How to become a Data Analyst FAST (By 2025)

Summary

This video offers a comprehensive roadmap for aspiring data analysts to secure their first role by 2025. It emphasizes the importance of consistent effort, understanding statistics as the language of data, and mastering Excel for data manipulation. The speaker also highlights the necessity of learning SQL for database management and Python or R for scripting. The video suggests practical steps, including joining Discord communities, practicing with real datasets, and building a portfolio to showcase skills. It assures viewers that breaking into the field is achievable with dedication and the right resources, even without a formal degree.

Takeaways

• 😀 The video provides a roadmap to land a data analyst role by 2025, emphasizing the importance of consistency and discipline in learning.
• 🎓 You don't need a relevant degree or extensive experience to break into data analytics; the focus should be on practical skills.
• 🤝 Joining a community like a Discord server can provide valuable networking opportunities and support in learning analytics.
• 📊 Understanding statistics is crucial as it forms the basis for data analysis, including concepts like descriptive statistics, correlation, and probability.
• 📈 Excel and Google Sheets are essential tools for data analysts, with pivot tables, conditional formatting, and visualization being key skills to master.
• 💾 SQL is a must-have skill for data analysts, used for retrieving and manipulating data from relational databases, and it's important to learn its basics and efficiency.
• 🐍 Learning Python or R is beneficial, with Python being more versatile for various fields including data analysis, and key libraries to learn include pandas, numpy, and matplotlib.
• 📊 Visualization tools like Tableau, Looker, or PowerBI are important for presenting data insights effectively, and storytelling with data is a valuable skill.
• 💼 Building a portfolio showcasing your data analysis projects can significantly improve your job prospects in the analytics field.
• 🔗 Consistent practice and application of learned skills are necessary for mastering data analysis, and showing your work is more impactful than just stating your abilities.

Q & A

What is the main goal of the video?

The main goal of the video is to provide a roadmap to help viewers land their first data analyst role before 2025, regardless of their current profession or educational background.

Why is consistency and discipline emphasized in the video?

Consistency and discipline are emphasized because breaking into the field of data analytics requires daily effort and commitment, rather than relying on quick fixes or shortcuts.

What is the significance of joining a Discord server for beginners in data analytics?

Joining a Discord server is significant because it offers a community where beginners can receive referrals, collaborate on group projects, and get support from others in the field, which is beneficial for breaking into tech and data science.

Why is having a grasp of statistics important for a data analyst?

Statistics is important because it is the language of data and analytics. It helps in understanding and interpreting data, conducting analyses, and ensuring that the analysis is not plagued by bias.

What are the basic statistical concepts a data analyst should understand?

A data analyst should understand descriptive statistics like mean, median, mode, and standard deviation, as well as concepts of correlation, probability, and hypothesis testing.

How can one learn the necessary statistical concepts without a formal degree?

One can learn the necessary statistical concepts through free online courses on platforms like Coursera, Khan Academy, or by watching educational videos on YouTube.

Why is Excel or Google Sheets a crucial skill for a data analyst?

Excel or Google Sheets is crucial because it is a ubiquitous tool used across industries for data manipulation, analysis, and visualization, and is often required for day-to-day tasks in data analysis.

What are some key Excel or Google Sheets skills that a data analyst should master?

Key skills include creating pivot tables for data summarization, using conditional formatting to highlight data patterns, and creating visualizations and dashboards for data presentation.

What is SQL and why is it essential for a data analyst?

SQL (Structured Query Language) is a tool used to retrieve, manipulate, and analyze data stored in relational databases. It is essential because it allows data analysts to communicate with and extract insights from large datasets.

How can one practice SQL effectively?

One can practice SQL effectively by using online platforms like W3Schools, Codecademy, or by participating in coding challenges on websites like LeetCode or HackerRank. Additionally, creating databases with multiple tables using tools like SQLite can help practice joins and other SQL queries.

Why is learning Python or R recommended for a data analyst?

Python or R are recommended because they are scripting languages with powerful libraries for data manipulation, numerical operations, and visualization, which are crucial for advanced data analysis and automation.

What are some important Python libraries for data analysis that the video suggests learning?

The video suggests learning libraries such as pandas for data manipulation, numpy for numerical operations, matplotlib or seaborn for visualization, and scikit-learn if interested in machine learning.

How can one learn data visualization tools like Tableau?

One can learn data visualization tools like Tableau through online courses on platforms like Udemy or LinkedIn Learning, by practicing with Tableau Public, or by using free trials of other business intelligence tools.

What is the importance of creating a portfolio in the context of job hunting for data analyst roles?

Creating a portfolio is important because it showcases practical experience and technical abilities to potential employers, giving job candidates a competitive edge over others who may not have tangible examples of their work.

Outlines

00:00

🚀 Introduction to Breaking into Data Analytics

The speaker introduces a roadmap to secure a data analyst role by 2025, emphasizing the video's relevance to both working professionals and students. They highlight that despite changes in the job market, it's possible to break into data analytics without a relevant degree or extensive experience. The speaker encourages viewers to be consistent and disciplined in their efforts, dismissing the idea of a 'magic pill' for success. They recommend joining a Discord server for networking and group projects, and stress the importance of understanding statistics as the language of data analytics, suggesting that a fundamental grasp is sufficient rather than expertise.

05:02

📊 Importance of Statistics and Excel Proficiency

The speaker discusses the necessity of understanding descriptive statistics, correlation, and probability for data analysis. They clarify that correlation does not imply causation, using an example of marketing budget and sales. The speaker then transitions into the importance of Excel and Google Sheets, stating that these tools are ubiquitous across industries. They suggest that viewers should practice using Excel for pivot tables, conditional formatting, and visualizations to solidify their learning. The speaker also recommends using online resources and YouTube for learning Excel, and emphasizes the value of hands-on practice with datasets from kaggle.com.

10:03

💾 Mastering SQL for Data Manipulation

The speaker moves on to SQL, explaining that it's essential for retrieving and analyzing data from relational databases. They simplify SQL learning by breaking it down into basic components like SELECT, FROM, WHERE, JOIN, GROUP BY, and HAVING clauses. The speaker insists that SQL is a must-have skill for data analysts and that efficiency in querying is critical when dealing with large datasets. They recommend using W3Schools and YouTube for learning SQL and suggest practicing on platforms like LeetCode and HackerRank. The speaker also advises creating mock databases with multiple tables for practice, as real datasets often come as single tables.

15:03

🐍 Python and R for Advanced Data Analysis

The speaker recommends learning Python or R for more advanced data analysis, with a personal preference for Python due to its versatility and large community. They highlight key libraries such as pandas for data manipulation, numpy for numerical operations, and matplotlib or seaborn for data visualization. The speaker also touches on the utility of Python for automation and ETL processes. They suggest learning through practical application and recommend using resources like DataCamp and online courses for learning. The speaker concludes by emphasizing the importance of learning a BI tool like Tableau for data visualization, suggesting that storytelling with data is crucial for effective communication of insights.

📈 Visualization and Building a Portfolio

The speaker stresses the importance of data visualization and storytelling, recommending Tableau for its interactive capabilities and the availability of a free public version. They advise learning the science of visualization and practicing with tools like Google Sheets before moving on to more advanced tools. The speaker also encourages viewers to build a portfolio showcasing their projects, which can significantly boost their job market prospects. They conclude by emphasizing the time and consistent effort required to master these skills, suggesting that by 2025, viewers who follow the roadmap will be well-prepared for a career in data analytics.

Keywords

💡Data Analyst

A Data Analyst is a professional who collects, processes, and interprets complex digital data to help businesses make informed decisions. In the video, the main theme revolves around guiding viewers on how to secure a Data Analyst role, emphasizing the importance of various skills and tools necessary for the job.

💡Statistics

Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. In the context of the video, statistics is described as the 'language of data and analytics,' highlighting its fundamental role in understanding and communicating data insights effectively.

💡Descriptive Statistics

Descriptive statistics summarize and organize data to give an overall picture. The video mentions that understanding descriptive statistics like mean, median, mode, and standard deviation is crucial for painting a holistic view of a dataset before delving into detailed analysis.

💡Correlation

Correlation measures the relationship between two variables. The video script clarifies that while correlation indicates a relationship, it does not imply causation, which is a critical concept for data analysts to understand when interpreting data.

💡Probability

Probability is used to express the likelihood of a given event and is fundamental in making inferences and predictions from data. The video suggests that a basic understanding of probability and hypothesis testing is necessary for data analysts to test their assumptions about the data.

💡Excel

Excel is a widely used spreadsheet program for data organization, analysis, and visualization. The video emphasizes Excel's importance in data analysis, mentioning pivot tables, conditional formatting, and data visualization as key features that data analysts should master.

💡SQL

SQL (Structured Query Language) is used for managing and manipulating databases. The video stresses that SQL is a must-have skill for data analysts, as it allows them to retrieve, manipulate, and analyze data stored in relational databases, which is a common task in the role.

💡Python

Python is a versatile programming language that is highly recommended for data analysis due to its simplicity and the powerful libraries available for data manipulation and analysis. The video suggests learning Python for tasks such as data cleaning, numerical operations, and automation.

💡Pandas

Pandas is a Python library for data manipulation and analysis. It is mentioned in the video as an essential tool for data analysts to clean, organize, and analyze data efficiently, allowing for tasks like filtering and joining tables similar to SQL.

💡Visualization

Visualization refers to the graphical representation of data to make it easier to understand and interpret. The video discusses the importance of visualization in data analysis, suggesting that tools like Tableau and Python libraries can be used to create interactive and insightful visual representations of data.

💡Portfolio

A portfolio showcases an individual's work and skills. In the video, building a portfolio is recommended as a way to demonstrate practical experience and skills to potential employers, which can significantly enhance a job candidate's appeal in the job market.

Highlights

A guaranteed roadmap to land your first data analyst role before 2025 is presented, applicable for both working professionals and students.

The video promises a 99% success rate if viewers follow through with the tips and make them actionable.

It's emphasized that breaking into data analytics is possible from unrelated fields, and viewers are encouraged to commit to consistent effort.

Joining a Discord server with over 5,000 members can provide valuable networking and collaboration opportunities.

Statistics is described as the language of data and analytics, with a focus on understanding descriptive statistics like mean, median, mode, and standard deviation.

The importance of recognizing correlation without assuming causation is highlighted.

Probability and hypothesis testing are introduced as fundamental concepts for making inferences and testing them with data.

Excel and Google Sheets are touted as essential tools for data analysts, with a focus on pivot tables, conditional formatting, and visualizations.

SQL is a must-have skill for data analysts, with a focus on understanding queries, joins, and aggregation functions.

Efficiency in SQL querying is stressed as crucial when dealing with large datasets.

Learning Python or R is recommended, with a focus on libraries like pandas, numpy, and matplotlib for data manipulation and visualization.

Data visualization tools like Tableau, Looker, and PowerBI are discussed, with Tableau being recommended for its free public version.

The importance of storytelling with data and creating dashboards that simplify complexity and provide actionable insights is emphasized.

Building a portfolio and showcasing practical experience is advised as a key strategy for standing out in the job market.

Consistency in learning and practicing is highlighted as the key to success in breaking into data analytics by 2025.

Transcripts

so today I'm going to be walking you

through a guaranteed road map to land

your first dat analyst role before 2025

whether you're working professional

student this video is for you now the

information in this video is 99%

guaranteed if you actually follow

through to the video to the end take

notes and also take the tips and make

them actionable listen times have

changed and so has the job market I've

seen countless of people break into Data

from unrelated Fields unrelated Gees in

2024 and I know you can too you might

think you need this relevant degree or

you might need years of experience or

100 projects on the side to land your

first rooll but I can tell you firsthand

you can break in without these now

before we get into the specifics I want

you to make a commitment to yourself and

myself if you do want to break into

analytics you do need to be consistent

and you need to be disciplined and put

in the work every single day to actually

break in there is no magic pill there is

no magic formula that you can just watch

a 8 Hour course and become a data

analyst that's not true you didn't make

a consistent effort every single day now

if you are completely new to the field I

recommend joining the Discord server

down below there over 5,000 people in it

people provide referrals people work on

group projects together and it's

honestly one of the best is good servers

for actually breaking into Tech and data

science and analytics now let's talk

statistics now it might be pretty

intimidating you might be thinking math

uh it sounds too complicated it's not

for me but you have to understand

statistics is the language of data and

analytics well you don't need to be an

expert by any means you don't need a PhD

you do need to have a fundamental grasp

of the basics of Statistics you might be

asked question like what is the average

customer spent what is the variance in

the data can you conduct the split test

for me and actually to answer these

questions you do need to have some grasp

of Statistics to make sure you aren't

plagued by bias and the analysis

actually makes sense for your

stakeholders so the first part of stats

is understanding descriptive statistics

this can be the mean median mode and

standard deviation of the data set they

basically paint a holistic picture of

the data set before you actually dive

into the specific analysis you're

actually doing next I want you to

understand what correlation means it's

basically a relationship between the two

variables but I do want you to note that

correlation does not equal causation so

for example correlation could be does

marketing budget actually lead to higher

sales you can actually figure out this

correlation and report it to your

manager and if it does have a high

correlation or relationship you can

actually increase your marketing budget

and hopefully increase sales now the

next thing you need to know is

probability and maybe a bit of

hypothesis testing this is basically

when you make some inference or

hypothesis on the data set and you

actually want to conduct some sort of

experiment to test it out so this could

be like hey does this new marketing

campaign actually have an effect on the

bottom line sales for the company so you

actually don't need a University degree

to learn these Concepts that pretty

straightforward and simple I recommend

just going on Con Academy or taking a

free course and auditing it on corsera

if you do want to you don't need to

spend thousands of dollars if you don't

want to there are tons of self-paced

courses online and there are tons of

videos on YouTube itself for statistics

I've also released videos on hypothesis

testing and split testing as well now as

I said you don't need a PhD in math or

statistics or computer science to

succeed in this field you just need to

know the basics in order to interpret

and present data in a nice way to your

customers and your fellow stakeholders

all right now that you have a

fundamental grasp of what you need to

learn statistics the next thing I want

you to learn is Excel and you might have

some background in Excel but Excel is

one of those things or Google Sheets is

one of those things that will be

ubiquitous in almost all of Industry no

matter if you're data analyst or your

investment banker or your sales or

marketing or even customer service you

probably have used Excel to some degree

now do you remember all the concepts we

covered in statistics earlier you can

actually use them in Excel you can

figure out the sum the max the Min all

these descriptive statistics you can

figure out V lookups index match and so

many other functions that you can

actually use on a day-to-day basis I

would say the most important Concepts to

cover in Excel or Google sheet are one

pivot tables it helps you actually

summarize the data set and visualize it

very easily normally when I'm making

visualizations in Excel and Google

Sheets I typically make a pivot table of

the data to begin with and then I

visualize that pivot table for my end

user most of the time data sets are very

large they could be thousands if not

tens of thousands of rows and pivot

tables just summarize the data so easily

for your customer so they don't have to

just look through tens of thousands of

rows of data they can just see a nice

summarized aggregated field of data in a

pivot table the next thing I think you

should learn in Excel and Google Sheets

is this idea of conditional formatting

conditional formatting is basically in

the name itself you set a condition and

then it formats the text so the way I

use it is to highlight anomalies or

outliers in the data set so let's say

the sales is actually going down for the

quarter over quarter you'd actually

maybe do red conditional formatting to

indicate bad green to be good and yellow

if it just doesn't make much of a

difference so I typically like to do

this in summary this feature just helps

you see patterns in the data and

summarize it easier so once you've done

the basics with V lookups pivot tables

conditional formatting the next step is

actually work on visualizations and

actually visualizing your data set so

once you actually figure out

visualization with bar charts pie charts

line charts you actually want to build

what are we call dashboards dashboard is

basically a visual hierarchy to present

a paino or problem statement to someone

with a group of visuals big numbers and

it's just a basically a way for like

let's say a sales team wants to see how

many sales they're closing per quarter

this could have like a conversion table

this can also have a big number saying

how many sales calls were booked how

many sales calls were closed and it's

basically the way for a team to get to

make better decisions based on

visualization and data a welld design

dashboard can make the difference even

if your visualizations are amazing or

storytelling's amazing if your dashboard

is not visually appealing or welld

designed often times stakeholders will

be a bit biased and it won't actually

take suggestions from that dashboard so

make sure you're spending the extra time

to design it well and communicate your

findings effectively so where can you

actually learn Excel I recommend you can

audit a course on course era as well um

Excel is one of those things where you

can just honestly learn on YouTube I

think there is some documentation for

Microsoft as well on how to use

functions in Excel there's also

literally a help feature in Excel that

you can use to learn certain features

pretty easily one more thing I don't

want you to learn Excel just in theory

watching these courses I actually prefer

if you actually practice it so go on

kaggle.com download a data set and just

import into Google Sheets or Excel and

just start making pivot tables start

making visualizations and start using

maybe descriptive statistics and just

playing around the data set so

everything you actually learn in terms

of documentation you can actually

practice and then that way it it'll be

much easier to remember it and apply it

in your actual work now that you've

mastered statistics in Excel the next

step is actually move on to SQL SQL

stands for structured query language and

this is basically how you retrieve

manipulate and analyze data stored in

relational databases so think about it

like this companies have tons of data

sitting in various databases whether it

be sales Data customer information

product inventory SQL is the tool to

actually communicate with this data set

and bring useful insight for your

customers in my previous experience SQL

is one of those things that isn't a nice

to have it is a must have it is a

non-negotiable to learn SQL and be

effective as a data analyst that is the

tool that I use by far the most at as my

job as a data analyst then when I move

to a data scientist it is truly my bread

and butter here's the thing though I

don't think SQL is that difficult to

learn I think you can learn it fairly

quickly but you do have to understand

the basics first so the anatomy of a SQL

query you always want to use a select

before everything this basically allows

you to choose certain columns that you

actually want to Output in your query

next every SQL query should have a from

statement which basically chooses which

database or which table you actually

want to pull data from after this you

have the option of using aware Clause

think of this as like a filter you can

basically filter data Down based on

certain conditions you ask for it so

let's say you want to pull from a sales

data set you can only pull data from a

certain sales rep or a certain threshold

amount or a certain date it's just

basically a filter now often times

you'll have to pull for multiple data

sets and this is where the idea of a

join comes in join allows you to bring

in multiple data sets and then use them

together to actually pull make a query

joins are very common and they're

different types called inner left join

right join and outer join are the most

common joins you'll probably be using

and lastly you want to know what group

by and having functions are these are

basically aggregation tools so let's say

you want to group by months so your date

actually has individual rows for each

date but let's say you want to figure

out the total sales in a month you would

Group by month and all those days would

actually go into one row for that month

and then you can actually use an

aggregation function like a sum average

min max then you can have that value for

each of the months so you it's basically

called aggregation you don't want to

filter with wear Clauses on a group buy

you want to use a having for group buy

filtering now even if you master the

basics and you're able to make queries

out of nothing efficiency matters a lot

in this game a lot of the times you're

playing with large large data sets like

even hundreds of thousands of rows of

data so efficiency matters a lot and you

want to create the most efficient SQL

query as you possibly can using less

compute the better make the most memory

optimized SQL query

all of this stuff matters and once you

learn the basics the next step is to

learn efficiency of SQL querying so this

can be learning how to index tables or

how to use subqueries properly or how to

use common table expression these are

all tools to you work with to make

queries more efficient and during that

technical interview you're going to have

for data analyst knowing how to query

tables efficiently is what separates a

data analyst from another data analyst

even if they can both output the right

output now there are countless amount of

free tutorials and learning SQL W3

schools Con Academy and so many anym I

recommend W3 schools I learned on W3

schools I also learn on YouTube

tutorials but honestly the best way to

learn SQL is just practicing going on

lead code going on hacker ring these

interview prep tools and just doing

problem after problem until you get

comfortable I actually have a video up

on my channel of how I learn SQL in 10

or 15 days and I basically had to go

into work on the first day as a data

analyst right after school and I didn't

know SQL prior to joining that company

and my first task was to write SQL code

and bring the answers to the my boss's

question so I didn't really have the

luxury to take in a class I just had to

learn on the job and this was the best

way for me force me to learn so the one

thing I will note SQL probably isn't the

easiest tool to practice because often

times you want to download data sets

from kaggle but SQL it's really

important to be able to join tables

together and kaggle often times only has

one table depending on what data set

you're pulling so I recommend even using

chat GPD to populate your own databases

with multiple tables and then actually

practicing how to do joins and SQL

querying this data may not be the most

accurate it may not make the most sense

because it is AI generated but it is a

first step now after you learned

statistics Google Sheets Excel and SQL

the next step is to Learn Python or R

and these are what we call scripting

languages I personally recommend python

it has a really large community it's a

lot more versatile if you want to go

into software development or data

engineering python is just a much better

tool to learn in terms of Versatility R

is more for like Academia and statistics

youd probably use it more as a data

scientist if you know you want to stay

on that path but you can't really go

wrong with either uh for the purpose of

of this video we are going to just be

talking about python now you don't need

to be a software engineer by any means

but you should know like the basics and

a few packages that are data analytics

specific the two of the most important

libraries that I think you should learn

are pandas numpy and also maybe map plot

lib for visualization and pyit learn if

you want to go into more machine

learning so pandas is actually a data

manipulation library that allows you to

clean organize data quickly and

efficiently so you can basically think

of it as Excel pretty much on steroids

or a SQL typee package you can use

pandas to group data filter data and

join tables together and so much more

and also read csvs read Excel files and

import data into a data frame numpy is a

package you'd use for more numerical

operations I like to think of this as

like an advanced calculator you could

use in Python that's probably a good way

of looking at it now once you've

actually imported the data with pandas

and you've done your numerical

calculations with numpy I believe the

next step is probably visualization I

would use a package like Matt plot lib

or caborn to help visualize the data

with whatever type of visualizations you

want it's a lot more customizable than

tools like tableau or Google Sheets in

Python you can actually customize so

much more now this is a bonus once

you've learned these packages I think

you're good enough for data analysis but

python is also amazing at automation so

let's say you're doing a bit of ETL

which is extract transform load this is

more of a data engineering task python

can help you do this and also run

reports on saying hey has the data

loaded properly so once you learn these

basic packages I would also highly

recommend All of You To Learn Python for

automation as well now in terms of

actually learning data Camp's a great

resource for this of course is great but

as I said before the best way and the

best way that I've learned python is

personally on the job itself once I got

the job I would volunteer for python

projects the job itself didn't require

python to get but once I got it I used

to just volunteer to use Python cuz I

knew that was the best way to learn at

the time now the last thing I want you

to learn is some sort of bi tool or data

visualization tool this could be Tableau

looker powerbi or honestly any other

tool I've used probably four or five

tools throughout my career and just

depends on the company you're at each

company it own preferences like I've

used R shiny which is basically using R

for more customized visualizations I've

used Spotfire I've used Tableau I've

used looker I've used data Studio I've

used so many tools and One Thing Remains

the Same is the science of visualization

will stay the same no matter what tool

you have to use but being able to stay

versatile and be nimble and learn a new

tool quickly is very very important now

for the purposes of this video I

recommend that you actually learn

Tableau because Tableau public is free

for you to learn on and you can learn

the science visualization you can learn

the basics of visualization and then you

can apply for different sorts of uh VI

tools later down in your career I think

the best part about these visualization

tools is they're highly interactive they

can actually connect straight to your

database whether you're using SQL Presto

Hive any of these like SQL engines you

can actually make it as interactive as

you want you can include filters buttons

and you can do you can drag your mouse

over it and you can see some results so

it is very very interactive compared to

other visualization tools now I think

the best book on this is called

storytelling with data figuring out how

how to actually visualize data properly

I would learn how to create charts how

to create dashboards and how to actually

like present these cuz not only is

creating the dashboard's important

presentation is just as important when

it comes to your customers but here's

the most important thing to keep in mind

visualization is not just about making

simple and pretty charts it's about

making data easy to understand and

telling a story so whenever you're

making a dashboard you're making a

visualization you need to ask yourself

what is the key takeaway of this

visualization what value does this add

to the my audience the best dashboards

in my opinion simplify complexity as

much as possible and they provide clear

and actionable insights to your audience

in terms of learning I recommend just

going on Udi or LinkedIn learning I

think course there has a few courses on

this I would download Tableau public and

just start playing around with it before

you even get to Tableau public maybe

just start playing around with Google

Sheets that is free and start making

many dashboards there then slowly you

can progress check a bi tool like

Tableau or powerbi powerbi also has like

a free trial you can also probably use

if you want to get into it now I've

actually had videos on how to use Tablo

public with full tutorials I think we

used Airbnb data in New York city so you

can download kagle data an Excel or CSV

file upload it to Tableau and start

making dashboards immediately and follow

a walkr on YouTube like the one I have

once you have it you can actually just

put this on your resume your GitHub and

start branding yourself employers love

to see what you do it's much better for

an employer to see what you can do

rather than you tell them what you can

do they can actually see like your

abilities your technical abilities they

know like it's very hard to communicate

how good you are so often times just

practicing and and showing it off to

employers is the way to go now let's be

very clear this will not happen

overnight this will take anywhere

between 3 and 12 months depending on how

much time you have to dedicate and your

existing Background by the time 2025

rolls around you should have enough

experience if you followed this to a te

you took notes and you actually

practiced this every single day

consistently my last tip for you is to

build some sort of portfolio and put all

of these projects on that portfolio and

build a brand around yourself in data

analytics and data science employers

love to see practical experience and

will help you so much in the job market

if another candidate doesn't have this

portfolio that you do so if you Meed

this r on the video if you got any value

of this video please leave a like

subscribe and I'll see you next one

[Music]