Python for Data analysis and AI Specialist Certificate

1.Learning Methodology

• Instructor-Led Classroom Training (ILT).

2.Prerequisites:

• Basic skills with at least one programming language are desirable
• Familiar with the basic math and statistic concepts

3.Training Program Description:

• In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?
• This course will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:
  • python
  • jupyter notebooks
  • pandas
  • numpy
  • matplotlib
  • git
  • and many other tools.
• You will learn these tools all within the context of solving compelling data science problems.
• After completing this course, you’ll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.
• By learning these skills, you’ll also become a member of a worldwide community that seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science School and AI School.

• Duration of Program: 5 weeks

• Who is this class for?
  • This course is primarily for individuals who are passionate about the field of data science and data analysts and who are aspiring to apply machine learning in their business, industry or research.
  • Developers and Software Engineers
  • Analytics Managers and Professionals
  • Statisticians with an interest in Machine

What you will learn

• Use Python to access and analyze data from several different data sources.
• The basic process of data science
• Python and Jupyter notebooks
• An applied understanding of how to manipulate and analyze uncurated datasets
• Basic statistical analysis and machine learning methods
• How to effectively visualize results

5.Training Program Curriculum:

I-PYTHON 3 TOPICS

• INTRODUCTION
  • SETTING UP PYTHON
  • SYNTAX
  • FIRST PYTHON PROGRAM
  • VARIABLES & DATA TYPES
  • NUMBERS AND MATH
  • STRINGS
  • OPERATORS AND BITWISE
  • DATA STRUCTURES OF PYTHON
    • TUPLES
    • LISTS
    • SETS
    • DICTIONARIES
  • CONDITIONAL LOGIC AND CONTROL FLOW
  • FUNCTIONS AND LOOPS
• OBJECT-ORIENTED PROGRAMMING (OOP)
  • CLASSES
  • INHERITANCE
  • PYTHON GENERATORS
  • PYTHON DECORATORS
  • EXCEPTIONS
  • REGULAR EXPRESSIONS
  • MULTITHREADING AND MULTIPROCESSING SOCKETS AND APIS

II-DATA ANALYSIS TOPICS

• DATA ANALYSIS
  • ANACONDA / JUPYTER NOTEBOOKS
  • DATABASE WITH SQLITE
  • NUMPY AND MATRIX OPERATIONS
  • PANDAS
  • DATA VISUALIZATION
  • GIT VERSION CONTROL
  • WEB SCRAPING

III-DATA STRUCTURES & ALGORITHMS TOPICS

• INTRODUCTION
  • HOW TO SOLVE PROBLEMS
  • BIG O NOTATION
• DATA STRUCTURES
  • COLLECTION DATA STRUCTURES (LISTS, ARRAYS, LINKED LISTS, QUEUES, STACK)
  • RECURSION
  • TREES
  • MAPS AND HASHING
• BASIC ALGORITHMS
  • BINARY SEARCH
  • SORTING ALGORITHMS
  • DIVIDE & CONQUER ALGORITHMS
  • MAPS AND HASHING
  • PRACTICE PROBLEMS: RANDOMIZED BINARY SEARCH, K-SMALLEST ELEMENTS USING HEAPS, BUILD RED-BLACK TREE, BUBBLE SORT, MERGE SORT, QUICK SORT, SORTING STRINGS, LINEAR-TIME MEDIAN FINDING