Want to learn Data Science but confused about where to start and what to follow?

Here are the ultimate 12 months Learning path to becoming a Data Scientist ๐Ÿ‘จโ€๐ŸŽ“

Note: I'm personally following this roadmap

๐Ÿงต๐Ÿ‘‡

#DataScience #MachineLearning #100DaysOfCode #66DaysOfCode #Python
Since we're currently in July, so start from this month.

Understanding Data Science and getting started with Python
- what is data science?
- what does a data scientist do?
- find out various resources
- Set up the system
- Learn Python basics
- Introduction to Pandas & Numpy
August -

Mathematics, Statistics & SQL
- Linear Algebra
- Introduction to Probability
- Statistics - inferential & descriptive
- Exploratory Data Analysis
- SQL for Data science
- Projects on EDA and SQL

Start engaging in the Data Science & Machine Learning community
September -

Basic Machine Learning Algorithms
- Understand Machine Learning Pipeline
- Linear Regression
- Logistic Regression
- Regression Project
- Decision Tree
- Naive Bayes
- Support Vector Machines
- Feature Engineering
- Classification Project

Work on @github Profile
October -

Learn about Ensemble Models and Techniques
- Unsupervised Learning
- Unsupervised Projects
- Understand Ensemble Learning
- Random Forest
- Boosting Algorithms
- Advanced Ensemble Learning

Start Participating in competitions @kaggle
November -

Learn about Validation, Hyperparameter tuning & Time Series
- Validation Strategies
- Hyperparameter tuning
- Time Series
- Time Series Project

Build Resume and apply for Internships
December -

Getting started with Neural Networks & Deep Learning
- Setup the system for Deep Learning or learn using @GoogleColab
- Introduction to Deep Learning (ANN)
- Introduction to Keras

Start writing Articles
January 2022 -

Convolutional Neural Network
- Understand CNN
- Image classification using Keras
- Transfer Learning in Computer Vision

Explore @TensorFlow / @PyTorch
February -

Computer Vision Projects
- Project 1: Color Detection
- Project 2: Perform Face Detection on Family Photos.
- Project 3: Human Emotion and Gesture Recognition
March -

Natural Language Processing
- Understand RNN, LSTM, GRU
- Text Preprocessing & Cleaning
- Text Classification
April -

Advanced Natural Language Processing
- Text Summarisation
- Word Embeddings
- Topic Modelling
- NLP Project
- Transfer Learning in NLP
May -

Explore ML Cloud Platforms
- Amazon Web Services @awscloud
- Google Cloud @googlecloud
- Azure @Azure
- IBM Cloud @IBMcloud
June -

Big Data Tools
- Tableau or PowerBI or QlikView
- Hadoop
- Spark
- Hive

Congratulations ๐Ÿฅณ๐ŸŽ‰
You are ready as a Data Scientist.
That's it for the thread ๐Ÿ‘‹

A retweet for the first one would really mean a lot ๐Ÿ™

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More from @PiyalBanik

2 Jul
Best Data Science blogs to follow in 2021

๐Ÿงต๐Ÿ‘‡

#DataScience #66daysofdata #100DaysOfCode
1. Towards Data Science

TDS is a Medium publication having audience-oriented content about Data Science, along with blogs on related fields such as Machine Learning, Programming, Visualization, and Artificial Intelligence.

towardsdatascience.com
2. Data Science Central

DSC is one of the leading repositories of Data Science content that is regularly updated with the latest trends across domains such as Artificial Intelligence, Machine Learning, Deep Learning, Analytics, Big Data, and much more.

datasciencecentral.com
Read 9 tweets
1 Jul
NumPy ๐Ÿ”ฅ

It is a Linear Algebra Library for #Python, the reason it is so important for Data Science is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks๐Ÿ‘จโ€๐Ÿซ.

Here's everything you need๐Ÿงต๐Ÿ‘‡

#DataScience #100DaysOfCode
1โƒฃNumpy Arrays

NumPy arrays are the main way we use Numpy. Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column). Image
2โƒฃBuilt-in Methods

There are lots of built-in ways to generate Arrays
- zeros
- ones
- eye
- arange
- linspace Image
Read 11 tweets
19 Jun
Ever wondered how a Data Scientist thinks about a problem? Here are the major steps involved in tackling a data science problem.

Thread ๐Ÿงต๐Ÿ‘‡

#DataScience #MachineLearning #100DaysOfCode
1. Business Understanding: We should have clarity of what is the exact problem we are going to solve.

What is the problem that we are trying to solve? - Asking the right questions as a Data Scientist starts with understanding the goal of the business.
2. Analytical Approach: How can we use data to answer the question? We should decide the analytical approach to follow which can be of 4 types
- Descriptive
- Statistical
- Predictive
- Prescriptive
and it indicates the necessary data content, formats, and sources to be gathered
Read 12 tweets
18 Jun
Top 7 interesting careers related to Data Science to explore. Pick one and start learning.

Thread ๐Ÿงต๐Ÿ‘‡

#DataScience #ArtificialIntelligence #MachineLearning #BigData
1. Data Scientist

Data scientist use their analytical and technical capabilities to extract meaningful insight from data.
2. Machine Learning Engineer

Machine Learning engineer's final output is the working software, and their audience for this output consists of other software components that run automatically with minimal human supervision. The decisions are made by machines.
Read 9 tweets
18 Jun
Everything you need to know about Strings in Python for Data Science

Thread ๐Ÿงต๐Ÿ‘‡

#DataScience #Python #100daysofcodechallenge
๐Ÿ“ŒLooping Through a String

Since strings are arrays, we can loop through the characters in a string, with a for loop.
๐Ÿ“ŒString Length
To get the length of a string, use the len() function.

๐Ÿ“ŒCheck String
To check if a certain phrase or character is present in a string, we can use the keyword in.
Read 8 tweets
17 Jun
Python operators are easy and every aspiring Data Scientist need to know the common ones.

Thread ๐Ÿงต๐Ÿ‘‡

#Python #DataScience #100DaysOfCode #code #CodeNewbie
๐Ÿ“ŒPython Arithmetic Operators:

Arithmetic operators are used with numeric values to perform common mathematical operations Image
๐Ÿ“ŒPython Assignment Operators:

Assignment operators are used to assign values to variables Image
Read 8 tweets

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