How to Become a Data Analyst in 2024? (complete roadmap)

Summary

This video serves as a comprehensive guide for aspiring data analysts in 2024, focusing on self-teaching methods. It outlines the importance of data analysts in driving business decisions with data. The speaker suggests starting with understanding the role and requirements of a data analyst, particularly at one's target company. Key areas to learn include statistics, math fundamentals, data analysis basics, and proficiency in tools like Excel and SQL. The video also emphasizes the importance of soft skills like communication and storytelling, and the necessity of hands-on projects to build a strong portfolio. Lastly, it advises thorough interview preparation to secure a data analyst position.

Takeaways

• 🚀 Data analytics remains a popular career choice in 2024, making it a good time to become a data analyst.
• 🎓 There are three main paths to becoming a data analyst: earning a degree, attending boot camps, or self-teaching.
• 🛠 Self-teaching involves learning various subjects in data analytics, which is the focus of the video.
• 🔍 A data analyst's role is crucial for businesses to make data-driven decisions by analyzing historical data and identifying areas for improvement.
• 📊 Data analysts work within the broader data science field, which includes other roles like data scientists, machine learning engineers, and AI specialists.
• 🔑 To prepare for a data analyst role, research the specific skills and tools required by your target company and role.
• 📈 Start with the fundamentals of statistics and math, including descriptive and inferential statistics, and basic arithmetic and algebra.
• 💻 Learn essential tools like Excel or Google Sheets for data manipulation and visualization, and tools like Tableau or AWS QuickSight if they are industry standards.
• 💬 Develop strong soft skills, particularly communication and storytelling, to effectively present your findings and collaborate with teams.
• 💼 Build a portfolio by working on hands-on projects and using platforms like Kaggle or Analytics Vidhya for data sets and problem statements.
• 📝 Prepare for interviews by practicing common data analysis and behavioral questions, and use resources like LeetCode or SQL for practice.

Q & A

What are the three ways to become a data analyst mentioned in the video?

The three ways to become a data analyst mentioned are earning a degree, taking boot camps, and self-teaching.

What does the video primarily focus on for learning data analytics?

The video primarily focuses on self-teaching as a method for learning data analytics.

What is the role of a data analyst according to the video?

A data analyst helps businesses make data-driven decisions by analyzing historical data to understand performance, identify areas of improvement, and guide the company towards growth.

Why is the data analyst role important to a company?

The data analyst role is important because it provides insights into the business's current state, allowing for informed decision-making that can drive future growth.

What exercise does the video suggest to understand the target data analyst role?

The video suggests researching the role on the target company's career website, reading job descriptions, and looking at the profiles of current data analysts at the company on LinkedIn to understand the required skills, tools, and languages.

What are the statistics and math fundamentals a data analyst should know?

A data analyst should understand descriptive and inferential statistics, as well as have a basic understanding of arithmetic math and algebra.

Which tools does the video recommend learning for a data analyst?

The video recommends learning Excel or Google Sheets, and other tools like Tableau, QuickSight, or Looker Studio based on the target company's preferences.

Why is SQL important for a data analyst?

SQL is important because a data analyst is likely to spend most of their time using it to retrieve data from databases, perform filtering, join datasets, and conduct aggregate analysis.

What soft skills are emphasized for a data analyst in the video?

The video emphasizes the importance of communication and storytelling skills for a data analyst to effectively understand problems and present solutions.

How does the video suggest preparing for job interviews as a data analyst?

The video suggests researching common interview questions for the target role, practicing responses, and conducting mock interviews to prepare for job interviews as a data analyst.

What resources does the video recommend for learning data analysis fundamentals?

The video recommends using platforms like Khan Academy for stats and math, and a free ebook called 'Introduction to Data Analytics' by HubSpot for data analysis fundamentals.

Outlines

00:00

📈 Introduction to Becoming a Data Analyst in 2024

The video script introduces the continuing relevance of data analytics as a career in 2024 and offers a roadmap for aspiring data analysts. It outlines three main pathways to enter the field: obtaining a degree, participating in boot camps, or self-teaching. The focus of the video is on self-teaching, emphasizing the importance of understanding the role of a data analyst in making data-driven decisions within a company. The speaker suggests conducting research on specific job roles and company requirements, such as those at Amazon, to tailor the learning process. The video also touches on the broader field of data science, which includes various roles like data scientists, machine learning engineers, and AI specialists, but centers on the data analyst role.

05:01

📊 Fundamentals of Data Analysis: Statistics, Math, and Tools

This section delves into the foundational knowledge required for data analysis, including descriptive and inferential statistics, and the basics of arithmetic and algebra. The speaker recommends resources like Khan Academy for learning these topics and introduces a free ebook by HubSpot as a valuable guide. The importance of mastering tools such as Excel and Google Sheets is highlighted, along with the need to learn specific tools like Tableau or AWS services, depending on the target company's preferences. The paragraph also emphasizes the necessity of learning SQL and Python, with a focus on their applications in data analysis. Practical tips are given for transitioning from SQL to Python, suggesting the use of generative AI tools to aid in the learning process.

10:02

💬 Developing Soft Skills and Hands-On Experience for Data Analysts

The final paragraph emphasizes the importance of soft skills, particularly communication and storytelling, for effectively conveying data analysis insights to stakeholders. It suggests various methods for improving these skills, such as practicing at home, writing blogs, and gaining practical experience. The paragraph also stresses the need for hands-on projects to build a portfolio, which is crucial for demonstrating data analysis capabilities to potential employers. Resources like analytics Vidya, Kaggle, and Google Dataset Search are mentioned for finding project ideas and datasets. Lastly, the video script advises on interview preparation, including practicing common interview questions and engaging in mock interviews to simulate real-world interview scenarios and improve performance.

Keywords

💡Data Analyst

A data analyst is a professional who collects, processes, and interprets complex digital data to help businesses make data-driven decisions. In the context of the video, the role of a data analyst is crucial as they help companies understand their historical data, identify areas of improvement, and guide strategic decisions for future growth. The video emphasizes the importance of this role within the data science umbrella and provides a roadmap for aspiring data analysts.

💡Data-Driven Decisions

Data-driven decisions are choices made based on the analysis and interpretation of collected data. The video underscores the significance of data analysts in facilitating such decisions, as they analyze data to provide insights that inform business strategies. This concept is central to the video's theme, as it highlights the value of data analytics in guiding business growth and performance improvement.

💡Self-Teaching

Self-teaching refers to the process of acquiring knowledge or skills independently, without formal instruction. The video focuses on self-teaching as a pathway to becoming a data analyst, suggesting that individuals can learn the necessary skills and knowledge through self-directed learning. This approach is exemplified by the video's provision of a roadmap for learning data analytics, emphasizing the accessibility of education in the field.

💡Descriptive Statistics

Descriptive statistics involve summarizing and organizing data to describe its main features. In the video, descriptive statistics are mentioned as a fundamental concept that data analysts should understand. They are used to provide a snapshot of the data, helping analysts to comprehend the basic characteristics and patterns within the dataset.

💡Inferential Statistics

Inferential statistics are used to make predictions or draw conclusions about a population based on a sample. The video explains that inferential statistics take the data analysis a step further by allowing analysts to make educated decisions and predictions. This is crucial for data analysts as it enables them to extrapolate from the data to inform future business strategies.

💡Data Cleaning

Data cleaning is the process of removing inconsistencies and inaccuracies from data. The video mentions data cleaning as one of the fundamental data analysis skills that a data analyst should掌握. It is essential for ensuring the quality and reliability of the data used for analysis, which in turn affects the accuracy of the insights derived from the data.

💡Excel

Excel is a widely used spreadsheet program for data organization, analysis, and visualization. The video highlights Excel as a critical tool for data analysts, suggesting that proficiency in Excel is almost a prerequisite due to its widespread use in data manipulation and reporting. The video encourages viewers to become comfortable with tasks such as data cleaning, sorting, creating pivot tables, and data visualization within Excel.

💡SQL

SQL (Structured Query Language) is a programming language used for managing and manipulating databases. The video emphasizes the importance of SQL for data analysts, as it is the primary tool for extracting and managing data from databases. Proficiency in SQL is essential for data analysts to perform tasks such as data retrieval, filtering, and aggregation, which are common in their work.

💡Python

Python is a versatile programming language that is increasingly used in data analysis due to its simplicity and the powerful libraries available for data manipulation and analysis. The video suggests that while SQL is more commonly used, having knowledge of Python for data analysis can be beneficial, especially when dealing with more complex data processing tasks.

💡Portfolio

A portfolio in the context of the video refers to a collection of projects that demonstrate a data analyst's skills and expertise. The video advises aspiring data analysts to build a portfolio of hands-on projects to showcase their abilities. This portfolio is crucial for job applications as it provides tangible evidence of a candidate's skills and experience in data analysis.

💡Interview Prep

Interview prep involves preparing for job interviews, which includes practicing responses to potential questions and familiarizing oneself with the company and role. The video stresses the importance of interview preparation for aspiring data analysts, as it can be the determining factor in securing a job. The video suggests practicing with mock interviews and focusing on both technical skills and behavioral responses.

Highlights

Data analytics remains a popular career choice in 2024.

Three pathways to become a data analyst: earning a degree, attending boot camps, or self-teaching.

Focus on self-teaching methods for learning data analytics.

Data analysts help businesses make data-driven decisions by analyzing historical data.

Data analysis is part of the broader data science field, which includes various roles like data scientists and AI specialists.

Exercise for viewers: Research the data analyst role at a target company to understand required skills and tools.

Importance of understanding the target role and company's preferred tools and languages.

The necessity of statistical knowledge, including descriptive and inferential statistics, for data analysis.

Basic math and algebra are important foundational skills for data analysts.

Data analysis fundamentals such as data cleaning and collection are essential.

Recommendation to use resources like Khan Academy for learning statistics and math.

Introduction to data analytics ebook by HubSpot as a free learning resource.

Excel and Google Sheets are fundamental tools for data analysts.

Learning SQL is crucial for data analysts as it is widely used for database management.

Python is a valuable coding language for data analysis, especially when focusing on data manipulation and visualization.

Soft skills like communication and storytelling are vital for effectively presenting data analysis.

Hands-on projects and building a portfolio are essential for demonstrating data analysis skills to potential employers.

Interview preparation is key to landing a data analyst job, including practicing with mock interviews and solving SQL and Python problems.

Transcripts

data analytics was a hot career last

year the year before and it's not going

anywhere in 2024 so if you have been

wanting to become a data analyst then

you have clicked on the Right video

because in this video I'm going to be

sharing data analyst road map that you

can use to become a data analyst in 2024

there are three ways to become a data

analyst you can earn a degree you can

take boot camps which is a condensed

version of degree program and third you

can do self- teing in this video I'm

going to be focusing on self- teing and

how you can learn different subjects in

data analytics to become a data analyst

in my opinion learning data analytics is

not that difficult for somebody who is

just starting out they probably have no

idea where to get started what to learn

so this video is specifically dedicated

to you so before we talk about the road

map let's actually talk about what does

a data analyst do so businesses are

collecting a ton of data and they want

to make data driven decision and that's

exactly when a data analyst comes in

they help the company make datadriven

decision using that data so data analyst

in this case would be looking at

historical data understanding

performance where things are going well

where things could be improved and

helping business guide into making a

decision that would help them grow in

the future and this is why data

analyst's role is specifically important

to any company because a data analyst is

basically telling you what's happening

with your business so you can make data

driven decisions data analyst falls into

the data science umbrella there are

several roles in the data science

umbrella including data scientists

machine learning data engineer AI

Specialists but in this video we'll be

focusing on data analyst topic before we

jump into the road map I want you to do

one exercise I want you to research the

role I want you to figure out what your

target data analyst role looks like what

your target company looks like let's say

your target company is Amazon in this

case you will go to Amazon career

website look up data analyst role read

the job description and try to

understand what are are the skill sets

that they are asking for what are the

tools that they're asking for what is

the language that they're asking for

you're going to take a note and write it

down and we're going to come back to

this the next thing you're going to do

is you're going to go to LinkedIn and

find somebody who is already working as

a data analyst at Amazon and you're

going to see what their background is

did they take any specific courses did

they take any specific certificates

basically try to understand what their

day-to-day work is like and what exactly

is their background and you're going to

make a note of that information as well

now you have basically created a data

set based on your research of the role

where you will use that data and work

backward from it for example let's say

your target company prefers that you

know how to work with Google Sheets and

Tableau or in Amazon's case Amazon loves

to use their own specific native AWS

products such as quick site is one of

the tools that they would be using so

you might see that on the job

description as well as somebody who is

working there so by doing this exercise

you actually are basically creating a

road map for you which gives you an idea

of like okay what's your end state looks

like where do you want to go and this is

actually really really helpful I

actually do this for all of my work not

just for learning something new but also

like when I'm doing like my project

scoping I try to figure out like what

exactly do I want to what does my ideal

outcome looks like so it's very similar

to that you're doing project management

of your data analyst learning road map

so let's say you have understood the

role you know what you want to do what

your ideal role looks like now we're

going to jump into that road map there

are a few things that I'm going to be

mentioning I'm going to be talking about

how much statistics and math you need to

know what coding languages you need to

know what tools you need to know I'm

also going to be covering some soft

skills as well as project and Hands-On

work and finally ending with interview

prep so we're going to be covering a lot

in this video so start taking notes so

the first thing I want to talk about

statistics math and data analysis

fundamentals for statistics I would

suggest that you start with

understanding descriptive and

inferential statistics descriptive

statistics is like a summary of your

data or a snapshot of what your data

looks like it basically helps you

understand main characteristics of your

data inferential statistics is basically

taking it one step further it's taking

the snapshot of the data and making

educated decision think of like

experimentation prediction and things

like that that's where you will be using

inferential statistics to understand

your data in a more educated way and

make predictions from it this is a

visualization that basically explains

what is the difference between

statistics and an inferential statistic

you can learn these topics from anywhere

you can pick any book that talks about

these two topics just make sure that

it's focused on data analysis so that

learning can be catered to like your

needs for math I would say you need

basic understanding of arithmetic math

and algebra although you would likely

not be using as much math it's likely

when you have to do some statistics or

some machine learning work the chances

of you doing that as a data analyst is

low but it's always good to have some

knowledge of math the third thing that I

would like you to cover here is some

data analysis fundamentals what does it

mean when we say data cleaning data

collection like understanding

understanding what each of those terms

mean so you are able to talk in a data

analyst language there are several

resources for learning all the

statistics math and data analysis there

are YouTube channels I love KH Academy

for learning stats and math definitely a

great resource for data analysis

fundamentals you can pick up any book so

one of the free resource that I found on

learning data analysis fundamentals is

this free ebook called introduction to

data analytics this ebook is created by

HubSpot who is also sponsoring this

portion of the video the EB book covers

data analysis fundamentals and starts

from like very basic such as data

cleaning data collection and then jumps

into more advanced topics such as bias

versus variance which is one of the

topics that you will need to know for

inferential statistics it also talks

about what are some use cases for using

generative AI when it comes to doing

data analysis which I think is like

really cool there's several other

examples that are included for the types

of data analysis and data analysis

methods that you can use as a data

analyst all in all this is a super handy

ebook to have by your side when learning

data analytics and the best part is that

it is free it's linked in the

description below so feel free to

download it now let's say if you have

learned the statistics math and data

analysis fundamentals then we're going

to jump into tools for tools learn Excel

Excel is the bread and butter the

chances are you're going to be spending

a lot of time in Excel or Google sheet

depending on what your company prefers

so become very comfortable with Excel

some of the things that you should do in

Excel is data cleaning sorting creating

pivot tables writing formulas to do

descriptive statistics learn how to do

data visualization in Excel and also

learn how to make it pretty because

everybody loves pretty reports and

pretty Excel files Excel and Google

Sheets can be very similar so I would

suggest to focus on one and then you can

easily transition to other now if you

have done your research and you

understand that your target company and

Target role uses a tool like Tableau or

something like quicksite or looker

Studio then you will kind of like Target

which tool to learn based off of that

let's say the role that you looked at

what preferred learning Tableau so let's

say the research that you did based on

that research you learn that you need to

learn Tableau as a data analyst so

that's where you would kind of like

start learning Tableau and figure out

what kind of things that you need to do

in Tableau whether that is like creating

reports doing visualizations and so on

by learning this tool you should be able

to make reports and visualizations in

these tool because the chances are when

you're working as a data analyst the

business stakeholders will come to you

and will ask you to build reports in a

tableau or a quicksite or a looker

Studio type of tool so learning this

tool is definitely going to be helpful

so highly recommend that you pick one

and build on it and then eventually you

can transition between the tools but

just picking one and sticking to it just

simplifies your learning process these

two things you should definitely be very

very comfortable with now let's say you

have learned these tools the next thing

that you should be doing is learn the

coding languages obviously the first and

primary coding language that I'm going

to mention is SQL and I don't even have

to say it you need to know SQL because

the chances are that 90% of your time as

a data analyst you will will be using

SQL there might be a 5 to 10% chance

where you will be using python but if

you are starting out in coding like

definitely understand how to write your

select statements how to get data from a

database using SQL how to do filtering

how to join multiple data sets how to do

aggregate analysis how to use Advanced

analytics function in SQL basically

understanding logic is very important as

well in addition to like understanding

SQL syntax so definitely do a lot of

practice on learning SQL you can also

Learn Python but make sure when you're

learning python focus on python for

specifically for data analysis because

you can easily get lost when it comes to

learning python I have done a few videos

for learning python data analysis you

can watch one of those to get some ideas

obviously you can use generative AI to

kind of like help you create a road map

here and figure out what topics you need

to learn another cool thing that I would

like to share here like if you're going

from SQL to python it's also helpful

that you use tools such as chat GPT to

help you learn I have personally asked

it to convert coding language for

example you can give it SQL group ey and

say can you convert this to a python

coding snippet and then you can kind of

like learn through that process that how

you would do a group by in python or how

you would join two data sets in Python

so just a tip here if you are going from

SQL to python python again is super

intuitive and it's easy to learn so

definitely add it to your toolkit for

doing data analysis by now you have

basic understanding of stats maths data

analysis fundamentals you know which

tools to use you know how to use them

you know the coding languages as SQL and

python now we need to work on your soft

skills by now whatever I have talked

about it's called hard skills because

these are the skills that you need to

note in order to do your job now in

order to do your job effectively you

actually need to have soft skills and by

soft skills I primarily mean

communication and storytelling when

you're doing your analysis when you're

doing your work at the end of that

analysis or even at the start of your

project you actually need to understand

the problem and the only way that you

can get to the core of the problem let's

say if a product manager comes to you

and I ask you to do some data analysis

you need to ask them questions back and

you need to understand what exactly

they're trying to get to because

sometimes stakeholders ask you question

when they don't exactly know what they

want so by you asking them questions and

trying to understand the problem you are

able to get to a better solution

similarly at the end of your project you

actually you have to present your work

whether that you do it verbally or you

do it in a written format or you do it

in a presentation format so get better

at communication get better at writing

get better at storytelling because that

would make you effective data analyst

there are several ways to practice that

you can practice it at home with friends

in front of a mirror with a camera write

blogs get more Hands-On writing

experience all these things like make

you a better

Communicator I don't know if that's a

term but basically you need to have

really good soft skills in order to

succeed in a data analyst career after

doing all that you basically have the

toolkit for data analyst now you need to

do Hands-On project and build your

portfolio so you can show it on your

resume there are several ways to

approach this one is that you can Define

your your own problems for example you

can look at your credit card purchases

over the last 6 months and you can try

to analyze it or for me I can do like

how much I have walked between the

different months of the year and I can

do analysis on that I can do cool

visualization so that's one way to kind

of like figure out what your project

should be the other is that you can also

like go to these websites a website

called analytics Vidya which has like

specific problems and data set given to

you and you can like build the data

analysis project around it now the

reason I am saying that there are so

many ways that you can do projects

because I want you to do it the only way

you can learn is if you do it and if you

don't do it there are two downsides to

it one you don't learn it might seem

like that you have learned it but

eventually you'll forget it and second

it helps you build your portfolio that's

how companies are going to know that you

can do the data analyst work and that's

how they're going to be able to hire you

so you need to do the Hands-On work you

need to figure out your projects and you

need to kind of like start building your

different project portfolio you can also

find free data set on several sources

such as like kaggle has free data sets

it also sometimes has the problems along

with the data set that you can use the

data set download it and solve for that

problem there's also Google data set

search that you can use to download free

data based on your interest on anything

well not anything but like most of the

topics are covered and you can kind of

like build your projects around it one

of the other platforms that I mentioned

is analytics viia has a lot of problem

as well as data set available if you

thought you were done you're not I have

one more step because I really want you

to be ready for real world and the only

way you can be ready for a real world is

if you are able to land a job and the

only way you are able to land a job is

if you do the interview prep you have

done all the hard work you have learned

the statistics math data analysis

fundamentals you have learned the tools

you have learned the coding you have

figured out your communication you have

done Hands-On project you're applying

for jobs and you're getting a call now

you need to pass that interview in order

to get that job so this is where you

need to kind of sit down do your

research figure out what kind of

questions your dream job your target

company is asking for that Target role

that you have and start practicing and

do a lot of practice one of my favorite

ways to do is is I anticipate the

question I write it out on a document

and then I practice it this is more for

like behavioral questions and then I

practice with the friend I do mock

interviews with the friend because the

problem is that even though it might

seem like that you know it when you are

in a pressure setting talking to

somebody else sometimes you forget and

sometimes you're not able to identify

what things that you could be saying

better or shouldn't have said your

friend or or the person that you're mock

interviewing with is going to help you

figure those out so do a lot of mock

interviews do a lot of interview prep do

a lot of Hands-On SQL problems python

problems is that what they're asking one

of my favorite platforms obviously you

can use lead code one of my favorite

platforms that is more focused on data

analysis and data scientist work is St

of scratch I'm going to link them below

as well so make sure that you're

prepared for the real world because the

goal in doing all of this work is to

land a job and enter your career as a

data analyst and only by having good

interview prep you will be able to work

as a data analyst and be able to get

into your target role that we discussed

at the start of the video all right

that's all I wanted to say thank you so

much for watching if you found value in

this video give this video a thumbs up

because it will help with the algorithm

and let me know in comments if you have

any thoughts or questions and I will see

you in the next video have a good one

bye