Programming

Python is currently the most used programming language in data science. It has had an explosive growth of interest in the last few years. It is a simple yet powerful language with many libraries available to make data analytics and machine learning easy to implement.

This 4 part course will give you an introduction to programming in Python for use in data science. We will focus on libraries that are commonly used in data science such as numpy, matplotlib, pandas and more.

The course consists of four lessons and four labs, for a total of 16 hours. There are 4 mandatory assignments in the course.

You will receive a certificate upon successful completion of the course and assignments.

Who should take this course?

• Analysts transitioning into a Data Science Role
• Developers who want a career in Data Science
• Anyone who wants a thorough understanding of Python libraries for data wrangling and visualization

What do you get?

• Write real programs
• Personalized training
• Learn common libraries for working with data
• Non-alcoholic drinks and light snacks will be served
• Certificate of Completion

Prerequisites

• Basic knowledge of Python Programming (If you are unfamiliar with Python we recommend attending Introduction to Python Programming first)
• You need to bring your own laptop (Windows/Linux/macOS), you will not need to install anything on your laptop.
• You need access to a free Google account (such as Gmail)

Probability theory is important in data science as we often want to use data to make some predictions about the future. It is also fundamental for many machine learning algorithms.

Learn about probability in this four-part course where we will cover:

• Conditional probability
• Bayes theorem
• Bayesian inference
• Bayesian decision making
• Bayesian regression

The course consists of four lessons and four labs, for a total of 16 hours. There are also four assignments in the course, which will form part of your professional portfolio.

We take a learn-by-doing approach and will focus on writing code in python rather than study equations. Because this is more efficient and more relevant for your day to day work.

You will receive a certificate upon successful completion of the course and assignments.

Who should take this course?

• Aspiring Data Scientists or Machine Learning engineers without a base in probability theory
• Developers who want a career in Data Science
• Anyone who wants a thorough understanding of probability and Bayes theorem.

What do you get?

• Personalized training
• Learn common libraries for working with data
• Non-alcoholic drinks and light snacks will be served
• Professional portfolio
• Certificate of Completion

Prerequisites

• Working knowledge of Python with numpy and pandas libraries.
• You need to bring your own laptop (Windows/Linux/macOS), you will not need to install anything on your laptop.
• You need access to a free Google account (such as Gmail)

Pandas is the most popular Python library for data manipulation and analysis, and it is a must-have in the toolbox of any Data Scientist. With Pandas, you can import data from CSV files or even directly from a URL, and Pandas organizes it neatly for you in the form of a data frame with rows and columns.

In this four-week evening course, you will learn to master advanced techniques for data wrangling with Pandas.

The course consists of four lessons and four lab assignments

You will receive a certificate upon successful completion of the course.

Who should take this course?

• Analysts/data scientists who are used to working in excel or with other programming languages than python.
• Anyone who wants to learn how to manipulate data with Pandas and Python

What do you get?

• In-depth knowledge of the Pandas library
• Certificate of Completion upon successful completion of the course.
• Professional portfolio projects to showcase to potential employers

Prerequisites

• Working knowledge of Python programming
• Working knowledge of NumPy python library
• Participants must bring their own computer (Win/Linux/Mac)

Python has had explosive growth in interest in the last few years. It is a simple yet powerful language with many libraries available that make complex programs simple to write.

This four-part course will give you an introduction to programming in Python. We will cover fundamental programming concepts common to all languages, and you will get a good foundation in Python.

The course consists of four 2 hour lessons and four 2 hour labs. Total 16 hours. There are also 4 mandatory home assignments.

You will receive a certificate upon successful completion of the course and assignments.

Who should take this course?

• Data analysts who are considering transitioning to a data science role
• People considering a career in software development or a similar field
• Anyone who wants to learn programming

What do you get?

• Write real programs from day one
• Personalized training
• A great foundation for programming in Python
• Non-alcoholic drinks and light snacks will be served
• Certificate of Completion

Prerequisites

• No prior programming knowledge required
• You need to bring your own laptop (Windows/Linux/macOS), you will not need to install anything on your laptop.
• You need access to a free Google account (such as Gmail)

Gathering, cleaning and analyzing messy data

If you already started your data science journey on your own you might have worked with some tutorials and courses that provide you with a clean and neat data set to train your machine learning models on. This is great for learning the principles of the algorithms, but the real world is not neat and clean. Real-world data is messy, incomplete, and probably not what you were looking for in the first place.

In this workshop we will look at techniques for gathering data, we will build a program for scraping web content and we will clean and preprocess that data so we can analyze it.

This course is great if you are trying to build a portfolio of machine learning and data science projects. Employers like to see that you are able to work with real data. And if you can gather your own data you are not limited by the existence of nicely prepared datasets to conduct your machine learning experiments.

The workshop is 3 hours. It requires some basic programming experience, preferably in Python. Participants must bring their own laptop.

What do you get?

• Hands-on training in web scraping
• Learn about working with messy and incomplete data
• Gain valuable skills for portfolio building
• Certificate of completion
• Food will be served

Prerequisites

• Working knowledge in programming (preferrably Python)
• Participants must bring their own laptop (Win/Linux/Mac)

Get expert help with your own projects!

Are you working through an online course, or are you stuck in your personal programming project? Come get hands-on help from our expert instructors! 

Our instructors are industry experts with experience teaching people from a wide range of backgrounds. They can provide support in Python, C, Java, Raspberry Pi, TensorFlow and more. 

You will work on your own project and our instructors will help you with anything you need.

Who should come for drop-in lab hours?

• Professionals who need help with a project they are working on
• Online programming course students
• CS or Bootcamp students who need extra help with course material
• Anyone who wants help with personal programming or machine learning projects. 

What do you get?

• Hands-on help with (almost anything) from Nova Institute Instructors. 

Prerequisites

• Bring your own project (all levels welcome)
• You need to bring your own laptop (Windows/Linux/MacOS)