Week 8 (Classification)
-- Slides

Reading

OpenIntro Statistics Chapters 8
Week 7 (Regression)
Week 5 (R Practice)
-- Code

Week 4 (Distributions and Byes Rule)
Week 3 (SQL)
Notes:

Reading

Week 2 (Probability)
Notes:
Slides: http://vsokolov.org/courses/41000/notes/41000secti...
Recording: https://youtu.be/j6QSqcbB9DQ
Practice: https://www.dropbox.com/s/by11g2bullsyes8/hw2.pdf?dl=1

Reading
OpenIntro Statistics Chapters 2 and 3

Additional
Persi Diaconis
Week 1 (R)
Notes:
Code: https://novafoundation-vsokolov.notebooks.azure.co...
HW: https://novafoundation-vsokolov.notebooks.azure.co...
Recording

Reading
list for this week is a sequence of datacamp tutorials. As I have mentioned, the only way to become fluent in a programming language is to practice. Try to do as many courses from this list as possible this week. You can continue doing those as our bootcamp goes. Keep working on your R skills.
- Introduction to R
- Intermediate R
- Introduction to the Tidyverse
- Data Manipulation with dplyr in R
- Importing Data in R (Part 1)
- Introduction to Data
- Exploratory Data Analysis
- Foundations of Probability in R
- Data Visualization in R
- Correlation and Regression
- Foundations of Inference
- A/B Testing in R (Ch 1 and 3)

Optional:
- Intermediate R: Practice
- Inference for Numerical Data
- Working with Dates and Times in R
- Cleaning Data in R
- Importing & Cleaning Data in R: Case Studies

Practice
Problem for this week is in materials/practice/1_wearher.ipynb

Additional Resources
- R cheat sheet. Print it and have it on your desk when you work on problems for our class
- RStudio: For those of you who prefer to use RStudio, you can download it here. Alternatively, you can use Anaconda (this is what I do). Anaconda comes with many widely used packages thus you do not need to install those as you work through practice problems. To convert my Jupiter notebooks to R scripts, use jupyter nbconvert --to script *. I converted notebooks from last week already. You will need to re-clone the NovaFoundation project.
- R in a Nutshell book
- Data Manipulation with R book

Note on <- vs =. Most of the R tutorials and documentation uses <- for assignment. I use =. Those are equivalent. There is one minor case when those are not and I have never encountered it in my practice. So if you prefer using = please do so.


Data Science Foundations Course
Materials:
- Azure
- Dropbox
To join slack, click here.

Evaluation
If you would like to use your course instructors as a reference in the future, please submit your homework assignments (by email). We will keep track of the homework problems and projects you did as well as your attendance. When asked for a reference, we will provide this information to your potential employer.

I also encourage you to create a GitHub repository to post your solutions there so they are publicly available and you can link those in your resume/LinkedIN profile. If you need help with GitHub, ask me for help.

Course Description
Nova Data School uses a complete immersive learning experience that reinforces what is presented and heard with instructor-led hands-on labs and projects. As a part of this 9-week Data Science Foundations course, you will develop a variety of a technical skills that will serve as the basis of your data science skills. As you progress through the Data Science Intermediate and Data Science Advanced programs, we will support you further in upskilling your data skills with data science office hours, dedicated career mentors, and networking events to help you connect and find your next career move.

Who will benefit from this course?

Professionals: Are you faced with problem of analyzing data on a regular basis? Do you use old-school techniques such as Excel and wish you could increase your efficiency in analyzing data? Or potentially tackle more complex data analysis problems to grow professionally and get promoted?
Data Analysts: Do you wish you had a structured and rigorous introduction into statistical and probabilistic tools of data science? Tools which will enable you to extend your skills and will form foundation for learning more advanced tools of machine learning and predictive analytics that are currently in high demand
Beginners: Have you been thinking of becoming a data scientist and was not sure what is a good place to start? This course assumes no previous background and will be accessible for those who are new to the field and trying to learn the basics and to understand if this is a good fit for a future career.

When: August 3 - September 28, every Saturday, 9am to noon

Where: Fairfax Campus.

Price: $1500.
1
What is Data Science and How to Become a Data Scientist?
If you would like to learn more about data science see our recent webinar via the link below
LEARN MORE
2
WEEKS 1-2: R and SQL
- Using SQL to create tables, insert data, update data, and delete data
-Using SQL to query data for reporting
-Using SQL to load a set of tables to build a data warehouse
-Working with data in relational databases or cloud databases
-Data visualization in R
-R libraries for data science
3
WEEKS 3-5: Decisions
- AB Testing
- Bayes Rule
- Decision Trees
- Random variables
- Hypothesis testing
4
WEEKS 6-8: Prediciton

- Machine learning and linear models
- Overfitting and assessing quality of a model
- Logistic regression for classification
- Practical aspects of linear models: feature engineering and regularization
5
WEEK 9: Tableau
- Using Tableau Public Desktop to visually analyze and explore data
- Using Tableau Public Desktop to build interactive dashboards
- Using Tableau Public Desktop to prepare for the Tableau Desktop Specialist certification
- Using Tableau Public Desktop to build a portfolio of data visualizations