Top 20 Beginner level Data Analytics Courses

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.

Python is the preferred language for many fields within the scientific community. This trend is also expanding to businesses that invest in analytics resources. Fundamentals in Data Analytics in Python LiveLessons provides a coherent, narrative tutorial that strikes a balance between the "how" as well as the "why" of Python data analytics. This video starts with a brief primer on Python and then moves on to discuss open-source Python tools that can be used to solve everyday engineering and scientific programming problems.

For additional information and commentary about this LiveLesson, visit wakari.io/training

About the Authors

Peter Wang is the co-founder and president at Continuum Analytics. Peter holds a B.A. Peter holds a B.A. in Physics from Cornell University. He has been using Python to develop applications professionally since 2001. Peter spent seven years working at Enthought, designing and developing applications for a variety companies, including oil companies, investment bankers, high frequency trading firms, and oil companies. Peter was appointed Director of Technical Architecture in 2007 and served as client liaison for high-profile projects. Chaco, an open-source Python-based toolkit that allows interactive data visualization, was also developed by Peter. Peter's roles at Continuum Analytics are product design and development as well as software management and training.

Aron Ahmadia works as a research scientist for Continuum Analytics. Aron has a Ph.D. from Columbia University in Applied Mathematics and has been using Python for technical computing since 2003. Aron is the founder of PyClaw, a Python-based open-source toolkit for modeling wave propagation on a large scale. His software is used in industry, academia, and government. It runs on workstations and the cloud as well as 65,536-core supercomputers. Aron has extensive experience in teaching Python to academic, government, and industrial users. He has also taught it as part the Software Carpentry curriculum across four continents.

Nearly all companies around the globe are evaluating their digital strategy and looking for ways they can capitalize on the promise that digitization offers. This strategy is based on big data analytics and machine-learning. This strategy is based on understanding the fundamentals of data processing, artificial intelligence, and executive information technology (OT) professionals. Barton and Henry discuss the basics and show demos of the most commonly used tools (such as Hadoop and TensorFlow, Matlab/Octave and R) in different fields used by data scientists and researchers.
This video course will equip you with the knowledge and skills to communicate big data analytics and machine-learning principles and possibilities.

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.

This is the eighth course of the Google Data Analytics Certificate. An optional case study will be available, which will prepare you for the job search in data analytics. Employers often use case studies to evaluate analytical skills. You'll need to choose an analytics-based case study for your case study. The scenario will require you to ask questions, process, analyze and visualize the data, as well as take action on it. Videos with common interview questions and answers, as well as helpful materials for building a portfolio online and other job hunting skills will be shared. The current Google data analysts will continue teaching and providing hands-on methods to complete common data analyst tasks using the best tools and resources.

This certificate program prepares learners to apply for data analyst jobs at introductory levels. You don't need any previous experience. This course will teach you how to use case studies and portfolios for job searches. You will learn about real-world job interview situations and common interview questions. Learn how case studies can help you prepare for job interviews. Consider different scenarios in case studies. - You can create your case study to add to your portfolio.

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.

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

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.

Big data technology plays a crucial role in our daily lives, from using search engines like Google to sharing on social media platforms like Facebook, Twitter, and Instagram. It also supports our smartphones, smartwatches, and even automobiles. This technology has become essential for the growth and survival of businesses worldwide. Understanding big data and how to leverage it can give businesses an edge in today's digital landscape.

This course is designed to provide a comprehensive understanding of big data and its applications. It consists of six modules that cover different aspects of big data technology. The course starts by reviewing the global market share rankings for big data software and hardware, giving insights into the industry landscape.

Next, it delves into the top products and services offered by major big data companies. This section provides valuable knowledge about the leading players in the industry and their offerings.

The course then focuses on three widely used big data technologies: Spark, Storm, and Hadoop. These technologies are explained in detail, enabling learners to gain practical knowledge of big data analysis using these tools.

Finally, the course introduces IBM SPSS Statistics, a popular statistical analysis system widely used in big data analytics. This module provides an overview of its functionalities and how it can be leveraged for intelligent data analysis in the context of big data.

Overall, this course aims to equip learners with the necessary skills and knowledge to successfully navigate the big data era. By understanding big data technology and its various applications, businesses can make informed decisions and develop effective strategies for their growth and success in today's competitive landscape.

This Capstone Project will bring together all of the skills and insights that you have gained through the four courses. A'mock' problem for a client and a data set will be provided. To gain business insight, research the client's area of expertise, and make recommendations, you will need to analyze the data. The next step is to present the data to clients. In a recorded video presentation, you will bring it all together.

This course was created by PricewaterhouseCoopers LLP with an address at 300 Madison Avenue, New York, New York, 10017.

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.

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 capstone course will allow you to apply all you have learned and create your own GIS project. Your project will be planned by you writing a proposal. This brief description will explain what you intend to do and why. The next step is to find data on a topic or location you choose, perform analysis, and create maps that let you try different data sets and tools. Your work will be compiled into an Esri Story Map, which is a website with maps, images and text. You want to share your learnings and create a product you are proud of.

This course teaches you how to use Looker's modern analytics platform to perform data exploration and analysis that was previously only possible for SQL analysts or SQL developers. This course will teach you how to use Looker's modern analytics platform, to search and explore the relevant content within your organization's Looker instance. You can also ask questions and create new metrics, build visualizations, and share dashboards that facilitate data-driven decision making.

This course will teach you how to locate GIS data and create maps that communicate your message. This section will focus on the basics of GIS data. It will explain the differences between different types of GIS files and what the consequences of using one over the other. Next we will discuss metadata, which is information about a dataset. This will help you to understand how to evaluate the data set before using it. Next, we'll discuss how to convert non-GIS data such as addresses into "mappable data" using geocoding. You'll also learn how to use data you find and create a map by using cartographic principles. You will create your own quantitative maps and find your data in the course project.

This final course will require you to complete a Capstone Project that uses data analysis to suggest a way to improve profits for Watershed Property Management, Inc. Watershed manages thousands of residential rental properties across the United States. Your task is to convince Watershed's management to adopt a new strategy to manage its properties, which will result in increased profits. You will need to (1) Gather information on important variables; (2) Use your MySQL database skills to extract pertinent data from a real-estate database; (3) Create a Tableau dashboard that shows Watershed executives the results from a sensitivity assessment; (4) Present a Watershed business process improvement based upon your data analysis to the company's executives; and (5)

This course will teach you how to understand the data and why it is important to correctly classify data. Data will be described graphically as well as numerically with descriptive statistics and R software. Four probability distributions are commonly used for data analysis. The appropriate probability distribution will be used to analyze data sets. You will also learn about sampling error, sampling distributions and errors in decision making.

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 capstone project will combine the skills of all four specializations to make something really enjoyable: analysis social networks.

There are literally endless learning opportunities in a social network. What are the "influential members" of the network? What are the sub-communities within the network? What are the sub-communities in the network? Who is connected to whom and how many links do they have? These are just a few of the many questions you can explore with this project.

You will be provided with real-world data and infrastructure to get started. We'll also provide warm up tasks to help you get started. After that, it will be up to your decision where to take the project. We'll give you suggestions to help spark your imagination and creativity if you are stuck for ideas. To integrate your skills from course 4 and show off your final product, You will be required to make a video showcasing your final product.

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.