Top 30 Python Pattern Programs You Must Know About

Updated on 20 May, 2024

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28 min read
Python Pattern Programs

Summary

Pattern in Python or “Python patterns” is an essential part of Python programming, especially when you are just starting out with using algorithms to print various types of output in Python. Number pattern programs in Python are extremely popular when it comes to examinations and assessments. For example, pattern questions in Python such as the number pyramid pattern in Python are recurring questions given in various job interviews.

In this article, you will learn the top 18 python pattern programs you must know about. Take a glimpse below.

Pattern #1: Simple Number Triangle Pattern 

Pattern #2: Inverted Pyramid of Numbers

Pattern #3: Half Pyramid Pattern of Numbers

 Pattern #4: Inverted Pyramid of Descending Numbers

 Pattern #5: Inverted Pyramid of the Same Digit 

 Pattern #6: Reverse Pyramid of Numbers

 Pattern #7: Inverted Half Pyramid Number Pattern

 Pattern #8: Pyramid of Natural Numbers Less Than 10

 Pattern #9: Reverse Pattern of Digits from 10

 Pattern #10: Unique Pyramid Pattern of Digits

 Pattern #11: Connected Inverted Pyramid Pattern of Numbers

 Pattern #12: Even Number Pyramid Pattern …so on…

Read the full article to know more about all 18 Python Pattern Programs in detail.

Python is a user-friendly language, allowing diverse helpful features to simplify the coding process and enable users to develop exceptional programming prowess. Users are free to access diverse libraries containing modules with program codes for structuring any framework, and it is one of the greatest reasons why pattern question in python fame among programmers is unlikely to die down.

When put to use, these programming skills can reap exceptional results though making it through the interview round with challenging questionnaires can be a difficult task requiring more logical programming solutions. 

Preparing for technical interviews takes a lot of preparation, and it’s highly probable that you might have to create Python pattern programs there. That’s why we’ve sorted a list of multiple ideas for pattern printing in Python to start your preparations. 

Python is a user-friendly language, allowing diverse helpful features to simplify the coding process and enable users to develop exceptional programming prowess. Users are free to access diverse libraries containing modules with program codes for structuring any framework, and it is one of the greatest reasons why Python’s fame among programmers is unlikely to die down.

Python has its applications in many areas such as web development, game development, software development, network programming, and database access. Python pattern programs are also useful because it creates data visualisation, solves complex calculations, and helps in the analysation of the data. It has many other advantages also added to it, for example, its syntax is similar to the English language, so it is easier to code and also easily portable, and has massive libraries to use. There are some of the factors that contribute to python’s popularity.

When put to use, these programming skills can reap exceptional results though making it through the interview round with challenging questionnaires can be a difficult task requiring more logical programming solutions. 

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What are Python patterns? 

Python patterns encode programs in different shapes and formats to create recognized patterns. These patterns are built using different combinations of codes to allow programmers logical practice to implement the same strategy in real-life courses and improve programming skills.

Some of the most famous Python programs called the number triangle, pyramid pattern in Python, reverse, mirrored, equilateral, and star pattern in Python equip programmers to accomplish complex programming issues. Therefore, preparing them to take on kind of coding patterns for precision and explaining how to print pattern in Python.

We have multiple kinds of Python pattern programs in this list, so choose your expertise and experience. Make sure that you understand what part of code does what before you move onto the next pattern. Without a proper understanding of how the system works, you would face a lot of difficulty in explaining its working.

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Many times, the interviewer asks for an explanation of how you performed pattern printing in Python. Knowing how everything works will help you in answering those questions effectively. Data science certification in your resume improves your chance of getting hired. 

While how to print pattern in Python is a typical question for many people, these Python patterns can easily be printed using multiple combinations of multiple for, while, or for while loops. The working of pyramid and star pattern in pattern question in python depends on a few of the major points that include- the outer loop outputs the number of rows in a Python syntax, while to output the number of columns, an inner loop is used. 

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You’ll find the Python code along with every pattern below: 

Key Features of Python

Having gained an understanding of what Python pattern programs entail, it’s time to delve into several fundamental characteristics that define Python: 

1. Easy to Learn and Readable Language      

Python boasts exceptional ease of learning, whose syntax is remarkably straightforward, and its learning curve associated with Python pattern programs is notably gentle. Coding in Python is highly accessible, and using indentation instead of traditional curly braces contributes to the readability of Python code. This quality has led to many educational institutions, ranging from schools to colleges and universities, adopting Python as the initial coding language for their students embarking on their coding journeys.

2. Interpreted Language 

Python pattern programs function as the interpreted language, a programming paradigm where programs are typically interpreted rather than compiled into machine-level instructions. In this approach, the instructions aren’t executed directly by the target machine; instead, they are read and run by a separate program called an interpreter. Python includes an Interactive Development Environment (IDLE) as part of its package. This IDLE functions as an interpreter and follows the structure of REPL (Read Evaluate Print Loop), akin to the operation of Node.js. When using IDLE, Python code is executed, and its output is displayed one line at a time. Consequently, when running a line of Python code, any errors are promptly exhibited, including a comprehensive stack trace detailing the error.

3. Dynamically Typed Language

Python pattern programs operate as a dynamically typed language, implying that there’s no requirement to declare the data types of defined variables explicitly. Instead, the Python interpreter is responsible for ascertaining variable data types during runtime, guided by the kinds of components within an expression. While this characteristic enhances coding convenience for programmers, it also introduces the potential for runtime errors. To elaborate, Python adheres to the concept of duck typing.

4. Open Source and Free

Python is an open-source programming language, accessible for free download from its official website. The community of Python pattern programs enthusiasts consistently collaborates to enhance the Python codebase, striving for continual improvement.

5. High-Level Language

A high-level language (HLL) is a programming language that empowers programmers to create programs mainly agnostic to the specifics of a particular computer architecture. These languages are considered “high-level” due to their close resemblance to human languages and their significant abstraction from machine-level languages. Unlike C, Python belongs to the category of high-level languages. Python’s comprehensibility is notably high, and its proximity to the user surpasses that of middle-level languages like C. With Python, there’s no need to retain intricate system architecture details or handle memory management intricacies.

6. Portable

Python pattern programs possess portability, signifying that identical code may be employed across diverse machines. For instance, if you create a Python script on your Mac, it can be executed on Linux or Windows without necessitating any modifications. This eliminates the requirement to adapt the code for various platforms, eliminating the need to develop separate programs for multiple operating systems.

7. Object-Oriented and Procedure-Oriented

A programming language adopts an object-oriented approach when its design centers on data and objects rather than functions and logic. Conversely, a programming language is considered procedure-oriented when its emphasis lies more on parts that can be reused. A pivotal feature of Python is its capability to accommodate object-oriented and procedure-oriented programming paradigms.

8. Support for GUI

One of the critical aspects of any programming language is support for GUI or Graphical User Interface. A user may interact with software easily using the GUI. Moreover, Python pattern programs also offer several toolkits, like wxPython, Tkinter, and JPython, enabling the GUI’s fast and easy development.

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Eager to put your Python skills to the test or build something amazing? Dive into our collection of Python project ideas to inspire your next coding adventure.

Python Pattern Program List for Beginners With Examples

Pattern problems in python encode programs in different shapes and formats to create recognized patterns. These patterns are built using different combinations of codes to allow programmers logical practice to implement the same strategy in real-life courses and improve programming skills.

Python design patterns are important because they help to generate repeatable solutions to those problems which are occurring recurrently. So this way continuous coding to solve the same problem is not required. The design patterns help in creating a well-structured software using lesser time. These patterns also helps in creation of objects without specifying their type.

Some of the most famous Python pattern programs called the number triangle, pyramid pattern in Python, reverse, mirrored, equilateral, and star pattern in Python equip programmers to accomplish complex programming issues. Therefore, preparing them to take on kind of coding patterns for precision and explaining how to print pattern in Python.

While how to print pattern in Python is a typical question for many people, these Python patterns can easily be printed using multiple combinations of multiple for, while, or for while loops. The working of pyramid and star pattern in Python depends on a few of the major points that include- the outer loop outputs the number of rows in a Python syntax, while to output the number of columns, an inner loop is used. 

The Python pattern programs can be printed with the help of loops. The outer loop handles the number of rows, whereas the inner loop handles the number of columns. Once the pattern style has been worked with, the different patterns can be printed such as numbers, alphabet, stars, etc.

When working with Python pattern programs for practice, you can use an online emulator such as GDB Online Debugger. When it comes Python all pattern programs can be used with emulators without needing to install Python on your system. However, if you want to know how to print pattern in Python offline, we have got your back. You can simply go to Python’s official website and download the right Python version for your OS. Once you finish setting up Python in your system, you can make a .py file and start using the codes below to run programs such as the number pyramid pattern in Python or Python pattern programs using for loop.

Pattern #1: Simple Number Triangle Pattern

Pattern:

1  

2 2  

3 3 3  

4 4 4 4  

5 5 5 5 5

Code:

rows = 6
for num in range(rows):
    for i in range(num):
        print(num, end=" ") # print number
    # line after each row to display pattern correctly
    print(" ")

 

1  

2 2  

3 3 3  

4 4 4 4  

5 5 5 5 5

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Pattern #2: Inverted Pyramid of Numbers

Pattern:

1 1 1 1 1 

2 2 2 2 

3 3 3 

4 4 

5

Code:

rows = 5
b = 0
for i in range(rows, 0, -1):
    b += 1
    for j in range(1, i + 1):
        print(b, end=' ')
    print('\r')

 

1 1 1 1 1 

2 2 2 2 

3 3 3 

4 4 

5

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Pattern #3: Half Pyramid Pattern of Numbers

Pattern:

1 2 

1 2 3 

1 2 3 4 

1 2 3 4 5

Code:

rows = 5
for row in range(1, rows+1):
    for column in range(1, row + 1):
        print(column, end=' ')
    print("")

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Pattern #4: Inverted Pyramid of Descending Numbers

Pattern:

5 5 5 5 5 

4 4 4 4 

3 3 3 

2 2 

1

Code:

rows = 5
for i in range(rows, 0, -1):
    num = i
    for j in range(0, i):
        print(num, end=' ')
    print("\r")

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Pattern #5: Inverted Pyramid of the Same Digit

Pattern:

5 5 5 5 5 

5 5 5 5 

5 5 5 

5 5 

5

Code:

rows = 5
num = rows
for i in range(rows, 0, -1):
    for j in range(0, i):
        print(num, end=' ')
    print("\r")

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Pattern #6: Reverse Pyramid of Numbers

Pattern:

2 1 

3 2 1 

4 3 2 1 

5 4 3 2 1

Code:

rows = 6
for row in range(1, rows):
    for column in range(row, 0, -1):
        print(column, end=' ')
    print("")

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Pattern #7: Inverted Half Pyramid Number Pattern

Pattern:

0 1 2 3 4 5 

0 1 2 3 4 

0 1 2 3 

0 1 2 

0 1

Code:

rows = 5
for i in range(rows, 0, -1):
    for j in range(0, i + 1):
        print(j, end=' ')
    print("\r")

Pattern #8: Pyramid of Natural Numbers Less Than 10

Pattern:

2 3 4 

5 6 7 8 9

Code:

currentNumber = 1
stop = 2
rows = 3 # Rows you want in your pattern
for i in range(rows):
    for column in range(1, stop):
        print(currentNumber, end=' ')
        currentNumber += 1
    print("")
    stop += 2

Pattern #9: Reverse Pattern of Digits from 10 

Pattern:

1

3 2

6 5 4

10 9 8 7

Code:

start = 1
stop = 2
currentNumber = stop
for row in range(2, 6):
    for col in range(start, stop):
        currentNumber -= 1
        print(currentNumber, end=' ')
    print("")
    start = stop
    stop += row
    currentNumber = stop

Pattern #10: Unique Pyramid Pattern of Digits

Pattern:

1 2 1 

1 2 3 2 1 

1 2 3 4 3 2 1 

1 2 3 4 5 4 3 2 1

Code:

rows = 6
for i in range(1, rows + 1):
    for j in range(1, i - 1):
        print(j, end=" ")
    for j in range(i - 1, 0, -1):
        print(j, end=" ")
    print()

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Pattern #11: Connected Inverted Pyramid Pattern of Numbers

Pattern:

5 4 3 2 1 1 2 3 4 5 

5 4 3 2 2 3 4 5 

5 4 3 3 4 5 

5 4 4 5 

5 5

Code:

rows = 6
for i in range(0, rows):
    for j in range(rows - 1, i, -1):
        print(j, '', end='')
    for l in range(i):
        print(' ', end='')
    for k in range(i + 1, rows):
        print(k, '', end='')
    print('\n')

Pattern #12: Even Number Pyramid Pattern

Pattern:

10 

10 8 

10 8 6 

10 8 6 4 

10 8 6 4 2

Code:

rows = 5
LastEvenNumber = 2 * rows
evenNumber = LastEvenNumber
for i in range(1, rows+1):
    evenNumber = LastEvenNumber
    for j in range(i):
        print(evenNumber, end=' ')
        evenNumber -= 2
    print("\r")

Pattern #13: Pyramid of Horizontal Tables

Pattern:

0  

0 1  

0 2 4  

0 3 6 9  

0 4 8 12 16  

0 5 10 15 20 25  

0 6 12 18 24 30 36

Code:

rows = 7
for i in range(0, rows):
    for j in range(0, i + 1):
        print(i * j, end=' ')
    print()

Pattern #14: Pyramid Pattern of Alternate Numbers

Pattern:

3 3 

5 5 5 

7 7 7 7 

9 9 9 9 9

Code:

rows = 5
i = 1
while i <= rows:
    j = 1
    while j <= i:
        print((i * 2 - 1), end=" ")
        j = j + 1
    i = i + 1
    print()

Pattern #15: Mirrored Pyramid (Right-angled Triangle) Pattern of Numbers

Pattern:

           1 

         1 2 

      1 2 3 

   1 2 3 4 

 1 2 3 4 5

Code:

rows = 6
for row in range(1, rows):
    num = 1
    for j in range(rows, 0, -1):
        if j > row:
            print(" ", end=' ')
        else:
            print(num, end=' ')
            num += 1
    print("")

Pattern #16: Equilateral Triangle with Stars (Asterisk Symbol)

Pattern:

            *   

           * *   

          * * *   

         * * * *   

        * * * * *   

       * * * * * *   

      * * * * * * *

Code:

print("Print equilateral triangle Pyramid using stars ")
size = 7
m = (2 * size) - 2
for i in range(0, size):
    for j in range(0, m):
        print(end=" ")
    m = m - 1 # decrementing m after each loop
    for j in range(0, i + 1):
        # printing full Triangle pyramid using stars
        print("* ", end=' ')
    print(" ")

Pattern #17: Downward Triangle Pattern of Stars

Pattern:

        * * * * * * 

         * * * * * 

          * * * * 

           * * * 

            * * 

             * 

Code:

rows = 5
k = 2 * rows - 2
for i in range(rows, -1, -1):
    for j in range(k, 0, -1):
        print(end=" ")
    k = k + 1
    for j in range(0, i + 1):
        print("*", end=" ")
    print("")

Pattern #18: Pyramid Pattern of Stars

Pattern:

* * 

* * * 

* * * * 

* * * * *

Code:

rows = 5
for i in range(0, rows):
    for j in range(0, i + 1):
        print("*", end=' ')
    print("\r")

These are some of the most widely used Python patterns fueling expertise for core and advanced Python programming skills. The concept for the pattern for loop is popularly featured in interview questions to check your programming and logic skills. With the nature and syntax of any programming language limiting its uses, Python is comparatively programmer-friendly, with detailed modules to implement relevant coding. These pyramid and triangle pattern in Python can easily be printed using a sequence of multiple loops.  

The given patterns above such as star and pyramid pattern in Python, offer an in-depth understanding of patterns in Python and their logical implementation. Besides being used in interviews to check expertise level, patterns like triangle pattern in Python can be further used to learn areas like data science. 

Also Read: 42 Exciting Python Project Ideas & Topics for Beginners

Pattern #19: Spiral Star Pattern

Pattern:

def spiral_star_pattern(rows):

    matrix = [[‘ ‘] * rows for _ in range(rows)]

    directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]

    direction = 0

    row, col = 0, 0

    for i in range(1, rows * rows + 1):

        matrix[row][col] = ‘*’

        new_row, new_col = row + directions[direction][0], col + directions[direction][1]

        if 0 <= new_row < rows and 0 <= new_col < rows and matrix[new_row][new_col] == ‘ ‘:

            row, col = new_row, new_col

        else:

            direction = (direction + 1) % 4

            row, col = row + directions[direction][0], col + directions[direction][1]

    for i in range(rows):

        for j in range(rows):

            print(matrix[i][j], end=” “)

        print()

spiral_star_pattern(4)

Pattern #20: Prime Number Spiral Pattern

Pattern:

Code:

def is_prime(num):

    if num < 2:

        return False

    for i in range(2, int(num**0.5) + 1):

        if num % i == 0:

            return False

    return True

 

def prime_spiral_pattern(rows):

    matrix = [[‘ ‘] * rows for _ in range(rows)]

    directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]

    direction = 0

    row, col = 0, 0

    num = 1

    for i in range(1, rows * rows + 1):

        if is_prime(num):

            matrix[row][col] = num

        num += 1

        new_row, new_col = row + directions[direction][0], col + directions[direction][1]

        if 0 <= new_row < rows and 0 <= new_col < rows and matrix[new_row][new_col] == ‘ ‘:

            row, col = new_row, new_col

        else:

            direction = (direction + 1) % 4

            row, col = row + directions[direction][0], col + directions[direction][1]

    for i in range(rows):

        for j in range(rows):

            print(str(matrix[i][j]).rjust(3) if matrix[i][j] != ‘ ‘ else ‘   ‘, end=” “)

        print()

prime_spiral_pattern(5)

 

Pattern #21: Sierpinski Triangle

Pattern:

Code:

def sierpinski_triangle(n):

    def draw_triangle(height):

        triangle = []

        for i in range(height):

            spaces = ‘ ‘ * (height – i – 1)

            stars = ‘*’ * (2 * i + 1)

            triangle.append(spaces + stars + spaces)

        return triangle

    def merge_triangles(top, bottom):

        return [t1 + ‘ ‘ + t2 for t1, t2 in zip(top, bottom)]

    def generate_sierpinski(level):

        if level == 0:

            return [‘*’]

        else:

            lower = generate_sierpinski(level – 1)

            upper = draw_triangle(2 ** (level – 1))

            return merge_triangles(upper, lower)

    triangle = generate_sierpinski(n)

    for line in triangle:

        print(line)

sierpinski_triangle(4)

Pattern #22: Fractal Tree

Pattern:

Code:

def fractal_tree(height):

    def draw_branch(length):

        if length <= 0:

            return [”]

        else:

            trunk = ‘|’ * length

            branches = [‘/’.rjust(length – i, ‘ ‘) + ‘\\’ + ‘\n’ for i in range(length)]

            return branches + [trunk]

    def merge_branches(top, bottom):

        return [t1 + t2 for t1, t2 in zip(top, bottom)]

    def generate_tree(level):

        if level == 0:

            return [”]

        else:

            lower = generate_tree(level – 1)

            upper = draw_branch(2 ** (level – 1))

            return merge_branches(upper, lower)

    tree = generate_tree(height)

    for line in tree:

        print(line)

fractal_tree(4)

Pattern #23: Peano Curve

Pattern:

Code:

def peano_curve(order, size):

    def draw_peano(order, size, direction):

        if order == 0:

            return []

        else:

            sub_curve = draw_peano(order – 1, size, direction)

            sub_curve.append(direction)

            sub_curve += draw_peano(order – 1, size, 0)

            sub_curve += draw_peano(order – 1, size, direction)

            sub_curve.append(0)

            sub_curve += draw_peano(order – 1, size, -direction)

            sub_curve += draw_peano(order – 1, size, 0)

            sub_curve += draw_peano(order – 1, size, -direction)

            sub_curve += draw_peano(order – 1, size, 0)

            sub_curve += draw_peano(order – 1, size, direction)

            sub_curve.append(0)

            sub_curve += draw_peano(order – 1, size, direction)

            sub_curve += draw_peano(order – 1, size, 0)

            sub_curve += draw_peano(order – 1, size, -direction)

            sub_curve += draw_peano(order – 1, size, direction)

            return sub_curve

    def scale_curve(curve, size):

        scaled_curve = []

        for step in curve:

            scaled_curve.extend([step] * size)

        return scaled_curve

    curve = draw_peano(order, size, 1)

    scaled_curve = scale_curve(curve, size)

    # Displaying the output

    for step in scaled_curve:

        if step == 1:

            print(‘|’, end=”)

        elif step == -1:

            print(‘-‘, end=”)

        else:

            print(‘ ‘, end=”)

    print()

# Example with order=2 and size=3

peano_curve(2, 3)

Pattern #24: Hilbert Curve

Pattern:

Code:

def hilbert_curve(order, size):

    def draw_hilbert(order, size, direction):

        if order == 0:

            return []

        else:

            sub_curve = draw_hilbert(order – 1, size, -direction)

            sub_curve.append(direction)

            sub_curve += draw_hilbert(order – 1, size, direction)

            sub_curve.append(0)

            sub_curve += draw_hilbert(order – 1, size, direction)

            sub_curve.append(-direction)

            sub_curve += draw_hilbert(order – 1, size, -direction)

            return sub_curve

    def scale_curve(curve, size):

        scaled_curve = []

        for step in curve:

            scaled_curve.extend([step] * size)

        return scaled_curve

    hilbert = draw_hilbert(order, size, 1)

    scaled_hilbert = scale_curve(hilbert, size)

    for step in scaled_hilbert:

        if step == 1:

            print(‘|’, end=”)

        elif step == -1:

            print(‘-‘, end=”)

        else:

            print(‘ ‘, end=”)

    print()

hilbert_curve(3, 3)

Pattern #25: Dragon Curve

Pattern:

Code:

def dragon_curve(order):

    def generate_curve(order):

        if order == 0:

            return [1]

        else:

            prev_curve = generate_curve(order – 1)

            return prev_curve + [1] + [1 if x == 0 else 0 for x in reversed(prev_curve)]

    curve = generate_curve(order)

    for step in curve:

        if step == 1:

            print(‘+’, end=”)

        else:

            print(‘-‘, end=”)

    print()

dragon_curve(5)

Pattern #26: Alternating Pyramid Pattern

The alternating pyramid pattern is a very popular pattern program in Python.

Pattern:

Code:

def alternating_pyramid_pattern(rows):

    def is_even(num):

        return num % 2 == 0

    def get_element(row, col):

        if is_even(row + col):

            return str(row)

        else:

            return ‘*’

    for i in range(1, rows + 1):

        for j in range(1, 2 * rows):

            if j <= rows – i or j >= rows + i:

                print(‘ ‘, end=”)

            else:

                print(get_element(i, j – rows), end=”)

        print()

alternating_pyramid_pattern(5)

Pattern #27: Butterfly Pattern With Numbers

Let us create a program that prints a butterfly pattern where each half of the butterfly consists of numbers in increasing order and the middle column is left empty. The numbers represent the column-wise sequence. The butterfly wings are symmetrical, and the numbers are printed in a zigzag fashion.

Pattern:

Code:

def butterfly_pattern(rows):

    for i in range(1, rows + 1):

        for j in range(1, 2 * rows + 1):

            if j <= i or j > 2 * rows – i:

                print(j, end=’ ‘)

            else:

                print(‘ ‘, end=’ ‘)

        print()

    for i in range(rows, 0, -1):

        for j in range(1, 2 * rows + 1):

            if j <= i or j > 2 * rows – i:

                print(j, end=’ ‘)

            else:

                print(‘ ‘, end=’ ‘)

        print()

butterfly_pattern(4)

Pattern #28: Diamond Pattern With Alphabets

Let us make a program that will print a diamond pattern where each level of the diamond consists of characters from ‘A‘ to the current row’s character.

Pattern:

Code:

def diamond_alphabet_pattern(rows):

    start_char = ord(‘A’)

    for i in range(1, rows + 1):

        for j in range(1, rows – i + 1):

            print(” “, end=” “)

        for j in range(1, 2 * i):

            print(chr(start_char + i – 1), end=” “)

        print()

    for i in range(rows – 1, 0, -1):

        for j in range(1, rows – i + 1):

            print(” “, end=” “)

        for j in range(1, 2 * i):

            print(chr(start_char + i – 1), end=” “)

        print()

diamond_alphabet_pattern(4)

Pattern #29: Pyramid of Squares

Let us build a program that prints a pyramid of squares where each level of the pyramid consists of numbers in an increasing and then decreasing order. The numbers represent the row-wise sequence.

Pattern:

Code:

def pyramid_of_squares(rows):

    for i in range(1, rows + 1):

        for j in range(1, rows – i + 1):

            print(” “, end=” “)

        for j in range(1, i + 1):

            print(j, end=” “)

        for j in range(i – 1, 0, -1):

            print(j, end=” “)

        print()

pyramid_of_squares(5)

Pattern #30: Flipped Mountain Pattern

Let us look at an example of the flipped mountain pattern program in Python. This is another example of creating Python pattern programs using for loop.

Pattern:

Code:

def flipped_mountain_pattern(height):

    for i in range(height, 0, -1):

        spaces = ” ” * (height – i)

        mountains = “*” * (2 * i – 1)

        print(spaces + mountains)

flipped_mountain_pattern(5)

Now that we have covered these 30 pattern questions in Python, you should have a strong understanding of patterns in Python.

Learn More About Python Programs list

These are some of the most widely used Python pattern programs fueling expertise for core and advanced Python programming skills. The concept for the pattern for loop is popularly featured in interview questions to check your programming and logic skills.

With the nature and syntax of any programming language limiting its uses, Python is comparatively programmer-friendly, with detailed modules to implement relevant coding. These pyramid and triangle pattern in Python can easily be printed using a sequence of multiple loops.  

Python pattern programs is used to develop multiple applications which are compatible with various platforms. It helps in handling data analysis, data visualisation, text processing, etc. The number of patterns depends upon the number of loops. The two loops have their own significance such as the first loop is used for the row. And the second loop works for the column. Pattern matching python is also one of the features which facilitates providing a pattern and the action associated with it. The action can be taken forward if the data fits.

The given pattern program in python above such as star and pyramid pattern in Python, offer an in-depth understanding of patterns in Python and their logical implementation. Besides being used in interviews to check expertise level, patterns like triangle pattern in Python can be further used to learn areas like data science. 

The reason why Python is useful is that its syntax is similar to the English language. This syntax helps the programmers in developing a code that can be coded using fewer lines. It follows the interpreter system where the code can be executed immediately as it is written.

If you’re interested in learning more about Python, go to our blog and find multiple detailed articles on this topic.  

If you have any questions regarding the Python pattern programs we’ve shared here, please let us know through the comments below. We’d love to hear from you. 

There are plenty of Python pattern programs out there and the possibilities are endless when it comes to programs with symbol patterns in Python or number pattern programs in Python. Pattern problems in Python or patterns in Python are an essential part of Python programming and Data Science in general.

If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Frequently Asked Questions (FAQs)

1. How do you print a pattern in Python?

In the pattern questions in python language, for loop is used for printing different patterns. Printing different patterns with a few twists is the most common type of programming question that is asked in the interviews. A pattern can be printed by using multiple loops. It is not possible to print a pattern with a single for loop. The concept followed by a majority of pattern programs is: 1. For printing the number of rows, the outer loop is utilized. 2.For printing the number of columns, the inner loop is utilized. 3. As per the required place in Python, a variable is used to print whitespaces in the program. Every pattern program in python is made with the use of these concepts itself. By defining three different variables for rows, columns, and whitespaces, respectively, you can create any pattern based on your requirements.

2. What are Python functions?

A function is a block of reusable, organized code that is useful for performing a single action multiple times. Functions make it pretty easy to increase modularity and reuse the code and make it easy to maintain the application code. Python allows the users to create different functions along with the availability of different built-in functions like print(), ascii(), and many more. There are four types of Python functions: 1. No return and no argument value of the function 2. Function with a return value but no arguments 3. Function with no return value but an argument 4. Function with both return value and arguments.

3. What are the different types of design patterns being used in Python?

There are three different types of design patterns in Python, with each of them being used for performing different functions. Patterns are used in Python for emphasizing code readability with the use of notable indentation. Programmers are able to write clear and logical code for small as well as huge projects with the help of design patterns. Different types of design patterns are: 1. Creational Patterns 2. Structural Patterns 3. Behavioral Patterns A majority of enterprise development software is built with the usage of these design patterns. By gaining a proper understanding of these design pattern programs in python, one can make its use pretty simple and also make the code easy to understand.

4. What does do in python?

In python is called a backlash or escape, it is a special character representing white space characters. is a carriage return. In python whenever a string has been added with the prefix ‘r’ or ‘R’ that string becomes a raw string.

5. Is python good for design patterns?

Python is a very useful programming language that has many reasons attached to it. It is a very strong language for object-oriented programming. It is portable and quick. Design patterns are another powerful feature of python pattern questions. Software engineers find it useful because the design patterns generate solutions for the constantly occurring problems, which saves the time and effort to code monotonous for the constantly occurring problem.

6. Should I use R or Python?

The usage is dependent upon the kind of task at hand. While both of them are good programming languages and serve different purposes, python would be considered better than R for various reasons. For example, pattern programs in python is portable and easily readable also python is known to be serving multiple purposes.

7. What is = = in python?

There are certain operators in python for better usage. The = = is also one of the functions that represent an equal sign. For example, x= = y, where the x and y can be any values. For example, x=1 y= 2 Print ( x == y) # returns False because 1 is not equal to 2.

8. What is the I in python?

Such like the operators, variables are also a feature of python. Where the ‘i’ is also one of the operators. Where the variable i is used to storing the integer value in the loop. This value will be stored within the loop only.

9. What does mean in python?

In python is called a backlash or escape, it is a special character representing white space characters. Just like is a carriage return similarly /n is also having a meaning attached to it. The represents a new line character. This represents the end of a line of text.

10. What is a literal string in python?

The literal string is the sequence of characters from the source. They are used to represent the sequence. There is a way of writing the literal strings, they can be written by adding the text or group of characters, and they should be surrounded by either single, double, or triple quotes.

11. What are some examples of Python pattern programs?

What are some examples of Python pattern programs? Python pattern programs are great exercises for learning about nested loops and conditional statements. Some common examples include creating patterns like the pyramid, diamond, or star patterns using asterisks. For instance, students might write a program to display a simple pyramid pattern where the number of rows is determined by user input. Other examples involve creating number patterns, where numbers increment or decrement across rows and columns, or character patterns using ASCII values.

Did you find this article helpful?

Rohit Sharma

Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

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UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself
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by Omkar Pradhan

09 Oct'16
Data Analytics Student Speak: Story of Thulasiram

5.68K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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by Apoorva Shankar

07 Dec'16
Decoding Easy vs. Not-So-Easy Data Analytics

5.12K+

Decoding Easy vs. Not-So-Easy Data Analytics

Authored by Professor S. Sadagopan, Director – IIIT Bangalore. Prof. Sadagopan is one of the most experienced academicians on the expert panel of UpGrad & IIIT-B PG Diploma Program in Data Analytics. As a budding analytics professional confounded by jargon, hype and overwhelming marketing messages that talk of millions of upcoming jobs that are paid in millions of Rupees, you ought to get clarity about the “real” value of a data analytics education. Here are some tidbits – that should hopefully help in reducing your confusion. Some smart people can use “analytical thinking” to come up with “amazing numbers”; they are very useful but being “intuitive”, they cannot be “taught.” For example: Easy Analytics Pre-configuring ATMs with Data Insights  “We have the fastest ATM on this planet” Claimed a respected Bank. Did they get a new ATM made especially for them? No way. Some smart employee with an analytical mindset found that 90% of the time that users go to an ATM to withdraw cash, they use a fixed amount, say Rs 5,000. So, the Bank re-configured the standard screen options – Balance Inquiry, Withdrawal, Print Statement etc. – to include another option. Withdraw XYZ amount, based on individual customer’s past actions. This ended up saving one step of ATM operation. Instead of selecting the withdrawal option and then entering the amount to be withdrawn, you could now save some time – making the process more convenient and intuitive. A smart move indeed, however, this is something known as “Easy Analytics” that others can also copy. In fact, others DID copy, within three months! A Start-Up’s Guide to Data Analytics Hidden Data in the Weather In the sample data-sets that used to accompany a spreadsheet product in the 90’s, there used to be data on the area and population of every State in the United States. There was also an exercise to teach the formula part of the spreadsheet to compute the population density (population per sq. km). New Jersey, with a population of 467 per sq. km, is the State with the highest density. While teaching a class of MBA students in New Jersey, I met an Indian student who figured out that in terms of population density, New Jersey is more crowded than India with 446 people per sq. km!  An interesting observation, although comparing a State with a Country is a bit misleading. Once again, an Easy Analytics exercise leading to a “nice” observation! Some simple data analytics exercises can be routinely done, and are made relatively easier, thanks to amazing tools: B-School Buying Behavior Decoded In a B-School in India that has a store on campus, (campus is located far from the city center) some smart students put several years of sales data of their campus store. They were excited by the phenomenal computer power and near, idiot-proof analytics software. The real surprise, however, was that eight items accounted for 85% of their annual sales. More importantly, these eight items were consumed in just six days of the year! Everyone knew that a handful of items were the only fast-moving items, but they did not know the extent (85%) or the intensity (consumption in just six days) of this. It turns out that in the first 3 days of the semester the students would stock the items for the full semester! The B-School found it sensible to request a nearby store to prop up a temporary stall for just two weeks at the beginning of the semesters and close down the Campus Store. This saved useful space and costs without causing major inconvenience to the students. A good example of Easy Analytics done with the help of a powerful tool. Top 4 Data Analytics Skills You Need to Become an Expert! The “Not So Easy” Analytics needs deep analytical understanding, tools, an ‘analytical mindset’ and some hard work. Here are two examples, one taken from way back in the 70’s and the other occurring very recently: Not-So-Easy Analytics To Fly or Not to Fly, That is the Question Long ago, the American Airlines perfected planned overbooking of airline seats, thanks to SABRE Airline Reservation system that managed every airline seat. Armed with detailed past data of ‘empty seats’ and ‘no show’ in every segment of every flight for every day through the year, and modeling airline seats as perishable commodities, the American Airlines was able to improve yield, i.e., utilization of airplane capacity. They did this through planned overbooking – selling more tickets than the number of seats, based on projected cancellations. Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications If indeed more passengers showed up than the actual number of seats, American Airlines would request anyone volunteering to forego travel in the specific flight, with the offer to fly them by the next flight (often free) and taking care of hotel accommodation if needed. Sometimes, they would even offer cash incentives to the volunteer to opt-out. Using sophisticated Statistical and Operational Research modeling, American Airlines would ensure that the flights went full and the actual incidents of more passengers than the full capacity, was near zero. In fact, many students would look forward to such incidents so that they could get incentives, (in fact, I would have to include myself in this list) but rarely were they rewarded!) upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 What American Airlines started as an experiment has become the standard industry practice over the years. Until recently, a team of well-trained (often Ph.D. degree holders) analysts armed with access to enormous computing power, was needed for such an analytics exercise to be sustained. Now, new generation software such as the R Programming language and powerful desktop computers with significant visualization/graphics power is changing the world of data analytics really fast. Anyone who is well-trained (not necessarily requiring a Ph.D. anymore) can become a first-rate analytics professional. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Unleashing the Power of Data Analytics Our learners also read: Free Python Course with Certification Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   Cab Out of the Bag Uber is yet another example displaying how the power of data analytics can disrupt a well-established industry. Taxi-for-sure in Bangalore and Ola Cabs are similar to Uber. Together, these Taxi-App companies (using a Mobile App to hail a taxi, the status monitor the taxi, use and pay for the taxi) are trying to convince the world to move from car ownership to on-demand car usage. A simple but deep analytics exercise in the year 2008 gave such confidence to Uber that it began talking of reducing car sales by 25% by the year 2025! After building the Uber App for iPhone, the Uber founder enrolled few hundreds of taxi customers in San Francisco and few hundreds of taxi drivers in that area as well. All that the enrolled drivers had to do was to touch the Uber App whenever they were ready for a customer. Similarly, the enrolled taxi customers were requested to touch the Uber App whenever they were looking for a taxi. Thanks to the internet-connected phone (connectivity), Mobile App (user interface), GPS (taxi and end-user location) and GIS (location details), Uber could try connecting the taxi drivers and the taxi users. The real insight was that nearly 90% of the time, taxi drivers found a customer, less than 100 meters away! In the same way, nearly 90% of the time, taxi users were connected with their potential drivers in no time, not too far away. Unfortunately, till the Uber App came into existence, riders and taxi drivers had no way of knowing this information. More importantly, they both had no way of reaching each other! Once they had this information and access, a new way of taxi-hailing could be established. With back-end software to schedule taxis, payment gateway and a mobile payment mechanism, a far more superior taxi service could be established. Of course, near home, we had even better options like Taxi-for-sure trying to extend this experience even to auto rickshaws. The rest, as they say, is “history in the making!” Deep dive courses in data analytics will help prepare you for such high impact applications. It is not easy, but do remember former US President Kennedy’s words “we chose to go to the Moon not because it is easy, but because it is hard!” Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.  
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by Prof. S. Sadagopan

14 Dec'16
Launching UpGrad&#8217;s Data Analytics Roadshow &#8211; Are You Game?

5.14K+

Launching UpGrad&#8217;s Data Analytics Roadshow &#8211; Are You Game?

We, at UpGrad, are excited to announce a brand new partnership with various thought leaders in the Data Analytics industry – IIIT Bangalore, Genpact, Analytics Vidhya and Gramener – to bring to you a one-of-a-kind Analytics Roadshow! As part of this roadshow, we will be conducting several back-to-back events that focus on different aspects of analytics, creating interaction points across India, to do our bit for a future ready and analytical, young workforce.  Also Read: Analytics Vidhya article on the UpGrad Data Analytics Roadshow Here is the line-up for the roadshow, to give you a better sense of what to expect: 9 webinars – These webinars (remote) will be conducted by industry experts and are aimed at increasing analytics awareness, providing a way for aspirants to interact with industry practitioners and getting their tough questions answered. 11 workshops – The workshops will be in-person events to take these interactions to the next level. These would be spread across 6 cities – Delhi, Bengaluru, Hyderabad, Chennai, Mumbai and Pune. So, if you are in any of these cities, we are looking forward to interact with you. Featured Data Science program for you: Master of Science in Data Science from from IIIT-B 2 Conclaves – These conclaves are larger events with a pre-defined agendas and time for networking. The first conclave is happening on the 17th of December in Bengaluru.  Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Hackathon – Time to pull up your sleeves and showcase your nifty skills. We will be announcing the format of the event shortly. “We find that the IT in­dustry is ab­sorb­ing al­most half of all of the ana­lyt­ics jobs. Banking is the second largest, but trails at al­most one fourth of IT’s re­cruit­ing volume. It is in­ter­est­ing that data rich in­dus­tries like Retail, Energy and Insurance are trail­ing near the bot­tom, lower than even con­struc­tion or me­dia, who handle less data. Perhaps these are ripe for dis­rup­tion through ana­lyt­ics?” Our learners also read: Learn Python Online for Free Mr. S. Anand, CEO of Gramener, wonders aloud. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4   Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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by Apoorva Shankar

15 Dec'16
What&#8217;s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

5.22K+

What&#8217;s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

Team Data Analytics is creating the most immersive learning experience for working professionals at UpGrad. Data Insider recently checked in to me to get my insights on the data analytics industry; including trends to watch out for and must-have skill sets for today’s developers. Here’s how it went: How competitive is the data analytics industry today? What is the demand for these types of professionals? Let’s talk some numbers, a widely-quoted McKinsey report states that the United States will face an acute shortage of around 1.5 million data professionals by 2018. In India, which is emerging as the global analytics hub, the shortage of such professionals could go up to as high as 200,000. In India alone, the number of analytics jobs saw a 120 percent rise from June 2015 to June 2016. So, we clearly have a challenge set out for us. Naturally, because of acute talent shortage, talented professionals are high in demand. Decoding Easy vs. Not-So-Easy Analytics What trends are you following in the data analytics industry today? Why are you interested in them? There are three key trends that we should watch out for: Personalization I think the usage of data to create personalized systems is a key trend being adopted extremely fast, across the board. Most of the internet services are removing the anonymity of online users and moving towards differentiated treatment. For example, words recommendations when you are typing your messages or destinations recommendations when you are using Uber. Our learners also read: Learn Python Online for Free End of Moore’s Law Another interesting trend to watch out for is how companies are getting more and more creative as we reach the end of Moore’s Law. Moore’s Law essentially states that every two years we will be able to fit double the number of transistors that could be fit on a chip, two years ago. Because of this law, we have unleashed the power of storing and processing huge amounts of data, responsible for the entire data revolution. But what will happen next? IoT Another trend to watch out for, for the sheer possibilities it brings. It’s the emergence of smart systems which is made possible by the coming together of cloud, big data, and IoT (internet of things). Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses What skill sets are critical for data engineers today? What do they need to know to stay competitive? A good data scientist sits at a rare overlap of three areas: Domain Knowledge This helps understand and appreciate the nuances of a business problem. For e.g, an e-commerce company would want to recommend complementary products to its buyers. Statistical Knowledge Statistical and mathematical knowledge help to inform data-driven decision making. For instance, one can use market basket analysis to come up with complementary products for a particular buy. Technical Knowledge This helps perform complex analysis at scale; such as creating a recommendation system that shows that a buyer might prefer to also buy a pen while buying a notebook. How Can You Transition to Data Analytics? Outside of their technical expertise, what other skills should those in data analytics and business intelligence be sure to develop? Ultimately, data scientists are problem solvers. And every problem has a specific context, content and story behind it. This is where it becomes extremely important to tie all these factors together – into a common narrative. Essentially all data professionals need to be great storytellers. In this respect, one of the key skills for analysts to sharpen would be, breaking down the complexities of analytics for others working with them. They can appreciate the actual insights derived – and work toward a common business goal. In addition, what is as crucial is getting into a habit of constantly learning. Even if it means waking up every morning and reading what’s relevant and current in your domain. Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications What should these professionals be doing to stay ahead of trends and innovations in the field? Professionals these days need to continuously upskill themselves and be willing to unlearn and relearn. The world of work and the industrial landscape of technology-heavy fields such as data analytics is changing every year. The only way to stay ahead, or even at par with these trends, is to invest in learning, taking up exciting industry-relevant projects, participating in competitions like Kaggle, etc. How important is mentorship in the data industry? Who can professionals look toward to help further their careers and their skills? Extremely important. Considering how fast this domain has emerged, academia and universities, in general, have not had the chance to keep up equally fast. Hence, the only way to stay industry-relevant with respect to this domain is to have industry-specific learning. This can only be done in two ways – through real-life case studies and mentors who are working/senior professionals and hail from the data analytics industry. In fact, at UpGrad, there is a lot of stress on industry mentorship for aspiring data specialists. This is in addition to a whole host of case studies and industry-relevant projects. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   Where are the best places for data professionals to find mentors? upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 While it’s important for budding or aspiring data professionals to tap into their networks to find the right mentors, it is admittedly tough to do so. There are two main reasons that can be blamed for this. First, due to the nascent stage, the industry is at, it is extremely difficult to find someone with the requisite skill sets to be a mentor. Even if you find someone with considerable experience in the field, not everybody has the time and inclination to be an effective mentor. Hence most people don’t know where to go to be mentored. That’s where platforms like UpGrad come in, which provide you with a rich, industry-relevant learning experience. Nowhere else are you likely to chance upon such a wide range of industry tie-ups or associations for mentorship from very senior and reputed professionals. How Can You Transition to Data Analytics? What resources should those in the data analytics industry be using to ensure they’re educated and up-to-date on developments, trends, and skills? There are many. For starters, here are some good and pretty interesting blogs and resources that would serve aspiring/current data analysts well to keep up with Podcasts like Data Skeptic, Freakonomics, Talking Machines, and much more.   This interview was originally published on Data Insider.  
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by Rohit Sharma

23 Dec'16