קורס Python – ניתוח ועיבוד נתונים

[learn_more caption="Part 1 – Data Analysis using NumPy"]

Module 1 – NumPy Basics

• The need for numpy arrays
• About numpy
• Creating a simple numpy array
• Inspecting the numpy array properties
• Python lists vs numpy arrays

Module 2 – Generating Data Using NumPy

• Creating customised numeric sequences
• Create repeating sequences
• Generate random numbers
• Using random.choice()

Module 3 – Subsetting Numpy Arrays

• Subsetting 1d numpy array
• Subsetting 2d numpy array

Module 4 – Performing Calculations on a Numpy Array

• 1D numpy Array arithmatics
• Calculating two 1D numpy arrays
• Performing calculations on 2D Numpy array

Module 5 – Filtering Data

• Filtering 1d numpy array
• Filtering 2d numpy array
• Find elements within range
• Using isin()
• Using isnan()
• Using any() & all()

Module 6 – Sort a NumPy Array

• Using sort()
• Using argsort()
• Using lexsort()

Module 7 – Applying Functions

• Working with numerics
• Working with dates
• The np.datetime64 object
• Extracting specific Parts of the date
• Datetime arithmetic
• Adding months and years
• Create a sequence of dates
• Compare against dates
• Apply functions on vectors
• Np.vectorize()
• Apply_along_axis()

Module 8 – Aggregate Functions

• Basic functions
• Dealing with nan values
• Group by

Module 9 – Set Operations

• Concatenate two arrays columnwise and row-wise
• Union all
• Union
• Intersect
• Subtract
• Union / intersect / substract with 2d

Module 10 – Reshaping and Flattening Multidimensional arrays

• Reshaping
• Flatenning

Module 11 – NumPy's Structured Arrays

• Introduction
• Creating numpy's structured arrays
• Loading csv into a structured array

Module 12 – Introduction to SciPy

• What is SciPy
• What is the difference between SciPy and NumPy
• Solving Linear Algebra Equations with SciPy

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[learn_more caption="Part 2 – Data Analysis using Pandas"]

Module 1 – Basic Select Operations

• Selecting columns dataset
• Calculations
• Concatenations
• Adding new columns
• Renaming existing columns
• Retreiving distinct list of values
• Creating a simple dataframe from a python list
• Getting info on your dataframe

Module 2 – Filtering & Sorting

• Simple operators
• Complex operators
• Isin() method
• Between() method
• Text matching
• Filtering empty values
• Sort a dataframe
• Using sort_values()
• Understanding inplace
• Understanding na_position
• Sort_index()
• Top-n rows
• Filter by index
• The query() method

Module 3 – Applying Scalar Functions

• String functions
• Numeric functions
• Date functions
• Conversion functions
• Nan functions
• Using the apply function

Module 4 – Aggregative Functions

• Basic aggregations
• The groupby() method
• Filter a grouping set
• Groupby() multiple columns

Module 5 – SET Operations

• Union all
• Union
• Intersect
• Minus / except

Module 6 – JOIN Opearions

• Inner join
• Outer join left
• Outer join right
• Outer join full

Module 7 – Schema Modifications

• Adding / dropping a column
• Renaming a column
• Deleting rows
• Updating rows
• Create random samples

Module 8 – Pandas Indexes

• The need for indexes
• Basic methods
• Retrieve rows by index label
• Retreive rows by index position
• Handling missing values
• Setting multi-indexes
• Extracting rows from multi-indexes
• Using traspose()

Module 9 – Pivot operations

• Using pivot_table()
• Using melt()

Module 10 – Input / Output Operations

• Using remote datasets
• Working with CSV
• Working with XML
• Working with JSON
• Reading multiple files

Module 11 – Working with Analytic Functions

• Using RANK
• LAG and LEAD
• NTILE

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[learn_more caption="Data Visualization using Matplotlib"]

MatplotLib Basics

• Working with Matplotlib
• types of matplotlib charts
• basic customizations
• advanced customizations
• 3d graphing

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