1. INTRODUCTION TO DATA SCIENCE

• What is a Data Science?
  Who is a Data Scientist?
  Who can become a Data Scientist?
  What is an Artificial Intelligence?
  What is a Machine Learning?
  What is Deep Learning?
  Artificial Intelligence vs. Machine Learning vs. deep Learning
      Real-Time Process of Data Science
  Data Science Real-Time Applications
  Technologies Used in Data Science
  Prerequisites Knowledge to Learn Data Science

2. INTRODUCTION TO MACHINE LEARNING

• What is Machine Learning?
  Machine Learning Vs Statistics
  Traditional Programming Vs Machine Learning
  How Machines Will Learn Like Human Learning
  Machine Learning Engineer Responsibilities
  Types of Machine Learning
  Supervised learning
  Un-Supervised learning
  Reinforcement Learning

3.CORE PYTHON PROGRAMMING

• PYTHON Programming Introduction
  History of Python
  Python is Derived from?
  Python Features
  Python Applications
  Why Python is Becoming Popular Now a Day?
  Existing Programming Vs Python Programming
  Writing Programs in Python Top Companies Using Python
  Python Programming Modes
     o Interactive Mode Programming
     o Scripting Mode Programming
  Flavors in Python, Python Versions
  Download & Install the Python in Windows & Linux
  How to set Python Environment in the System?
  Anaconda – Data Science Distributor
  Downloading and Installing Anaconda, Jupyter Notebook &
  Spyder
  Python IDE – Jupyter Notebook Environment
  Python IDE – Spyder Environment
  Python Identifiers(Literals), Reserved Keywords
  Variables, Comments
  Lines and Indentations, Quotations
  Assigning Values to Variables
  Data Types in Python
  Mutable Vs Immutable
  Fundamental Data Types: int, float, complex, bool, str
  Number Data Types: Decimal, Binary, Octal, Hexa Decimal &
  Number Conversions
  Inbuilt Functions in Python
  Data Type Conversions
  Priorities of Data Types in Python
  Python Operators
• o Arithmetic Operators
  o Comparison (Relational) Operators
  o Assignment Operators
  o Logical Operators
  o Bitwise Operators
  o Membership Operators
  o Identity Operators
• Slicing & Indexing
  o Forward Direction Slicing with +ve Step
  o Backward Direction Slicing with -ve Step
  Decision-Making Statements
  o if Statement
  o if-else Statement
  o Elif Statement
  Looping Statements
  o Why do we use Loops in Python?
  o Advantages of Loops
  o for Loop
• o Nested for Loop
  o Using else Statement with for Loop
  o while Loop
  o Infinite while Loop
  o Using else with Python while Loop
  Conditional Statements
  o break Statement
  o continue Statement
  o Pass Statement

4. ADVANCED PYTHON PROGRAMMING

• Advanced Data Types: List, Tuple, Set, Frozenset, Dictionary,
  Range, Bytes & Bytearray, None
  List Data Structure
  o List indexing and splitting
  o Updating List values
  o List Operations
  o Iterating a List
  o Adding Elements to the List
  o Removing Elements from the List
  o List Built-in Functions
  o List Built-in Methods
  Tuple Data Structure
  o Tuple Indexing and Splitting
  o Tuple Operations
  o Tuple Inbuilt Functions
  o Where use Tuple
  o List Vs Tuple
  o Nesting List and Tuple
  Set Data Structure
  o Creating a Set
  o Set Operations
  o Adding Items to the Set
  o Removing Items from the Set
  o Difference Between discard() and remove()
  o Union of Two Sets
  o Intersection of Two Sets
  o Difference of Two Sets
  o Set Comparisons
  Frozenset Data Structure
  Dictionary Data Structure
  o Creating the Dictionary
  o Accessing the Dictionary Values              o Updating Dictionary Values
  o Deleting Elements Using del Keyword
  o Iterating Dictionary
  o Properties of Dictionary Keys
  o Built-in Dictionary Functions
  o Built-in Dictionary Methods
  List Vs Tuple Vs Set Vs Frozenset Vs Dict
  Range, Bytes, By tearray & None
  Python Functions
  o Advantage of Functions in Python
  o Creating a Function
  o Function Calling
  o Parameters in Function
  o Call by Reference in Python
  o Types of Arguments
  Required Arguments
  Keyword Arguments
  Default Arguments
  Variable-Length Arguments
  Scope of Variables
  Python Built-in Functions
  Python Lambda Functions
  String with Functions
  o Strings Indexing and Splitting
  o String Operators
  o Python Formatting Operator
  o Built-in String Functions
  Python File Handling
  o Opening a File
  o Reading the File
  o Read Lines of the File
  o Looping through the File
  o Writing the File
  o Creating a New File
  o Using with Statement with Files
  o File Pointer Positions
  o Modifying File Pointer Position
  o Renaming the File & Removing the File
  o Writing Python Output to the Files
  o File-Related Methods
  Python Exceptions
  o Common Exceptions
  o Problem without Handling Exceptions        o except Statement with no Exception
  o Declaring Multiple Exceptions
  o Finally Block
  o Raising Exceptions
  o Custom Exception
  Python Packages
  o Python Libraries
  o Python Modules
  Collection Module
  Math Module
  OS Module
  Random Module
  Statistics Module
  Sys Module
  Date & Time Module
  o Loading the Module in our Python Code
  import Statement
  from-import Statement
  o Renaming a Module
  Regular Expressions
  Command Line Arguments
  Object Oriented Programming (OOP)
  o Object-oriented vs Procedure-oriented Programming
  languages
  o Object
  o Class
  o Method
  o Inheritance
  o Polymorphism
  o Data Abstraction
  o Encapsulation
  Python Class and Objects
  o Creating Classes in Python
  o Creating an Instance of the Class
  Python Constructor
  o Creating the Constructor in Python
  o Parameterized Constructor
  o Non-Parameterized Constructor
  o In-built Class Functions
  o In-built Class Attributes
  Python Inheritance
  o Python Multi-Level Inheritance
  o Python Multiple Inheritance                          o Method Overriding
  o Data Abstraction in Python
  Graphical User Interface (GUI) Programming
  Python Tkinter
  o Tkinter Geometry
  pack() Method
  grid() Method
  place() Method
  o Tkinter Widgets 

5. DATA ANALYSIS WITH PYTHON NUMPY

• NumPy Introduction
  o What is NumPy
  o The Need for NumPy
  NumPy Environment Setup
   N-Dimensional Array (ndarray)
  o Creating a Ndarray Object
  o Finding the Dimensions of the Array
  o Finding the Size of Each Array Element
  o Finding the Data Type of Each Array Item
  o Finding the Shape and Size of the Array
  o Reshaping the Array Objects
  o Slicing in the Array
  o Finding the Maximum, Minimum, and Sum of the Array
  Elements
  o NumPy Array Axis
  o Finding Square Root and Standard Deviation
  o Arithmetic Operations on the Array
  o Array Concatenation
   NumPy Datatypes
  o NumPy D type
  o Creating a Structured Data Type
   Numpy Array Creation
  o Numpy. empty
  o Numpy. Zeros
  o NumPy.ones
   Numpy Array from Existing Data
  o Numpy. as array
   NumPy Arrays within the Numerical Range
  o Numpy. arrange
  o NumPy. space
  o Numpy. logspace
   NumPy Broadcasting  o Broadcasting Rules
   NumPy Array Iteration
  o Order of Iteration
   F-Style Order
   C-Style Order
  o Array Values Modification
   NumPy String Functions
   NumPy Mathematical Functions
  o Trigonometric Functions
  o Rounding Functions
   NumPy Statistical functions
  o Finding the Min and Max Elements from the Array
  o Calculating Median, Mean, and Average of Array Items
   NumPy Sorting and Searching
   NumPy Copies and Views
   NumPy Matrix Library
   NumPy Linear Algebra
   NumPy Matrix Multiplication in Python

6. DATA ANALYSIS WITH PYTHON PANDAS

•  Pandas Introduction & Pandas Environment Setup
  o Key Features of Pandas
  o Benefits of Pandas
  o Python Pandas Data Structure
   Series
   Data Frame
   Panel
   Pandas Series
  o Creating a Series
   Create an Empty Series
   Create a Series using Inputs
  o Accessing Data from Series with Position
  o Series Object Attributes
  o Retrieving Index Array and Data Array of a Series Object
  o Retrieving Types (type) and Size of Type (item size)
  o Retrieving Shape
  o Retrieving Dimension, Size, and Number of Bytes
  o Checking Emptiness and Presence of NaNs
  o Series Functions
   Pandas Data Frame
  o Create a Data Frame
   Create an Empty Data Frame
   Create a data frame using Inputs                         Column Selection, Addition & Deletion
   Row Selection, Addition & Deletion
   Data Frame Functions
   Merging, Joining & Combining Data Frames
   Pandas Concatenation
   Pandas Time Series
  o Datetime
  o Time Offset
  o Periods
  o Convert String to Date
   Viewing/Inspecting Data (loc & loc)
   Data Cleaning
   Filter, Sort, and Group by
   Statistics on Data Frame
   Pandas Vs NumPy
   Data Frame Plotting
  o Line: Line Plot (Default)
  o Bar: Vertical Bar Plot
  o Barh: Horizontal Bar Plot
  o Hist: Histogram Plot
  o Box: Box Plot
  o Pie: Pie Chart
  o Scatter: Scatter Plot

7. DBMS – Structured Query Language

• Introduction & Models of DBMS
   SQL & Sub Language of SQL
   Data Definition Language (DDL)
   Data Manipulation Language (DML)
   Data Query/Retrieval Language (DQL/DRL)
   Transaction Control Language (TCL)
   Data Control Language (DCL)
   Installation of MySQL & Database Normalization
   Sub Queries & Key Constraints
   Aggregative Functions, Clauses & Views

8. Importing & Exporting Data

• Data Extraction from CSV (pd.read_csv)
   Data Extraction from TEXT File (pd.read_table)
   Data Extraction from CLIPBOARD (pd.read_clipboard)
   Data Extraction from EXCEL (pd.read_excel)
   Data Extraction from URL (pd.read_html)
   Writing into CSV (df.to_cs                                   Writing into EXCEL (df.to_excel)
   Data Extraction from DATABASES
  o Python MySQL Database Connection
   Import my sql. connector Module
   Create the Connection Object
   Create the Cursor Object
   Execute the Query

9.DATA VISUALIZATION WITH PYTHON MATPLOTLIB

•  Data Visualization Introduction
   Tasks of Data Visualization
   Benefit of Data Visualization
   Plots for Data Visualization
   Matplotlib Architecture
   General Concept of Matplotlib
   MatPlotLib Environment Setup
   Verify the matplotlib Installation
   Working with PyPlot
   Formatting the Style of the Plot
   Plotting with Categorical Variables
   Multi-Plots with Subplot Function
   Line Graph
   Bar Graph
   Histogram
   Scatter Plot
   Pie Plot
   3Dimensional – 3D Graph Plot
   mpl_toolkits
   Functions of Mat Plot Lib
   Contour Plot, Quiver Plot, Violin Plot
   3D Contour Plot
   3D Wireframe Plot
   3D Surface Plot
   Box Plot
  o What is a Boxplot?
  o Mean, Median, Quartiles, Outliers
  o Inter Quartile Range (IQR), Whiskers
  o Data Distribution Analysis
  o Boxplot on a Normal Distribution
  o Probability Density Function
  o 68–95–99.7 Rule (Empirical rule)

10.DATA VISUALIZATION