Interview Questions


  • 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 a 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


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


  • 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 &
    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 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


  • 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, Bytearray & 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 (OOPs)
    o Object-oriented vs Procedure-oriented Programming
    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 


  • NumPy Introduction
    o What is NumPy
    o The Need of 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
    o NumPy Array Axis
    o Finding Square Root and Standard Deviation
    o Arithmetic Operations on the Array
    o Array Concatenation
     NumPy Datatypes
    o NumPy dtype
    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.asarray
     Numpy Arrays within the Numerical Range
    o Numpy.arrange
    o NumPy.linspace
    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


  •  Pandas Introduction & Pandas Environment Setup
    o Key Features of Pandas
    o Benefits of Pandas
    o Python Pandas Data Structure
     Series
     DataFrame
     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 (dtype) and Size of Type (itemsize)
    o Retrieving Shape
    o Retrieving Dimension, Size and Number of Bytes
    o Checking Emptiness and Presence of NaNs
    o Series Functions
     Pandas DataFrame
    o Create a DataFrame
     Create an Empty DataFrame
     Create a DataFrame using Inputs                         Column Selection, Addition & Deletion
     Row Selection, Addition & Deletion
     DataFrame Functions
     Merging, Joining & Combining DataFrames
     Pandas Concatenation
     Pandas Time Series
    o Datetime
    o Time Offset
    o Time Periods
    o Convert String to Date
     Viewing/Inspecting Data (loc & iloc)
     Data Cleaning
     Filter, Sort, and Groupby
     Statistics on DataFrame
     Pandas Vs NumPy
     DataFrame 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 mysql.connector Module
     Create the Connection Object
     Create the Cursor Object
     Execute the Query


  •  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 MatPlotLib
     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)


  • Bar Chart 
  • Histogram
  • Box whisker
  •  plot Line plot
  • Scatter Plot and Heat Maps

11.Machine Learning

  •  What is Machine Learning
     Importance of Machine Learning
     Need for Machine Learning
     Statistics Vs Machine Learning
     Traditional Programming Vs Machine Learning
     How Machine Learning like Human Learning
     How does Machine Learning Work?
     Machine Learning Engineer Responsibilities
     Life Cycle of Machine Learning
    o Gathering Data
    o Data preparation
    o Data Wrangling
    o Analyze Data
    o Train the model
    o Test the model
    o Deployment
     Features of Machine Learning
     History of Machine Learning
     Applications of Machine Learning
     Types of Machine Learning
    o Supervised Machine Learning
    o Unsupervised Machine Learning
    o Reinforcement Learning

12.Supervised Machine Learning

  • How Supervised Learning Works?
     Steps Involved in Supervised Learning
     Types of supervised Machine Learning Algorithms
    o Classification
    o Regression
     Advantages of Supervised Learning
     Disadvantages of Supervised Learning

13.Unsupervised Machine Learning

  •  How Unsupervised Learning Works?
     Why use Unsupervised Learning?
     Types of Unsupervised Learning Algorithm
    o Clustering
    o Association
     Advantages of Unsupervised Learning
     Disadvantages of Unsupervised Learning
     Supervised Vs Unsupervised Learning     Reinforcement Machine Learning
     How to get Datasets for Machine Learning?
    o What is a Dataset?
    o Types of Data in Datasets
    o Popular Sources for Machine Learning Datasets 

14.Data Preprocessing in Machine Learning

  • Why do we need Data Preprocessing?
    o Getting the Dataset
    o Importing Libraries
    o Importing Datasets
    o Finding Missing Data
     By Deleting the Particular Row
     By Calculating the Mean
    o Encoding Categorical Data
     LableEncoder
     OneHotEncoder
    o Splitting Dataset into Training and Test Set
    o Feature Scaling
     Standardization
     Normalization

15.Classification Algorithms in Machine Learning

  • What is the Classification Algorithm?
     Types of Classifications
    o Binary Classifier
    o Multi-class Classifier
     Learners in Classification Problems
    o Lazy Learners
    o Eager Learners
     Types of ML Classification Algorithms
    o Linear Models
     Logistic Regression
     Support Vector Machines
    o Non-linear Models
     K-Nearest Neighbors
     Naïve Bayes
     Decision Tree Classification
     Random Forest Classification
     Kernel SVM
     Evaluating a Classification Model
    o Confusion Matrix
     What is a Confusion Matrix?                                 True Positive
     True Negative
     False Positive – Type 1 Error
     False Negative – Type 2 Error
     Why need a Confusion matrix?
     Precision
     Recall
     Precision vs Recall
     F1-score
     Confusion Matrix in Scikit-Learn
     Confusion Matrix for Multi-Class Classification
    o Log Loss or Cross-Entropy Loss
    o AUC-ROC curve
     Use cases of Classification Algorithms

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