Top 20 Beginner level Data Analytics Courses

First, you'll create data visualizations and dashboards. You will consider your audience to be the most effective. Next, you will be able to create presentations using storytelling techniques and visualizations.

You will learn the fundamentals of programming to make a career out of data science. You will be able Python, SQL and Command Line by the end of this program.

This is the second course of the Google Data Analytics Certificate. These courses will give you the skills necessary to apply for introductory-level jobs as a data analyst. These courses will build upon your knowledge of topics covered in the Google Data Analytics Certificate course. This material will teach you how to ask the right questions and make data-driven decisions while communicating with stakeholders. The current Google data analysts will continue their instruction and provide practical ways for you to complete common data analyst tasks using the best tools and resources.

Data science projects can be difficult because it is often the hardest part of data science. Before any insights can be gained, data must be imported and harmonised into a consistent format. This course will teach you how to import data from common formats into R and how to harmonize different types of datasets from different sources. This course is essential if you work in an organisation where data is collected by different departments using different storage formats and systems.

Microsoft Excel is a powerful tool that goes beyond simple calculations. It offers a range of functions that can help organizations and institutions convert large amounts of data into meaningful insights. This course teaches students how to save time and clean up data using Excel. Through hands-on practice, participants will learn how to analyze the factors contributing to project success. The course is designed specifically for those interested in data analytics and is suitable for both beginners and experienced professionals looking to enhance their skills. By the end of the course, students will be equipped with the knowledge and skills to effectively analyze data using Excel, making it a valuable resource for data analysts and professionals working in various industries.

This is the third course of the Google Data Analytics Certificate. These courses will give you the skills necessary to apply for introductory-level jobs as a data analyst. You'll continue to expand your knowledge of the topics covered in the first two courses and be exposed to new topics to help you develop data analytics skills. Learn how to use spreadsheets and SQL to extract the data you need and how to protect it. You will be guided by current Google data analysts to help you use the best tools and resources to complete common data analyst tasks.

Moneyball was a revolutionary book in the analysis and interpretation of performance statistics in professional sport. It showed that data analytics could be used for team winning percentage. This course teaches you how to program Python data to verify the Moneyball claims and examine the evolution in Moneyball statistics over the years. This course guides the learner through the process of computing baseball performance statistics using publicly available data. The course covers everything from the analysis and slugging of on base percentages to advanced measures such as wins over replacement (WAR) derived using run expectancy matrix. The learner will be able use these statistics to perform their own player and team analyses by the end of the course.

Good samples are the foundation of good data collection. There are many options for choosing the right samples. Although it is possible to randomly or conveniently select people, records, networks or other units from the population, one should question their quality and what these selection methods are for drawing accurate conclusions after analysis. Although samples can be selected more carefully based on the researcher's judgement, one must ask if that judgment is biased by personal factors. You can draw samples in statistically rigorous and precise ways. This includes using control and random selection methods. These will provide sound representations and cost control. These last types of samples will be covered in this course. Simple random sampling can be used to collect records or people, cluster sampling can be used to gather records or networks of people, stratification can be used to stratify simple random or cluster samples, systematic selection and stratified multistage sample. This course will conclude with a short overview on how to estimate and summarize uncertainty in random sampling.

Although data storytelling has been a part of news practice since its inception, it is currently experiencing a revival. The graphic desks used to be considered "the art department", a subfield of the newsrooms' work, but are now a central part of newsrooms. These people often have many titles, including data journalists, news editors, graphic reporters and developers. Designers of news graphics will be expected to work closely with editors and reporters. This class teaches you how to visualize data and explain how it works. Learn how to create graphs such as those found in The New York Times, Vox and Pew. You will be able to share-embed your charts in blogs, publications, websites, and other media.

This course will focus on the concepts and tools that are used to report modern data analyses in a reproducible way. The idea of reproducible research means that data analyses and, more generally, scientific claims are published together with the data and code to allow others to verify and expand upon the results. As data analyses become more complex and involve larger data sets and more complicated computations, the need for reproducibility has increased dramatically. Because of reproducibility, people can focus on the content of a data analyse rather than on details in a written summary. Reproducibility also makes it easier to share an analysis with others, as the code and data that actually performed the analysis are readily available. This course will teach you how to use literate statistical analysis software to publish data analyses in one document. This allows others to perform the same analysis and get the same results.

Data products are the output of a statistical analysis. Data products are used to automate complicated analysis tasks, or use technology for greater utility of data informed models, algorithms or inferences. This course will cover the basics of creating data products with Shiny, R packages and interactive graphics. This course will cover the statistical basics of creating data products that can be used to tell stories about data to a large audience.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to create a Data Fusion instance and deploy a sample pipeline

This is a self-paced lab that takes place in the Google Cloud console.
This hands-on lab will show you how to store and query data in Google Cloud Datastore using the Google Cloud Platform.

Edureka's QlikView Certification Training can help you become an expert in Data Visualization using QlikView. You will learn how to transform data into interactive charts and graphs with QlikView features such as dashboards, system tables, incremental loads, and object formatting.

This is Part 1 in a series of two Excel data visualization classes. It's designed to provide a complete, deep understanding of Excel's most recent data visualization tools.

This section will introduce you to key data visualization techniques and best practices. I'll also guide you through interactive, hands on demos and exercises. Finally, I'll show you when, Why and How to use each 20+chart type that Excel 2016 offers, including:

• Bar & Column charts
• Histograms & Pareto charts
• Trend lines & line charts
• Area charts
• Pies &Donuts
• Bubble charts & Scatter plots
• Box & Whisker charts
• Tree Maps and Sunbursts
• Waterfall and Funnel charts
• Radar & Stock charts
• Heat maps,3-D surface & contour charts
• Geospatial maps & Chloropleths
• Custom combination charts & graphs
• Sparklines

Part II will test your skills once you have mastered the basics. There are advanced demos and case studies you won't find anywhere else, so you can't go wrong.

This series can be used to help you get started with Excelskills, diversify your Excelskills, or improve your data viz skills.

What are the requirements for ?

• Microsoft Excel, ideal 2016 for PC (some charts not available in older Excel versions).
• Mac users are welcomed, but please note that the user experience across platforms will be vastly different.

What will I get from this course?

• Step-by-step guide for visualizing data in Excel using graphs & charts
• Excel 2016 chart types: A deep understanding of WHEN, WHY and HOW to use them
• An award-winning analytics expert shares his top data visualization tips and tricks.
• You won't find this kind of unique content in any other course

Who are the target audiences?

• Excel users who want to create stunning, custom data visualizations
• Excel users with basic skills who want to master advanced charts and graphs.
• Students who are looking for a hands-on, interactive and engaging approach to training

This course will teach you how to analyze map data using various data types and methods to answer geographical questions. You will first learn how to filter data using different queries to find the information you need. Next, we'll discuss simple but powerful analysis methods that use vector information to discover spatial relationships between and within data sets. This section will teach you how to use ModelBuilder which is a powerful but simple tool that allows you to create analysis flowcharts and then run them as models. The next section will teach you how to use remote-sensible data, such as satellite imagery as a rich source for GIS data. The next step is to learn how to analyze raster information. You will then complete your own project, where you can use the skills and tools that you have learned in this course.

How can data be put to work for you? How can data in spreadsheets tell us about past and present business activities? And how can they be used to predict the future? Building quantitative models is the answer. This course will help you to understand the basics of this fundamental business skill. You will learn how to build your own quantitative models through a series short lectures, demonstrations, assignments, and exercises. You will be able to create your own models using a variety of commonly-used quantitative models by the end of the course. These building blocks will also be used in the Specialization's other courses.

Capstone is an individual assignment.

Participants choose the topic they wish to study and the problem they want solved. Your "playing field" should include data from different sectors, such as agriculture and nutrition, culture, economics, education & research, international & Europe, Housing, Sustainable, Development & Energies, Health & Social, Society, Territories & Transport. Participants are encouraged not to combine the data from different areas and to use the information with other open data sets (properly sourced).

Deliverable 1 is the initial preparation and problem qualification. It is important to determine the who, what, and how. What problem are we trying to solve? It promises value to citizens, public authorities, and companies. What data can we use to our advantage?

Deliverable 2 requires that the participant presents the intermediate outputs and adjusts to the analysis framework. This is done to verify the relevance and how the first results were obtained.

Deliverable 3 requires that the participant presents the final outputs as well as the value case. It is important to clarify the why. It will create value for citizens, public authorities, and companies.

Evaluation and grading: Participants will regularly present their results to each other. Participants will receive an evaluation framework to help them evaluate the quality of their deliverables.

This course offers a comprehensive review of research on survey design and data quality, focusing on the impact of data collection decisions on survey errors. It covers various methods for collecting survey data, including interview-based approaches such as face-to-face and telephone interviews, as well as self-administered methods like paper questionnaires and online surveys. The course also explores mixed-mode designs and hybrid methods for collecting sensitive data, such as self-administering sensitive questions during face-to-face interviews. It discusses the latest methods in data collection, including SMS interviews and mobile web surveys, as well as the use of social media as a data source. The main emphasis of the course is on understanding how different data collection techniques affect survey data quality, including measurement error, nonresponse error, and coverage error. It also explores the tradeoffs involved in choosing a mode of survey design or data collection method. Overall, this course provides valuable insights for researchers and survey designers looking to improve their understanding of data collection decisions and their impact on survey outcomes.

Data is increasingly important to your success, whether it's used to personalize advertising for millions of people or to streamline inventory ordering in a small restaurant. We often don't know how to use data to answer the questions that will help us be more successful in our work. This course will help you understand data and what your questions are that can be answered with data. You will be able to create a research question based on existing data and describe variables and their relationships. Calculate basic statistics and present your findings clearly. You will learn how to use powerful data analysis tools, either SAS or Python, to visualize and manage your data. This includes dealing with missing data, variable groupings, graphs, and other issues. You will be able to share your progress with other students to get valuable feedback and learn from your peers how they use data to answer their questions.