Data Analytics Roadmap from Basic to Advanced in Real life

DATA ANALYST BASIC TO ADVANCED LEVEL ROADMAP OF  USAGE IN THE REAL LIFE.

If you want to become a data analyst, there are several essential skills you

need to master. Let's type in and check them out one by one. Being a data analyst is all about analyzing data to help make better business decisions. You'll need to get good at various skills from math and

programming to data handling and Mathematics & Statistics

Visualisation, let's jump in. First up, you

need a solid foundation in mathematics

and statistics. This is crucial because

data analysis relies heavily on these

principles focus on understanding basic

concepts like mean, median, standard

deviation probability and hypothesis

Testing takes about a month or two

getting comfortable with these topics

Excel

Next, you need to get really good at

Excel. Excel is a powerful tool for data

analysis and many companies still rely

on it, learn how to use functions pivot

tables and charts spend about 2 to 3

weeks mastering Excel as it's a

fundamental skill for any data analyst

after Excel you should get comfortable

SQL

With SQL, SQL stands for 'structured query'.

language is a simple language we use

for managing and querying databases

learn how to write queries to access

organize and analyze data SQL is pretty

simple and you can get a decent grasp of

in about a month or two. Next you need

Python

to get the hang of Python, it's a

versatile language that's widely used in

data analysis focus on learning the

basics of Python, including libraries

like pandas and nonp, you will also hear

About R: that's another language used

in data analysis, however, if you're

Starting out, it's best to stick with

python first and think about learning R

later spend about a month or two getting

the hang of Python, you should also learn

GitHub

git is a Version Control System we

use to track changes to our code and

collaborate with others. Git has a ton of

features but you don't need to learn all

of them think of it like the 80/20 rule

80% of the time you use 20% of the GS.

features so one to two weeks of practice

is enough to get up and running by the

way to help you on this journey I've

created a free supplementary PDF that

breaks down the specific Concepts you

need to learn for each skill. It's a

great resource to review your progress

find gaps in your knowledge and prepare

For interviews, you can find the link in

the description also I have a bunch of

tutorials on this channel and complete

courses on my website if you're looking

for structured learning again links are

in the description next focus on data

Data Collection and Preprocessing

collection and preparation. This means

Gathering data from various sources and

cleaning it up so it's ready for

Analysis: Learn how to use Python

libraries like pandas to manipulate and

clean data spend about a month or two.

Once your data is clean you need to

Data Visualization

visualize it to spot patterns and

Communicate results. Learn how to use

Python libraries like Matplotlib and

Seaborn also check out businesses.

intelligence tools like Tableau or

Power BI, they are widely used for

creating interactive and sharable

Dashboards: Power BI is especially cool

because it's getting more popular and

since it's a Microsoft product, it works

great with other Microsoft tools you

might be using spend about a month or

two on data visualisation now, while not

Machine Learning Fundamentals

essential for every data analyst role

having a basic understanding of machine

learning can be a plus machine learning

involves teaching computers to make

predictions Based on data if you're

interested spend a month or two learning

the basics of machine learning including

python libraries like TensorFlow and

psychic learn now as you advance you

Big Data

might encounter situations where you

need to work with my massive data sets

that's where Big Data comes in. Big Data

is all about handling and processing

huge amounts of data quickly tools like

Hadoop and Spark are super handy for

this spend a month or two getting

familiar with these tools so if you

dedicate 3 to 5 hours every day you can

follow this road map and pick up all the

skills you need to apply for an

entry-level data analyst job in about 8 to 16 months.