Data Science and Machine Learning Curriculum

Published Jun 14 2022 02:37 AM 4,374 Views
Microsoft

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 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: 

The Curriculum: 

 

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 

 

 

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‎Jul 04 2022 12:27 AM
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