Welcome
IPN-Dharma AI Lab
This is an IPN CIC - DHARMA initiative to provide an Artificial Intelligence Laboratory to motivate researchers, professors and students to take advantage of the courses, resources and tools of the main technology platforms of the industry in the areas of Machine Learning, Data Science, Cloud Computing, Artificial Intelligence and Internet of Things with the purpose of generating a practical experience through a learning model between peers and by objectives.
Level 1: Literacy and Foundations
Google Data Analytics Professional
Data analytics is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision making.
In this program you will:
- Gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job.
- Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau).
- Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming.
- Learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms.
Courses in this program
1) Foundations: Data, Data, Everywhere
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.
By the end of this course, you will:
By the end of this course, you will:
- Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystem.
- Evaluate the role of analytics in the 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.
Estimated effort 20 hours
Spanish & English language
2) Ask Questions to Make Data-Driven Decisions
You’ll build on your understanding of the topics that were introduced in the first course. The material will help you learn how to ask effective questions to make data-driven decisions, while connecting with stakeholders’ needs.
By the end of this course, you will:
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.
- 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.
3) Prepare Data for Exploration
As you continue to build on your understanding of the topics from the first two courses, you’ll also be introduced to new topics that will help you gain practical data analytics skills. You’ll learn how to use tools like spreadsheets and SQL to extract and make use of the right data for your objectives and how to organize and protect your data.
By the end of this course, you will:
By the end of this course, you will:
- Find out how analysts decide which data to collect for analysis.
- Learn about structured and unstructured data, data types, and data formats.
- Discover how to identify different types of bias in data to help ensure data credibility.
- Explore how analysts use spreadsheets and SQL with databases and data sets.
- Examine open data and the relationship between and importance of data ethics and data privacy.
- Gain an understanding of how to access databases and extract, filter, and sort the data they contain.
- Learn the best practices for organizing data and keeping it secure.
- Explain factors to consider when making decisions about data collection.
- Discuss the difference between biased and unbiased data.
- Describe databases with references to their functions and components.
- Describe best practices for organizing data.
4) Process Data from Dirty to Clean
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.
By the end of this course, you will:
By the end of this course, you will:
- Learn how to check for data integrity and define data integrity with reference to types of integrity and risk to 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.
5) Analyze Data to Answer Questions
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.
By the end of this course, you will:
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.
6) Share Data Through the Art of Visualization
You’ll learn how to visualize and present your data findings as you complete the data analysis process. This course will show you how data visualizations, such as visual dashboards, can help bring your data to life. You’ll also explore Tableau, a data visualization platform that will help you create effective visualizations for your presentations.
By the end of this course, you will:
By the end of this course, you will:
- Examine the importance of data visualization.
- Learn how to form a compelling narrative through data stories.
- Gain an understanding of how to use Tableau to create dashboards and dashboard filters.
- Discover how to use Tableau to create effective visualizations.
- Explore the principles and practices involved with effective presentations.
- Learn how to consider potential limitations associated with the data in your presentations.
- Understand how to apply best practices to a Q&A with your audience.
7) Data Analysis with R Programming
In this course, you’ll learn about the programming language known as R. You’ll find out how to use RStudio, the environment that allows you to work with R. This course will also cover the software applications and tools that are unique to R, such as R packages. You’ll discover how R lets you clean, organize, analyze, visualize, and report data in new and more powerful ways.
By the end of this course, you will:
By the end of this course, you will:
- Examine the benefits of using the R programming language.
- Discover how to use RStudio to apply R to your analysis.
- Explore the fundamental concepts associated with programming in R.
- Explore the contents and components of R packages including the Tidyverse package.
- Gain an understanding of dataframes and their use in R.
- Discover the options for generating visualizations in R.
- Learn about R Markdown for documenting R programming.
8) Google Data Analytics Capstone: Complete a Case Study
Case studies are commonly used by employers to assess analytical skills. For your case study, you’ll choose an analytics-based scenario. You’ll then ask questions, prepare, process, analyze, visualize and act on the data from the scenario. You’ll also learn other useful job hunt skills through videos with common interview questions and responses, helpful materials to build a portfolio online, and more.
By the end of this course, you will:
By the end of this course, you will:
- Learn the benefits and uses of case studies and portfolios in the job search.
- Explore real world job interview scenarios and common interview questions.
- Discover how case studies can be a part of the job interview process.
- Examine and consider different case study scenarios.
- Have the chance to complete your own case study for your portfolio.