A humble welcome into the 26-Weeks-Of-Data-Science Study Plan from Team GreyAtom. We at GreyAtom, have trained 1000s of professionals from a multitude of backgrounds and know exactly what it takes to be a data scientist and how to make you one. For a motivated learner like you, in these 26 weeks, you should be able to make steady strides in the discipline of Data Science. In these 26 weeks, you’d be receiving one email from us per week, each of them equipped with the following resources:

1. Jupyter Notebooks with code snippets relevant to the pertaining topic dedicated for the week
2. Bonus materials from industry experts to upskill your understanding on the topic and help you see in new light
3. Cheatsheets for every topic to help you think in the nick of time
4. Reality anchoring tips - once per week - to keep you focussed on the common goal.
5. Access to career resources to help fine tune your approach to the market and make you market ready.

Below is a brief description of what a learner can expect from this program:

• The initial weeks would lay the foundations in programming & statistics on which we would model our study on machine learning.
• We'd start off with a primer on Git followed by the basics of Object Oriented programming in Python. Following this we'll slowly advance into Numpy, Pandas and Visualizations
• Once we get a tight hold on Python, we'll venture into statistics and exploratory data anlaysis which would involve a lot of the python and visualzation skills we'd learnt previously, to make the data ready for applying machine learning models.
• In the middle of the course you’ll learn about the supervised algorithms like Linear Regession, Logistic Regression,Decision Trees,Random Forests etc, and unsupervised ones like K-Means Clustering and a few more, how to implement them from scratch and how to use them for prediction tasks etc.
• In addition to our notebooks, the bonus materials in accordance to the respective topics will serve you well by giving you the industry perspective of machine learning.
• Towards the end of the study plan, we’ll delve a little into Natural Language Processing & Recommender Systems and make you adept at how to apply your machine learning knowledge for real world applications.