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Python: Working with Predictive Analytics (LinkedIn Learning)

Durée de la formation : 2,22h

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 data scientist Isil Berkun in this course 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. Isil describes what models to use and when, and explains the concepts in such a way that you can immediately apply them to your own work. Check out this course and learn to leverage Python libraries like pandas and NumPy and choose the right prediction models for your projects. This course includes Code Challenges powered by CoderPad. Code Challenges are interactive coding exercises with real-time feedback, so you can get hands-on coding practice alongside the course content to advance your programming skills.

Topics include:
  • Explain how predictive analytics can assist with decision-making.
  • Differentiate between the types of data that are used.
  • Apply the correct functions to Python code to produce optimal results.
  • Explain why data needs to be preprocessed before using predictive models.
  • Distinguish between the different predictive models available.

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.

Data Science Foundations: Fundamentals (LinkedIn Learning)

Data science is driving a world-wide revolution that touches everything from business automation to social interaction. It’s also one of the fastest growing, most rewarding careers, employing analysts and engineers around the globe. This course provides an accessible, nontechnical overview of the field, covering the vocabulary, skills, jobs, tools, and techniques of data science. Instructor Barton Poulson defines the relationships to other data-saturated fields such as machine learning and artificial intelligence. He reviews the primary practices: gathering and analyzing data, formulating rules for classification and decision-making, and drawing actionable insights. He also discusses ethics and accountability and provides direction to learn more. By the end, you’ll see how data science can help you make better decisions, gain deeper insights, and make your work more effective and efficient.

Topics include:
  • Assess the skills required for a career in data science.
  • Evaluate different sources of data, including metrics and APIs.
  • Explore data through graphs and statistics.
  • Discover how data scientists use programming languages such as R, Python, and SQL.
  • Assess the role of mathematics, such as algebra, in data science.
  • Assess the role of applied statistics, such as confidence intervals, in data science.
  • Assess the role of machine learning, such as artificial neural networks, in data science.
  • Define the components of effective data visualization.

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

Excel: Economic Analysis and Data Analytics (LinkedIn Learning)

Durée de la formation : 2,29h

Economics affects us all each day—from issues like inflation to the health of the economy and risk of a recession. In this course, learn how to harness economic data to do economic analysis and forecasting to gain key business insights and analyze market conditions. Join professor Michael McDonald as he demonstrates how to harness the wealth of information available on the internet to forecast statistics such as industry growth, GDP, and unemployment rates, as well as factors that directly affect your business like property prices, future interest rate hikes, supply chain issues, and more. All you need is the built-in formulas, functions, and calculations of Microsoft Excel. Along the way, get tips on using regression analysis, confidence intervals, and forecasting tools with your company’s key performance indicators (KPIs), building your skill set in data analytics to better meet the needs of your business.

Topics include:
  • Explore the different kinds of economics data.
  • Differentiate between forecasting methods.
  • Identify the correct regression technique for different data sets.
  • Define types of correlation.
  • Review the types of variables used in various forms of regression analysis.
  • Identify proper Stata techniques.

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

Data Science on Google Cloud Platform: Exploratory Data Analytics (LinkedIn Learning)

Cloud computing brings unlimited scalability and elasticity to data science applications. Expertise in the major platforms, such as Google Cloud Platform (GCP), is essential to the IT professional. This course—one of a series by cloud engineering specialist and data scientist Kumaran Ponnambalam—shows how to conduct exploratory data analytics with GCP. First, review the concepts of segmentation and profiling. Then get hands on, as you learn to perform both text and visual analysis of data using tools provided by GCP: Cloud Datalab, BigQuery, Cloud Dataflow, and Data Studio. Finally, look at an end-to-end use case that applies what you've learned in the course.

Topics include:
  • Setting up Cloud DataLlb for exploratory data analytics
  • Segmentation and profiling
  • Reading and writing data from BigQuery
  • Managing cloud storage buckets
  • Creating visualizations of BigQuery data with the GCP Charting API
  • Managing Datalab instances

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

Data Science on Google Cloud Platform: Predictive Analytics (LinkedIn Learning)

Predictive analytics use historic data to look forward, enabling organizations to make better decisions. However, making accurate predictions from big data can be an overwhelming task. Enter Google Cloud Platform (GCP), a suite of cloud-computing services that bring scalability, elasticity, and automated machine learning to predictive analytics. This course—one of a series by data scientist Kumaran Ponnambalam—shows how to apply the power of GCP to generate predictions for your business. Start off by exploring the different tools and features for predictive analytics in GCP, including Cloud Dataproc, Cloud ML Engine, and the machine learning APIs such as Cloud Translation, Cloud Vision, and Cloud Video Intelligence. Then explore learn how to build, train, and deploy models to create predictions. Plus, learn best practices for cost control, testing, and performance monitoring of predictive models.

Topics include:
  • Evaluating the machine learning tools in GCP
  • Understanding the predictive analytics process
  • Building models
  • Training models with jobs
  • Building and running predictions
  • Best practices for cost control, testing, and performance monitoring

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

Learning Data Analytics: 1 Foundations (LinkedIn Learning)

Are you interested in pursuing a career in data analytics? In this course, instructor Robin Hunt brings you into the mind of an analyst. She defines and explains foundational concepts, such as how to think about data, how to work with others in different roles to get the data you need, and the tools you need to work with data, such as Excel and Microsoft Access. She introduces you to SQL queries, PowerBI, and more. Robin goes into syntax and explains how to interpret the data you see, find the data you need, and clean the data for effective data work. She explains data governance and how to ask the right questions of different departments to gather the data you need. Robin shows how to work with data, including how to import data, work with flat files such as CSVs, and create datasets for others. Robin goes into what cleaning and modeling mean, as well as how to use Power Query in Excel. She has also added challenge/solution sets in each chapter to help you evaluate your skills.

Topics include:
  • Recognize the skills of a data analyst.
  • Apply SQL statements using the correct syntax.
  • Interpret existing data.
  • Explain how data is cleaned.
  • Demonstrate how to use joins.
  • Identify different types of data.
  • Describe how to model data.

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

OpenRefine

Support du cours sur l'outil OpenRefine : "A free, open source, powerful tool for working with messy data"

Predictive Customer Analytics (LinkedIn Learning)

Use big data to tell your customer's story, with predictive analytics. In this course, instructor Kumaran Ponnambalam teaches you about the customer life cycle and how predictive analytics can help improve every step of the customer journey. Start off by learning about the various phases in a customer's life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review multiple use cases for predictive analytics in each phase of the customer's life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, Kumaran introduces best practices for creating a customer analytics process from the ground up.

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

Executive Guide to Predictive Modeling Strategy at Scale (LinkedIn Learning)

Durée de la formation : 1,05h

Building world-class predictive analytics solutions requires recognizing that the challenges of scale and sample size fluctuate greatly at different stages of a project. How do you know how much data to use? What is too little, what is too much? How does your infrastructure need to scale with the volume and demands of the project? This course walks step by step through the strategic and tactical aspects of determining how much data is needed to build an effective predictive modeling solution based on machine learning and what volumes of data are so large that they will create challenges. Instructor Keith McCormick reviews each stage—data selection, data preparation, modeling, scoring, and deployment—with scalability in mind, providing IT professionals, data scientists, and leadership with new insights, perspectives, and collaboration tools. Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only.

Topics include:
  • Evaluating the proper amount of data
  • Assessing data quality and quantity
  • Seasonality and time alignment
  • Data preparation challenges
  • Data modeling challenges
  • Scoring machine-learning models
  • Deploying models and adjusting data prep and scoring
  • Monitoring and maintenance

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

Learning path: Advance Your Skills in Predictive Analytics (LinkedIn Learning)

Predictive analytics is one of the richest disciplines within the realm of data science. As the tools and techniques for using data to predict future outcomes have evolved, business and data analysis professionals can use this learning path to stay up to date with the latest advancements.

Discover how machine learning is changing predictive analytics.

Apply predictive analytics techniques for financial forecasting.

Explore the predictive analytics functions of R, Python, etc.

    Learning Path: Become a Data Analytics Specialist (LinkedIn Learning)

    Durée de la formation : 18,02h

    Get a thorough grounding in the concepts and skills needed for data analytics, including statistics, financial forecasting, data mining, predictive analytics, and meta-analysis.

    Explore the expanding applications for data analytics skills.

    Master statistics, the core skill needed in analytics work.

    Build skills in financial forecasting and data mining.