Advanced Certification Python for Data Science

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PART-1 Core Python

Lesson 1
Getting started with Python programming

  • Overview
  • Introductory Remarks about Python
  • A Brief History of Python
  • How python is differ from other languages
  • Python Versions
  • Installing Python and Environment Setup
  • IDLE
  • Getting Help
  • How to execute Python program
  • Writing your first Python program
  • How to work on different Popular IDE’s [Pycharm, Jupyter Notebook, Spyder etc.]

Lesson 2
Variables, Keywords and Operators

  • Variables
  • Memory mapping of variables
  • Keywords in Python
  • Comments in python
  • Operators
    1. Arithmetic Operators
    2. Assignment Operators
    3. Comparison Operators
    4. Logical Operators
    5. Membership Operators
    6. Identity Operators
    7. Bitwise Operators
  • Basics I/O and Type casting
  • Getting user input

Lesson 3
Data types in Python

  1. Numbers
  2. Strings
  3. Lists
  4. Tuples
  5. Dictionary
  6. Sets

Lesson 4
Numbers and Strings

  • Introduction to Python ‘Number’ & ‘string’ data types
  • Properties of a string
  • String built-in functions
  • Programming with strings
  • String formatting

Lesson 5
Lists and Tuples

  • Introduction to Python ‘list’ data type
  • Properties of a list
  • List built-in functions
  • Programming with lists
  • List comprehension
  • Introduction to Python ‘tuple’ data type
  • Tuples as Read only lists

Lesson 6
Dictionary and Sets

  • Introduction to Python ‘dictionary’ data type
  • Creating a dictionary
  • Dictionary built-in functions
  • Introduction to Python ‘set’ data type
  • Set and set properties
  • Set built-in functions

Lesson 7
Decision making & Loops

  • Introduction of Decision Making
  • Control Flow and Syntax
  • The if Statement
  • The if…else Statement
  • The if…elif…else Statement
  • Nested if…else Statement
  • The while Loop
  • break and continue  Statement
  • The for Loop
  • Pass statement
  • Exercise

Lesson 8
User defined Functions

  • Introduction of functions
  • Function definition and return
  • Function call and reuse
  • Function parameters
  • Function recipe and docstring
  • Scope of variables
  • Recursive functions
  • Lambda Functions / Anonymous Functions
  • Map , Filter & Reduce functions

Lesson 9
Lessons and Packages

  • Lessons
  • Importing Lesson
  • Standard Lesson – sys
  • Standard Lesson – OS
  • The dir Function
  • Packages
  • Exercise

Lesson 10
Regular expression

  • Pattern matching
  • Meta characters for making patterns
  • re flags
  • Use of match() , sub() , findall(), search(), split() methods

Part -2 Data Analysis

LESSON 1
GETTING STARTED WITH PYTHON LIBRARIES

  • What is data analysis?
  • Why python for data analysis?
  • Essential Python Libraries Installation and setup
  • Ipython
  • Jupyter Notebook

LESSON 2
NUMPY ARRAYS

  • Introduction to Numpy
  • Numpy Arrays
  • Numpy Data types
  • Numpy Array Indexing
  • Numpy  Mathematical Operations
  • Indexing and slicing
  • Manipulating array shapes
  • Stacking arrays
  • Sorting arrays
  • Creating array views and copies
  • I/O with NumPy
  • Numpy Exercises

LESSON 3
WORKING WITH PANDAS

  1. Introduction to Pandas
  • Data structure of pandas
  • Pandas Series
  • Pandas dataframes
  • Data aggregation with Pandas
  • DataFrames Concatenating and appending
  • DataFrames Joining
  • DataFrames Handling missing data
  • Data Indexing and Selection
  • Operating on data in pandas
  • loc and iloc
  • map,apply,apply_map
  • group_by
  • string methods
  • Querying data in pandas
  • Dealing with dates
  • Reading and Writing to CSV files with pandas
  • Reading and Writing to Excel with pandas
  • Reading and Writing to SQL with pandas
  • Reading and Writing to HTML files with pandas
  • Pandas Exercises

Part -3 Data Visualization

LESSON 1
Matplotlib

  • Introduction of Matplotlib
  • Basic matplotlib plots
  • Line Plots
  • Bar Plots
  • Pie Plots
  • Scatter plots
  • Histogram Plots
  • Saving plots to file
  • Plotting functions in matplotlib
  • Matplotlib Exercises

LESSON 2
Seaborn

  • Introduction of Seaborn
  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Bar Plots
  • Box Plots
  • Strip Plots
  • Violin Plots
  • Clustermap Plots
  • Heatmaps Plots
  • KDE Plots
  • Regression Plots
  • Style and Color
  • Seaborn Exercise

LESSON 3
Plotly and Cufflinks

  • Introduction to Plotly and Cufflinks
  • Plotly and Cufflinks

LESSON 4
Geographical Plotting

  • Introduction to Geographical Plotting
  • Choropleth Maps – Part 1
  • Choropleth Maps – Part 2
  • Choropleth Exercises
  • Projects using Analysis and Visualisation

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OBJECTIVE OF THE COURSE
REQUIREMENTS AND PREREQUISITES FOR THE COURSE
Outcome