How I Would Learn Data Science in 2022

Summary

The video script provides a practical guide on learning data science in 2022, emphasizing a breadth-first approach centered around project-based learning. It outlines essential topics including coding, statistics, data visualization, exploratory data analysis (EDA), machine learning, data scraping, APIs, databases, and deployment. The speaker recommends starting with Python due to its simplicity and rich data science libraries. The script also advises on learning SQL for database management and highlights the importance of domain knowledge and communication skills for a data scientist. It suggests using interactive platforms like Free Code Camp and resources like Kaggle for practical learning and emphasizes the evolving landscape of data science with automation of repetitive tasks, stressing the need for understanding algorithms and their application in specific contexts.

Takeaways

• 📈 **Practical Guide Focus**: The video emphasizes a practical approach to learning data science, focusing on effective learning methods and persistence.
• 🔍 **Interdisciplinary Nature**: Data science involves coding, math, statistics, and business acumen, necessitating a breadth-first learning approach.
• 🛠️ **Project-Based Learning**: A project-based learning approach is recommended for its effectiveness in encoding information deeply and retaining knowledge.
• 🐍 **Python for Coding**: Python is suggested as the starting language for coding due to its simplicity, great documentation, and data science libraries.
• 📊 **Statistics Fundamentals**: Basic statistical knowledge is crucial, including mean, median, mode, standard deviation, and distributions.
• 📈 **Data Visualization**: Learning a visualization library like seaborn is important for graphically representing data insights.
• 🔬 **Exploratory Data Analysis (EDA)**: EDA is introduced as a method to explore and familiarize oneself with data sets, looking for trends and patterns.
• 📚 **Learning Timeline**: The video provides a suggested timeline for learning each topic, emphasizing the importance of starting with the basics and progressing to projects.
• 🤖 **Machine Learning Algorithms**: Understanding common machine learning algorithms is key, with an intuitive grasp being more important initially than deep mathematical understanding.
• 🌐 **Data Scraping and APIs**: As one progresses, learning to scrape data and work with APIs becomes essential for obtaining and manipulating data sets.
• 💡 **Domain Knowledge**: With automation on the rise, domain knowledge and the ability to communicate the impact of data science work becomes increasingly important.

Q & A

What is the main focus of the video regarding learning data science?

The main focus of the video is to provide a practical guide on how to effectively learn data science, emphasizing a breadth-first approach centered around project-based learning.

Why is project-based learning recommended for learning data science?

Project-based learning is recommended because it allows learners to apply theoretical knowledge in practice, which helps in deeper encoding of information into the brain and better retention of knowledge.

What is the recommended first step in learning data science according to the video?

The recommended first step is learning coding, specifically starting with Python, as it is a general-purpose language with great libraries for data science.

Why is a breadth-first approach preferred over a depth-first approach when learning data science?

A breadth-first approach is preferred because it helps learners avoid getting overwhelmed by the depth of each subject, allows them to start implementing what they learn sooner, and keeps the learning process engaging.

What are some of the key topics to cover when learning data science?

Key topics include programming, statistics, data visualization, exploratory data analysis (EDA), machine learning, data scripting, APIs, databases, and deployment, as well as specific niches like NLP and computer vision.

What is the significance of understanding the theory behind machine learning algorithms?

Understanding the theory behind machine learning algorithms is important for applying them effectively to specific use cases and ensuring they function properly in a given context.

Why is domain knowledge considered crucial for a data scientist?

Domain knowledge is crucial because it helps a data scientist understand the business context, communicate the value of their work, and ensure that their analyses and models provide real impact and are used by the organization.

What is the recommended timeline for learning the basics of coding in the context of data science?

The recommended timeline for learning the basics of coding is one to two weeks at four hours per day.

How does the video suggest approaching the learning of statistics for data science?

The video suggests brushing up on statistics with a focus on high school to first-year university stats, such as mean, median, mode, standard deviation, distributions, central limit theorem, and confidence intervals.

What is the role of accountability in the learning process as discussed in the video?

Accountability is built into the learning process to maximize the chances of not giving up, especially for those who may not have the strongest willpower and tend to give up easily.

How does the video suggest one should engage with existing projects to enhance their learning?

The video suggests taking someone else's project and working through it, understanding each line of code and the rationale behind it, rather than just copying code, to gain a practical understanding of how to approach a project.

Outlines

00:00

🎓 Mastering Data Science in 2022: A Practical Guide

The video provides a practical guide to learning data science in 2022, emphasizing effective learning strategies over the sheer volume of information. It covers the interdisciplinary nature of data science, involving coding, math, statistics, and business acumen. The presenter outlines a step-by-step approach, starting with a breadth-first, project-based learning method. This approach encourages learners to understand the minimum required theory before diving into practical projects. The video also discusses the importance of not giving up and the role of accountability in learning.

05:01

👩‍💻 Coding and Project-Based Learning: Starting Strong

The presenter suggests starting with coding, finding it more motivating to see immediate results. Python is recommended as the language of choice due to its simplicity and rich data science libraries. The basics of coding, including variables, functions, loops, and conditionals, are covered, along with the importance of learning data science modules like pandas and numpy. The video also touches on statistics, visualization, and the first project milestone, which involves exploratory data analysis (EDA). Timelines for learning these topics are provided, with an emphasis on the breadth-first approach and the integration of theory and practice.

10:02

📈 Diving Deeper: Statistics, Visualization, and Machine Learning

The video moves on to more advanced topics, beginning with statistics, where a foundational understanding is crucial. It then transitions into data visualization, recommending the seaborne library for its intuitive interface and aesthetic appeal. The presenter highlights the importance of EDA for understanding data trends and patterns. Following this, the focus shifts to machine learning, introducing common algorithms and the importance of understanding their workings intuitively. The video also discusses the process of learning through other people's projects on platforms like Kaggle and emphasizes the practical aspects of machine learning, such as data preprocessing and model optimization.

15:03

🔍 Scraping, APIs, and Databases: Expanding Data Science Skills

The presenter covers data scraping and APIs, which are essential for sourcing data when pre-built datasets are unavailable. Beautiful Soup is recommended for web scraping, and the importance of learning SQL for database manipulation is emphasized. The video outlines the types of databases, including relational, NoSQL, and cloud databases. It also provides a timeline for learning these skills and suggests practical exercises like importing datasets into a personal database. The presenter encourages the use of interactive learning platforms and emphasizes the importance of project-based learning.

🚀 Deployment, Niche Topics, and the Future of Data Science

The video discusses deployment, which involves putting machine learning models into a live environment, and explores niche areas such as natural language processing (NLP) and computer vision. The presenter provides resources for learning SQL, machine learning algorithms, and databases. They also stress the importance of understanding the automated aspects of data science and the growing significance of domain knowledge. The video concludes with the importance of communication and presenting findings in a business context, as well as the role of data scientists in ensuring their work provides value to the company.

Keywords

💡Data Science

Data Science is an interdisciplinary field that utilizes scientific methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data. In the video, the speaker discusses how to effectively learn data science, emphasizing the importance of understanding both the theoretical and practical aspects of the field.

💡Project-Based Learning

Project-Based Learning (PBL) is an educational approach where students gain knowledge and skills by working for an extended period of time to investigate and respond to an authentic, engaging, and complex question, problem, or challenge. The speaker advocates for a breadth-first approach centered around PBL, which facilitates deeper learning and understanding by applying concepts in real-world scenarios.

💡Python

Python is a high-level, general-purpose programming language that is widely used in data science due to its simplicity and the availability of robust libraries for data manipulation and analysis. The video recommends starting with Python for coding in data science, highlighting its ease of understanding and extensive documentation.

💡Pandas and Numpy

Pandas and Numpy are two fundamental Python modules for data science. Pandas is used for data manipulation and analysis, while Numpy is a library that supports large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The script mentions that understanding these modules is crucial for handling datasets in data science projects.

💡Statistics

Statistics is a branch of mathematics dealing with data collection, analysis, interpretation, presentation, and organization. In the context of the video, the speaker suggests brushing up on basic statistical concepts such as mean, median, mode, standard deviation, and distributions, which are essential for understanding the nature of datasets in data science.

💡Data Visualization

Data Visualization is the graphical representation of information and data. It enables viewers to understand the significance of data by summarizing and consolidating the data in a visual format. The video mentions seaborne as a preferred visualization module, emphasizing the importance of visual representations for interpreting data insights.

💡Exploratory Data Analysis (EDA)

Exploratory Data Analysis is an approach to analyze data sets to summarize their main characteristics, often using visual methods. EDA provides an initial understanding of the data by discovering patterns, spotting anomalies, and testing the hypothesis. The speaker discusses EDA as a project that integrates coding, statistics, and visualization skills to explore and familiarize oneself with the dataset.

💡Machine Learning

Machine Learning is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The video outlines the importance of understanding common machine learning algorithms and their applications in predicting outcomes based on data, which is a significant part of data science.

💡Data Scraping and APIs

Data Scraping is the process of extracting data from websites, while APIs (Application Programming Interfaces) are protocols for building software applications. In the context of the video, the speaker discusses the necessity of learning how to scrape data from websites or use APIs to gather data for analysis, which is a critical skill when pre-built datasets are not available.

💡Databases

Databases are structured sets of data stored electronically. The video emphasizes the importance of understanding different types of databases, such as relational and NoSQL databases, and learning SQL, a domain-specific language used in programming and managing relational databases. SQL is particularly important for data professionals as it is a common requirement in job interviews.

💡Deployment

Deployment in the context of data science refers to the process of putting a developed machine learning model into a live environment where it can be used to make predictions or decisions. The speaker touches on the importance of this step for moving from experimental models to operational models that can be integrated into other software and used in real-world applications.

💡Domain Knowledge

Domain Knowledge refers to the specific knowledge and expertise in a particular field or industry. The video stresses that as data science tasks become more automated, domain knowledge becomes increasingly important for data scientists to understand the context and business reasons behind their analyses, ensuring their work provides real value to the organization.

Highlights

The video provides a practical guide on learning data science effectively, emphasizing not just what to learn but how to learn it.

Data science is described as an interdisciplinary field involving coding, math, statistics, and business acumen.

A breadth-first approach centered around project-based learning is recommended for learning data science.

The importance of understanding the difference between theory and practice in technical subjects is highlighted.

Python is suggested as the starting language for coding due to its simplicity and rich data science libraries.

Pandas and NumPy are identified as key modules for data manipulation in data science.

Statistics knowledge is crucial for understanding data sets, with a focus on concepts from high school to first-year university stats.

Visualization is emphasized as an important aspect of data science, with Seaborn recommended as an intuitive module.

Exploratory Data Analysis (EDA) is introduced as a foundational project type for beginners to familiarize with data sets.

A timeline is provided for learning the basics of coding, statistics, and visualization, suggesting 1-2 weeks for each.

The video encourages learning from other data scientists and building upon existing projects to enhance understanding.

Machine learning is discussed, with an emphasis on understanding common algorithms and their applications.

Data scraping and APIs are highlighted as essential skills when moving beyond pre-built data sets.

SQL is identified as a vital language to learn for database manipulation and is often a job requirement for data roles.

Deployment and niche areas like NLP and computer vision are considered advanced topics in data science.

Recommended resources for learning include interactive platforms, online courses, and practical project-based learning.

As data science tasks become automated, the importance of domain knowledge and effective communication of findings increases.

The presenter shares personal strategies for staying accountable and motivated during the learning process.

The landscape of data science is evolving, with a growing emphasis on domain expertise and the ability to apply AI tools effectively.

Transcripts

welcome back to another recall by data

iq video

in this video i'm going to walk you

through how i would learn data science

in 2022. you've probably already seen a

couple other videos on this topic before

but what i'm going to be focusing on

here is a very practical guide because

from my experience the hardest part

about learning data science is that you

can't figure out what to learn but

rather how to learn effectively and kind

of like how to not give up essentially

because data science is hard it's an

interdisciplinary field that involves

coding math and stats and business staff

product sense first i'm going to outline

the topics to cover and my step-by-step

approach

kind of like framework for how to learn

then i'll go through the approximate

timeline for each topic some recommended

resources and finally ending with where

i see data science heading and how you

should adjust your learning plan to suit

it so do stick to the end of the video

because data science is a rapidly

changing field and i think it's

important to understand the landscape if

you're genuinely interested in getting

into the field throughout the video i'll

also be pointing out where i very

intentionally built in accountability

which is basically how to maximize your

chances to not give up because at least

for me i don't have the strongest

willpower and i tend to give up easily

so if you can kind of relate to this

maybe these tips and checkpoints will

also help you as well okay topics to

cover there's programming stats data

visualization exploratory data analysis

or eda machine learning data scripting

apis databases deployment and specific

niches like nlp and computer vision

don't worry i'm just listing these here

now but we're going to go through each

of these later in the video and talk

about why each topic is relevant and why

i recommend learning them in this

specific order but first i want to share

with you what is called meta learning

where how to learn the general approach

i recommend is what is called a

breadth-first approach centered around

project-based learning basically what i

mean by this is say we take the topics i

listed before right breadth-first

approach means that you should cover

just enough for the minimum amount of

theory for each topic before doing a

project surrounding it then you can

learn more about the topics and do a

more complex project and you do this

over and over still being learning more

about each topic and expanding your

skills this is called a breath first

approach to learning and as opposed to a

depth first approach where you would

attempt to learn every single thing

about a topic and then move on to the

next topic and then again try to learn

every single thing about that and after

you learn each topic thoroughly then you

would try to do the project the reason

why i recommend this breadth-first

approach centered around project based

learning is for three major reasons the

first reason is that technical subjects

like coding math and stats etc are

really different in theory and in

practice if you try to learn how to code

before you may have experienced

something like you do a course on coding

right and you're like okay makes sense i

know how to code now and then when you

actually sit down to code something

yourself you're kind of like uh where do

i even start the reason for this is

because implementation is really a

separate beast and the whole point of

learning to code and data science is

that you can actually implement and do

cool projects right so you do want to

know how to implement the second reason

is that if you try to deeply learn each

subject in turn you will be there

learning until the end of days each

subject of coding stats and machine

learning is huge and you can really go

down the rabbit hole and find yourself

super overwhelmed not knowing what is

actually relevant and important and at

some point you're probably going to give

up before you've been starting to use

these things that you learned trust me i

know this from experience and finally

another plug for project-based learning

studies have actually shown that project

based learning is the best form of

learning because by doing things and

figuring things out yourself you're

actually more deeply encoding that

information into your brain and more

likely to retain the information as

opposed to just like kind of passively

absorbing information if you're just

watching someone else code for example

so yes breadth first approach centered

around project based learning hopefully

i have convinced you alright let's now

go through the topics in my opinion you

should start with coding first and the

reason why i recommend coding first is

because it's a lot more motivating at

least for me to be able to see the

results of things that i do as opposed

to starting with more theoretical topics

like math and stats which of course is

extremely important and you'll certainly

get to them later but i find these

topics more abstract and less engaging

aka easier to get bored and give up for

choice of language i would recommend

starting with python the reason why i

recommend starting with python is

because it's a general purpose language

that is super simple to understand has

great documentation and also has great

libraries for data science including

machine learning so what to learn for

coding you should know the basics

including how to declare variable

functions loops and if statements then

you should get familiar with two

specific data science modules pandas and

numpy pandas is built on top of numpy

and is like the data science module

where you can manipulate your data sets

and feed them into other more

specialized libraries for data

visualization and machine learning for

example after you learn the basis of

coding next i recommend learning we're

brushing up on your stats and i'm not

talking about like crazy stuff here like

we're talking about high school to first

year university stats mean median mode

standard deviation distributions central

limit theorem confidence intervals

things like that this comes in really

handy when you're understanding the

nature of your data set now what's

really cool is that because you know how

to code now you can actually implement

the stats on your data sets which again

i think is a lot more fun because you

can see the things that you do next up

is visualizations there's a lot of

different visualization modules out

there but honestly if you learn one of

them the rest are kind of just

variations with different

functionalities i personally like

seaborne because it's really intuitive

to use and the graphs are automatically

really pretty as well at this point you

should know the basics of coding stats

and visualizations and you're ready now

for your first project which is some

exploratory data analysis or eda eda is

just a fancy way of saying exploring

your data set and familiarizing yourself

with it by seeing if there's any trends

patterns correlations between variables

etc with the basis in coding stats and

visualizations you're now well equipped

to do eda by taking a data set playing

around with it a bit and doing some

stats like finding the mean distribution

of variables and making some

visualizations okay let's talk about

timelines to get to this point in terms

of timeline i would say coding should

take you about one or two weeks at four

hours per day so that should take you

again one to two weeks maybe a little

more maybe a little longer depending on

how much stats that you remember and

visualization should take you only about

one to two hours to a day to get a hang

of now you might be thinking this is

probably a lot longer than i thought and

that's okay because remember breadth

first approach centered around

project-based learning you don't have to

know everything just enough the basics

that you can start doing a project which

will help you learn even faster so what

exactly should you do for your first

project well let me let you in on a

secret so much of data science is

learning from other data scientists and

working on top of what others have built

i find that the best projects to start

with when you're new in the field is to

take someone else's project and work

through it for example you can start

with the famous titanic data set on

cable and pick one of the highly rated

notebooks then if you're feeling daring

you can add something onto it and take

it a step further word of warning here

is of course don't just go and copy code

right like that clearly will not help

you learn but if you understand what

each line of code is doing and the

rationale behind it you'll gain an

understanding on how to approach a

project then next time when you're doing

another project you will know how to

approach it honestly even now when i

want to learn something that i'm not

super familiar with i find the fastest

way to learn is to start by doing a

project that someone else has done and

then applying it to my own project later

so by now after working through a kaggle

notebook or two you'll probably notice

that for many kaggle notebooks after

some initial exploration of the data

they start jumping into machine learning

for example some exploratory data

analysis may show that the likelihood of

survival when you're male is far lower

than if you're female and also your

class has to do with survival then the

question becomes can you predict

survival and the answer is yes with

machine learning so now it's time to

learn about machine learning there's

around 10 to 15 common machine learning

algorithms and there's a lot of ways of

classifying them one example is dividing

them into supervised learning

unsupervised learning and reinforcement

learning i recommend intuitively

understanding how the algorithms work

without worrying too much about the

exact math behind it for example linear

regression is the simplest machine

learning model and intuitively how it

works is that it tries to draw a

straight line that minimizes the

distance between each data point and

that line and the model is the line you

drew that can predict for example the

probability of survival on the titanic

given an age the good news is that most

machine learning algorithms are actually

quite intuitive and not super difficult

to understand to learn the basics of the

common machine learning algorithms i

would say it should take you about like

three to four weeks

again assuming four hours per day

definitely feel free to go deeper into

the math if you are interested however

depending on your math proficiency you

may need to refresh your calculus and go

deeper into statistics okay cool now you

can continue working through the

notebook of someone else's project and

trying out the different machine

learning algorithms it's also super

useful here to understand the notebook

author's reason for the data

pre-processing that's being done the

reason why certain machine learning

algorithms are chosen and their pros and

cons as well as how to optimize the

models these are super practical things

that are extremely important to doing

machine learning so be sure to really

understand the reasoning behind choices

that are being made now we'll cover

things up to machine learning and next

up is data scraping slash apis this

comes into play when you graduate out of

using pre-built data sets especially if

you want to do your own project it's

actually really rare that you'll find

kind of like just a nice data set laid

out for you already the more likely

situation you find yourself in is having

to scrape the data yourself from

websites or using apis which stand for

application programming interface for

scraping data a module i would recommend

checking out is beautiful soup very

useful and quite cute and whimsical too

if i do say so myself it shouldn't take

you more than a couple days to a week to

have a good grasp for apis we're

application programming interfaces

they are software built by other people

that you can use to get access to data

amongst other functions but what is

relevant here is that you can get data

using apis to learn how to use an api it

may take you some time to understand how

to use it because it involves

understanding how to use other people's

software and this really has to do with

how well documented the api is reading

documentation is in itself a skill in

both understanding how to read

documentation as well as like developing

the patients to read documentation again

remember the approach that i guess i've

already beaten into you at this point

brad first approach project-based

learning learn the minimum and do the

project next up databases for databases

what to learn here is understanding the

different types of databases like

relational databases nosql databases

cloud databases etc a language that you

may especially want to pick up here is

sql it's a much easier language to learn

compared to python and shouldn't take

you more than a week or two to learn it

well pro tip here is if you're

interested in getting a job as a data

scientist data analyst or data engineer

almost all companies will ask you sql

questions as part of the interview

process in my opinion the minimum here

to learn is relational databases and the

language behind them which is sql

especially if you're primarily learning

data science to get a job timeline here

is two weeks for the basics for database

projects i recommend downloading some

data sets like from kaggle for example

and then importing that data into your

own database this teaches you how to

create a database create tables inside

the database and manipulate the data

okay we're almost done so for the next

two topics deployment and specific

niches i consider these more advanced

topics deployment comes into play when

you want to take the machine learning

model you develop and put into a live

environment instead of just having it in

a notebook that you have you can deploy

the model across different code

environments and also integrate them

into other software then if you're

interested in a specific field of data

science you can also explore niches like

natural language processing or nlp which

has to do with developing algorithms

that understand human languages known as

natural languages it's really a very

cool interdisciplinary field there's

also niches like computer vision that

has applications in self-driving cars

for example it's kind of hard for me to

give you a timeline on these niches

because theoretically you can easily do

a project in nlp for example with the

skills you learned so far by employing

modules that other people have developed

which abstract away a lot of the

underlying concepts and this will take

you like a few hours to a few days to

learn but if you're interested in these

niche topics i would also assume you

would want to understand more of the

theory behind it and i mean there are

people who have phds in the field so in

terms of timeline really depends on how

far you want to go now let's talk about

some recommended resources i personally

prefer interactive interfaces to learn

coding like free code camp for example

because you can see what it is that you

were coding for basic statistics and

theory and math behind machine learning

algorithms the top resources i would

recommend are stat quests by josh summer

and data aiku's own guides both of which

are free for projects to follow i

already mentioned it before but kaggle

is great because there's notebooks where

you can see how people approach projects

from different perspectives a great free

resource to learn sql is moat which is

what i personally use to learn sql from

scratch and pass my own data science

interview to learn more about databases

in general there's also great moocs

available finally for deployment and

more niche topics i would personally go

with highly rated courses from moocs and

again rely heavily on working on my own

projects because at this point you

should already be quite proficient in

the basics so it's more about building

on top of them and doing specific

projects that interest you honestly

there are so many amazing free and low

cost options for learning data science

out there and i just listed a food that

i personally used and liked my

preference is to choose resources that

are interactive and already have

projects built into them i get it though

if you prefer learning from online video

courses or books for example and that's

totally fine my only recommendation is

that you should also intentionally work

through project so you can learn to

implement and in summary if you want

like the most simplistic guide possible

for how to choose a good resource and if

you're willing to spend a little money

you just absolutely cannot go wrong with

choosing a highly rated course on the

topic on a mooc platform there are many

many courses to cover each of these

topics that we discussed now finally

let's talk a little bit about how the

landscape of data science is progressing

as the data science field develops and

becomes more mature a lot of repetitive

tasks in data science like data cleaning

pre-processing exploratory data analysis

machine learning and even deployment are

becoming automated in fact data iq does

just this data iq is a platform for

everyday ai that systemizes the use of

data for business results by using

dataiku you're able to create share and

reuse applications that leverage data

and machine learning to extend and

automate decision making data iq also

allows you to scale ai safely and

effectively and deliver advanced

analytics using the latest techniques at

big data skills data iq is really

powerful and you should check out more

about the platform if you're interested

link in the descriptions below but wait

a second

if you've been paying attention

you are probably thinking right now why

should i learn all the things we just

talked about earlier if it's becoming

automated there's actually still very

good reason to do so first it's still

important for you to understand how

things work so you can understand how to

apply analyses and algorithms to

specific use cases and learn how to best

leverage these tools available because

after all they still are tools even if

they're automating things and we need to

make sure that they're doing what

they're supposed to be doing another

implication of so much of the data

science and machine learning pipelines

being automated is that it's become more

and more important for a data scientist

to have domain knowledge which by the

way is the third pillar of data science

that we haven't really discussed until

now domain knowledge or business product

sets this is just as important as the

coding and the stats coding and stats

and the machine learning algorithms and

all the other technical stuff is only as

valuable as how much value it can

provide to the company so even if you

make the fanciest and best algorithm

ever honestly nobody actually would care

if it doesn't provide value to the

company so it's very much the data

scientists job to understand the

business reason for doing an analysis

where building a model to make sure that

what they're doing also has real impact

in the organization it is also a data

scientist job to communicate the value

of what they're doing and make sure what

they do is actually going to be used for

those of you who are not in industry you

might think that this is kind of weird

right it's like of course it has so much

value and impact but believe me in

practice it's actually really crucial

and it's not just a given because if

decision makers don't understand why

your analysis or your model is useful

then they don't want to use it right and

even if you make the best model ever and

it has a lot of impact your effort would

be for nothing if it's not being used so

to summarize this section since many

repetitive data science tasks are being

automated it's important to one

understand how the algorithms work and

make sure that they're functioning

properly in your given context and two

focus on gaining domain knowledge and

learn how to communicate and present

your findings and the impact of your

work in the business context alright

that's all i have for you today i've

linked all the resources i've talked

about in the description below

do also share your thoughts on this

guide on how to learn data science i

will see you guys in the next video