Andmeanalüüs Pythonis JupyterLabiga (e-õpe, inglise keeles)

Koolituse maht: 16 akadeemilist tundi ehk 2 õppepäeva, mille saad endale sobivale ajale jaotada.

Sihtgrupp: This course is for students new to NumPy and Pandas.

Koolitusel osalemise eeldused: Basic Python programming experience. In particular, you should be very comfortable with: working with strings, working with lists, tuples and dictionaries, loops and conditionals, writing your own functions.

To complete the training and get a certification you need to pass the quiz 100%.

Koolituse teemad:

If you or your team are using or plan to use Python for data science or data analytics, then this is the right Python course for you. The course assumes that you already have had a good amount of Python training and/or experience. You will start the class by learning how to use Jupyter Notebook, a great tool for writing, testing, and sharing quick Python programs. Even if you do not end up using Jupyter Notebook as your main Python IDE, you will appreciate having it as a tool in your Python toolkit.

You will learn NumPy, which makes working with arrays and matrices (in place of lists and lists of lists) much more efficient, and pandas, which makes manipulating, munging, slicing, and grouping data much easier. You will also learn some simple data visualization techniques with matplotlib.

1. JupyterLab

• Exercise: Creating a Virtual Environment
• Exercise: Getting Started with JupyterLab
• Jupyter Notebook Modes
• Exercise: More Experimenting with Jupyter Notebooks
• Markdown
• Exercise: Playing with Markdown
• Magic Commands
• Exercise: Playing with Magic Commands
• Getting Help
• Test

2. NumPy

• Exercise: Demonstrating Efficiency of NumPy
• NumPy Arrays
• Exercise: Multiplying Array Elements
• Multi-dimensional Arrays
• Exercise: Retrieving Data from an Array
• More on Arrays
• Using Boolean Arrays to Get New Arrays
• Random Number Generation
• Exploring NumPy Further
• Test

3. Pandas

• Getting Started with pandas
• Introduction to Series
• np.nan
• Accessing Elements in a Series
• Exercise: Retrieving Data from a Series
• Series Alignment
• Exercise: Using Boolean Series to Get New Series
• Comparing One Series with Another
• Element-wise Operations and the apply() Method
• Series: A More Practical Example
• Introduction to DataFrames
• Creating a DataFrame using Existing Series as Rows
• Creating a DataFrame using Existing Series as Columns
• Creating a DataFrame from a CSV
• Exploring a DataFrame
• Exercise: Practice Exploring a DataFrame
• Changing Values
• Getting Rows
• Combining Row and Column Selection
• Boolean Selection
• Pivoting DataFrames
• Be careful using properties!
• Exercise: Series and DataFrames
• Plotting with matplotlib
• Exercise: Plotting a DataFrame
• Other Kinds of Plots
• Test

Koolituse eesmärk

The main purpose of this e-course is to give students a good understanding of NumPy and Pandas.

Koolituse õpiväljundid

After completing this course, students will:

• Learn to work with Jupyter Notebook.
• Learn to use NumPy to work with arrays and matrices of numbers.
• Learn to work with pandas to analyze data.
• Learn to work with matplotlib from within pandas.

Koolitus toimub e-õppe keskkonnas, kuhu saab siseneda isikliku kasutajanime ja parooliga. Need saadetakse Teile pärast koolitusarve tasumist või erikokkuleppel.

Täienduskoolituse õppekavarühm: tarkvara ja rakenduste arendus ning analüüs