Data Analysis

Data Analysis and Scientific Python

מספר הקורס 40834

32 סה"כ שעות אקדמאיות
4 מפגשים
* מספר המפגשים והשעות למפגש עשויים להשתנות בין קורס לקורס

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ספרו לי עוד


Python is an easy to learn and powerful programming language that is used in many computer science areas.

This course covers the power and flexibility of NumPy, SciPy and Matplotlib when dealing with heavy mathematical, engineering or scientific problems. Using the Pandas library, Python provides fast, flexible, and expressive data structures designed to make working with data both easy and intuitive.

Explore the concise and expressive use of Python’s advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting and other applications

Python is considered to be one of the most efficient programming languages for data analysis.


On Completion, Delegates will be able to


Who Should Attend

This course is mainly intended for Data Analysts, Developers, Business Intelligence professionals, Data Engineers, and other roles responsible for analyzing the organization’s data.


תכנית הלימודים

Full syllabus
PDF להורדה

Python Refresher / Crash Course

  • Introduction
  • IDEs and Tools for data analysis
  • Data types and strings
  • Control structures
  • Functions and functional programming
  • Collections
  • Object Oriented
  • Modules and Packages

Introduction to Data Analysis

  • Data Science
  • Data Analysis and ML Packages overview


  • Python arrays and NumPy Arrays
  • Multi-dimensional Arrays
  • Array slicing
  • Fancy Indexing
  • Data types
  • Array calculation methods
  • Statistics methods
  • Universal functions
  • Broadcasting
  • Universal function methods
  • Linear algebra
  • Linear regression with Numpy 
  • Feature Engineering
  • Feature selections
  • Dummy variables
  • Converting continuous variable to discrete

SciPy package

  • Interpolation
  • Curve fit
  • Optimization
  • Statistics
  • Image processing
  • Integration and more
  • Interpolation
  • FFT
  • Linear algebra
  • Scikits


  • Overview
  • Series and Dataframes
  • Working with data

Pandas – Series

  • Understanding one-dimensional labeled arrays
  • Create a Series from Python objects
  • Using the read_csv() method
  • Attributes
  • Methods
  • Arguments and Parameters
  • Extracting Series values

 Pandas – DataFrames

  • Understanding two-dimensional data structures
  • Selecting columns from a DataFrame
  • Adding new columns to a DataFrame
  • Working with Nulls
  • Sorting a DataFrame
  • Filter a DataFrame – conditions and methods
  • Retrieving rows by Index position
  • Delete rows or columns from a DataFrame
  • Rename Index labels or Columns in a DataFrame
  • Common String methods

Using MultiIndex

  • Understanding multiIndexes in Pandas
  • Creating a multiIndex
  • Extracting rows from a multiIndex
  • Common methods

Group By

  • Basic operations
  • Retrieving groups
  • Common methods
  • Group by multiple columns
  • Iterating through groups

Joining and Concatenating Data

  • Join operations between DataFrame objects
  • Combining together Pandas objects

Working with Dates

  • Understanding Python’s datetime module
  • Pandas Timestamp and DateTimeIndex objects
  • Pandas DateOffset and TimeDelta objects
  • Common methods

Pandas I/O API

  • Object conversions
  • Export DataFrame to csv
  • Importing and exporting Excel files

Data visualization

  • Overview
  • Learning from the data

Matplotlib package

  • Line plots
  • Scalar plots
  • Bar plots
  • Histograms
  • Interactive graphs and Events
  • Animation


  • Overview
  • Statistics graphs
  • Histograms and distributions
  • Bar plots and Box-plot
  • Heat map and correlation
  • Grids

Data Preparation

  • Data preparation and cleaning
  • Dealing with Missing values
  • Central tendency
  • Mean/median/mode
  • Bias
  • Variance and standard deviation
  • Standard scores
  • Feature scaling – standardization and normalization

Machine Learning Basic Concepts

  • Overview
  • Why Learn
  • Applications
  • Machine Learning Process
  • Learning Types:
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement learning
  • Examples


  • Basic understanding of Programming concepts

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