" The goal is to turn data into information, and information into insight. β

β Carly Fiorina

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**#1 Choose your weapon First**

Choose your weapon First:

Here I mean choose the Right Language to learn Data Science, you can go with R or Python you can also choose Java to learn data science, but I will not recommend learning data science with Java.

However, the most alluring factor of Python is that it is easy to learn. As compared to other data science languages like R, Python provides an easy-to-understand syntax.

If you've decided to go with Python, develop good hands-on Jupyter NoteBook and various libraries Like Pandas, Numpy, Seaborn, SciPy, XGBoost and more.

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**#2 Learn Basic Maths**

Three main Basic Mathematics Topics are

- Calculus
- Linear algebra
- Probability and statistics

some theories:-

- Discrete math
- Graph theory
- information theory

fun fact:-

If you're scared of math or not ready to look at an equation, you won't have much fun as a data scientist.

However, you if have taken high school level math and are willing to invest some time to improve your familiarity with probability and statistics and to learn the principles underlying calculus and linear algebra, math will not come in your way of becoming a professional data scientist.

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**#3 Do Some Online Courses**

there are some really good courses out there to getting started with data science please note here Doing Courses is Different Thing and playing with real-world data is a different thing after

here are some examples of quality courses for beginners

Do Some Online Courses like:

DataCamp

Kaggle Courses

Coursera

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**#4 Real-World Projects and Communication are keys!!**

The Business leaders of the past generation, the visionaries of the current, and the research community are Keys!!!

as I said above Doing Courses is Different Thing and playing with real-world data is a different thing After experiencing many different types of learning, I found that learning by doing real-world projects is the most effective way to learn things quickly.

Doing courses is a different thing and playing with real-world data is a different thing.

Here are some sources where you can find some real-world open-source data sets for practice and Making projects

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**#5 Do Read some good books like:**

- Python for data analysis by Wes McKinny
- Automate the boring stuff with python
- Machine Learning with Python Cookbook
- Python Cookbook
- Hands-On machine learning with scikit-Learn & TensorFlow

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**#6 Divide Your Learning Time into two parts**

In the first part try learning some algorithms, ask questions to your data,

search some good projects on Kaggle and GitHub, and do your analysis of what people are doing with their Jupyter notebook.

Learn various algorithms and understand the math behind the scenes. Don't worry the math behind them is easy, don't get scared after hearing their names.

- Linear Regression
- K-means(k-NN)
- k-means and more...

After some practice, you will be able to solve advanced data science problems. Try collecting data to make your project, practice data science life cycle processes, and put it on Kaggle and take reviews of your seniors.

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**What's Next??**

Hopefully, this blog will give you a clear path on how to navigate in this field. It took me years to properly understand data science and life concepts and I'm still learning every day!

Thank you so much for reading and good luck on your data science journey!!

*As always, I welcome feedback, constructive criticism, and hearing about your projects. I can be reached on Linkedin, and also on my website.*

## Top comments (6)

I do feel the conceptual mathematics part is important, as when we are implementing models we tend to use libraries and functions which just act as a black box. So a well read concepts help us explain the results, interpretations and explanations to our analysis.

Yes!! totally agreed π―

2 lines of code can implement whole model But it will not help you to explain!! And if we have strong basic maths background we can write books just by watching π numbers π

NICE

Thanksπ

hey! Nice posts. There's some hyperlinks broken on step 4!

Thank you For Like and Comment!! Bug Was Fixed Thanks For Your Contributionπ