MITx: Machine Learning with Python: from Linear Models to Deep Learning (Free Course)

 

What you'll learn

Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning

Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models

Choose suitable models for different applications

Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.

Syllabus

Lectures :

Introduction

Linear classifiers, separability, perceptron algorithm

Maximum margin hyperplane, loss, regularization

Stochastic gradient descent, over-fitting, generalization

Linear regression

Recommender problems, collaborative filtering

Non-linear classification, kernels

Learning features, Neural networks

Deep learning, back propagation

Recurrent neural networks

Generalization, complexity, VC-dimension

Unsupervised learning: clustering

Generative models, mixtures

Mixtures and the EM algorithm

Learning to control: Reinforcement learning

Reinforcement learning continued

Applications: Natural Language Processing

Projects :

Automatic Review Analyzer

Digit Recognition with Neural Networks

Reinforcement Learning

Join Free - MITx: Machine Learning with Python: from Linear Models to Deep Learning

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IBM: Machine Learning with Python: A Practical Introduction (Free Course)

 

About this course

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.

Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

Join Free - IBM: Machine Learning with Python: A Practical Introduction

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Write a program to generate all Pythagorean Triplets with side length less than or equal to 50.

 Certainly! Let me explain the code step by step:

# Function to check if a, b, c form a Pythagorean triplet

def is_pythagorean_triplet(a, b, c):

    return a**2 + b**2 == c**2

This part defines a function is_pythagorean_triplet that takes three arguments (a, b, and c) and returns True if they form a Pythagorean triplet (satisfy the Pythagorean theorem), and False otherwise.

# Generate and print all Pythagorean triplets with side lengths <= 50

for a in range(1, 51):

    for b in range(a, 51):

Here, two nested for loops are used. The outer loop iterates over values of a from 1 to 50. The inner loop iterates over values of b from a to 50. The inner loop starts from a to avoid duplicate triplets (e.g., (3, 4, 5) and (4, 3, 5) are considered duplicates).

        c = (a**2 + b**2)**0.5  # Calculate c using the Pythagorean theorem

        if c.is_integer() and c <= 50:

            print(f"Pythagorean Triplet: ({a}, {b}, {int(c)})")

Within the loops, the program calculates the value of c using the Pythagorean theorem (c = sqrt(a^2 + b^2)). The is_integer() method is then used to check if c is a whole number. If it is, and if c is less than or equal to 50, the triplet (a, b, c) is printed as a Pythagorean triplet.

# Function to check if a, b, c form a Pythagorean triplet

def is_pythagorean_triplet(a, b, c):

    return a**2 + b**2 == c**2

# Generate and print all Pythagorean triplets with side lengths <= 50

for a in range(1, 51):

    for b in range(a, 51):

        c = (a**2 + b**2)**0.5  # Calculate c using the Pythagorean theorem

        if c.is_integer() and c <= 50:

            print(f"Pythagorean Triplet: ({a}, {b}, {int(c)})")

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Rewrite the following program using for loop. lst = ['desert', 'dessert', 'to', 'too', 'lose', 'loose'] s = 'Mumbai' i = 0 while i < len(lst) : if i > 3 : break else : print(i, lst[i], s[i]) i += 1

 Rewrite the following program using for loop.

lst = ['desert', 'dessert', 'to', 'too', 'lose', 'loose']

s = 'Mumbai'

i = 0

while i < len(lst) :

if i > 3 :

break

else :

print(i, lst[i], s[i])

i += 1

Here's the equivalent program using a for loop:

lst = ['desert', 'dessert', 'to', 'too', 'lose', 'loose']

s = 'Mumbai'

for i in range(len(lst)):

    if i > 3:

        break

    else:

        print(i, lst[i], s[i])

In this version, the for loop iterates over the indices of the lst list using range(len(lst)). The if condition inside the loop checks whether i is greater than 3, and if so, it breaks out of the loop. Otherwise, it prints the values of i, lst[i], and s[i]. The loop increment is handled automatically by the for loop construct, eliminating the need for an explicit increment (i += 1).

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Write a program to print first 25 odd numbers using range( ).

 You can use the range() function with a step of 2 to generate odd numbers and then use a loop to print the first 25 odd numbers. Here's a simple Python program to achieve this:

# Using range() to generate the first 25 odd numbers

odd_numbers = list(range(1, 50, 2))[:25]

# Printing the first 25 odd numbers

for number in odd_numbers:

    print(number)

In this program:

range(1, 50, 2) generates odd numbers starting from 1, up to, but not including 50, with a step of 2.

[:25] is used to select the first 25 numbers from the list generated by range().

The loop then prints each of the first 25 odd numbers.

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a. range(5) b. range(1, 10, 3) c. range(10, 1, -2) d. range(1, 5) e. range(-2)

a. range(5)

b. range(1, 10, 3) 

c. range(10, 1, -2)

d. range(1, 5) 

e. range(-2) 

Let's break down each of the provided ranges:

a. range(5) - This generates a sequence of integers from 0 to 4 (5 is the stopping value, exclusive). So, it produces the numbers 0, 1, 2, 3, and 4.

b. range(1, 10, 3) - This generates a sequence of integers starting from 1, up to, but not including 10, with a step of 3. So, it produces the numbers 1, 4, 7.

c. range(10, 1, -2) - This generates a sequence of integers starting from 10, down to, but not including 1, with a step of -2. So, it produces the numbers 10, 8, 6, 4, 2.

d. range(1, 5) - This generates a sequence of integers starting from 1, up to, but not including 5. So, it produces the numbers 1, 2, 3, 4.

e. range(-2) - This is a bit different. range() requires at least two arguments (start, stop), but here you've provided only one. If you want to generate a range starting from 0 up to, but not including -2, you would need to provide the start and stop values. If you intended to start from 0 and go up to -2, you could use range(0, -2), which would produce the numbers 0, -1.

So, to summarize:

a. 0, 1, 2, 3, 4

b. 1, 4, 7

c. 10, 8, 6, 4, 2

d. 1, 2, 3, 4

e. (Assuming you meant range(0, -2)) 0, -1

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Can a do-while loop be used to repeat a set of statements in Python?

 Python does not have a built-in "do-while" loop like some other programming languages do. However, you can achieve similar behavior using a "while" loop with a break statement.

Here's an example of how you can simulate a "do-while" loop in Python:

while True:

    # Code block to be repeated

    # Ask the user if they want to repeat the loop

    user_input = input("Do you want to repeat? (yes/no): ")

    # Check the user's input

    if user_input.lower() != 'yes':

        break  # Exit the loop if the user does not want to repeat

In this example, the loop will continue to execute the code block and prompt the user for input until the user enters something other than "yes." The break statement is used to exit the loop when the desired condition is met.

This pattern achieves the same effect as a "do-while" loop where the loop body is executed at least once before the condition is checked.

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Can a while/for loop be used in an if/else and vice versa in Python?

 Yes, it is entirely valid to use loops within conditional statements (if/else) and vice versa in Python. You can nest loops inside if/else statements and if/else statements inside loops based on the requirements of your program.

Here's an example of a while loop inside an if/else statement:

x = 5

if x > 0:

    while x > 0:

        print(x)

        x -= 1

else:

    print("x is not greater than 0")

And here's an example of an if/else statement inside a for loop:

numbers = [1, 2, 3, 4, 5]

for num in numbers:

    if num % 2 == 0:

        print(f"{num} is even")

    else:

        print(f"{num} is odd")

The key is to maintain proper indentation to denote the block of code within the loop or the conditional statement. This helps Python understand the structure of your code.

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Can a while loop be nested within a for loop and vice versa?

Yes, both while loops can be nested within for loops and vice versa in Python. Nesting loops means placing one loop inside another. This is a common programming practice when you need to iterate over elements in a nested structure, such as a list of lists or a matrix.

Here's an example of a while loop nested within a for loop:

for i in range(3):

    print(f"Outer loop iteration {i}")

    

    j = 0

    while j < 2:

        print(f"  Inner loop iteration {j}")

        j += 1

And here's an example of a for loop nested within a while loop:

i = 0

while i < 3:

    print(f"Outer loop iteration {i}")

    

    for j in range(2):

        print(f"  Inner loop iteration {j}")

    

    i += 1

In both cases, the indentation is crucial in Python to define the scope of the loops. The inner loop is indented and considered part of the outer loop based on the indentation level.

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Can range( ) function be used to generate numbers from 0.1 to 1.0 in steps of 0.1?

 No, the range() function in Python cannot be used directly to generate numbers with non-integer steps. The range() function is designed for generating sequences of integers.

However, you can achieve the desired result using the numpy library, which provides the arange() function to generate sequences of numbers with non-integer steps. Here's an example:

import numpy as np

# Generate numbers from 0.1 to 1.0 with steps of 0.1

numbers = np.arange(0.1, 1.1, 0.1)

print(numbers)

In this example, np.arange(0.1, 1.1, 0.1) generates an array of numbers starting from 0.1, incrementing by 0.1, and stopping before 1.1.

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If a = 10, b = 12, c = 0, find the values of the following expressions: a != 6 and b > 5 a == 9 or b < 3 not ( a < 10 ) not ( a > 5 and c ) 5 and c != 8 or c

Questions - 

 If a = 10, b = 12, c = 0, find the values of the following expressions:

a != 6 and b > 5

a == 9 or b < 3

not ( a < 10 )

not ( a > 5 and c )

5 and c != 8 or c

Solution and Explanations 

Let's evaluate each expression using the given values:

a != 6 and b > 5

a != 6 is True (because 10 is not equal to 6).

b > 5 is also True (because 12 is greater than 5).

The and operator returns True only if both expressions are True.

Therefore, the result is True.

a == 9 or b < 3

a == 9 is False (because 10 is not equal to 9).

b < 3 is False (because 12 is not less than 3).

The or operator returns True if at least one expression is True.

Since both expressions are False, the result is False.

not (a < 10)

a < 10 is False (because 10 is not less than 10).

The not operator negates the result, so not False is True.

not (a > 5 and c)

a > 5 is True because 10 is greater than 5.

c is 0.

a > 5 and c evaluates to True and 0, which is False because and returns the first False value encountered.

The not operator negates the result, so not (True and 0) is not False, which is True.

Therefore, the output of the code will be True.

5 and c != 8 or c

5 is considered True in a boolean context.

c != 8 is True (because 0 is not equal to 8).

The and operator returns the last evaluated expression if all are True, so it evaluates to True.

The or operator returns True if at least one expression is True.

Therefore, the result is True.

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Hands-On Data Analysis with NumPy and pandas (Free PDF)

 

Key Features

• Explore the tools you need to become a data analyst
• Discover practical examples to help you grasp data processing concepts
• Walk through hierarchical indexing and grouping for data analysis

Book Description

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning.

Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python's NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python's pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them.

By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.

What you will learn

• Understand how to install and manage Anaconda
• Read, sort, and map data using NumPy and pandas
• Find out how to create and slice data arrays using NumPy
• Discover how to subset your DataFrames using pandas
• Handle missing data in a pandas DataFrame
• Explore hierarchical indexing and plotting with pandas

Who This Book Is For

Hands-On Data Analysis with NumPy and Pandas is for you if you are a Python developer and want to take your first steps into the world of data analysis. No previous experience of data analysis is required to enjoy this book.

Table of Contents

1. Setting Up a Python Data Analysis Environment
2. Diving into NumPY
3. Operations on NumPy Arrays
4. Pandas Are Fun! What Is pandas?
5. Arithmetic, Function Application and Mapping with pandas
6. Managing, Indexing, and Plotting

Kindle - Hands-On Data Analysis with NumPy and pandas: Implement Python packages from data manipulation to processing

PDF Link - 

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Python Coding challenge - Day 72 | What is the output of the following Python code?

 

Code - 

j = 1

while j <= 2 :

  print(j)

  j++

Explanation - 

It looks like there's a small syntax error in your code. In Python, the increment operator is +=, not ++. Here's the corrected version:

j = 1

while j <= 2:

    print(j)

    j += 1

This code initializes the variable j with the value 1 and then enters a while loop. The loop continues as long as j is less than or equal to 2. Inside the loop, the current value of j is printed, and then j is incremented by 1 using j += 1.

When you run this corrected code, the output will be:

1

2

Each number is printed on a new line because the print function automatically adds a newline character by default.

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a = 10 b = 60 if a and b > 20 : print('Hello') else : print('Hi')

Code - 

a = 10

b = 60

if a and b > 20 :

  print('Hello')

else :

  print('Hi')

Explanation -

The above code checks the condition a and b > 20. In Python, the and operator has a higher precedence than the comparison (>), so the expression is evaluated as (a) and (b > 20).

Here's how it breaks down:

The value of a is 10.

The value of b is 60.

The first part of the condition, a, is considered "truthy" in Python.

The second part of the condition, b > 20, is also true because 60 is greater than 20.

So, both parts of the and condition are true, and the print('Hello') statement will be executed. Therefore, the output of the code will be:

Hello

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a = 10 a = not not a print(a)

CODE - 

a = 10

a = not not a

print(a)

Solution - 

In the above code, the variable a is initially assigned the value 10. Then, the line a = not not a is used. The not operator in Python is a logical NOT, which negates the truth value of a boolean expression.

In this case, not a would be equivalent to not 10, and since 10 is considered "truthy" in Python, not 10 would be False. Then, not not a is applied, which is equivalent to applying not twice. So, not not 10 would be equivalent to not False, which is True.

Therefore, after the execution of the code, the value of a would be True, and if you print a, you will get: True 

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Rewrite the following code in 1 line

 Rewrite the following code in 1 line

x = 3 y = 3.0 if x == y : print('x and y are equal') else : print('x and y are not equal')

In one line

x, y = 3, 3.0

print('x and y are equal') if x == y else print('x and y are not equal')

Explanation -

Initializes two variables, x and y, with the values 3 and 3.0, respectively. Then, it uses a conditional expression (also known as a ternary operator) to check if x is equal to y. If they are equal, it prints 'x and y are equal'; otherwise, it prints 'x and y are not equal'.

In this case, x and y are equal because the values are numerically the same, even though one is an integer (x) and the other is a float (y). The output of the code would be: x and y are equal

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msg = 'Aeroplane' ch = msg[-0] print(ch)

msg = 'Aeroplane'

ch = msg[-0]

print(ch)

In Python, indexing starts from 0, so msg[-0] is equivalent to msg[0]. Therefore, ch will be assigned the value 'A', which is the first character of the string 'Aeroplane'. If you run this code, the output will be: A

Step by Step : 

msg = 'Aeroplane': This line initializes a variable msg with the string 'Aeroplane'.

ch = msg[-0]: This line attempts to access the character at index -0 in the string msg. However, in Python, negative indexing is used to access elements from the end of the sequence. Since -0 is equivalent to 0, this is the same as accessing the character at index 0.

print(ch): This line prints the value of the variable ch.

Now, let's evaluate the expression step by step:

msg[-0] is equivalent to msg[0], which accesses the first character of the string 'Aeroplane', so ch is assigned the value 'A'.

Therefore, when you run the code, the output will be : A

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Python Coding challenge - Day 71 | What is the output of the following Python code?

 

Questions - 

for index in range(20, 10, -3):

    print(index, end=' ')

Solution - 

range(20, 10, -3): This creates a range of numbers starting from 20, decrementing by 3 at each step, until it reaches a number less than 10. So, the sequence is 20, 17, 14, 11.

for index in range(20, 10, -3):: This is a for loop that iterates over each value in the specified range. In each iteration, the variable index takes on the next value in the range.

print(index, end=' '): This prints the value of index followed by a space. The end=' ' argument is used to specify that a space should be printed at the end of each value instead of the default newline character.

When you run this code, the output will be:

Output :   20 17 14 11

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Process Data from Dirty to Clean

 

What you'll learn

Define data integrity with reference to types of integrity and risk to data integrity

Apply basic SQL functions for use in cleaning string variables in a database

Develop basic SQL queries for use on databases

Describe the process involved in verifying the results of cleaning data

There are 6 modules in this course

This is the fourth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll continue to build your understanding of data analytics and the concepts and tools that data analysts use in their work. You’ll learn how to check and clean your data using spreadsheets and SQL as well as how to verify and report your data cleaning results. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will be able to do the following:

 - Learn how to check for data integrity.

 - Discover data cleaning techniques using spreadsheets. 

 - Develop basic SQL queries for use on databases.

 - Apply basic SQL functions for cleaning and transforming data.

 - Gain an understanding of how to verify the results of cleaning data.

 - Explore the elements and importance of data cleaning reports.

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Analyze Data to Answer Questions

 

What you'll learn

Discuss the importance of organizing your data before analysis with references to sorts and filters

Demonstrate an understanding of what is involved in the conversion and formatting of data

Apply the use of functions and syntax to create SQL queries for combining data from multiple database tables

Describe the use of functions to conduct basic calculations on data in spreadsheets

There are 4 modules in this course

This is the fifth course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. In this course, you’ll explore the “analyze” phase of the data analysis process. You’ll take what you’ve learned to this point and apply it to your analysis to make sense of the data you’ve collected. You’ll learn how to organize and format your data using spreadsheets and SQL to help you look at and think about your data in different ways. You’ll also find out how to perform complex calculations on your data to complete business objectives. You’ll learn how to use formulas, functions, and SQL queries as you conduct your analysis. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

 - Learn how to organize data for analysis.

 - Discover the processes for formatting and adjusting data. 

 - Gain an understanding of how to aggregate data in spreadsheets and by using SQL.

 - Use formulas and functions in spreadsheets for data calculations.

 - Learn how to complete calculations using SQL queries.

JOIN FREE - Analyze Data to Answer Questions

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Go Beyond the Numbers: Translate Data into Insights

 

What you'll learn

Apply the exploratory data analysis (EDA) process

Explore the benefits of structuring and cleaning data

Investigate raw data using Python

Create data visualizations using Tableau 

There are 5 modules in this course

This is the third of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn how to find the story within data and tell that story in a compelling way. You'll discover how data professionals use storytelling to better understand their data and communicate key insights to teammates and stakeholders. You'll also practice exploratory data analysis and learn how to create effective data visualizations. 

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you build your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.  

By the end of this course, you will:

-Use Python tools to examine raw data structure and format

-Select relevant Python libraries to clean raw data

-Demonstrate how to transform categorical data into numerical data with Python

-Utilize input validation skills to validate a dataset with Python

-Identify techniques for creating accessible data visualizations with Tableau

-Determine decisions about missing data and outliers 

-Structure and organize data by manipulating date strings

Join Free- Go Beyond the Numbers: Translate Data into Insights

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Decisions, Decisions: Dashboards and Reports

 

What you'll learn

Design BI visualizations

Practice using BI reporting and dashboard tools

Create presentations to share key BI insights with stakeholders

Develop professional materials for your job search

There are 6 modules in this course

You’re almost there! This is the third and final course in the Google Business Intelligence Certificate. In this course, you’ll apply your understanding of stakeholder needs, plan and create BI visuals, and design reporting tools, including dashboards. You’ll also explore how to answer business questions with flexible and interactive dashboards that can monitor data over long periods of time.

Google employees who currently work in BI will guide you through this course by providing hands-on activities that simulate job tasks, sharing examples from their day-to-day work, and helping you build business intelligence skills to prepare for a career in the field. 

Learners who complete the three courses in this certificate program will have the skills needed to apply for business intelligence jobs. This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.  

By the end of this course, you will:

-Explain how BI visualizations answer business questions

-Identify complications that may arise during the creation of BI visualizations

-Produce charts that represent BI data monitored over time

-Use dashboard and reporting tools

-Build dashboards using best practices to meet stakeholder needs

-Iterate on a dashboard to meet changing project requirements

-Design BI presentations to share insights with stakeholders

-Create or update a resume and prepare for BI interviews

Join Free - Decisions, Decisions: Dashboards and Reports

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Ask Questions to Make Data-Driven Decisions

 

What you'll learn

Explain how each step of the problem-solving road map contributes to common analysis scenarios.

Discuss the use of data in the decision-making process.

Demonstrate the use of spreadsheets to complete basic tasks of the data analyst including entering and organizing data.

Describe the key ideas associated with structured thinking.

There are 4 modules in this course

This is the second course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs. You’ll build on your understanding of the topics that were introduced in the first Google Data Analytics Certificate course. The material will help you learn how to ask effective questions to make data-driven decisions, while connecting with stakeholders’ needs. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

- Learn about effective questioning techniques that can help guide analysis. 

- Gain an understanding of data-driven decision-making and how data analysts present findings.

- Explore a variety of real-world business scenarios to support an understanding of questioning and decision-making.

- Discover how and why spreadsheets are an important tool for data analysts.

- Examine the key ideas associated with structured thinking and how they can help analysts better understand problems and develop solutions.

- Learn strategies for managing the expectations of stakeholders while establishing clear communication with a data analytics team to achieve business objectives.

Join Free- Ask Questions to Make Data-Driven Decisions

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Google Data Analytics Capstone: Complete a Case Study

 

What you'll learn

Differentiate between a capstone, case study, and a portfolio

Identify the key features and attributes of a completed case study

Apply the practices and procedures associated with the data analysis process to a given set of data

Discuss the use of case studies/portfolios when communicating with recruiters and potential employers

There are 4 modules in this course

This course is the eighth course in the Google Data Analytics Certificate. You’ll have the opportunity to complete an optional case study, which will help prepare you for the data analytics job hunt. Case studies are commonly used by employers to assess analytical skills. For your case study, you’ll choose an analytics-based scenario. You’ll then ask questions, prepare, process, analyze, visualize and act on the data from the scenario. You’ll also learn other useful job hunt skills through videos with common interview questions and responses, helpful materials to build a portfolio online, and more. Current Google data analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

 - Learn the benefits and uses of case studies and portfolios in the job search.

 - Explore real world job interview scenarios and common interview questions.

 - Discover how case studies can be a part of the job interview process. 

 - Examine and consider different case study scenarios. 

 - Have the chance to complete your own case study for your portfolio.

Join FREE - Google Data Analytics Capstone: Complete a Case Study

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Foundations: Data, Data, Everywhere

 

What you'll learn

Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystem

Conduct an analytical thinking self assessment giving specific examples of the application of analytical thinking

Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics

Describe the role of a data analyst with specific reference to jobs/positions

There are 5 modules in this course

This is the first course in the Google Data Analytics Certificate. These courses will equip you with the skills you need to apply to introductory-level data analyst jobs. Organizations of all kinds need data analysts to help them improve their processes, identify opportunities and trends, launch new products, and make thoughtful decisions. In this course, you’ll be introduced to the world of data analytics through hands-on curriculum developed by Google. The material shared covers plenty of key data analytics topics, and it’s designed to give you an overview of what’s to come in the Google Data Analytics Certificate. Current Google data analysts will instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources.

Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary.

By the end of this course, you will:

- Gain an understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job. 

- Learn about key analytical skills (data cleaning, data analysis, data visualization) and tools (spreadsheets, SQL, R programming, Tableau) that you can add to your professional toolbox. 

- Discover a wide variety of terms and concepts relevant to the role of a junior data analyst, such as the data life cycle and the data analysis process. 

- Evaluate the role of analytics in the data ecosystem. 

- Conduct an analytical thinking self-assessment. 

- Explore job opportunities available to you upon program completion, and learn about best practices in the job search.

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Google Business Intelligence Professional Certificate

 

What you'll learn

Explore the roles of business intelligence (BI) professionals within an organization

Practice data modeling and extract, transform, load (ETL) processes that meet organizational goals 

Design data visualizations that answer business questions

Create dashboards that effectively communicate data insights to stakeholders

Professional Certificate - 3 course series

Get professional training designed by Google and take the next step in your career with advanced skills in the high-growth field of business intelligence. There are over 166,000 open jobs in business intelligence and the median salary for entry-level roles is $96,000.¹

Business intelligence professionals collect, organize, interpret, and report on data to help organizations make informed business decisions. Some responsibilities include measuring performance, tracking revenue or spending, and monitoring progress.

This certificate builds on your data analytics skills and experience to take your career to the next level. It's designed for graduates of the 

Google Data Analytics Certificate

 or people with equivalent data analytics experience. Expand your knowledge with practical, hands-on projects, featuring BigQuery, SQL, and Tableau.

After three courses, you’ll be prepared for jobs like business intelligence analyst, business intelligence engineer, business intelligence developer, and more. At under 10 hours a week, the certificate program can be completed in less than two months. Upon completion, you can apply for jobs with Google and over 150 U.S. employers, including Deloitte, Target, and Verizon.

75% of certificate graduates report a positive career outcome (e.g., new job, promotion or raise) within six months of completion2

¹Lightcast™ US Job Postings (Last 12 Months: 1/1/2022 – 12/31/2022) 

2Based on program graduate survey responses, US 2022

Applied Learning Project

This program includes over 70 hours of instruction and 50+ practice-based assessments, which will help you simulate real-world business intelligence scenarios that are critical for success in the workplace. The content is highly interactive and exclusively developed by Google employees with decades of experience in business intelligence. Through a mix of videos, assessments, and hands-on labs, you’ll get introduced to BI tools and platforms and key technical skills required for an entry-level job.

Platforms and tools you will learn include: BigQuery, SQL, Tableau

In addition to expert training and hands-on projects, you'll complete a portfolio project that you can share with potential employers to showcase your new skill set. Learn concrete skills that top employers are hiring for right now.

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Foundations of Data Science

 

What you'll learn

Understand common careers and industries that use advanced data analytics

Investigate the impact data analysis can have on decision-making

Explain how data professionals preserve data privacy and ethics 

Develop a project plan considering roles and responsibilities of team members

There are 5 modules in this course

This is the first of seven courses in the Google Advanced Data Analytics Certificate, which will help develop the skills needed to apply for more advanced data professional roles, such as an entry-level data scientist or advanced-level data analyst. Data professionals analyze data to help businesses make better decisions. To do this, they use powerful techniques like data storytelling, statistics, and machine learning. In this course, you’ll begin your learning journey by exploring the role of data professionals in the workplace. You’ll also learn about the project workflow PACE (Plan, Analyze, Construct, Execute) and how it can help you organize data projects.   

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.  

By the end of this course, you will:

-Describe the functions of data analytics and data science within an organization

-Identify tools used by data professionals 

-Explore the value of data-based roles in organizations 

-Investigate career opportunities for a data professional 

-Explain a data project workflow 

-Develop effective communication skills

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Google Cybersecurity Professional Certificate

 

What you'll learn

Understand the importance of cybersecurity practices and their impact for organizations.

Identify common risks, threats, and vulnerabilities, as well as techniques to mitigate them.

Protect networks, devices, people, and data from unauthorized access and cyberattacks using Security Information and Event Management (SIEM) tools.

Gain hands-on experience with Python, Linux, and SQL.

Prepare for a career in cybersecurity

Receive professional-level training from Google

Demonstrate your proficiency in portfolio-ready projects

Earn an employer-recognized certificate from Google

Qualify for in-demand job titles: cybersecurity analyst, security analyst, security operations center (SOC) analyst

Professional Certificate - 8 course series

Prepare for a new career in the high-growth field of cybersecurity, no degree or experience required. Get professional training designed and delivered by subject matter experts at Google and have the opportunity to connect with top employers.

Organizations must continuously protect themselves and the people they serve from cyber-related threats, like fraud and phishing. They rely on cybersecurity to maintain the confidentiality, integrity, and availability of their internal systems and information. Cybersecurity analysts use a collection of methods and technologies to safeguard against threats and unauthorized access — and to create and implement solutions should a threat get through.

During the 8 courses in this certificate program, you’ll learn from cybersecurity experts at Google and gain in-demand skills that prepare you for entry-level roles like cybersecurity analyst, security operations center (SOC) analyst, and more. At under 10 hours per week, you can complete the certificate in less than 6 months. 

Upon completion of the certificate, you can directly apply for jobs with Google and over 150 U.S. employers, including American Express, Deloitte, Colgate-Palmolive, Mandiant (now part of Google Cloud), T-Mobile, and Walmart.

The Google Cybersecurity Certificate helps prepare you for the CompTIA Security+ exam, the industry leading certification for cybersecurity roles. You’ll earn a dual credential when you complete both.

Applied Learning Project

This program includes 170 hours of instruction and hundreds of practice-based assessments and activities that simulate real-world cybersecurity scenarios that are critical for success in the workplace. Through a mix of videos, assessments, and hands-on labs, you’ll become familiar with the cybersecurity tools, platforms, and skills required for an entry-level job.

Skills you’ll gain will include: Python, Linux, SQL, Security Information and Event Management (SIEM) tools, Intrusion Detection Systems (IDS), communication, collaboration, analysis, problem solving and more! 

Additionally, each course includes portfolio activities through which you’ll showcase examples of cybersecurity skills that you can share with potential employers. Acquire concrete skills that top employers are hiring for right now.

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Free Courses from Cisco

 

Free Courses from Cisco

1. Cybersecurity Operations Fundamentals Specialization

2. Introduction to Network Automation

3. Security Operations Center (SOC)

4. Network Automation Engineering Fundamentals Specialization

5. Network Security

6. DevOps for Network Automation (NetDevOps)

7. Ansible for Network Automation

8. Using APIs for Network Automation

9. Data Security

10. Introducing Model-Driven Programmability

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Machine Learning with Apache Spark (Free Course)

 

What you'll learn

Describe ML, explain its role in data engineering, summarize generative AI, discuss Spark's uses, and analyze ML pipelines and model persistence. 

Evaluate ML models, distinguish between regression, classification, and clustering models, and compare data engineering pipelines with ML pipelines. 

Construct the data analysis processes using Spark SQL, and perform regression, classification, and clustering using SparkML. 

Demonstrate connecting to Spark clusters, build ML pipelines, perform feature extraction and transformation, and model persistence. 

There are 4 modules in this course

Explore the exciting world of machine learning with this IBM course. 

Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos.

Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML.  

In practical labs, you'll utilize SparkML for regression, classification, and clustering, enabling you to construct prediction and classification models. Connect to Spark clusters, analyze SparkSQL datasets, perform ETL activities, and create ML models using Spark ML and sci-kit learn. Finally, demonstrate your acquired skills through a final assignment. 

This intermediate course is suitable for aspiring and experienced data engineers, as well as working professionals in data analysis and machine learning. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is highly recommended for this course.

Free Course - Machine Learning with Apache Spark

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Data Science Coding Challenge: Loan Default Prediction (Free Project)

 

Objectives

Load, clean, analyze, process, and visualize data using Python and Jupyter Notebooks

Produce an end-to-end machine learning prediction model using Python and Jupyter Notebooks

Skills you’ll demonstrate

Data Science

Data Analysis

Python Programming

Machine Learning

About this Project

In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model.

Important Information:

How to register?

To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register.

Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes. We’ll send you a profile link the week of the challenge.

When does the challenge start?

The coding challenge begins Tuesday, August 29th, at 8 AM (PST) and closes Thursday, August 31st, at 11:59 PM (PST). If you’re registered, you’ll receive a reminder email on the challenge start date. 

Please note this is a timed competition. Once the challenge is unlocked, you’ll have 72 hours to complete it. You can submit as many times as you would like within this timeframe.

What will the winners receive?

Participants will be evaluated based on their model’s prediction accuracy. The top 20% of participants will receive an achievement badge on their Coursera Skills Profile, highlighting their performance to recruiters.  The top 100 performers will get complimentary access to select Data Science courses.

All participants can showcase their projects to potential employers on their Coursera Skills Profile.

Winners will be notified by email the week of September 10th.

Good luck, and have fun!

Project plan

This project requires you to independently complete the following steps:

•Importing and preprocessing data

•Analyze the data

•Build machine learning models

•Evaluate machine learning models

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