/Data-Science-RoadMap-1

Data Science RoadMap

Data-Science-RoadMap

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What is Data Science ?

Data Science is an interdisciplinary field that focuses on extracting knowledge from data sets which are typically huge in amount. The field encompasses analysis, preparing data for analysis, and presenting findings to inform high-level decisions in an organization.

Getting Started 🚀

In this roadmap I will recommend Python, although you may encounter R in more Data Analytics related jobs. Python mastery will come with time - learn enough basics to be able to read code and implement some simple projects with it.

Beginner Level 👇

Descriptive Stats :

To become a successful data scientist, you should have knowledge of Statistics. Statistics knowledge will give you the ability to decide which algorithm is good for a certain problem

Excel :

Python :

Now let's dive into python's libraries :

Notes I strongly recommend to study these libraries from This Book , in case you aren't familiar with reading i will put some resources i hope it help!

Pandas

Pandas is used for data cleaning and data preprocessing

Numpy

NumPy is short for numerical Python and is one of Python’s most important libraries it used for matrix and multidimensional array operations in Python

Data Cleaning :

feel free to take a look at a buch of Notebooks of others through kaggle :)

EDA :

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As a Data scientist, you have to showcase your findings in a visual form, so that stakeholders can understand them properly

Dashboards :

SQL and DB :

Note,All you need from week 1 to week 5

Data Science Tools

Intermediate Level 👇

Quick Remember, going into ML is a lifelong learning commitment - you are going to learn new things time after time no matter how far you are in your career.

Getting start with machine learning the first thing is to study math as much as you can !

Questions about Machine Learning?

  • How do I choose which attributes of my data to include in the model?
  • How do I choose which model to use?
  • How do I optimize this model for best performance?
  • How do I ensure that I'm building a model that will generalize to unseen data?
  • Can I estimate how well my model is likely to perform on unseen data?

To learn theory:

Hands On:

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Advanced statistics :

last but not least everything you need to know about statistics feel free to go ahead to this Amazing YouTupe chanel BrandonFoltz

Time Series Analysis :

Feature Engineering :

Join Data Science Communities :

Instagram Pages :

Reddit communities :

Twitter Accounts :

Useful Links :

Next:

In the upcomming years i will update the roadmap with the Advanced Level

Happy learning.