Learn data science and machine learning with a complete end-to-end data science roadmap in 2023 (Don't miss it!🤯)
A🧵👇
1) Python programming fundamentals
Python is one of the most widely used programming languages today.
It is named one of the most popular programming languages according to the StackOverflow developers' survey in 2022.
CC: @DataKwery
🔗 datakwery.com/tools/python/?utm_source=avi&utm_medium=blog
2) Statistics and math essentials
After learning Python programming, you should learn statistics and mathematics essentials to learn the data science tech stack and become a proficient data scientist.
CC: @DataKwery
🔗 datakwery.com/techniques/statistical-analysis/?utm_source=avi&utm_medium=blog
3) Data wrangling and data visualization
Data wrangling and data manipulation is a crucial skill to develop as a data scientist.
Python's libraries like Pandas, Numpy, Matplotlib, and seaborn are necessary to learn Data wrangling.
CC: @DataKwery
🔗 datakwery.com/post/complete-data-science-roadmap-2023/?utm_source=avi&utm_medium=blog
Are you looking to learn data analysis and visualization for FREE?
Yes, you heard it right. I and @SanthoshKumarS_ are conducting a live class on Feb 18th
Perks:
Certificate
Live interactive class
Q&A support
Click the link to register👇
🔗 lighthall.co/class/039c72c6-8051-45f8-92b8-907782bbe5bf
4) SQL programming language and MySQL database
data scientists should also be proficient in SQL programming language to store and manipulate relational databases in order to work with a vast amount of data
CC: @DataKwery
🔗 datakwery.com/tools/sql/?utm_source=avi&utm_medium=blog
5) Machine learning using sci-kit learn
Learn:
1. Supervised ML
2. Unsupervised ML
3. Feature selection and FE
4. Hyperparameter tuning
5. Pipelining
CC: @DataKwery
🔗 datakwery.com/techniques/machine-learning/?utm_source=avi&utm_medium=blog
6) Deep learning using Keras
Deep learning is a powerful technique one should learn as a data scientist.
It helps to process text, image, audio, and video data. learn Deep learning by using below resources.
CC: @DataKwery
🔗 datakwery.com/techniques/deep-learning/?utm_source=avi&utm_medium=blog
7) Natural language processing (NLP) techniques and concepts
NLP is a sub-field of machine learning which leverages analysis, generation, and understanding of human languages in order to derive meaningful insights from it.
CC: @DataKwery
🔗 datakwery.com/techniques/natural-language-processing/?utm_source=avi&utm_medium=blog
8) Machine learning model deployment
Model deployment is the sort after-skill that will set you apart in the job market.
Learn: Model serving, Containerization, and Cloud platforms
Cc: @DataKwery
🔗 datakwery.com/techniques/cloud-computing/?utm_source=avi&utm_medium=blog
9) Portfolio projects
Now, to showcase your skills building a portfolio is the best way to go🚀
Some of the project ideas you can consider
1) Predicting road accident severity
2) Energy intensity prediction
3) Wlid blue-berry prediction
4) Patient survival prediction
10) Interview prep and job application
Here are a few tips for data science job interviews
1. Understand the company and the job
2. Brush up on key skills
3. Show your passion
4. Be able to explain your work
5. Practice data-based ques
Check more at: datakwery.com/post/complete-data-science-roadmap-2023/?utm_source=avi&utm_medium=blog
TL;DR
1) Python lang
2) Statistics & math
3) Data wrangling & data viz
4) SQL programming language & MySQL database
5) Machine learning using sci-kit learn
6) Deep learning using Keras
7) NLP techniques
8) ML model deployment
9) Portfolio projects
10) Interview prep
The most awaited roadmap for data science is out now⚡
Check out the data science roadmap to following in 2023 right now👇
Click the link below👇👍
🔗 datakwery.com/post/complete-data-science-roadmap-2023/?utm_source=avi&utm_medium=blog
Avi Kumar Talaviya
@avikumart_
Simplifying Data Science and Machine learning for beginners🤖 I share valuable threads & resources on DS/ML/DL @kaggle Master|Python|ML|Data|Analytics|Tech