Data Science with Python Course Catalog

Transcription

1 Enhance Your Contribution to the Business, Earn Industry-recognized Accreditations, and Develop Skills that Help You Advance in Your Career March

2 Table of Contents Syllabus Overview Time Duration: 45H To 50H 1 Introduction I. Why Do People Use Python? II. Is Python a Scripting Language? III. Who Uses Python Today? IV. What are the Python s Technical Strengths? V. How Python Runs Program VI. How You Run Python Program 2 Installation I. Installation of Python in Windows, Linux, Mac OS II. Installation of Eclipse IDE Windows, Linux, Mac OS III. Installation of Pip. 3 Object Types or Built-in Types I. Python s Core Data Types II. Numbers III. Strings IV. Lists V. Dictionaries VI. Tuples VII. Files 4 Statements and Syntax I. Assignments, Expressions and Prints II. If Test and Syntax Rules III. While and for Loops IV. break, continue, pass, and the Loop else V. Iterations and Comprehensions 5 Functions and Generators I. Function Basics. II. Scopes III. Arguments IV. Anonymous Functions: lambda V. Comprehensions and Generations

3 6 Modules and Packages I. Module Coding Basics II. How Imports Work III. The module Search Path IV. Package Import Basics V. Package Import Example 7 Objet Oriented Programming I. Why Use Classes? II. Classes and Instances III. Method calls IV. Inheritance (Multilevel and Multiple Inheritance) V. Overriding VI. Polymorphism VII. Method Overloading and Operator Overloading VIII. Encapsulation IX. Abstraction 8 Exceptions and Tools I. Default Exception Handler II. Catching Exceptions III. Raising Exceptions IV. User-Defined Exceptions V. Termination Actions 9 File Handling I. File Handling Basics II. Work with Text Files III. Work with Doc Files IV. Works with Excel-Sheet

4 10 Introduction to Data Science with Python I. Why Python for Data Science? II. Essential Python Libraries I. numpy II. pandas III. Matplotlib and scikit-learn IV. Ipython and Jupyter 11 Installation and Setup I. Install IPython in Windows, Linux and Mac OS II. Install Jupyter in Windows, Linux and Mac OS III. Install numpy in Windows, Linux and Mac OS IV. Install pandas in Windows, Linux and Mac OS V. Install matplotlib in Windows, Linux and Mac OS VI. Install scikit-learn in Windows, Linux and Mac OS 12 NumPy Basics I. A Multidimensional Array Object ( ndarrays ) I. Creating ndarrays II. Data Types for ndarrays III. Basic Indexing and Slicing IV. Fancy Indexing V. Transposing Arrays and Swapping Axes II. Fast Element-Wise Array Functions III. Mathematical and Statistical Methods IV. Sorting V. File Input and Output with Arrays VI. Linear Algebra 13 Getting Started with pandas I. Series, DataFrame and Index Objects II. Reindexing III. Indexing, Selection and Filtering IV. Sorting and Ranking V. Axis Indexes with Duplicate Labels

5 14 Data Loading, Storage, and File Formats I. Reading and Writing Data in Text Format II. Reading Text Files in Pieces III. JSON Data IV. XML and HTML: Web Scrapping V. Reading Microsoft Excel Files VI. Interacting with Web API VII. Interacting with Databases 15 Data Cleaning and Preparation I. Handling Missing Data II. Filtering Out Missing Data III. Filling in Missing Data IV. Removing duplicates V. Replacing values VI. Renaming Axis index VII. Discretization and Binning VIII. Permutation and Random sampling IX. String Manipulation X. String Object methods XI. Vectorized String Functions in pandas 16 Data Wrangling I. Hierarchical Indexing II. Indexing with a DataFrame s columns III. Combining and Merging Datasets IV. Reshaping and Pivoting 17 Plotting and Visualization I. Figures and Subplots II. Colors, Markers and Line Style III. Ticks, Labels, and Legends IV. Annotations and Drawing on a Subplot V. Saving Plots to File VI. Matplotlib Configuration

6 18 Data Aggregation and Group Operations I. Iterating Over Groups II. Selecting a column or Subset of Columns III. Grouping with Dicts and Series IV. Grouping with Functions V. Grouping with Index Levels VI. Data Aggregation VII. Suppressing the Group Keys VIII. Example: Filling Missing Values with Group-Specific Values IX. Example: Random Sampling and Permutation X. Pivot Tables and Cross-Tabulation 19 Time Series I. Date and Time Data Types and Tools II. Time Series Basics III. Indexing, Selecting, Subsetting IV. Date, Range, Frequencies, and Shifting V. Time Zone Handling VI. Periods and Period Arithmetic VII. Resampling and Frequency Conversion VIII. Downsampling IX. Upsampling and Interpolation X. Moving Window Functions XI. User Defined Moving Window Functions

7 Python Course Catalog Course Description Target Audience Data science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.[3] It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science,. Fresher, Experienced who want to switch their technology. Enthusiastic Techie who want to work on future technologies. Duration Style Delivery Prerequisites Hours Self-paced Class Room Training, e-learning None