Become a Data Analytics Specialist

• 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.

 

 

 

01

Learning Data Analytics with Robin Hunt Learn the basics of data analytics: using data for analysis and reporting. This beginner-level data science course is for anyone who works with data. 1h 39m • COURSE Every person who works with data has to perform analytics at some point. This popular training course—dramatically expanded and enhanced for 2018—teaches analysts and non-analysts alike the basics of data analytics and reporting. Robin Hunt defines what data analytics is and what data analysts do. She then shows how to identify your data set—including the data you don't have—and interpret and summarize data. She also shows how to perform specialized tasks such as creating workflow diagrams, cleaning data, and joining data sets for reporting. Coverage continues with best practices for data analytics projects, such as verifying data and conducting effective meetings, and common mistakes to avoid. Then learn techniques for repurposing, charting, and pivoting data. Plus, get helpful productivity-enhancing shortcuts and troubleshooting tips for the most popular data analytics program, Microsoft Excel. Topics include: Define data analysis and data analyst. List roles in data analysis. Explain data fields and types. Define syntax. Explain how to interpret existing data. Describe data best practices. Repurpose data. Create a data dictionary. Compare and contrast linking versus embedding charts and data. Build pivot charts with slicers.

02

Statistics Foundations: 1 with Eddie Davila All types of jobs use statistics. Learn the most common statistics, including mean, median, standard deviation, probability and more, in these beginner-level statistics lessons. 2h 6m • COURSE Statistics is not just the realm of data scientists. All types of jobs use statistics. Statistics are important for making decisions, new discoveries, investments, and predictions. Whether the subject is political races, sports rankings, shopping trends, or healthcare advancements, statistics is an instrument for understanding your favorite topic at a deeper level. With these beginner-level lessons, you too can master the terms, formulas, and techniques needed to perform the most common types of statistics. Professor Eddie Davila covers statistics basics, like calculating averages, medians, modes, and standard deviations. He shows how to use probability and distribution curves to inform decisions, and how to detect false positives and misleading data. Each concept is covered in simple language, with detailed examples that show how statistics are used in real-world scenarios from the worlds of business, sports, education, entertainment, and more. These techniques will help you understand your data, prove theories, and save time, money, and other valuable resources—all by understanding the numbers. Topics include: Why statistics matter Evaluating your data sets Finding means, medians, and modes Calculating standard deviation Measuring distribution and relative position Understanding probability and multiple-event probability Describing permutations: the order of things Calculating discrete and continuous probability distributions

03

Statistics Foundations: 2 with Eddie Davila Get practical, example-based learning of the intermediate skills associated with statistics: samples and sampling, confidence intervals, and hypothesis testing. 1h 56m • COURSE Statistics are a core skill for many careers. Basic stats are critical for making decisions, new discoveries, investments, and even predictions. But sometimes you need to move beyond the basics. Statistics Fundamentals – Part 2 takes business users and data science mavens into practical, example-based learning of the intermediate skills associated with statistics: samples and sampling, confidence intervals, and hypothesis testing. Eddie Davila first provides a bridge from Part 1, reviewing introductory concepts such as data and probability, and then moves into the topics of sampling, random samples, sample sizes, sampling error and trustworthiness, the central unit theorem, t-distribution, confidence intervals (including explaining unexpected outcomes), and hypothesis testing. This course is a must for those working in data science, business, and business analytics—or anyone else who wants to go beyond means and medians and gain a deeper understanding of how statistics work in the real world. Topics include: Data and distributions Sample size considerations Random sampling Confidence intervals Hypothesis testing

04

Statistics Foundations: 3 with Eddie Davila Complete your mastery of statistics. Get an advanced understanding of concepts such as t-distribution, degrees of freedom, chi-square testing, regression testing, and ANOVA. 1h 41m • COURSE Statistics are everywhere, in every industry, but they're a must for anyone working in data science, business, or business analytics. If you're in one of these specialized fields, chances are you need an advanced understanding of statistics. Complete your mastery in this course, part 3 of our Statistics Fundamentals series. Eddie Davila covers concepts such as small sample sizes, t-distribution, degrees of freedom, chi-square testing, and more. This advanced skills training moves learners into the practical study and application of experimental design, analysis of variance, population comparison, and regression analysis. Use these lessons to go beyond the basics and dive deeper into the specific factors that influence your own calculations and results. Topics include: Working with small sample sizes Using t-statistic vs. z-statistic Calculating confidence intervals with t-scores Comparing two populations (proportions) Comparing two population means Chi-square testing ANOVA testing Regression testing

05

Financial Forecasting with Big Data with Michael McDonald Quickly create financial forecasts using big data, predictive analytics, and Microsoft Excel. 1h 21m • COURSE Big data is transforming 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. 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. LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here. The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc. Topics include: Understanding big data and predictive analytics Gathering financial data Cleaning up your data Calculating key financial metrics Using regression analysis for business-specific forecasts Performing scenario analysis Calculating confidence intervals Stress testing

06

The Essential Elements of Predictive Analytics and Data Mining with Keith McCormick Learn the basics of data mining and predictive analytics. Learn the steps of a real-world project, from defining the problem to putting the solution into practice, and review CRISP-DM and the 9 laws of data mining. 1h 28m • COURSE A proper predictive analytics and data-mining project can involve many people and many weeks. There are also many potential errors to avoid. A "big picture" perspective is necessary to keep the project on track. This course provides that perspective through the lens of a veteran practitioner who has completed dozens of real-world projects. Keith McCormick is an independent data miner and author who specializes in predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules. Here he shares his knowledge with you. Walk through each step of a typical project, from defining the problem and gathering the data and resources, to putting the solution into practice. Keith also provides an overview of CRISP-DM (the de facto data-mining methodology) and the nine laws of data mining, which will keep you focused on strategy and business value. Topics include: What makes a successful predictive analytics project? Defining the problem Selecting the data Acquiring resources: team, budget, and SMEs Dealing with missing data Finding the solution Putting the solution to work Overview of CRISP-DM

07

Predictive Customer Analytics with Kumaran Ponnambalam Learn about the customer life cycle and how predictive analytics can help improve every step of the customer journey. Use predictive analytics to identify, attract, and retain the best customers for your business. 1h 37m • COURSE   Use big data to tell your customer's story, with predictive analytics. In this course, you can learn 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 three 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, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up. Topics include: Understanding the customer life cycle Acquiring customer data Applying big data concepts to your customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers Uncovering attrition patterns Applying predictive analytics in multiple use cases Designing data processing pipelines Implementing continuous improvement

08

Excel: Economic Analysis and Data Analytics with Michael McDonald Use big data to forecast economic trends. Find out how to perform regression analysis for economic forecasting using Microsoft Excel. 1h 37m • COURSE Big data is transforming 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 by showing how to use large volumes of economic data to gain key business insights and analyze market conditions. Professor Michael McDonald 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 and future interest rate hikes. 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. He also covers time series exponential smoothing, fixed effects regression, and difference estimators. You'll walk away from the course able to immediately begin creating forecasts for your own business needs. LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here. The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc. Topics include: Understanding big data and economic forecasting Predicting values with regressions Analyzing economic trends and economic cycles Using fixed-effects regressions and binary regressions for forecasting Assessing the accuracy of an economic forecast Using scenario analysis

09

Meta-analysis for Data Science and Business Analytics with Conrad Carlberg Enhance your understanding of meta-analysis. Learn about raw mean differences—specifically for experimental and comparison groups—and how to convert useful outcome measures such as relative risk and odds ratios to commensurate measures of effect size. 49m 5s • COURSE In a world where nearly everyone uses data to inform their business methodologies, an emerging consensus is that more emphasis needs to be placed on validating data; verifying that data-driven conclusions are accurate; and minimizing the risk that your conclusions are incorrect. Although most researchers know what meta-analysis is, few understand how to calculate an effect size from popular metrics such as risk ratios, or how the distinction between fixed and random effects can lead the meta-analyst astray. This advanced-level course for data science and statistics practitioners and researchers covers raw mean differences—specifically for experimental and comparison groups—and how to convert useful outcome measures such as relative risk and odds ratios to commensurate measures of effect size. Plus, learn about how confidence intervals are created for binary outcome measures. Topics include: Rationale for meta-analysis Straightforward effect sizes Standardized mean differences Correlation coefficients Complex effect sizes: Risk ratios and odds ratios Confidence intervals in meta-analysis Building confidence intervals around binary-outcome effect sizes

 

01. Introduction

02. Getting Started with Data Analysis

03. Fundamentals of Data Understanding

04. Key Elements to Understand When Starting Data Analysis

05. Getting Started with a Data Project

06. Repurposing Data versus Remanufacturing Data

07. Working with Business Data

08. Chart Data Anytime and Anywhere

09. Pivot Data Anytime and Anywhere

10. Excel Tips and Tricks for Data Analysts

11. Conclusion

00. Introduction

01. The World of Statistics

02. The Center of the Data

03. Data Variability

04. Distribution and Relative Position

05. Probability Explained

06. Multiple Event Probability

07. How Objects Are Arranged

08. Discrete vs. Continuous Probability Distributions

09. Discrete Probability Distributions

10. Continuous Probability Distributions

11. Conclusion

01. Introduction

1. Beyond Data and Probability

2. Sampling

3. Sample Size

4. Confidence Intervals

5. Hypothesis Testing

<--PAGEBREAK

06. Conclusion

01. Introduction

02. The Statistics Series_ A Look Back and Forward

03. Small Sample Sizes

04. Comparing Two Populations (Proportions)

05. Comparing Two Populations (Means)

06. Chi-Square

07. ANOVA: Analysis of Variance

08. Introduction to Regression

Conclusion

Financial Forecasting with Big Data with Michael McDonald

Quickly create financial forecasts using big data, predictive analytics, and Microsoft Excel.

1h 21m • COURSE

Big data is transforming 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. 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.

LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.

The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.

Topics include:

• Understanding big data and predictive analytics
• Gathering financial data
• Cleaning up your data
• Calculating key financial metrics
• Using regression analysis for business-specific forecasts
• Performing scenario analysis
• Calculating confidence intervals
• Stress testing

01. Introduction

02. The Basics

03. Forecasting in Finance

04. Performing Forecasting

05. Interpreting Forecast Results

06. Wrap Up

01. Introduction

02. What Is Data Mining and Predictive Analytics?

03. Problem Definition

04. Data Requirements

05. Resources You'll Need

06. Problems You'll Face

07. Finding the Solution

08. Putting the Solution to Work

09. CRISP-DM and the Nine Laws

10. Conclusion

Predictive Customer Analytics with Kumaran Ponnambalam

Use big data to tell your customer's story, with predictive analytics. In this course, you can learn 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 three 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, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up.

0. Introduction

1. Customer Analytics Overview

2. Will you become My Customer

3. What else are you interested in

4. How much is you future business worth

5. Are you happy with me

6. Will you leave me

7. Best Practices

Conclusion

Excel: Economic Analysis and Data Analytics with Michael McDonald

Use big data to forecast economic trends. Find out how to perform regression analysis for economic forecasting using Microsoft Excel.

1h 37m • COURSE

Big data is transforming 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 by showing how to use large volumes of economic data to gain key business insights and analyze market conditions.

Professor Michael McDonald 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 and future interest rate hikes. 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. He also covers time series exponential smoothing, fixed effects regression, and difference estimators. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.

LinkedIn Learning (Lynda.com) is a PMI Registered Education Provider. This course qualifies for professional development units (PDUs). To view the activity and PDU details for this course, click here.

The PMI Registered Education Provider logo is a registered mark of the Project Management Institute, Inc.

Topics include:

• Understanding big data and economic forecasting
• Predicting values with regressions
• Analyzing economic trends and economic cycles
• Using fixed-effects regressions and binary regressions for forecasting
• Assessing the accuracy of an economic forecast
• Using scenario analysis

0. Introduction

1. Meta-Analysis: The Basic Idea

2. Two Groups: Continuous Outcome Measure

3. Two Groups: Binary Outcome

4. Confidence Intervals

Conclusion