25.6.19
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Courses

58 Credentials
Viewing 1-10 of 58

Microsoft Copilot for Excel

This badge is earned by successfully completing the Microsoft Copilot for Excel course at Maven Analytics.

COURSE HOURS: 2

COURSE DESCRIPTION:

This is a hands-on, project-based course designed to help you leverage Copilot in Microsoft Excel to solve real-world data analytics problems.

We’ll start by reviewing Microsoft Copilot’s basic features and limitations, compare strengths and weaknesses against GenAI tools like ChatGPT, and get you up and running with Copilot for Microsoft 365 on your machine.

From there, we’ll dive into each of Copilot’s core use cases for data management and analysis. You’ll practice adding new formula columns using natural language, applying conditional formatting rules to highlight key data points, and analyzing data to find insights using pivot tables and charts.

Last but not least, we’ll showcase how to use Copilot’s generative AI engine for more advanced use cases, including writing dynamic array formulas and generating Python code within Excel.

Throughout the course, you’ll play the role of an HR Admin at ACME Corporation, a manufacturing company that makes outlandish products based on classic cartoons. Using the skills you learn throughout the course, you’ll manipulate employee data, monitor performance metrics, and make data-driven recommendations to your HR supervisor.

Whether you’re a casual Excel user looking to level up your skills and productivity with Generative AI, or a data professional looking to stay on top of Microsoft's newest features, this is the course for you.

Skills
  • Data Analysis

Data Science in Python: Unsupervised Learning

This badge is earned by successfully completing the Data Science in Python: Classification course at Maven Analytics. COURSE HOURS: 28 COURSE DESCRIPTION: This is a hands-on, project-based course designed to help you master the foundations for unsupervised learning in Python. We’ll start by reviewing the data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You’ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization. From there we'll fit, tune, and interpret 3 popular clustering models using scikit-learn. We’ll start with K-Means Clustering, learn to interpret the output’s cluster centers, and use inertia plots to select the right number of clusters. Next, we’ll cover Hierarchical Clustering, where we’ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we’ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score. We’ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You’ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots. Next, we’ll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We’ll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization. Last but not least, we’ll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition. Throughout the course you'll play the role of an Associate Data Scientist for the HR Analytics team at a software company trying to increase employee retention. Using the skills you learn throughout the course, you'll use Python to segment the employees, visualize the clusters, and recommend next steps to increase retention. If you're an aspiring or seasoned data scientist looking for a practical overview of unsupervised learning techniques in Python with a focus on interpretation, this is the course for you.
Skills
  • Machine Learning
  • Python
  • Unsupervised Learning

Data Literacy Foundations

This badge is earned by successfully completing the Data Literacy Foundations course at Maven Analytics.

COURSE HOURS: 2.5

COURSE DESCRIPTION:

We live in a world that runs on data.

It’s how Amazon and Netflix know which movies and products to recommend, how Starbucks manages a global supply chain, how banks detect fraud, and how Uber connects drivers with passengers in real-time.

But data skills aren’t just for tech companies or professional analysts anymore.

Whether you’re a teacher using test scores to improve lesson plans, a sales manager tracking monthly quotas, or a marketer exploring customer trends, data can help you work smarter and make better, more impactful decisions.

In this course, we’ll set the stage by discussing what data literacy means, share frameworks to help you assess and benchmark your skills, and review the elements of a successful data ecosystem, including data democratization, strategy, architecture, and governance.

From there we’ll dig into each core component of the data literacy skill set: interpreting, managing, analyzing and communicating with data.

You’ll practice interpreting tabular datasets and charts, learn how to apply profiling and QA techniques, and review methods for accessing, storing, and transforming data for analysis.

Next we’ll introduce proven frameworks designed to help you think and problem-solve like a world-class data professional, and break down the differences between descriptive, diagnostic, predictive and prescriptive analytics.

We’ll also explore the power of data visualization and storytelling. We’ll review key principles and best practices for communicating with data, walk through common visualization mistakes and how to correct them, and show you how to craft clear, data-driven narratives.

Last but not least we’ll talk about what data literacy means in the age of AI. We’ll demo some incredible use cases for generative AI tools like ChatGPT and Gemini, share prompt engineering tips and best practices, and address common limitations and pitfalls to be aware of.

Whether you’re an individual looking to build confidence, a leader seeking to empower and upskill your team, or a data professional just trying to stay ahead of the curve, this is the course for you.

Skills
  • Data Literacy
  • Data Prep
  • Data Analysis
+2 more skills

Data Science in Python: Classification

This badge is earned by successfully completing the Data Science in Python: Classification course at Maven Analytics. COURSE HOURS: 16 COURSE DESCRIPTION: This is a hands-on, project-based course designed to help you master the foundations for classification modeling in Python. We’ll start by reviewing the data science workflow, discussing the primary goals & types of classification algorithms, and do a deep dive into the classification modeling steps we’ll be using throughout the course. You’ll learn to perform exploratory data analysis, leverage feature engineering techniques like scaling, dummy variables, and binning, and prepare data for modeling by splitting it into train, test, and validation datasets. From there, we’ll fit K-Nearest Neighbors & Logistic Regression models, and build an intuition for interpreting their coefficients and evaluating their performance using tools like confusion matrices and metrics like accuracy, precision, and recall. We’ll also cover techniques for modeling imbalanced data, including threshold tuning, sampling methods like oversampling & SMOTE, and adjusting class weights in the model cost function. Throughout the course, you'll play the role of Data Scientist for the risk management department at Maven National Bank. Using the skills you learn throughout the course, you'll use Python to explore their data and build classification models to accurately determine which customers have high, medium, and low credit risk based on their profiles. Last but not least, you'll learn to build and evaluate decision tree models for classification. You’ll fit, visualize, and fine tune these models using Python, then apply your knowledge to more advanced ensemble models like random forests and gradient boosted machines. If you're an aspiring data scientist looking for an introduction to the world of classification modeling with Python, this is the course for you.
Skills
  • Machine Learning
  • Classification
  • Python

Intro to Google Sheets

This badge is earned by successfully completing the Intro to Google Sheets course at Maven Analytics. COURSE HOURS: 10.0 CPE CREDITS: 10.0 COURSE DESCRIPTION: In this course you’ll build the skills you need to manage, explore, analyze, and visualize data in Google Sheets, using real-world projects and datasets. We’ll start by introducing the Sheets interface and workflow, then dive into spreadsheet fundamentals like table structures, data types, formatting, sorting and filtering. From there we’ll dig into formulas and functions, introduce key topics like syntax, reference types, and common errors, and practice applying some of the most common and powerful techniques for data management and analysis. We’ll use functions to count and aggregate values, create logical tests, join data across tables, manipulate text and date values, and more. Next we’ll introduce Pivot Tables, an essential spreadsheet tool for data exploration. We’ll prepare raw data for analysis, create views to slice and dice the data, and apply tools like sorting, grouping, and calculated fields. From there we’ll dive into the world of data visualization and storytelling, review best practices for effective design, and bring our data to life using visuals like bars and columns, line and area charts, scatterplots and maps. Last but not least, we’ll demonstrate some of Google Sheets’ unique sharing and collaboration features, including comments, notifications, edit history, sheet protection, and more. This is a hands-on and project-based course, designed to help you build practical data literacy and analytical thinking skills. You’ll work with unique, real-world datasets, and apply your skills with hands-on assignments every step of the way. CPE ACCREDITATION DETAILS: - CPE Credits: 10.0 - Field of Study: Information Technology - Delivery Method: QAS Self Study Maven Analytics LLC is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards of accountancy have the final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.nasbaregistry.org.
Skills
  • Business Intelligence
  • data visualization
  • data analytics
+3 more skills

Launching Your Data Career

This badge is earned by successfully completing the Launching Your Data Career course at Maven Analytics. COURSE HOURS: 7.5 COURSE DESCRIPTION: Launching Your Data Career is a step-by-step, practical guide to landing your analytics dream job. You'll learn from Maven's top instructors as they walk you through proven frameworks and case studies designed to help you build your brand, grow your network, ace your interviews, and launch a successful career in data. We'll kick things off by reviewing common analytics roles and sharing interactive tools to help you find the right path, whether you skew towards business intelligence, data visualization, database engineering or data science. Next we'll help you create the exact assets you'll need to build your brand and market yourself to hiring managers. We'll walk through step-by-step guides to creating bulletproof resumes, polished LinkedIn profiles, and project portfolios that prove you have what it takes to succeed on the job. From there we'll share a 30-day action plan to help you build your professional network, connect with valuable leads, and identify key connections and job opportunities. Last but not least, we'll share tools and tactics to help you build confidence and ace even the toughest interviews. We'll discuss the interviewing mindset, review common questions, and introduce proven frameworks to help you breeze through technical assessments and live case studies. By the end of the course you'll have the exact roadmap you need to impress recruiters, outshine the competition, and launch the career you've dreamed of.
Skills
  • Business Intelligence
  • Data Analysis

Machine Learning 1: Data Profiling

This badge is earned by successfully completing the Machine Learning 1: Data Profiling course at Maven Analytics. COURSE HOURS: 3.5 COURSE DESCRIPTION: This course is Part 1 of a 4 part series designed to help you build a fundamental understanding of machine learning, including data profiling, classification, regression & forecasting, and unsupervised learning. In this course we’ll introduce the machine learning landscape and workflow, and review critical QA tips for cleaning and preparing raw data for analysis, including variable types, empty values, range & count calculations, table structures, and more. We’ll cover univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation. Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll clean up product inventory data for a local grocery, explore demographics for Olympic athletes, visualize traffic accident frequency in New York Ciy, and more. If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Skills
  • machine learning
  • data science
  • data profiling
+1 more skill

Machine Learning 2: Classification

This badge is earned by successfully completing the Machine Learning 2: Classification course at Maven Analytics. COURSE HOURS: 4.0 COURSE DESCRIPTION: This course is Part 2 of a 4-Part series designed to help you build a fundamental understanding of machine learning, including data profiling, classification, regression & forecasting, and unsupervised learning. In this course we’ll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting. From there we'll review common classification models including K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization. Throughout the course we’ll introduce case studies to solidify key concepts and tie them back to real world scenarios. You’ll help build a recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for a travel company, extract sentiment from customer reviews, and much more. If you’re ready to build the foundation for a successful career in data science, this is the course for you.
Skills
  • machine learning
  • data science
  • data analysis
+1 more skill

Tableau Certification Prep

This badge is earned by successfully completing the Tableau Certification Prep course at Maven Analytics. COURSE HOURS: 9.5 COURSE DESCRIPTION: This course is designed to help you build the exact skills you need to ace the Tableau Desktop Specialist and Certified Associate exams, guaranteed. Throughout the course, you’ll play the role of a BI Analyst for BankMaven, a financial institution based in the US. You’ll use Tableau Desktop, and the skills you learn throughout the course, to design and build an executive dashboard from scratch. Our project will incorporate all of the skills required for certification, including connecting and blending data, creating charts and geospatial maps, applying analytics tools and calculations, filtering and examining data, and designing custom, interactive dashboards and stories. We’ll also walk through everything you need to know about the certification process itself, including exam options and structure, topic breakdowns, question types, rules, scheduling options, and tips for success. Finally, we'll wrap things up with two full-length practice exams, with hands-on and knowledge-based questions designed to mirror the actual exam experience and make sure you're ready for the real deal. If you're ready to level-up your skills, increase your earning potential, and become a certified Tableau expert, this is the course for you.
Skills
  • data analysis
  • data visualization
  • data prep

Tableau Performance Optimization

This badge is earned by successfully completing the Tableau Performance Optimization course at Maven Analytics. COURSE HOURS: 5.0 COURSE DESCRIPTION: If you’re looking to improve the speed and performance of your Tableau dashboards, this is the course for you. You’ll be playing the role of a Business Intelligence Analyst at Bike Maven, a bicycle sharing company based in New York City. You’ve inherited a Tableau dashboard containing three years of accident data, but it’s so slow that it’s practically unusable. Your mission? Use the optimization tools and techniques covered in this course to make the dashboard fast, scalable, and user-friendly. We’ll start by reviewing Tableau’s performance evaluation tools, then dive into four key optimization areas, including data design, filters, calculations, and layouts & visuals. Finally, we’ll address some external factors specific to Tableau Server & online. During the course you’ll learn how to record and interpret query performance, understand exactly how Tableau processing and rendering actually works, and review common event types and their impact on speed and usability. We’ll also discuss the pros and cons of various data modeling techniques and connection types, share best practices for filter optimization, introduce pro tips to help you write efficient calculations, review key data visualization and dashboard design principles, and much more. Throughout the course we’ll practice applying these techniques to our Bike Maven dashboard, and track our performance improvements each step of the way.
Skills
  • data analysis
  • data visualization
  • tableau
Viewing 1-10 of 58