Storytelling is one of the most important skills of an Analyst because the analysis has to be communicated to the stakeholders. The best way to communicate the analysis obtained from the data is by telling the story of the data. Using animations as a result of communication methods can assist the audience in rapidly grasping the point and absorbing the message delivered by the teller. This article will introduce a python framework called ipyVizzu which will help to create animated analysis within the notebook itself for presentation. Following are the topics to be covered.

The ipyvizzu is an animated graphing tool for notebooks like Jupyter, Google Colab, Databricks, Kaggle, and Deepnote, among others. It enables data scientists and analysts to use Python animation for data storytelling. It is based on Vizzu, an open-source Javascript/C++ charting toolkit.

There is a new ipyvizzu extension, ipyvizzu-story, that allows animated charts to be shown directly from notebooks. Because the syntax of ipyvizzu-story differs from that of ipyvizzu, we recommend starting with the ipyvizzu-story repo if you want to use animated charts to show your findings live or as an HTML file. It makes use of a generic DataViz engine to produce several types of charts and easily transition between them. It is intended for creating animated data tales since it allows viewers to quickly follow multiple viewpoints of the data.

Main characteristics:

Are you looking for a complete repository of Python libraries used in data science,check out here.

The article will use data related to sales, it is a time series data for multiple products and sub-products. To use the ipyVizzu needed to be installed, so lets start with installing dependency.

Importing dependencies required for this article

Since the ipyvizzu module is completely compatible with Pandas dataframes, creating graphs straight from data is a breeze. To include a dataframe in an ipyvizzu chart, first, create a Data() object and then add the dataframe to it.

We are all set to create stories with ipyVizzu story, for creating stories it must be in the form of slides. Its similar to a video which is a set of different frames.

The plots could be designed according to the need, one change the labels, colours, font size of the texts, orientation of the labels, etc. To customize the plots use the below code.

To create a slide the components like x-axis, y-axis, hue and title need to be configured by using channels. As shown in the below code.

Then after these slides are built, you need to add them to the story built above so that it can be aggregated in one place and be in a sequence. To display the story, use the play() function.

The ipyVizzu is really simple and easy to use once its property is properly understood and one can create animated stories. With this article, we have understood the use of the ipyVizzu package.

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Create interactive presentations within the Python notebook with ipyVizzu - Analytics India Magazine

Right now, the market for those knowledgeable in data is growing quickly. Its estimated that US data professional job openings grew by 364,000 openings by 2020 alone. However, when you see terms like machine learning, data science and data analytics out there, its hard to know which ones which. What is the difference between them?

What Is Machine Learning?

Lets start with machine learning. Its something youll hear a lot about, as its being used in all kinds of industries right now to get better results in marketing, sales and even HR. Essentially, its the practice of using algorithms to extract data, and learning from it to inform future actions.

Youre likely interacting with machine learning every day, without even knowing it. For example, Facebook uses machine learning to understand more about their users. It gathers information about the behaviors you exhibit on the site, and with that information it can offer more relevant ads and interests to you. Product recommendation on Amazon works in the same way too, as the machine learning AI gathers information on what you buy, and then recommends you similar products.

What Is Data Science?

Next, lets look at data science. This is quite a broad term, and definitions have been changing over the last decade or so. In essence, data science is the combination of hacking skills, math and statistics, and subject expertise.

What does that mean in practice? Data science is used to tackle big data, and understand what information can be taken from it. As such, it can include data cleansing, preparation, and analysis. This data is collected from multiple sources, such as machine learning outputs, predictive analysis, and so on. With these multiple data sets, analysis can happen, and predictions can be made for the future. This is especially important when it comes to businesses, as they need to be able to stay ahead of the curve.

What is Data Analytics?

Finally, lets look at data analytics. This is the process of understanding the data gathered for a business, and making recommendations based on the results. A data analyst will need to understand statistics, PIG/HIVE, coding, and more. They use all these skills to gather the results, and make sense of them.

This is something that is becoming crucial for businesses, no matter what industry theyre in. As a business you have access to more data than ever before, and you need to be able to make sense of it says Bill Styles, a tech blogger from Write My X and 1 Day 2 Write. Data analysts are invaluable for interpreting the data and helping a business grow with it.

Expertise In Each Role

As youve seen, machine learning, data science and data analytics are all different disciplines that all feed into each other. As such, if youre looking to make your career in data, youll need to consider where youll start learning. All three areas have a lot of overlap, but theres some differences that you should be aware of.

Machine learning: To work as a machine learning expert, you need to have a foundation in working with AI programs. As such, youll need expertise in computer fundamentals, as well as data modeling and evaluation skills, stats and probability knowledge, and in depth programming expertise.

Data scientist: To work as a data scientist, youll need a knowledge of machine learning, so there is some overlap here. Youll also need strong programming knowledge, such as in Python, SAS, R or Scala. Youll need to have experience with SQL database coding too. There is a good amount of overlap with machine learning here says Michelle Robin, a Origin Writings and Brit Student. Whether you work in machine learning or data science, youll need to understand techniques like regression and supervised clustering.

Data analyst: To work in data analysis, youll be required to be able to code in R or Python, as well as understand PIG/HIVE. A knowledge of data wrangling and mathematical statistics is critical, too.

While data science, machine learning and data analysis are all different, they do have a lot of overlap. Whether youre a business looking to put them to use, or someone looking to make their career in data, its critical to know how they differ.

Author: George J. Newton is a tech writer working with Research paper writing services and PhD Kingdom. His speciality is in data and AI. He also blogs for Coursework Help

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Machine Learning, Data Science And Data Analytics: Whats The Difference? - EuroScientist

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The era of Big Data has helped democratize information, creating a wealth of data and growing revenues at technology-based companies. But for all this intelligence, were not getting the level of insight from the field of machine learning that one might expect, as many companies struggle to make machine learning (ML) projects actionable and useful. A successful AI/ML program doesnt start with a big team of data scientists. It starts with strong data infrastructure. Data needs to be accessible across systems and ready for analysis so data scientists can quickly draw comparisons and deliver business results, and the data needs to be reliable, which points to the challenge many companies face when starting a data science program.

The problem is that many companies jump feet first into data science, hire expensive data scientists, and then discover they dont have the tools or infrastructure data scientists need to succeed. Highly-paid researchers end up spending time categorizing, validating and preparing data instead of searching for insights. This infrastructure work is important, but also misses the opportunity for data scientists to utilize their most useful skills in a way that adds the most value.

When leaders evaluate the reasons for success or failure of a data science project (and 87% of projects never make it to production) they often discover their company tried to jump ahead to the results without building a foundation of reliable data. If they dont have that solid foundation, data engineers can spend up to 44% of their time maintaining data pipelines with changes to APIs or data structures. Creating an automated process of integrating data can give engineers time back, and ensure companies have all the data they need for accurate machine learning. This also helps cut costs and maximize efficiency as companies build their data science capabilities.

Machine learning is finicky if there are gaps in the data, or it isnt formatted properly, machine learning either fails to function, or worse, gives inaccurate results.

When companies get into a position of uncertainty about their data, most organizations ask the data science team to manually label the data set as part of supervised machine learning, but this is a time-intensive process that brings additional risks to the project. Worse, when the training examples are trimmed too far because of data issues, theres the chance that the narrow scope will mean the ML model can only tell us what we already know.

The solution is to ensure the team can draw from a comprehensive, central store of data, encompassing a wide variety of sources and providing a shared understanding of the data. This improves the potential ROI from the ML models by providing more consistent data to work with. A data science program can only evolve if its based on reliable, consistent data, and an understanding of the confidence bar for results.

One of the biggest challenges to a successful data science program is balancing the volume and value of the data when making a prediction. A social media company that analyzes billions of interactions each day can use the large volume of relatively low-value actions (e.g. someone swiping up or sharing an article) to make reliable predictions. If an organization is trying to identify which customers are likely to renew a contract at the end of the year, then its likely working with smaller data sets with large consequences. Since it could take a year to find out if the recommended actions resulted in success, this creates massive limitations for a data science program.

In these situations, companies need to break down internal data silos to combine all the data they have to drive the best recommendations. This may include zero-party information captured with gated content, first-party website data, and data from customer interactions with the product, along with successful outcomes, support tickets, customer satisfaction surveys, even unstructured data like user feedback. All of these sources of data contain clues if a customer will renew their contract. By combining data silos across business groups, metrics can be standardized, and theres enough depth and breadth to create confident predictions.

To avoid the trap of diminishing confidence and returns from an ML/AI program, companies can take the following steps.

By building the right infrastructure for data science, companies can see whats important for the business, and where the blind spots are. Doing the groundwork first can deliver solid ROI, but more importantly, it will set up the data science team up for significant impact. Getting a budget for a flashy data science program is relatively easy, but remember, the majority of such projects fail. Its not as easy to get budget for the boring infrastructure tasks, but data management creates the foundation for data scientists to deliver the most meaningful impact on the business.

AlexanderLovell is head of product atFivetran.

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20-month program to provide students with critical skill set to meet evolving business demands

NEW YORK, July 18, 2022 /PRNewswire/ -- As part of a commitment to prepare the business leaders of tomorrow, Columbia Business School and Columbia University's School of Engineering and Applied Science will offer a new dual-degree program that pairs the foundational skill sets of business with those of engineering. Students in the 20-month program will receive two degrees: a Master of Business Administration and an Executive Master of Science in Engineering and Applied Science. The program will officially launch in September 2023 and interested students can beginapplying now.

Designed to meet the evolving needs of leaders in technology, product managers, entrepreneurs, and other roles associated with technology and business, the Dual MBA/Executive MS in Engineering and Applied Science curriculumwill cover core engineering, areas of "tough tech," and applied science foundations, as well as essential business courses in leadership, strategy, finance, economics, and marketing. Students will take courses with both Columbia Business School and Columbia Engineering faculty, spend a summer pursuing an entrepreneurial venture or interning at a technology company, and complete a capstone project.

"Today's business challenges are multidisciplinary, and their solutions often lean on technological innovations. Students need, on one hand, a broad exposure to and understanding of how technology and engineering breakthroughs are shaping our lives today and the world of tomorrow. And, on the other hand, they need a deep understanding of business and, importantly, how to manage and lead in this dynamic environment," said Columbia Business SchoolDean Costis Maglaras, the David and Lyn Silfen Professor of Business. "In this competitive marketplace, Columbia's new MBAxMS: Engineering & Applied Science equips students with both the management skills and the science and technology core that enables them to move seamlessly from the classroom to product development to large-scale innovation and ultimately help create and grow companies and drive change."

The MBAxMS: Engineering & Applied Science core curriculum will focus on the creative application of technology and will include a variety of new and existing courses, including Digital Disruption & Tech Transfer, Business Analytics, Human-Centered Design and Innovation, and more. Students will also choose from an extensive array of electives designed to stimulate innovation, strengthen analytical skills, and bolster critical knowledge for their specific entrepreneurial or enterprise path.

"Technology, data, and analytics are transforming every aspect of modern businesses, especially those prized by the ambitious and entrepreneurial students who come to Columbia University," said Columbia Engineering Dean Shih-Fu Chang, the Morris A. and Alma Schapiro Professor of Engineering. "We recognize how important it is to provide students with broad exposures to emerging technology breakthroughs, the comprehensive training of business leadership skills, the unique experience in applying the human-centric design approach to innovative products and solutions, and importantly the ability to apply these unique skills in confronting major challenges facing our society and business world today. We look forward to partnering with Columbia Business School to launch an unprecedented program that can give our students a major boost."

The dual degree program, which is based in New York City, provides students with unmatched access and opportunities to work with and learn from the world's leaders in business, technology, data, analytics, and more. This includes opportunities to learn from guest speakers, meet with in-house mentors, and pursue internship opportunities that extend beyond the summer months. With one of the largest tech and entrepreneurial ecosystems in the country, the NYC location provides a unique, one-of-a-kind experience for the Dual MBA/Executive MS in Engineering and Applied Science students and graduates.

To learn more about the program, please visit https://academics.gsb.columbia.edu/mbaxms.

About Columbia Business SchoolColumbia Business School is the only world-class, Ivy League business school that delivers a learning experience where academic excellence meets with real-time exposure to the pulse of global business. The thought leadership of the School's faculty and staff members, combined with the accomplishments of its distinguished alumni and position in the center of global business, means that the School's efforts have an immediate, measurable impact on the forces shaping business every day. To learn more about Columbia Business School's position at the very center of business, please visitwww.gsb.columbia.edu.

About Columbia Engineering Columbia Engineering, based in New York City, is one of the top engineering schools in the U.S. and one of the oldest in the nation. Also known as The Fu Foundation School of Engineering and Applied Science, the School expands knowledge and advances technology through the pioneering research of its more than 250 faculty, while educating undergraduate and graduate students in a collaborative environment to become leaders informed by a firm foundation in engineering. The School's faculty are at the center of the University's cross-disciplinary research, contributing to the Data Science Institute, Earth Institute, Zuckerman Mind Brain Behavior Institute, Precision Medicine Initiative, and the Columbia Nano Initiative. Guided by its strategic vision, "Columbia Engineering for Humanity," the School aims to translate ideas into innovations that foster a sustainable, healthy, secure, connected, and creative humanity. To learn more about Columbia Engineering, please visit engineering.columbia.edu.

SOURCE Columbia Business School

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Columbia Business School and Columbia Engineering to Offer New "Dual MBA/Executive MS in Engineering and Applied Science" Program - PR...

The Financial Conduct Authority (FCA) is using technology investments to proactively identify and act upon potential risks to financial services customers.

Rather than react to problems after consumers have been affected, technology is improving the regulators use of analytics to gain insights and prevent future problems.

In a speech at the Peterson Institute of International Economics, FCA CEO Nikhil Rath said: This is helping us to take a more proactive stance and, crucially, spot harm and intervene more quickly and more broadly.

For example, the FCA has moved systems to the cloud as part of a project to put 50,000 finance firms and thousands of users onto a new regulatory data platform.

Using our data lake, we aim to more swiftly identify, connect and react to firm and market issues, said Rath. Our analytics tools are speeding up case management and providing improved visibility of risk in each firm.

He said the FCA is currently rolling out analytics screening tools to check that finance forms are complying with sanctions on Russia. This includes an automated tool to test firms ability to identify sanctioned firms.

The FCA now scans100,000 websites every dayand removes hundreds of fraudulent or scam-related websites targeting UK consumers every year.There is and must remain a laser focus on the quality of data coming in in the first place, said Rath. This is an area where we are holding firms increasingly to account.

He said the FCA is keeping an eye on the potential systemic risks caused to the finance sector as a result of its resilience of services provided by a small number of critical third parties, including cloud providers.

The FCA is changing its approach to digital regulation in the UK through itsDigital Regulators Cooperation Forum, which creates collaboration with communications, privacy and competition regulators.

Rath said the FCA will influence tech companies to make changes on its behalf where it can. We are testing our powers to the limit, and where we lack them, using our influence and other means to affect change, he said, citing a success story where Google agreedto stop non-FCA verified firms from advertising financial products on its platform.

Last month, the FCA announced it is looking for a significant number of tech experts as it invests in the use of data to prevent online fraud.

It wants to hire people with expertise in artificial intelligence, analytics and data science, as well as cloud engineering and digital technology.

The new recruits come on top of the100 people hired since 2020 as part of the FCAs data strategy.

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Data and tech platforms helping finance regulator spot problems earlier - ComputerWeekly.com