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Excel and ChatGPT: Data Analysis Power Tips (LinkedIn Learning)

Durée de la formation : 1,7h

Data analytics revolutionized the way we work, and now, with the rise of artificial intelligence, data professionals have access to tools that let them work smarter and faster than ever before. In this beginner-friendly course, instructor Chris Dutton shows you have to leverage tools like Excel and ChatGPT together to work more efficiently and make smarter, data-driven decisions. Chris and the Maven Analytics team discuss how AI is revolutionizing data analysis, introduce the art of prompt engineering, review tips and best practices for generating consistent accurate model outputs, and address common limitations and pitfalls. Throughout the course you’ll learn some of the most powerful ChatGPT use cases for data science and analytics, and get hands-on practice using ChatGPT to explain and interpret code, generate formulas, troubleshoot errors, optimize complex queries, and much more.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Using Data in Financial Analysis (LinkedIn Learning)

Durée de la formation : 1,74h

Big data has transformed the world of business. Yet many people don't understand what big data and business intelligence are, or how to apply the techniques to their day-to-day jobs. This course addresses that knowledge gap, giving businesspeople practical methods to create quick and relevant business forecasts using big data. Join Professor Michael McDonald and discover how to use predictive analytics to forecast key performance indicators of interest, such as quarterly sales, projected cash flow, or even optimized product pricing. Learn how to gather, compute, and use basic financial ratios, and then how to apply those ratios for forecasting purposes looking at key outcomes for the firm. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.

Topics include:
  • Identify strategies for using data to enhance business performance.
  • Describe the steps in tying data to financial statements.
  • Explain the reason for using ratios.
  • Differentiate between various sources of data.
  • Define the reason for using regression to make forecasts.
  • Review the output of regression analysis in forecasting.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Preparing to Get a Job in Data Analytics (LinkedIn Learning)

Thinking about a career in data analytics or looking to land a more senior-level role? This course offers a friendly guide to landing a job in this in-demand field. Jess Ramos, a senior data analyst, helps you gauge your initial interest, providing her honest take on what entry-level and senior-level data analytics jobs entail and the amount of work it takes to land such a role. Once you determine whether your interests are a match for this field, Jess goes over the tools and skills you’ll need to excel as a data analyst, as well as how to advertise those skills on LinkedIn, make important connections, and land interviews. And to ensure that you ace those interviews, Jess explains how to build a data analytics portfolio that’s tailored to your target role and industry.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Statistics Foundations 3: Using Data Sets (LinkedIn Learning)

Durée de la formation : 1,68h

Statistics are a core skill for many careers. Basic stats are critical for making decisions, discoveries, investments, and even predictions. But sometimes you need to move beyond the basics. This third course in the Statistics Foundations series gives you practical, example-based lessons on the intermediate skills associated with statistics: Samples and sampling, standard errors, confidence intervals, and hypothesis testing. Eddie Davila takes a look at topics like sampling, random samples, sample sizes, sampling error, trustworthiness, the central unit theorem, confidence intervals, and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world.

Topics include:
  • Describe how non-representative samples can lead to biased conclusions in the context of polling.
  • Recognize the characteristics and process of obtaining a convenience sample.
  • Explain how concepts such as the law of large numbers and the central limit theorem are relevant to understanding the impact of sample size.
  • Assess how confidence intervals contribute to the overall reliability and validity of election poll results.
  • Discuss the role of test statistics in hypothesis testing and how they are used to find the p-value.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Cert Prep: Certified Analytics Professional (CAP) (LinkedIn Learning)

Durée de la formation : 2,57h

Want to accelerate your career in data science and analytics? Consider earning the Certified Analytics Professional (CAP) credential. This premier data science certification shows potential employers that you can glean insights from data and use your findings to determine logical next steps. In this course, Jungwoo Ryoo provides test takers with an understanding of how a core set of data science topics are relevant and necessary to obtain a CAP credential in an expedited fashion. Jungwoo covers the seven domains of the exam: business problem framing, analytics problem framing, data, methodology, model building, deployment, and lifecycle management. He also shares case studies that demonstrate how the CAP knowledge domain concepts work in the real world.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Data Analytics for Students (LinkedIn Learning)

Analytics is such a broad topic that it's hard to know where to get started. In this course designed for students, explore how to use data analytics to make informed decisions, and build core analytics skills that can prepare you to enter into the business or data science landscape. Learn about the basics of analytics, how data is typically captured, and how it impacts the day-to-day of a business. This course also provides an introduction to common tools used in analytics, as well as stories designed to help students get an overview of careers that require strong analytical skills.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Data Science Foundations: Data Assessment for Predictive Modeling (LinkedIn Learning)

CRISP-DM, the cross-industry standard process for data mining, is composed of six phases. Most new data scientists rush to modeling because it's the phase in which they have the most training. But whether the project succeeds or fails is actually determined far earlier. This course introduces a systematic approach to the data understanding phase for predictive modeling. Instructor Keith McCormick teaches principles, guidelines, and tools, such as KNIME and R, to properly assess a data set for its suitability for machine learning. Discover how to collect data, describe data, explore data by running bivariate visualizations, and verify your data quality, as well as make the transition to the data preparation phase. The course includes case studies and best practices, as well as challenge and solution sets for enhanced knowledge retention. By the end, you should have the skills you need to pay proper attention to this vital phase of all successful data science projects.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Predictive Analytics Essential Training: Estimating and Ensuring ROI (LinkedIn Learning)

Nothing is more important to the future of predictive analytics teams than proving their projects have long-term value. Measuring the return on investment (ROI) often can help turn analytics into a visible profit center for your organization. Estimating ROI early—before a project even begins—can also help fast-track approval. Here Keith McCormick shows how to address ROI both before and after the predictive model is built. Learn how to create your estimate before the project starts by estimating the overall size of the problem, assigning value to possible outcomes, and judging the impact of model performance. Keith then shows a different method for calculating ROI after the model is built, during the evaluation and deployment phases, and provides tips for the ongoing monitoring of the project. He also takes a retrospective look assessed one year after model deployment. These two strategies will give you the data you need to get buy-in for your projects and provide ongoing metrics on their performance.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Learning Data Analytics Part 2: Extending and Applying Core Knowledge (LinkedIn Learning)

It’s one thing to work with data. It’s another to provide quality data sets and accurate visualizations for decisions to be made. If you are considering a course emphasis or a career in data analytics, this course can help you get off to a good start. Robin Hunt, CEO and co-founder of ThinkData Solutions, shares practical skills to help you get the most from your data and jumpstart your career in data. Robin discusses business rules, filtering out the noise in the data we all deal with, and how to deliver what decision-makers want. She covers how to create data sets with queries, joins, and appends, then goes into building aggregate data with total queries. Robin goes over pivots, how to use pivots to build basic dashboards and visualizations, and how to use Power Query for data transformations. She concludes with best practices for meetings and taking your work to the next level for your organization.

Topics include:
  • Explore concepts relating to data analytics.
  • Explain the use of wildcard characters in building queries.
  • Review types of data visualizations.
  • Describe how slicers are used.
  • Define terminology used in creating visualizations.
  • Differentiate between duplicate and reference data sets.
  • Identify licensing and permissions considerations.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Introduction to Machine Learning with KNIME (LinkedIn Learning)

KNIME is an open-source workbench-style tool for predictive analytics and machine learning. It is highly compatible with numerous data science technologies, including R, Python, Scala, and Spark. With KNIME, you can produce solutions that are virtually self-documenting and ready for use. These reasons and more make KNIME one of the most popular and fastest-growing analytics platforms around. In this course, expert Keith McCormick shows how KNIME supports all the phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) in one platform. Get up and running quickly—in 15 minutes or less—or stick around for the more in-depth training covering merging and aggregation, modeling, and data scoring. Plus, learn how to increase the power of KNIME with extensions and integrate R and Python.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Data Analytics: Graph Analytics (LinkedIn Learning)

Since the inception of data analytics, the way in which analysts view and interpret data has evolved tremendously. New technologies, tools, and approaches have advanced what's possible with data analytics, and network analysis is no exception. In this course, longtime data analyst and data visualization expert Heather Johnson shares the fundamentals of using graph analytics, or network analysis, when analyzing data. Heather begins by reviewing the components of a network analysis and detailing the advantages of using a graph analytics approach. She then walks through key applications of using graph analytics when reviewing data. To wrap up the course, Heather discusses career opportunities within the realm of graph analytics. Upon completion, you’ll be equipped with the knowledge of what graph analytics is and how it can be leveraged within your analytics career. This course was created by Madecraft. We are pleased to host this training in our library.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Python: Working with Predictive Analytics (LinkedIn Learning)

Data can tell many stories: where it came from and where it’s going. Predictive analytics gives programmers a tool to tell stories about the future: to extract usable information and make accurate predictions. These predictions, in turn, allow business to make more informed, impactful decisions. Join Isil Berkun, data scientist, to explore predictive analytics with Python. Discover how to prepare data—fill in missing values, perform feature scaling, and more—and use prebuilt Python libraries to make and evaluate prediction models. She describes what models to use when, and explains the concepts in such a way that you can immediately apply them to your own work. By the end of the course, you’ll be able to leverage Python libraries like pandas and NumPy and choose the right prediction models for your projects.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Amazon Web Services: Data Analytics (LinkedIn Learning)

Many modern organizations have a wealth of data that they can draw from to inform their decisions. But all of this information can't truly benefit a business unless the professionals working with that data can efficiently extract meaningful insights from it. Amazon Web Services (AWS) offers data scientists an array of tools and services that they can leverage to analyze data. In this course, learn about best practices, patterns, and tools for designing and implementing data analytics using AWS. Explore key analytics concepts, common methods of approaching analytics challenges, and how to work with services such as Athena, RDS, and QuickSight. Plus, discover how to visualize text-based data in a more visually intuitive way, use partner solutions for analytics from the AWS Marketplace, and more.

Topics include:
  • Explain the difference between files and databases.
  • Identify examples of batching, micro-batching, and streaming.
  • Prepare helpful data visualizations with QuickSight.
  • Recognize the different types of analytics available in AWS.
  • Demonstrate how to set up AWS CLI.
  • Describe common analytics architecture patterns.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Financial Forecasting with Analytics Essential Training (LinkedIn Learning)

Big data is transforming the world of business. Yet many people don't understand what big data and business intelligence are, or how to use that data to evaluate key metrics for their firm in the future. This course addresses that knowledge gap, giving business people practical methods to create financial forecasts with business analytics and big data. Join Professor Michael McDonald and discover how to use predictive analytics to forecast key performance indicators of interest, such as quarterly sales, projected cash flow, or even optimized product pricing. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Data Analytics: Dashboards vs. Data Stories (LinkedIn Learning)

In the world of data analytics, you're consistently presented with the same decision when it comes to how you'll communicate your data and insights. For each project, you need to decide whether to use a dashboard or tell a data story. In this course, business intelligence architect Sara Anstey provides you with the necessary information you need to make this decision with confidence. First, Sara covers the fundamentals of making decisions with data and shares how the role of a data analyst makes this possible for organizations. She then dives into the topics of data science dashboards and data storytelling, highlighting the pros and cons of each and providing the details you need to determine which approach is right for you. Sara closes by recapping key concepts, leaving you prepared to pick between each of these options with ease and intentionality. This course was created by Madecraft. We are pleased to host this training in our library.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Predictive Analytics Essential Training for Executives (LinkedIn Learning)

Organizations in nearly every industry are seeking and hiring data scientists, but many of these professionals don’t remain at their posts for long. Even though data analytics skills are highly valued, individuals with this skill set can't make an impact unless middle and senior management know how to leverage analytics for the long-term benefit of their organization. The challenge is that most of the people overseeing advanced analytics don’t have backgrounds in data science themselves. In this course, Keith McCormick shows executives who aren't fluent in data analytics how to hire data science professionals, manage data science teams, and transform their business with effectively deployed advanced analytics. Keith details how to hire a well-rounded team, including how to identify top-performing data scientists. Plus, he shares how to navigate the different analytics and machine learning software options on the market, fit data science into your organizational structure, and more.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Predictive Analytics Essential Training: Data Mining (LinkedIn Learning)

Durée de la formation : 1,91h

Are you a data science practitioner, looking to develop or enhance your skills in predictive analysis and data mining? This course provides several “big picture” insights, via instructor Keith McCormick, a veteran practitioner who has completed dozens of real-world projects. Keith begins by introducing you to key definitions and processes that you will need to complete the course successfully. He steps you through defining the problem you need your predictive analysis to address, then focuses on how to make sure you meet the data requirements and how good data preparation improves your data mining projects. Keith dives into the skill sets and resources that you need and the problems you will face. Then he goes over the steps to find the solution and put it to work with probabilities, propensities, missing data, meta modeling, and much more. Keith finishes up with detailed explanations of CRISP-DM and Tom Khabaza’s nine laws of data mining, plus Tom’s new 10th law.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Business Analytics Foundations: Descriptive, Exploratory, and Explanatory Analytics (LinkedIn Learning)

Durée de la formation : 0,69h

Business analytics allows us to learn from the past and make better predictions for the future. There are three types of analytics used for learning from the past. Descriptive analytics summarizes historical data; exploratory analytics uncovers hidden patterns; and explanatory analytics reveals the reasons for business results. Each type encompasses a different set of tools, technologies, processes, and best practices to derive insights from data. This course by Kumaran Ponnambalam explains why they matter and how and when to use them. He starts by setting the context for business analytics and its various stages. You then explore the stages that focus on the past: descriptive, exploratory, and explanatory. With each stage, you learn about the processes, techniques, and best practices used in the field. Finally, you walk through a use case (the results of an email marketing campaign) that demonstrates how analysis is performed at each stage.

Topics include:
  • Recall the data sources utilized in business analytics.
  • Explain potential problems with having an excessive number of metrics and reports when conducting descriptive analytics.
  • Identify the questions each type of business analytics is intended to answer.
  • Recognize the appropriate type of business analytics given a scenario.
  • Describe the business analytics that can be used with various types of data.
  • Determine next steps after completing an analysis.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Business Analytics Foundations: Predictive, Prescriptive, and Experimental Analytics (LinkedIn Learning)

Business analytics encompasses a set of tools, technologies, processes, and best practices that are required to derive knowledge from data. It's an iterative and methodical exploration of data to derive insights from it—and, in turn, make smarter, more strategic decisions that are grounded in facts. In this course, learn about the stages in business analytics that are used to predict and build the future—predictive analytics, prescriptive analytics, and experimental analytics. This course dives into each stage, discussing the tools and techniques used for each, as well as best practices leveraged in the field. In addition, the course lends a real-world context to these concepts by using a use case to demonstrate how to execute analytics in each stage.

Topics include:
  • Distinguish between the different stages of business analytics.
  • Identify the movement of data during business analytics.
  • Examine prescriptive analytics tools and techniques.
  • Explain experimental analysis tools and techniques.
  • Identify factors that should be considered when using A/B testing.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

Data Analytics for Pricing Analysts in Excel (LinkedIn Learning)

Discover how to make smarter product pricing decisions that maximize your organization's profits. In this course, instructor Michael McDonald goes over using scenario analysis, price optimization, and variance analysis to model the data analytics behind pricing . Michael explains how to determine bundle pricing in a scenario, estimate price elasticity, compute price optimization profits with one variable or many variables, balance price and sales volume considerations, and more. If you'd like to pursue a career in corporate finance—particularly as a pricing analyst in an insurance, retail, manufacturing, or technology firm—then this course can help equip you with the skills you need to help your company succeed in today's economy.

Topics include:
  • Break down how price affects both supply and demand.
  • Explain how to use linear interpolation to make optimal pricing decisions.
  • Identify different types of price discrimination.
  • Give examples of inelastic pricing.
  • Predict the effect higher margins for your products would have on your competitors.
  • Compare the value of a unit of price to a unit of volume.

Ce cours n´est disponible qu´en anglais. Si ce n´est pas un problème pour vous, soumettez votre demande.

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