DATA SCIENCE INTERVIEW QUESTIONS PART 1

1. INTRODUCTION TO DATA SCIENCE

• What is a Data Science?
• Who is a Data Scientist?
• Who can become a Data Scientist?
• What is 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
• Arithmetic Operators
• Comparison (Relational) Operators
•  Assignment Operators
• Logical Operators
• Bitwise Operators
• Membership Operators
• Identity Operators
• Slicing & Indexing
• Forward Direction Slicing with +ve Step
• Backward Direction Slicing with -ve Step
• Decision-Making Statements
• if Statement
• if-else Statement
• Elif Statement
• Looping Statements
• Why do we use Loops in Python?
• Advantages of Loops
• for Loop
• Nested for Loop
• Using else Statement with for Loop
• while Loop
•  Infinite while Loop
• Using else with Python while Loop
• Conditional Statements
• break Statement
• continue Statement
• 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)
• Object-oriented vs Procedure-oriented Programming
• languages
• Object
• Class
• Method
• Inheritance
• Polymorphism
• Data Abstraction
• Encapsulation
• Python Class and Objects
• Creating Classes in Python
• Creating an Instance of the Class
• Python Constructor
• Creating the Constructor in Python
• Parameterized Constructor
• Non-Parameterized Constructor
• In-built Class Functions
• In-built Class Attributes
• Python Inheritance
• Python Multi-Level Inheritance
• Python Multiple Inheritance                         
   Method Overriding
• Data Abstraction in Python
• Graphical User Interface (GUI) Programming
• Python Tkinter
• Tkinter Geometry
• pack() Method
• grid() Method
• place() Method
• Tkinter Widgets 

5. DATA ANALYSIS WITH PYTHON NUMPY

• NumPy Introduction
• What is NumPy
• The Need for NumPy
• NumPy Environment Setup
• N-Dimensional Array (array)
• Creating a Ndarray Object
• Finding the Dimensions of the Array
• Finding the Size of Each Array Element
• Finding the Data Type of Each Array Item
• Finding the Shape and Size of the Array
• Reshaping the Array Objects
• Slicing in the Array
• Finding the Maximum, Minimum, and Sum of the Array
• Elements
• NumPy Array Axis
• Finding Square Root and Standard Deviation
• Arithmetic Operations on the Array
• Array Concatenation
• NumPy Datatypes
• NumPy D type
• Creating a Structured Data Type
• Numpy Array Creation
• Numpy. empty
• Numpy. Zeros
• NumPy.ones
• Numpy Array from Existing Data
• Numpy. as array
• NumPy Arrays within the Numerical Range
•  Numpy. arrange
• NumPy. space
• Numpy. logspace
• NumPy Broadcasting  or Broadcasting Rules
• NumPy Array Iteration
• Order of Iteration
• F-Style Order
• C-Style Order
• Array Values Modification
• NumPy String Functions
• NumPy Mathematical Functions
• Trigonometric Functions
• Rounding Functions
• NumPy Statistical functions
• Finding the Min and Max Elements from the Array
• 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
• Key Features of Pandas
• Benefits of Pandas
• Python Pandas Data Structure
• Series
• Data Frame
• Panel
• Pandas Series
• Creating a Series
• Create an Empty Series
• Create a Series using Inputs
• Accessing Data from Series with Position
• Series Object Attributes
• Retrieving Index Array and Data Array of a Series Object
• Retrieving Types (type) and Size of Type (item size)
• Retrieving Shape
• Retrieving Dimension, Size, and Number of Bytes
• Checking Emptiness and Presence of NaNs
• Series Functions
• Pandas Data Frame
• 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
• Datetime
• Time Offset
• Periods
• 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
• Line: Line Plot (Default)
• Bar: Vertical Bar Plot
• Barh: Horizontal Bar Plot
• Hist: Histogram Plot
• Box: Box Plot
• Pie: Pie Chart
• 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
• 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
• What is a Boxplot?
• Mean, Median, Quartiles, Outliers
• Inter Quartile Range (IQR), Whiskers
• Data Distribution Analysis
• Boxplot on a Normal Distribution
• Probability Density Function
• 68–95–99.7 Rule (Empirical rule)

10.DATA VISUALIZATION

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 Gathering Data Data preparation Data Wrangling Analyze Data Train the model Test the model Deployment Features of Machine Learning History of Machine Learning Applications of Machine Learning Types of Machine Learning Supervised Machine Learning Unsupervised Machine Learning Reinforcement Learning 12. Supervised Machine Learning How Supervised Learning Works? Steps Involved in Supervised Learning Types of Supervised Machine Learning Algorithms Classification 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 Clustering Association Advantages of Unsupervised Learning Disadvantages of Unsupervised Learning Supervised Vs Unsupervised Learning     Reinforcement Machine Learning How to get Datasets for Machine Learning? What is a Dataset? Types of Data in Datasets Popular Sources for Machine Learning Datasets  14. Data Preprocessing in Machine Learning Why do we need Data Preprocessing? Getting the Dataset Importing Libraries Importing Datasets Finding Missing Data By Deleting the Particular Row By Calculating the Mean Encoding Categorical Data Label Encoder One Hot Encoder Splitting Dataset into Training and Test Set Feature Scaling Standardization Normalization 15. Classification Algorithms in Machine Learning What is the Classification Algorithm? Types of Classifications Binary Classifier Multi-class Classifier Learners in Classification Problems Lazy Learners Eager Learners Types of ML Classification Algorithms Linear Models Logistic Regression Support Vector Machines Non-linear Models K-Nearest Neighbors Naïve Bayes Decision Tree Classification Random Forest Classification Kernel SVM Evaluating a Classification Model 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 Log Loss or Cross-Entropy Loss AUC-ROC curve Use cases of Classification Algorithms