to collaborate on various software programs and share insights with fellow team members and seniors to learn more about the programming language and software.

Parting Words

While machine learning and AI simplify numerous tasks and activities related to software development, it doesn’t mean that testers and developers are going to lose their jobs. A hired android app developer will still write codes in a faster, better, and more efficient environment, supported by AI and machine learning.

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Artificial Intelligence

Generative Adversarial Transformers: Using GANsformers to Generate Scenes

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@whatsaiLouis Bouchard

I explain Artificial Intelligence terms and news to non-experts.

They basically leverage transformers’ attention mechanism in the powerful StyleGAN2 architecture to make it even more powerful!

Watch the Video:

Chapters:
0:00​ Hey! Tap the Thumbs Up button and Subscribe. You’ll learn a lot of cool stuff, I promise.
0:24​ Text-To-Image translation
0:51​ Examples
5:50​ Conclusion

References

Paper: https://arxiv.org/pdf/2103.01209.pdf
Code: https://github.com/dorarad/gansformer
Complete reference:
Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021), Published on Arxiv., abstract:

“We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables longrange interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model’s strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-theart results in terms of image quality and diversity, while enjoying fast learning and better data efficiency. Further qualitative and quantitative experiments offer us an insight into the model’s inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at https://github.com/dorarad/gansformer​.”

Video Transcript

Note: This transcript is auto-generated by Youtube and may not be entirely accurate.

00:00

the basically leveraged transformers

00:02

attention mechanism in the powerful stat

00:04

gun 2 architecture to make it even more

00:06

powerful

00:10

[Music]

00:14

this is what’s ai and i share artificial

00:16

intelligence news every week

00:18

if you are new to the channel and would

00:19

like to stay up to date please consider

00:21

subscribing to not miss any further news

00:24

last week we looked at dali openai’s

00:27

most recent paper

00:28

it uses a similar architecture as gpt3

00:31

involving transformers to generate an

00:33

image from text

00:35

this is a super interesting and complex

00:37

task called

00:38

text to image translation as you can see

00:41

again here the results were surprisingly

00:43

good compared to previous

00:45

state-of-the-art techniques this is

00:47

mainly due to the use of transformers

00:49

and a large amount of data this week we

00:52

will look at a very similar task

00:54

called visual generative modelling where

00:56

the goal is to generate a

00:58

complete scene in high resolution such

01:00

as a road or a room

01:02

rather than a single face or a specific

01:04

object this is different from delhi

01:06

since we are not generating the scene

01:08

from a text but from a trained model

01:10

on a specific style of scenes which is a

01:13

bedroom in this case

01:14

rather it is just like style gun that is

01:17

able to generate unique and non-existing

01:19

human faces

01:20

being trained on a data set of real

01:22

faces

01:24

the difference is that it uses this gan

01:26

architecture in a traditional generative

01:28

and discriminative way

01:29

with convolutional neural networks a

01:32

classic gun architecture will have a

01:34

generator

01:35

trained to generate the image and a

01:36

discriminator

01:38

used to measure the quality of the

01:40

generated images

01:41

by guessing if it’s a real image coming

01:43

from the data set

01:44

or a fake image generated by the

01:46

generator

01:48

both networks are typically composed of

01:50

convolutional neural networks where the

01:52

generator

01:53

looks like this mainly composed of down

01:56

sampling the image using convolutions to

01:58

encode it

01:59

and then it up samples the image again

02:02

using convolutions to generate a new

02:04

version

02:05

of the image with the same style based

02:07

on the encoding

02:08

which is why it is called style gun then

02:12

the discriminator takes the generated

02:14

image or

02:15

an image from your data set and tries to

02:17

figure out whether it is real or

02:18

generated

02:19

called fake instead they leverage

02:22

transformers attention mechanism

02:24

inside the powerful stargane 2

02:26

architecture to make it

02:27

even more powerful attention is an

02:30

essential feature of this network

02:32

allowing the network to draw global

02:34

dependencies between

02:36

input and output in this case it’s

02:39

between the input at the current step of

02:41

the architecture

02:42

and the latent code previously encoded

02:44

as we will see in a minute

02:46

before diving into it if you are not

02:48

familiar with transformers or attention

02:50

i suggest you watch the video i made

02:52

about transformers

02:54

for more details and a better

02:55

understanding of attention

02:57

you should definitely have a look at the

02:58

video attention is all you need

03:01

from a fellow youtuber and inspiration

03:03

of mine janik

03:04

kilter covering this amazing paper

03:07

alright

03:07

so we know that they use transformers

03:09

and guns together to generate better and

03:12

more realistic scenes

03:13

explaining the name of this paper

03:15

transformers

03:17

but why and how did they do that exactly

03:20

as for the y they did that to generate

03:22

complex and realistic scenes

03:24

like this one automatically this could

03:26

be a powerful application for many

03:28

industries like movies or video games

03:30

requiring a lot less time and effort

03:33

than having an

03:34

artist create them on a computer or even

03:36

make them

03:37

in real life to take a picture of it

03:40

also

03:40

imagine how useful it could be for

03:42

designers when coupled with text to

03:44

image translation generating many

03:46

different scenes from a single text

03:48

input

03:48

and pressing a random button they use a

03:51

state-of-the-art style gun architecture

03:53

because guns are powerful generators

03:55

when we talk about the general image

03:58

because guns work using convolutional

04:00

neural networks

04:01

they are by nature using local

04:03

information of the pixels

04:05

merging them to end up with the general

04:07

information regarding the image

04:09

missing out on the long range

04:11

interaction of the faraway pixel

04:13

for the same reason this causes guns to

04:15

be powerful generators for the overall

04:18

style of the image

04:19

still they are a lot less powerful

04:21

regarding the quality of the small

04:23

details in the generated image

04:25

for the same reason being unable to

04:27

control the style of localized regions

04:30

within the generated image itself this

04:33

is why they had the idea to combine

04:34

transformers and gans in one

04:36

architecture they called

04:38

bipartite transformer as gpt3 and many

04:41

other papers already proved transformers

04:44

are powerful for long-range interactions

04:46

drawing dependencies between them and

04:48

understanding the context of text

04:50

or images we can see that this simply

04:53

added attention layers

04:54

which is the base of the transformer’s

04:56

network in between the convolutional

04:58

layers of both the generator and

05:00

discriminator

05:01

thus rather than focusing on using

05:03

global information and controlling

05:05

all features globally as convolutions do

05:07

by nature

05:08

they use this attention to propagate

05:10

information from the local pixels to the

05:12

global high level representation

05:14

and vice versa like other transformers

05:17

applied to images

05:18

this attention layer takes the pixel’s

05:20

position and the style gun to latent

05:23

spaces w

05:24

and z the latent space w is an encoding

05:27

of the input into an intermediate latent

05:30

space

05:30

done at the beginning of the network

05:32

denoted here

05:34

as a while the encoding z is just the

05:37

resulting features of the input at the

05:39

current step of the network

05:40

this makes the generation much more

05:42

expressive over the whole image

05:44

especially in generating images

05:46

depicting multi-object

05:48

scenes which is the goal of this paper

05:51

of course this was just an overview of

05:53

this new paper by facebook ai research

05:55

and stanford university

05:57

i strongly recommend reading the paper

05:59

to have a better understanding of this

06:00

approach it’s the first link in the

06:02

description below

06:03

the code is also available and linked in

06:05

the description as well

06:07

if you went this far in the video please

06:08

consider leaving a like

06:10

and commenting your thoughts i will

06:12

definitely read them and answer you

06:14

and since there’s still over 80 percent

06:16

of you guys that are not subscribed yet

06:18

please consider clicking this free

06:20

subscribe button

06:21

to not miss any further news clearly

06:23

explained

06:24

thank you for watching

06:33

[Music]

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Artificial Intelligence

How I’d Learn Data Science If I Were To Start All Over Again

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@santiviquezSantiago Víquez

Physicist turned data scientist. Creator of datasciencetrivia.com

A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting.

I’m aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that’s ok, the important thing is to learn and enjoy it.

So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.

As you will see, my favorite way to learn is going from simple to complex gradually. This means starting with practical examples and then move to more abstract concepts.

Kaggle micro-courses

I know it may be weird to start here, many would prefer to start with the heaviest foundations and math videos to fully understand what is happening behind each ML model. But from my perspective starting with something practical and concrete helps to have a better view of the whole picture.

In addition, these micro-courses take around 4 hours/each to complete so meeting those little goals upfront adds an extra motivational boost.

Kaggle micro-course: Python

If you are familiar with Python you can skip this part. Here you’ll learn basic Python concepts that will help you start learning data science. There will be a lot of things about Python that are still going to be a mystery. But as we advance, you will learn it with practice.

Link: https://www.kaggle.com/learn/python

Price: Free

Kaggle micro-course: Pandas

Pandas is going to give us the skills to start manipulating data in Python. I consider that a 4-hour micro-course and practical examples is enough to have a notion of the things that can be done.

Link: https://www.kaggle.com/learn/pandas

Price: Free

Kaggle micro-course: Data Visualization

Data visualization is perhaps one of the most underrated skills but it is one of the most important to have. It will allow you to fully understand the data with which you will be working.

Link: https://www.kaggle.com/learn/data-visualization

Price: Free

Kaggle micro-course: Intro to Machine Learning

This is where the exciting part starts. You are going to learn basic but very important concepts to start training machine learning models. Concepts that later will be essential to have them very clear.

Link: https://www.kaggle.com/learn/intro-to-machine-learning

Precio: Free

Kaggle micro-course: Intermediate Machine Learning

This is complementary to the previous one but here you are going to work with categorical variables for the first time and deal with null fields in your data.

Link: https://www.kaggle.com/learn/intermediate-machine-learning

Price: Free

Let’s stop here for a moment. It should be clear that these 5 micro-courses are not going to be a linear process, you are probably going to have to come and go between them to refresh concepts. When you are working in the Pandas one you may have to go back to the Python course to remember some of the things you learned or go to the pandas documentation to understand new functions that you saw in the Introduction to Machine Learning course. And all of this is fine, right here is where the real learning is going to happen.

Now, if you realize these first 5 courses will give you the necessary skills to do exploratory data analysis (EDA) and create baseline models that later you will be able to improve. So now is the right time to start with simple Kaggle competitions and put in practice what you’ve learned.

Kaggle Playground Competition: Titanic

Here you’ll put into practice what you learned in the introductory courses. Maybe it will be a little intimidating at first, but it doesn’t matter it’s not about being first on the leaderboard, it’s about learning. In this competition, you will learn about classification and relevant metrics for these types of problems such as precision, recall, and accuracy.

Link: https://www.kaggle.com/c/titanic

Kaggle Playground Competition: Housing Prices

In this competition, you are going to apply regression models and learn about relevant metrics such as RMSE.

Link: https://www.kaggle.com/c/home-data-for-ml-course

By this point, you already have a lot of practical experience and you’ll feel that you can solve a lot of problems, buuut chances are that you don’t fully understand what is happening behind each classification and regression algorithms that you have used. So this is where we have to study the foundations of what we are learning.

Many courses start here, but at least I absorb this information better once I have worked on something practical before.

Book: Data Science from Scratch

At this point, we will momentarily separate ourselves from pandas, scikit-learn ,and other Python libraries to learn in a practical way what is happening “behind” these algorithms.

This book is quite friendly to read, it brings Python examples of each of the topics and it doesn’t have much heavy math, which is fundamental for this stage. We want to understand the principle of the algorithms but with a practical perspective, we don’t want to be demotivated by reading a lot of dense mathematical notation.

Link: Amazon

Price: $26 aprox

If you got this far I would say that you are quite capable of working in data science and understand the fundamental principles behind the solutions. So here I invite you to continue participating in more complex Kaggle competitions, engage in the forums, and explore new methods that you find in other participants’ solutions.

Online Course: Machine Learning by Andrew Ng

Here we are going to see many of the things that we have already learned but we are going to watch it explained by one of the leaders in the field and his approach is going to be more mathematical so it will be an excellent way to understand our models even more.

Link: https://www.coursera.org/learn/machine-learning

Price: Free without the certificate — $79 with the certificate

Book: The Elements of Statistical Learning

Now the heavy math part starts. Imagine if we had started from here, it would have been an uphill road all along and we probably would have given up easier.

Link: Amazon

Price: $60, there is an official free version on the Stanford page.

Online Course: Deep Learning by Andrew Ng

By then you have probably already read about deep learning and play with some models. But here we are going to learn the foundations of what neural networks are, how they work, and learn to implement and apply the different architectures that exist.

Link: https://www.deeplearning.ai/deep-learning-specialization/

Price: $49/month

At this point it depends a lot on your own interests, you can focus on regression and time series problems or maybe go more deep into deep learning.

I wanted to tell you that I launched a Data Science Trivia game with questions and answers that usually come out on interviews. To know more about this Follow me on Twitter.

Also published at https://towardsdatascience.com/if-i-had-to-start-learning-data-science-again-how-would-i-do-it-78a72b80fd93

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