Creative Thinking: Techniques and Tools for Success (Free Course)

 

What you'll learn

Understand what creative thinking techniques are

Comprehend their importance in tackling global challenges as well as in everyday problem-solving scenarios

Select and apply the appropriate technique based on the opportunity to seize or the problem to tackle

There are 7 modules in this course

In today’s ever-growing and changing world, being able to think creatively and innovatively are essential skills. It can sometimes be challenging to step back and reflect in an environment which is fast paced or when you are required to assimilate large amounts of information. Making sense of or communicating new ideas in an innovative and engaging way, approaching problems from fresh angles, and producing novel solutions are all traits which are highly sought after by employers.

This course will equip you with a ‘tool-box’, introducing you to a selection of behaviours and techniques that will augment your innate creativity. Some of the tools are suited to use on your own and others work well for a group, enabling you to leverage the power of several minds.  You can pick and choose which of these tools or techniques suit your needs and interests, focusing on some or all of the selected approaches and in the order that fits best for you.

The practical approach of this course enables you to acquire an essential skill-set for generating ideas, with plenty of:

- Fun e-tivities and exercises;

- Practical lectures and tips;

- Video representations of the techniques in action.

By the end of this course you should be able to:

- Pick a type of brainstorming you think will be useful to apply to a challenge

- Use alphabet brainstorming in tackling a challenge

- Use grid brainstorming in tackling a challenge

- Use a morphological chart to synthesise a solution to a challenge

- Use the TRIZ contradiction matrix to identify recommended inventive principles

- Apply SCAMPER to a range of challenges

The greatest innovators aren’t necessarily the people who have the most original idea. Often, they are people- or teams- that have harnessed their creativity to develop a new perspective or more effective way of communicating an idea. You can train your imagination to seize opportunities, break away from routine and habit, and tap into your natural creativity.

Join this course and a community of practitioners in CREATIVITY!

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Introduction to Artificial Intelligence (AI)

 

What you'll learn

Describe what is AI, its applications, use cases, and how it is transforming our lives

Explain terms like Machine Learning, Deep Learning and Neural Networks 

Describe several issues and ethical concerns surrounding AI

Articulate advice from experts about learning and starting a career in AI 

There are 4 modules in this course

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI.  You will also demonstrate AI in action with a mini project.

This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not. 

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Process Mining: Data science in Action (Free Course)

 

There are 6 modules in this course

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action".

The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.

The course covers the three main types of process mining.

1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log.

2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa.

3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases.

Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development.

The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field.

After taking this course you should:

- have a good understanding of Business Process Intelligence techniques (in particular process mining),

- understand the role of Big Data in today’s society,

- be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification,

- be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools),

- be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools),

- be able to extend a process model with information extracted from the event log (e.g., show bottlenecks),

- have a good understanding of the data needed to start a process mining project,

- be able to characterize the questions that can be answered based on such event data,

- explain how process mining can also be used for operational support (prediction and recommendation), and

- be able to conduct process mining projects in a structured manner.

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Introduction to Mathematical Thinking (Free Course)

 

There are 9 modules in this course

Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years.

Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking.

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Mathematics for Machine Learning Specialization

 

Specialization - 3 course series

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Applied Learning Project

Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

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Improving your statistical inferences (Free Course)

 

There are 8 modules in this course

This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles. 

In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework.

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10 FREE coding courses from University of Michigan

 

•  Python for Everybody Specialization

• Applied Data Science with Python Specializati

• Master of Applied Data Science

• Python 3 Programming Specialization

• Web Design for Everybody: Basics of Web Development & Coding Specialization

• Web Applications for Everybody Specialization

• Statistics with Python Specialization

• Using Python to Access Web Data

• PostgreSQL for Everybody Specialization

• Introduction to Structured Query Language (SQL)

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Inferential Statistical Analysis with Python

 

What you'll learn

Determine assumptions needed to calculate confidence intervals for their respective population parameters.

Create confidence intervals in Python and interpret the results.

Review how inferential procedures are applied and interpreted step by step when analyzing real data.

Run hypothesis tests in Python and interpret the results.

There are 4 modules in this course

In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.  

At the end of each week, learners will apply what they’ve learned using Python within the course environment. During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera. 

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From Excel to Power BI (Free Course)

 

What you'll learn

Learners will be instructed in how to make use of Excel and Power BI to collect, maintain, share and collaborate, and to make data driven decisions

There is 1 module in this course

Are you using Excel to manage, analyze, and visualize your data? Would you like to do more? Perhaps you've considered Power BI as an alternative, but have been intimidated by the idea of working in an advanced environment. The fact is, many of the same tools and mechanisms exist across both these Microsoft products. This means Excel users are actually uniquely positioned to transition to data modeling and visualization in Power BI! Using methods that will feel familiar, you can learn to use Power BI to make data-driven business decisions using large volumes of data. 

We will help you to build fundamental Power BI knowledge and skills, including: 

• Importing data from Excel and other locations into Power BI.

• Understanding the Power BI environment and its three Views.

• Building beginner-to-moderate level skills for navigating the Power BI product.

• Exploring influential relationships within datasets. 

• Designing Power BI visuals and reports.

• Building effective dashboards for sharing, presenting, and collaborating with peers in Power BI Service.

For this course you will need:

• A basic understanding of data analysis processes in Excel.

• At least a free Power BI licensed account, including:

• The Power BI desktop application.

• Power BI Online in Microsoft 365.

Course duration is approximately three hours. Learning is divided into five modules, the fifth being a cumulative assessment. The curriculum design includes video lessons, interactive learning using short, how-to video tutorials, and practice opportunities using COMPLIMENTARY DATASETS. Intended audiences include business students, small business owners, administrative assistants, accountants, retail managers, estimators, project managers, business analysts, and anyone who is inclined to make data-driven business decisions. Join us for the journey!

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Meta Front-End Developer Professional Certificate

 

What you'll learn

Create a responsive website using HTML to structure content, CSS to handle visual style, and JavaScript to develop interactive experiences. 

Learn to use React in relation to Javascript libraries and frameworks.

Learn Bootstrap CSS Framework to create webpages and work with GitHub repositories and version control.

Prepare for a coding interview, learn best approaches to problem-solving, and build portfolio-ready projects you can share during job interviews.

Prepare for a career in Front-end Development

Receive professional-level training from Meta

Demonstrate your proficiency in portfolio-ready projects

Earn an employer-recognized certificate from Meta

Qualify for in-demand job titles: Front-End Developer, Website Developer, Software Engineer

Professional Certificate - 9 course series

Want to get started in the world of coding and build websites as a career? This certificate, designed by the software engineering experts at Meta—the creators of Facebook and Instagram, will prepare you for a career as a front-end developer.

In this program, you’ll learn: 

How to code and build interactive web pages using HTML5, CSS and JavaScript. 

In-demand design skills to create professional page layouts using industry-standard tools such as Bootstrap, React, and Figma. 

GitHub repositories for version control, content management system (CMS) and how to edit images using Figma. 

How to prepare for technical interviews for front-end developer roles.

By the end, you’ll put your new skills to work by completing a real-world project where you’ll create your own front-end web application. Any third-party trademarks and other intellectual property (including logos and icons) referenced in the learning experience remain the property of their respective owners. Unless specifically identified as such, Coursera’s use of third-party intellectual property does not indicate any relationship, sponsorship, or endorsement between Coursera and the owners of these trademarks or other intellectual property.

Applied Learning Project

Throughout the program, you’ll engage in hands-on activities that offer opportunities to practice and implement what you are learning. You’ll complete hands-on projects that you can showcase during job interviews and on relevant social networks.

At the end of each course, you’ll complete a project to test your new skills and ensure you understand the criteria before moving on to the next course. There are 9 projects in which you’ll use a lab environment or a web application to perform tasks such as:  

Edit your Bio page—using your skills in HTML5, CSS and UI frameworks

Manage a project in GitHub—using version control in Git, Git repositories and the Linux Terminal 

Build a static version of an application—you’ll apply your understanding of React, frameworks, routing, hooks, bundlers and data fetching. 

At the end of the program, there will be a Capstone project where you will bring your new skillset together to create the front-end web application.

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Introduction to Statistics (Free Course)

 

There are 12 modules in this course

Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.

Free Course - Introduction to Statistics

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IBM Full Stack Software Developer Professional Certificate

 

What you'll learn

Master the most up-to-date practical skills and tools that full stack developers use in their daily roles

Learn how to deploy and scale applications using Cloud Native methodologies and tools such as Containers, Kubernetes, Microservices, and Serverless

Develop software with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap

Build your GitHub portfolio by applying your skills to multiple labs and hands-on projects, including a capstone

Professional Certificate - 12 course series

Prepare for a career in the high-growth field of software development. In this program, you’ll learn in-demand skills and tools used by professionals for front-end, back-end, and cloud native application development to get job-ready in less than 4 months, with no prior experience needed. 

Full stack refers to the end-to-end computer system application, including the front end and back end coding. This Professional Certificate covers development for both of these scenarios. Cloud native development refers to developing a program designed to work on cloud architecture. The flexibility and adaptability that full stack and cloud native developers provide make them highly sought after in this digital world. 

You’ll  learn how to build, deploy, test, run, and manage full stack cloud native applications. Technologies covered includes Cloud foundations, GitHub, Node.js, React, CI/CD, Containers, Docker, Kubernetes, OpenShift, Istio, Databases, NoSQL, Django ORM, Bootstrap, Application Security, Microservices, Serverless computing, and more. 

After completing the program you will have developed several applications using front-end and back-end technologies and deployed them on a cloud platform using Cloud Native methodologies. You will publish these projects through your GitHub repository to share your portfolio with your peers and prospective employers.

This program is ACE® recommended—when you complete, you can earn up to 18 college credits.

Applied Learning Project

Throughout the courses in the Professional Certificate, you will develop a portfolio of hands-on projects involving various popular technologies and programming languages in Full Stack Cloud Application Development. These projects include creating:

HTML pages on Cloud Object Storage

An interest rate calculator using HTML, CSS, and JavaScript

An AI program deployed on Cloud Foundry using DevOps principles and CI/CD toolchains with a NoSQL database

A Node.js back-end application and a React front-end application

A containerized guestbook app packaged with Docker deployed with Kubernetes and managed with OpenShift

A Python app bundled as a package

A database-powered application using Django ORM and Bootstrap

An app built using Microservices & Serverless

A scalable, Cloud Native Full Stack application using the technologies learned in previous courses

You will publish these projects through your GitHub repository to share your skills with your peers and prospective employers.

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Meta Back-End Developer Professional Certificate

 

What you'll learn

Gain the technical skills required to become a qualified back-end developer

Learn to use programming systems including Python Syntax, Linux commands, Git, SQL, Version Control, Cloud Hosting, APIs, JSON, XML and more

Build a portfolio using your new skills and begin interview preparation including tips for what to expect when interviewing for engineering jobs

Learn in-demand programming skills and how to confidently use code to solve problems

Professional Certificate - 9 course series

Ready to gain new skills and the tools developers use to create websites and web applications? This certificate, designed by the software engineering experts at  Meta—the creators of Facebook and Instagram, will prepare you for an entry-level career as a back-end developer. 

In this program, you’ll learn:

Python Syntax—the most popular choice for machine learning, data science and artificial intelligence.

In-demand programming skills and how to confidently use code to solve problems. 

Linux commands and Git repositories to implement version control.

The world of data storage and databases using MySQL, and how to craft sophisticated SQL queries. 

Django web framework and how the front-end consumes data from the REST APIs. 

How to prepare for technical interviews for back-end developer roles.

Any third-party trademarks and other intellectual property (including logos and icons) referenced in the learning experience remain the property of their respective owners. Unless specifically identified as such, Coursera’s use of third-party intellectual property does not indicate any relationship, sponsorship, or endorsement between Coursera and the owners of these trademarks or other intellectual property.

Applied Learning Project

Throughout the program, you’ll engage in applied learning through hands-on activities to help level up your knowledge. At the end of each course, you’ll complete 10 micro-projects that will help prepare you for the next steps in your engineer career journey. 

In these projects, you’ll use a lab environment or a web application to perform tasks such as:   

Solve problems using Python code. 

Manage a project in GitHub using version control in Git, Git repositories and the Linux Terminal. 

Design and build a simple Django app. 

At the end of the program, there will be a Capstone project where you will bring all of your knowledge together to create a Django web app.

Join - Meta Back-End Developer Professional Certificate

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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

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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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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.

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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

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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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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.

Join Free - Foundations: Data, Data, Everywhere

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