Beginning Data Science with Python

The Beginning Data Science is an ideal course for people involving data analytics or having interest in the subject of Data Science.

In the course, students will know how to load data from various sources , dealing with a big dataset, handling problematic data and processing and manipulating the data using Pandas and Numpy. Finally, data can be visualized and plotted graphically with the matplotlib.

The course is suitable for data analysts, data science or finance students or Excel users require computations on large datasets.

You'll Learn

Intalling Python

  • Python on OS X
  • Python on Windows

Working with variables and data

  • Naming and using variables
  • Numbers, strings and boolean
  • Stripping whitespaces
  • Concatenating strings
  • Stripping whitespaces
  • Tabs and newlines
  • Integers and floats
  • Writing comments
  • Strings manipulation

Working with Lists and Tuples

  • Accessing Elements in a List
  • Working with Elements (appending, adding, sorting, deleting )
  • For Loop
  • Looping through an entire list
  • Slicing and looping through a slice
  • Copying a list
  • Organizing a list
  • List comprehension
  • Vertical summation
  • Defining a tuple
  • Tuples and Lists conversion
  • Looping through a Tuple

If and Conditional Statements

  • Various conditional tests ( equality and inequality checking )
  • Conditional testing on a list
  • Boolean Expressions
  • if-else , if-elif-else
  • Using if statement with multiple lists
  • Nesting Blocks
  • While Loop using a list
  • While Loop using a flag
  • Flow control with break statement
  • Flow control with continue statement

Dictionaries

  • Accessing values in a Dictionary
  • Adding new key-value pairs
  • Looping through all key-value pairs
  • Looping through all values in a dictionary

Getting user Input

  • Using input() function
  • Handling numberical input

Functions

  • Defining a function
  • Arguments and parameters
  • Working with positional arguments
  • Working with keyword arguments
  • Setting arguments' default values
  • Returning result from a function

Working with Modules

  • Storing functions in a module file
  • Importing an entire module
  • Importing individual functions

Using Python's standard modules

  • Math module
  • Datetime module
  • Random module
  • OS module

Working with files

  • Reading data from a txt or csv files
  • Merging and working data from a file
  • Writing to a file

Errors handling

  • Handling error using Exception
  • The try-except blocks
  • The else block
  • Displaying errors messages

SCHEDULES
 
PYD8103 - 廣東話 06 Oct enrol
 
PYD8108 - 廣東話 23 Oct enrol
 
PYD8109 - 廣東話 26 Oct enrol
 
PYD81010 - 廣東話 27 Oct enrol
 
PYD8113 - 廣東話 20 Nov enrol
RELATING COURSES
  Access
  Access SQL
  Excel Dashboards and Reports
  Excel VBA
  Python Programming

Data Preparation and loading

  • Data loading using Pandas
  • Working with problematic data
  • Dealing with big datasets

Data Processing with Pandas

  • Introduction to Data Structures
  • Series / DataFrame / Index Objects
  • Viewing and selecting data
  • Statistical and histogramming operations
  • String methods
  • Concatenating / joining / appending data
  • Arithmetic methods
  • Operations between DataFrame and Series
  • Function application and mapping
  • Sorting and Ranking
  • Doing common Excel tasks in Pandas
  • Doing Pivot Table in Pandas

Data Processing with Numpy

  • Array Creation - N-dimensions
  • Printing Arrays
  • Numpy DataTypes and conversion
  • numpy.ndarray methods
  • Basic arrays elementwise operations
  • Matrix product using dot function
  • Universal functions
  • Indexing, slicing and iterating
  • Shape manipulation
  • Copies and views
  • Data Processing Using Arrays

Data Visualization

  • pyPlot Basics
  • Knowing different chart elements
  • Charting with Pandas
  • Plotting a single interactive chart
  • Saving your charts
  • Subplots
  • Plotting multiple charts

Machine Learning

  • Supervised and unsupervised Learning
  • Training set and testing set
  • Dimensionality reduction
  • The PCA Decomposition
  • K-Nearest Neighbours Classifier
  • Linear Regression
  • Polynominal interpolation and curve fitting
  • Support Vector Machine