Markovdata > Courses > Advanced Python for Data Science & Data Analysis

Advanced Python for Data Science & Data Analysis

Instructor
Markov Data
10 students enrolled

Abstract

This Course provides attendees with the knowledge and skills to work on different data formats, proceed with data preparation and analysis, create a full data model, and come up with business insights using advanced python techniques.

Instructor

Eng. Amr Helal, Data Scientist, Markov

Duration

6 Weeks

Sessions

Sundays, 6:00 to 11:30PM

Location

Live Streaming (on-line)

Prerequisites

In addition to their professional experience, students who attend this training should already have the following technical knowledge: • Basic knowledge of Microsoft Windows operating system and its core functionality.
• Basic knowledge of descriptive statistics.
• Basic knowledge of data analysis concepts
.• Basic knowledge of python programming.
• Some exposure to basic programming concepts (such as looping and conditioning).
• Basic knowledge of databases.
• Familiarity with Microsoft Office applications – particularly Excel.

NumPy Basics: Arrays and Vectorized Computation

1
1. NumPy ndarray: A Multidimensional Array Object
  • Creating ndarrays
  • Data Types for ndarrays
  • Arithmetic with NumPy Arrays
  • Basic Indexing and Slicing
  • Boolean Indexing
  • Fancy Indexing
  • Transposing Arrays and Swapping Axes
2
2. Universal Functions: Fast Element-Wise Array Functions
3
3. Array-Oriented Programming with Arrays
  • Expressing Conditional Logic as Array Operations
  • Mathematical and Statistical Methods
  • Methods for Boolean Arrays
  • Sorting
  • Unique and Other Set Logic
4
4. File Input and Output with Arrays
5
5. Linear Algebra
6
6. Pseudorandom Number Generation
7
7. Example: Random Walks
  • Simulating Many Random Walks at Once

Getting Started with pandas.

1
1. Introduction to pandas Data Structures
  • Series
  • Data Frame
  • Index Objects
2
2. Essential Functionality
  • Reindexing
  • Dropping Entries from an Axis
  • Indexing, Selection, and Filtering
  • Integer Indexes
  • Arithmetic and Data Alignment
  • Function Application and Mapping
  • Sorting and Ranking
  • Axis Indexes with Duplicate Labels
3
3. Summarizing and Computing Descriptive Statistics Correlation and Covariance Unique Values, Value Counts, and Membership

Data Loading and Storage and File Formats.

1
1. Reading and Writing Data in Text Format Reading Text Files in Pieces
  • Writing Data to Text Format
  • Working with Delimited Formats
  • JSON Data
  • XML and HTML: Web Scraping
2
2. Binary Data Formats
  • Using HDF5 Format
  • Reading Microsoft Excel Files
3
3. Interacting with Web APIs
4
4. Interacting with Databases

Data Cleaning and Preparation.

1
1. Handling Missing Data
  • Filtering Out Missing Data
  • Filling In Missing Data
  • Data Transformation
  • Removing Duplicates
  • Transforming Data Using a Function or Mapping
  • Replacing Values
  • Renaming Axis Indexes
  • Discretization and Binning
  • Detecting and Filtering Outliers
  • Permutation and Random Sampling
  • Computing Indicator/Dummy Variables
2
2. String Manipulation
  • String Object Methods
  • Regular Expressions
  • Vectorized String Functions in pandas

Data Wrangling: Join and Combine and Reshape.

1
1. Hierarchical Indexing
  • Reordering and Sorting Levels
  • Summary Statistics by Level
  • Indexing with a DataFrame’s columns
2
2. Combining and Merging Datasets
  • Database-Style Data Frame Joins
  • Merging on Index
  • Concatenating Along an Axis
  • Combining Data with Overlap
3
3. Reshaping and Pivoting
  • Reshaping with Hierarchical Indexing
  • Pivoting “Long” to “Wide” Format
  • Pivoting “Wide” to “Long” Format

Plotting and Visualization

1
1. A Brief matplotlib API Primer
  • Figures and Subplots
  • Colors, Markers, and Line Styles
  • Ticks, Labels, and Legends 261Annotations and Drawing on a
  • Subplot
  • Saving Plots to File
  • matplotlib Configuration
2
2. Plotting with pandas and seaborn
  • Line Plots
  • Bar Plots
  • Histograms and Density Plots
  • Scatter or Point Plots
  • Facet Grids and Categorical Data
3
3. Other Python Visualization Tools

Data Aggregation and Group Operations.

1
1. GroupBy Mechanics
  • Iterating Over Groups
  • Selecting a Column or Subset of Columns
  • Grouping with Dicts and Series
  • Grouping with Functions
  • Grouping by Index Levels
2
2. Data Aggregation
  • Column-Wise and Multiple Function Application
  • Returning Aggregated Data Without Row Indexes
3
3. Apply: General split-apply-combine
  • Suppressing the Group Keys
  • Quantile and Bucket Analysis
  • Example: Filling Missing Values with Group-Specific Values
  • Example: Random Sampling and Permutation
  • Example: Group Weighted Average and Correlation
  • Example: Group-Wise Linear Regression
4
4. Pivot Tables and Cross-Tabulation
  • Cross-Tabulations: Crosstab

Time Series.

1
1. Date and Time Data Types and Tools
  • Converting Between String and Date time
2
2. Time Series Basics
  • Indexing, Selection, Subsetting
  • Time Series with Duplicate Indices
3
3. Date Ranges, Frequencies, and Shifting
  • Generating Date Ranges
  • Frequencies and Date Offsets
  • Shifting (Leading and Lagging) Data
4
4. Time Zone Handling
  • Time Zone Localization and Conversion
  • Operations with Time Zone−Aware Timestamp Objects
  • Operations Between Different Time Zones
5
5. Periods and Period Arithmetic
  • Period Frequency Conversion
  • Quarterly Period Frequencies
  • Converting Timestamps to Periods (and Back)
  • Creating a PeriodIndex from Arrays
6
6. Resampling and Frequency Conversion
  • Downsampling
  • Upsampling and Interpolation
  • Resampling with Periods

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