How to ACTUALLY become a data analyst? | Data Analyst Roadmap 2024

Summary

In this video, the speaker, a data and analytics analyst with six years of experience, shares an efficient learning path for aspiring data analysts starting from scratch. They emphasize setting clear objectives and learning by doing, using tools like Microsoft Excel for mastering lookup functions and pivot tables. The speaker recommends creating small, interesting projects to apply skills and suggests learning SQL for database querying. They advise learning visualization tools like Tableau or Power BI based on job market demand and reserve coding in Python for later, focusing on data analysis libraries. The video also promotes Simply Learn's data analyst course in collaboration with IBM as a resource for hands-on experience.

Takeaways

• 🎯 Start with clear objectives and learn by doing to achieve them.
• 📚 Avoid aimless learning; focus on mastering specific skills like Excel's lookup functions.
• 🔍 Understand when and where to apply the formulas and functions you learn.
• 💡 Create projects that interest you to practice and apply your technical skills.
• 🛠️ Learn the basics of SQL for querying relational databases, which is crucial for data analysts.
• 📊 Master a visualization tool like Tableau or Power BI, as data visualization is almost a requirement in the field.
• 💼 Consider learning coding last, focusing on Python if you choose to, with an emphasis on data analysis libraries.
• 🏆 Aim to become an advanced Excel user, as it's a powerful tool for various data analysis tasks.
• 📈 Learn SQL to handle data extraction and insights from relational databases effectively.
• 🌐 For visualization, choose the tool that aligns with the needs of the companies you wish to work for.
• 📝 Simply Learn's Data Analyst course, in collaboration with IBM, is recommended for hands-on experience and real-world projects.

Q & A

What is the main message of the video regarding learning data analysis?

The main message is that to learn data analysis effectively, one should set clear objectives, learn by doing, and apply the skills to solve real-world problems rather than aimlessly consuming tutorials and courses.

Why does the speaker emphasize the importance of setting clear objectives when learning data analysis?

Setting clear objectives helps focus the learning process, ensuring that the skills acquired are directly applicable to specific tasks or problems, which is more effective than learning without a clear goal.

What is the speaker's view on learning Microsoft Excel for data analysis?

The speaker considers Microsoft Excel a fundamental and powerful tool for data analysis, capable of handling various tasks from data gathering to creating visuals and dashboards, and recommends mastering it as a beginner.

What is the common mistake the speaker observes when people learn Excel?

The common mistake is that people tend to memorize formulas and functions without understanding how and where to apply them to solve business problems, which is ineffective.

How does the speaker suggest applying Excel skills to enhance learning?

The speaker suggests mastering specific functions like lookup formulas and then applying them to extract information from various tables across worksheets and files to solve real problems.

What does the speaker recommend for creating projects to practice data analysis skills?

The speaker recommends creating fun and unique projects that one is genuinely interested in, starting with simple projects like a Tableau dashboard and progressing to more complex ones like an AWS ETL pipeline.

Why is it important to apply knowledge to a new dataset according to the speaker?

Applying knowledge to a new dataset forces the learner to think critically and apply what they've learned to an unknown scenario, which is where the learning truly sinks in and the learner starts thinking for themselves.

What tools and skills does the speaker recommend learning for data analysis?

The speaker recommends learning Microsoft Excel, SQL, data visualization tools like Tableau or Power BI, and coding in Python with a focus on data analysis libraries such as pandas, numpy, matplotlib, and seaborn.

What is the speaker's advice on the order in which to learn data analysis tools and skills?

The speaker advises starting with Excel, moving on to SQL, then learning a data visualization tool, and finally, learning coding in Python, preferably once already in an analyst role.

Why does the speaker suggest learning Python as the coding language for data analysis?

Python is recommended because it is versatile, an all-rounder coding language, and has a rich set of libraries specifically for data analysis, making it the preferred choice for the speaker.

What is the speaker's opinion on the necessity of coding skills for a data analyst?

The speaker considers coding, especially in Python, as a valuable skill for data analysts, but not as critical as other skills like Excel, SQL, and data visualization, and suggests learning it last.

Outlines

00:00

🎓 Learning Data Analysis from Scratch

The speaker, Moan, a data and analytics analyst with six years of experience, offers advice on how to efficiently learn data analysis from the ground up. Moan emphasizes the importance of setting clear objectives and learning by doing, rather than passively consuming tutorials and courses. The speaker suggests mastering specific tools like Microsoft Excel and learning to apply formulas and functions to solve real-world business problems. Moan also encourages creating small, interesting projects to practice and apply newly acquired skills, which can lead to a deeper understanding and more significant learning outcomes.

05:00

💼 Tools and Courses for Becoming a Data Analyst

The speaker introduces the Simply Learn data analyst course in collaboration with IBM as a resource for hands-on experience and real-world projects. The course covers a range of topics including Excel, SQL, Python, R, Tableau, and Power BI, and is recommended by Forbes. Moan then delves into the importance of mastering Excel for data analysis, suggesting a learning path that starts with basic formulas and functions, progresses to pivot tables and charts, and concludes with interactive dashboards. The paragraph also touches on the necessity of learning SQL for data extraction and analysis, positioning it as a fundamental skill for analysts working with relational databases.

10:03

💻 The Role of Visualization and Coding in Data Analysis

Moan discusses the evolution of data visualization from a 'nice-to-have' to a required skill for analysts, highlighting the importance of storytelling with data. The speaker advises learning a widely-used business intelligence (BI) tool, such as Power BI or Tableau, based on the preferences of the companies one aims to work for. The paragraph also addresses the topic of coding, suggesting that it should be learned later in one's analytical career, with a focus on Python for its versatility and applicability in data analysis. Moan recommends learning Python with a focus on data analysis libraries such as pandas, numpy, matplotlib, and seaborn, and provides a link to a dedicated video on learning Python for data analysis.

Keywords

💡Data Analysis

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of extracting useful information, drawing conclusions, and supporting decision-making. In the context of the video, the speaker emphasizes the importance of learning data analysis skills to improve job prospects and efficiency in handling complex data sets. The video aims to guide viewers on how to become proficient in data analysis, suggesting practical approaches and tools to master.

💡Excel

Microsoft Excel is a widely used spreadsheet program for organizing, analyzing, and presenting data. The video mentions Excel as a fundamental tool for data analysis, highlighting its capabilities for data manipulation, formula application, and visualization. The speaker advises learning specific functions like lookup formulas to extract information from various data tables, underscoring Excel's role in building foundational data analysis skills.

💡Learning by Doing

Learning by doing is an educational method that involves acquiring new skills through hands-on experience. The script advocates for this approach, suggesting that viewers should apply what they learn in practical projects rather than passively consuming tutorials. This method is emphasized as a way to solidify understanding and make learning more engaging and effective.

💡Projects

In the context of the video, projects refer to practical tasks or assignments that viewers can undertake to apply their data analysis skills. The speaker encourages creating 'fun and unique projects' to practice technical skills, such as creating a Tableau dashboard or an AWS ETL pipeline. These projects serve as a means to consolidate learning and demonstrate one's capabilities.

💡SQL

Structured Query Language (SQL) is a domain-specific language used in programming and software engineering as a standard language for managing and manipulating relational databases. The video positions SQL as a crucial skill for data analysts, necessary for querying and managing data stored in relational databases. The speaker recommends learning SQL after mastering Excel to enhance one's ability to handle more complex data analysis tasks.

💡Visualization Tools

Visualization tools, such as Tableau and Power BI, are software applications used to create visual representations of data. These tools help in data storytelling and making data insights accessible and understandable. The video discusses the importance of learning these tools to meet the demands of modern data analysis roles, where the ability to communicate data findings visually is often required.

💡Pivot Tables

Pivot tables in Excel are interactive tables used to summarize and analyze data. The video script mentions mastering pivot tables as a step towards becoming an advanced Excel user. Pivot tables are a key feature for data analysis as they allow for the quick summarization and exploration of large data sets.

💡Coding

Coding, or programming, involves writing instructions in a language that computers can understand. In the video, the speaker suggests learning to code, specifically in Python, as a final step in the data analysis learning journey. Coding is highlighted as a more advanced skill, useful for automating data analysis tasks and handling large-scale data processing.

💡Python

Python is a high-level, general-purpose programming language that is widely used in data analysis due to its simplicity and the powerful libraries available for data manipulation and analysis. The video recommends learning Python with a focus on data analysis libraries like pandas, numpy, and matplotlib. Python is positioned as a versatile tool that can significantly enhance a data analyst's capabilities.

💡Simply Learn

Simply Learn is mentioned as a platform offering a data analyst course in collaboration with IBM. The video script suggests this course as a resource for hands-on experience and real-world projects, indicating that it covers a range of data analysis tools and skills, from Excel and SQL to Python and visualization tools like Tableau and Power BI.

Highlights

The most efficient way to learn data analysis from scratch is emphasized, discouraging the idea of quick success through superficial learning.

The importance of setting clear objectives and learning by doing to achieve those objectives is highlighted.

A specific example using Microsoft Excel is provided to illustrate the application of learned skills in a practical context.

The common problem of memorizing formulas without knowing their practical application is identified.

The advice to focus on mastering specific functions, like lookup functions in Excel, for practical use is given.

The concept of creating fun and unique projects to practice technical skills is introduced.

The idea that learning by doing leads to a deeper understanding when faced with new data sets and scenarios is explained.

A recommendation for the Simply Learn data analyst course in collaboration with IBM is made for hands-on experience.

Excel is defended as a powerful and essential tool for data analysis, despite being considered old school by some.

A step-by-step guide for beginners to learn Excel, starting from basic functions to advanced features like pivot tables and dashboards, is provided.

SQL is introduced as a crucial skill for data analysts, with advice on focusing on data analysis rather than broader coding.

The necessity of learning a business intelligence (BI) tool, with a recommendation to choose based on the needs of the companies one wishes to work for.

A comparison between PowerBI and Tableau is made, highlighting their differences and use cases.

Coding, specifically in Python, is recommended as the last skill to learn, focusing on data analysis libraries for analysts.

The importance of learning coding with a specific focus on data analysis is stressed to avoid being overwhelmed.

A summary of the learning path for data analysis is provided, from Excel to SQL, BI tools, and finally coding in Python.

Transcripts

today I'll walk you through the most

efficient way I would learn data

analysis all over again if I had to

start from absolutely zero this is not a

video where I'll tell you that you can

become a data analyst or any analyst per

se in just 30 Days by learning a couple

tools enrolling in some courses and

doing some projects half-heartedly if

you don't know me my name is moan and I

currently work as a data and analytics

analyst and my ultimate goal is to help

you become better at data analysis like

much much better so that you can get a

better job that you actually enjoy doing

with more pay of course I have 6 years

of experience working with complex big

data in the financial services industry

and in this video I'll give you my

honest advice on what I could have done

better and differently to get to where I

am faster and easier first things first

set clear objectives and Learn by doing

to meet those objectives

now what do I mean by this it is very

simple really and please pay close

attention to what I'm about to say now

don't just watch endless tutorials don't

watch endless videos complete endless

courses do endless exercises on the same

topic or technique aimlessly make sure

you apply what you learn let's take a

specific example here and I'll use a

very basic but extremely powerful tool

here Microsoft Excel we we all know the

ability to navigate spreadsheets is a

fundamental data analysis skill when it

comes to excel what I see a lot of

people do is try and memorize all of the

formulas and functions without actually

knowing how and where to use them if you

cannot apply the formulas and functions

you learned then you may as well not

have learned them right let's say you

learned various lookup formulas like V

lookup X lookup or index match to the

extent that you can tell me the exact

formula by heart even if I wake you up

at 3:00 a.m. but you don't actually know

when to use these formulas to solve

business problems this is a very common

problem and can easily happen if you

solely focus on learning the technical

skills without paying any attention to

applying what you learned because if you

actually applied all of those lookup

functions you learned you would know

straight away that the most common use

case for these lookup formulas would be

to extract information that is not in

your current table from other tables

based on a unique identifier most likely

a single column like order ID customer

ID product ID Etc so let me just make my

point again here don't just learn

aimlessly instead of saying I will learn

Excel say I will Master lookup functions

in Excel so that I can extract

information from various tables across

worksheets and files you see the

difference instead of having one large

probably NeverEnding aimless learning

task you now have one super specific

learning task where you know exactly

what to learn and what you will gain

after you finished learning create fun

and unique projects that you genuinely

are interested in to practice your

technical skills and they don't have to

be the biggest projects in the world

they can be a simple Tableau dashboard

with someone analysis like this customer

Bank CH analysis I have in my ultimate

data portfolio you can slowly work your

way up to more advanced projects like my

AWS ETL pipeline where I used pypar SQL

python AWS red shift and many other

tools and Technologies where I created

an automated endtoend pipeline to

prepare clean data for airline data

analysts I can guarantee you even if you

create the simplest projects in in the

world you will learn so much more

because you'll have to apply your

knowledge to a new data set you'll have

to think about how to clean and organize

your data how to transform your data and

how to actually get the insights you

want whether it's um generating insights

from data in a relational database using

SQL creating dashboards in powerbi or

Tableau or pivot tables and pivot charts

in Excel the moment you actually have to

do something different

the moment you have to go off script the

moment you have to do something that is

not exactly what you learned in the

tutorials and videos that's the moment

where the learning will truly start to

sink in because you'll go from copying

the steps mindlessly to thinking for

yourself and applying what you already

know to an unknown scenario now that I

told you that you should definitely

focus on learning by doing let me tell

you which tools you should learn what

order you should Cho choose to learn

them in and where you could learn them I

know that there are so many courses out

there where you can learn data analysis

skills but if you're looking for a place

where you can get hands-on experience

using the latest tools and work on real

world projects then look no further than

simply learns data analyst course in

collaboration with IBM by taking this

course you can become a data analyst a

job where the average salary can easily

surpass The 100 $1,000 Mark simply learn

is reviewed and recommended by Forbes

and the course has great reviews that

you can easily check out both on trust

pilot and on course report business

analytics with Excel SQL course data

analytics with python and R Tableau

desktop specialist and pl300 Microsoft

powerbi certification training these are

all covered for you and of course You'

have many industry projects to work on

with real world data sets to really put

what you've learned into practice so if

you want to take a big step towards a

career in data analytics check out

Simply learns data analyst course using

the link in the description below and a

huge thanks to Simply learn for

sponsoring this video so spreadsheets

for me are the bread and butter of data

analysis lots of people say Microsoft

Excel is crap and old school but let's

be honest it's not going away is it it's

not going away because it is just so

powerful it's so popular because it is a

good tool and I would argue it's

probably the best tool for data analysis

you can do so much in Excel gather data

clean data transform data create visuals

create dashboards and I could go on and

on and on but I won't instead of listing

all the things Excel can do I'll tell

you what you should learn as a beginner

get familiar with the user interface

know the difference between worksheets

rows columns and cells once you get past

the super basic bits learn some basic

formulas and functions like date and

time functions text functions math

functions logical functions and lookup

functions then Master pivot tables and

pivot charts and create some dashboards

the next step is to make those

dashboards interactive by using slicers

and timeline filters if you can do do

all of these things in Excel you can

safely say you're an advanced Excel user

which is probably the right time for you

to start learning SQL which stands for

structured query language and I know it

can be a bit intimidating at first to

write a couple lines of code but I'm

telling you that you should not be

learning the basics of SQL is really not

that difficult especially if your focus

is on data analysis rather than data

science or data engineering if all

you're doing is extracting some data

getting some insights or loading data

from relational databases into your

chosen data analysis tool to do further

analysis like creating reports creating

visuals and dashboards then you should

just check out my full SQL database

tutorial course after you're done

watching this video because I still have

quite a couple technical skills to cover

here my SQL course has everything from

the really simple stuff like select star

or select all from to joining tables

using unions to group by Clauses and

even subqueries being able to write SQL

code is crucial for analysts because the

majority of databases at work will be

some sort of relational database and to

query these the language will be some

sort of SQL based language whether

that's postgress SQL MySQL or Microsoft

SQL Server once you've gotten

comfortable with writing SQL queries

it's time to move on to mastering a

visualization tool five six years ago

data visualization skills were nice to

have skills for data analysts or any

other sort of analyst but nowadays let's

be honest even though the job

description says data visualization

skills are optional or would be very

nice to have they're pretty much

required data storytelling skills are

very much in demand so having these

skills doesn't really make you stand out

anymore but not having them will in a

bad way if you know what I mean now you

must be wondering which bi tool should I

learn and the answer is simple just

learn the one that the majority of the

companies that you want to work for use

if that's powerbi then go with powerbi

if that's Tableau then learn Tableau the

two tools are fundamentally quite

different Tableau is OS independent and

it's solely a visualization tool the

calculated fields in it are based on SQL

powerbi uses the Dax language which by

the way is very different from SQL and

is way more than just a visualization

tool powerbi is more of an app it

integrates with other Microsoft products

like Excel power query or power automate

extremely well I know there are other

visualization tools out there but I

would highly recommend you learn one of

these just because they're by far the

most popular you really don't want to

waste your time learning a tool that

only very very few companies use so

that's the visualization tool covered

which leaves me with only one more Big

Technical skill coding now this is

something that I would urge you to learn

last preferably once you're already in

your Analyst job as it is by far the

most difficult I would say for me

learning Excel SQL Tableau and powerbi

combined wasn't as hard as learning

coding in Python and I'm mentioning

python here because it is my preferred

coding language it's so versatile it's

just an allrounder coding language now

that is not to say that if you learn

something else like R that's useless all

I'm saying here is that if I were in

your shoes and I only have the time to

learn one coding language that's

definitely Python and when it comes to

python please don't learn anything and

everything as if you do so I can

guarantee that you'll be completely lost

Learn Python with a focus on data

analysis by narrowing down your learning

to the main data analysis libraries like

the panda numpy math plot lip and

Seaborn libraries I already made a

dedicated video on how I'd learned

python all over again I'll put the link

in the description in case you're

interested coding for analysts is not

that important in my opinion now if you

want to become a data scientist or a

data engineer then coding is definitely

super important but given this channel

is focused around data analysis I guess

that's a story for another day for

another video I really hope you found

the last couple of minutes of me going

through how I'd learn data analysis all

over again helpful if you did then I'm

sure you'd really enjoy watching these

videos right here thank you so much for

taking just a little time out of your

day to watch this and I shall see you in

the next one