Data Science and Machine Learning Curriculum

[bethanyjep]

 Thank you for signing up to the 30 days of learning. In this post, I will cover what you can expect during the entire period of the Data Science and Machine Learning track. But first, make sure you watch and complete the onboarding tasks at: https://aka.ms/30DLOnboardingRecap  

In this track we will go from understanding the Python language to working with real life data and finally creating Machine Learning models both on Azure and in Python. The main role is understanding our data and using the knowledge to make decisions such as clustering Nigerian music based on their 'danceability' score, 'acousticness', loudness, 'speechiness', popularity and energy. 

Main Onboarding tasks: 

• Sign up for the Cloud Skills challenge: https://aka.ms/30DLDataScienceAndML   
• Create and activate your GitHub Account: https://aka.ms/30DLGitHubSessionRecap   
• Get Started with Azure for Students: https://aka.ms/30DLAzure4StudentRecap  
• Set up Your Local environment: https://youtu.be/6pMvovj7KbE  
• From GitHub to Visual Studio: https://youtu.be/Zxs1eK2acLk 

The Curriculum: 

• Python for Beginners: https://aka.ms/py4beginners    
• Data Science for Beginners: https://aka.ms/ds4beginners    
• Machine Learning for Beginners: https://aka.ms/ml4beginners     
• Student hub: http://aka.ms/learnstudent    

Below is the complete schedule for the program: 

Day	Date	Topic	Outcome	Main content	Other Resources
Monday	13-Jun	Getting Started with Python	Get started with Python!   Create your first python program	https://aka.ms/py4beginners  - module 1 to 5	Reactor video: https://aka.ms/pyBMI
Tuesday	14-Jun	Python Basics: Boolean | strings | operations | list | loops | dictionaries | functions	Write your first program in Python  Explore packages to better manage projects	https://aka.ms/py4beginners - module 5 -10	Self study on the given modules.
Wednesday	15-Jun	Data Preparation + Introduction to Data Science	Defining Data Science and what you can do with Data.  Basics of using Python for data exploration with libraries such as Pandas.  Data techniques for cleaning and transforming data to handle challenges of missing, inaccurate, or incomplete data.	https://aka.ms/ds4beginners do lessons 7 and 8.	Reactor video: https://aka.ms/analyseData  Data - https://aka.ms/pumpkinsDataset
Thursday	16-Jun	Data Visualization Part 1	Learn how to use Matplotlib to visualize bird data :duck:  Visualizing observations and trends within an interval.	https://aka.ms/ds4beginners - lessons 9 and 10	Follow up video:  https://aka.ms/manipulateData
Friday	17-Jun	Data Visualization Part 2	Visualizing discrete and grouped percentages.  Visualizing connections and correlations between sets of data and their variables.  Techniques and guidance for making your visualizations valuable for effective problem solving and insights.	https://aka.ms/ds4beginners - lessons 11 and 12	Video: https://aka.ms/data-viz  Data: https://aka.ms/birdsData
Saturday	18-Jun	General Track - Recap for the Week	An hour session to run through all the learning for the week and also answer questions	An hour session to run through all the learning for the week and answer questions
Monday	20-Jun	Analyzing your Data	This phase of the data science lifecycle focuses on techniques to analyze data.	https://aka.ms/ds4beginners - lessons 14 and 15	Task: go through the instructions on the two lessons and using what you have learnt, analyze and visualize https://aka.ms/spamdataset. Share your work on Discord and Twitter once done.
Tuesday	21-Jun	Data Science in the Cloud	This series of lessons introduces data science in the cloud and its benefits.  Training models using Low Code tools.	https://aka.ms/ds4beginners - lessons 17 to 19	Video Recording: https://aka.ms/30DL-dscloud
Wednesday	22-Jun	Techniques for Machine Learning + Intro to ML	Learn the basic concepts behind machine learning  What techniques do ML researchers use to build ML models?	https://aka.ms/ml4beginners   - lessons 1, 3 and 4
Thursday	23-Jun	Regression part 1	Get started with Python and Scikit-learn for regression models  Visualize and clean data in preparation for ML	https://aka.ms/ml4beginners   - lessons 5 and 6	Introduction to regression: https://aka.ms/30DL-regression   Resources: https://aka.ms/30DL-RegressionRe
Friday	24-Jun	Regression part 2	Build linear and polynomial regression models  Build a logistic regression model	https://aka.ms/ml4beginners   - lessons 7 and 8
Saturday	25-Jun	General Track - Recap for the Week		An hour session to run through all the learning for the week and also answer questions
Monday	27-Jun	Deploy Your ML Model Using Flask Framework	Build a web app to use your trained model	https://aka.ms/ml4beginners   - lesson 9	Data: https://aka.ms/30DL-ufodata  Live session: https://aka.ms/30DL-deploymodels Resources: https://aka.ms/30DL-WebDeployRe
Tuesday	28-Jun	Classification	Clean, prep, and visualize your data; introduction to classification  Build a recommender web app using your model	https://aka.ms/ml4beginners   - lessons 10 - 13
Wednesday	29-Jun	Introduction to Clustering	Build a recommender web app using your model	https://aka.ms/ml4beginners - lessons 14 and 15	Live session: https://aka.ms/30DL-Clustering  Resources: https://aka.ms/30DL-ClusteringRe
Thursday	30-Jun	Time Series forecasting in action	Time series forecasting with ARIMA  Time series forecasting with Support Vector Regressor	https://aka.ms/ml4beginners   - lesson 21 and 22
Friday	1-Jul	Introduction to natural language processing	Learn the basics about NLP by building a simple bot	https://aka.ms/ml4beginners   - lesson 16 and 17	Data: https://aka.ms/30DL-NLPData
Saturday	2-Jul	General Track - Recap for the Week	An hour session to run through all the learning for the week and also answer questions	An hour session to run through all the learning for the week and also answer questions
Monday	4-Jul	Machine Learning on Azure: Custom Vision	Deploying models with Custom Vision AI.	Create a Regression Model with Azure Machine Learning designer - Learn | Microsoft Docs and Use automated machine learning in Azure Machine Learning - Learn | Microsoft Docs	Live session: https://aka.ms/30DL-MLSumProject
Tuesday	5-Jul	Capstone project	You are expected to work on a project that will help them demonstrate all the things they have learnt during the program with proper documentation on GitHub
Wednesday	6-Jul	Capstone project
Thursday	7-Jul	Capstone project
Friday	8-Jul	Capstone project
Saturday	9-Jul	General Track - Moving on from Here
Sunday	10-Jul	Catch Up and Share Your Learning
Monday	11-Jul	Tidy Up your GitHub and LinkedIn Profile		You should be able to have updated LinkedIn Profile and GitHub Profile with their projects well documented
Tuesday	12-Jul	Graduation		Graduation