5 Must-Know Terms in Time Series Analysis by @snr14 via @TDataScience

A fundamental part of data science.

This would a useful reminder/quick tutor in time series analysis. Make sure you also think hard about the method you want to use to plot this analysis as sometimes the graph or notation you use can help of hinder your understanding.

A Complete Data Analytics Project with Python by Natassha Selvaraj via @TDataScience

Data collection, analysis, visualization, and presentation.

I really enjoyed this as it worked completely through the one example from start to finished explaining all of the thought processes. Go through this and use it as a bit of a blueprint on how to do this going forward.

WEBINAR: Using external data to accelerate business in a post-vaccinated world - 24 June 2021

 

NEWSLETTER . Using external data to accelerate business in a post-vaccinated world REGISTER NOW . You’re Invited! THURSDAY, JUNE 24 11AM PT | 2PM ET 60 MIN SESSION REGISTER NOW Join this webinar to learn how companies are developing insights to better prepare for growth opportunities, improve business performance, and mitigate risk in a post-pandemic economy. Join this webinar to learn how companies are using data to build and enhance visualizations, train machine-learning algorithms, and facilitate valuable insights. Attendees will learn: Use diverse data to help enrich business analytics. Integrate data into machine-learning models and data pipelines to create powerful visualizations. Utilize data analysis strategies to help apply faster insights and business outcomes. Leverage data to understand consumer behaviours from online purchasing patterns. Moderator: Samantha Gibson WW Leader, AWS Data Exchange Category Management, AWS Data Exchange Samantha Gibson leads the industry and regional verticals team for AWS Data Exchange at Amazon Web Services (AWS). In this capacity, her team engages with data subscribers globally across industries such as Financial Services, Healthcare and Life Sciences, Retail, Marketing & Advertising, and Media & Entertainment to discover, procure, and use traditional and alternative data assets in the AWS Cloud. Samantha’s team also works with data providers to help reduce their infrastructure, sales, and support costs and grow their customer base through cloud-native distribution. Prior to joining AWS, Samantha was a part of the Strategy and Corporate Development team at Bloomberg L.P. Samantha managed strategic initiatives and transactions spanning the company’s financial products, enterprise, and data businesses, as well as market surveillance and exploration of emerging market trends and financial technologies. Samantha was the inaugural product manager of the Bloomberg Gender-Equality Index, a first-of-its-kind reference index launched in May 2016, which measures the performance of global public companies recognized for supporting both data disclosure and best-in-class policies and practices in the gender-equality space. Samantha graduated Magna Cum Laude with a B.S. in Finance from the Stern School of Business at New York University, is a CFA charter holder, and a 2018 Aspen Institute First Movers Fellow. She is part of the NYU Stern Alumni Council and represents Amazon Web Services as an Executive Committee member of the Financial Information Services Association (FISD). Presenters: Jace McLean Director, Data Insights, Domo Jace McLean has more than 15 years of experience in data, analytics, and technology. His passion revolves around solving complex problems in a data-driven manner. Prior to Domo, he spent two years at Cargill building out analytics capabilities for its North American finance department. He also led analytics teams at Target in their Enterprise Data Analytics and Business Intelligence (EDABI) Center of Excellence with a focus on new products in e-commerce. Prior to that he spent nine years in the software industry. Jace has a bachelor’s degree in Computer Science from the University of Minnesota’s Institute of Technology, and a Master of Business Administration from The University of Chicago Booth School of Business. Jonathan Kay Founder and CEO, Apptopia Jonathan Kay co-founded Apptopia at the age of 25. As the CEO, he leads the daily operations and strategic direction, including product development and global sales. He’s an expert on the mobile landscape, app economy, and how data and predictive modeling add transparency to the ecosystem. As someone who believes deeply in the importance of customer engagement, he is constantly striving to find scalable intimacy. He’s extremely passionate about branding and storytelling. John Rogers Chief Innovation Officer, CoreLogic John Rogers holds the role of Chief Innovation Officer at CoreLogic. He is responsible for driving innovation through a state-of-the-art R&D platform to act as a catalyst to transform the industries CoreLogic serves. Prior to joining CoreLogic, John was a Partner with IBM Global Business Services where he focused on the delivery of large multi-million transformational programs within the financial sector. John earned a bachelor’s degree from University of Glasgow, United Kingdom in Aerospace Engineering. Colin Marden Senior Solutions Architect, AWS Colin Marden is a Solutions Architect in the Financial Services industry supporting AWS customers in their journey to modernize, transform, and migrate on-premises workloads to the AWS Cloud. Colin is a champion and specialist for Amazon QuickSight and AWS Data Exchange. He regularly works with AWS customers to create data engineering architectures and speaks at AWS and partner events on these subjects of interest. Kanchan Waikar Senior Solutions Architect for Machine Learning, AWS Kanchan Waikar is a Senior Partner Solutions Architect at Amazon Web Services with AWS Marketplace for machine learning group. She has over 14 years of experience building, architecting, and managing, NLP, and software development projects. She has a master’s degree in computer science (data science major), and she enjoys helping customers build solutions backed by AI/ML-based AWS services and partner solutions. REGISTER NOW © 2021 AWS Marketplace. 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3 Python Pandas Tricks for Efficient Data Analysis by @snr14 via TDataScience

Explained with examples. Pandas is one of the predominant data analysis tools.

Some handy hints in Python that may fix some minor issues in your code that you hadn't realised could be fixed so easily.

WEBINAR: Making Scaled Data Science Work For People - 30 March 2021

 

Making Scaled Data Science Work For People, March 30, 5 pm ET

Making Scaled Data Science Work For People, March 30, 5 pm ET Data science is too often discussed as a technical discipline, rather than a social and cultural one. But the role of data science is to both inform and automate decision-making processes, which require, in turn, humans to collaborate and communicate with each other and humans to collaborate with machines, both of which have key cultural and social dimensions. This webinar, presented by Hugo Bowne-Anderson, Head of Data Science Evangelism and Marketing at Coiled, will answer key questions, including Why do so many executives feel that so little of the data work in their organizations actually deliver returns? How can we reduce friction in factoring the process of turning business questions into business answers through the intermediaries of data questions and data answers? What provisions need to be in place to make sure that everybody is speaking enough of the same data languages to excel at their jobs? How do we promote data literacy throughout organizations while getting the job done? Join us on March 30th at 5 pm ET by signing up here Register Now

Dark Data: Why What You Don’t Know Matters by David Hand via @kdnuggets

In his latest book, a leading statistician Dr David Hand explores how we can be blind to missing or unseen data and how, in our rush to be a data-driven society, we might be missing things that matter, leading to dangerous decisions that can sometimes have disastrous consequences.

This is amazing and joins so many dots I didn't know I had. I recommend the full book makes it onto your Amazon wish list or similar as we are almost at Christmas and it would make a great present!!

10 Of his Favourite Python Libraries For Data Analysis by Emmett Boudreau via @TDataScience

 A quick rundown of some great analytical packages you should be using in Python.

Some great package suggestions here that I think you can use as part of learning Python in order to make sure you learn the right ones first.

Common Data Mistakes to Avoid by/via @geckoboard

“Statistical fallacies are common tricks data can play on you, which lead to mistakes in data interpretation and analysis.” Here’s a look at some of the common fallacies, with examples, a downloadable poster, and - more importantly - ways to avoid them.

This was really useful to remind you of all the potential mistakes you can make. There is also a great poster that can be downloaded to remind you of all these great points. Definitely, something to bookmark and keep.

5 predictions for when big data will become everyone's job by Larry Alton via @infomgmt

Even the best predictive analytics platforms will still need a human mind to tackle high-level analysis. Look for major changes in almost all human roles to come.

Some really great observations in this article that definitely warrants the time to read and think about what he has listed.

Dirty Data Is OK, How You Cleanse It Matters by Chirag Shivalker via @DZone

It has been an unsolved mystery for companies if they should get their data cleansed first to opt for data analytics or if they should opt for data analytics to conclude whether their data is dirty.

There are some really good points in this article.  I cannot emphasise enough the single source of truth point.  We must all have worked for organisations where department A's figures don't match department B's.  You cannot run an organisation if the numbers in your reporting don't match, and even worse you have no idea why they don't match. You need data management, agreed definitions for data, and just the one source of the truth across the entire company.

4 Ways Cities Can Change Their Data Game by @tnewcombe via @GOVERNING

From who they hire to how they share, adjusting municipal data use is a must for any city looking to improve its services.

An interesting look at a few possibilities of how data could help cities.

AI is changing the skills needed of tomorrow's data scientists by Ashish Thusoo via @infomgmt

It’s critical that today's students understand how analytics is evolving and how artificial intelligence can help solve real world problems.

This is a great summary of the kinds of skills that are becoming necessary. Worth reading and thinking hard about the kinds of skills you might need so you can fill any gaps that you have.

How can R Users Learn Python for Data Science? by @Manish_Saraswt via @HackerEarth

Python is a supremely powerful and a multi-purpose programming language. It has grown phenomenally in the last few years. It is used for web development, game development, and now data analysis / machine learning. Data analysis and machine learning is a relatively new branch in python. For a beginner in data science, learning python for data analysis can be really painful.

This blog entry is really interesting and is perfect if you know R but want an easy intro to Python as it gives you the relevant translation.

20 Data Analytics Careers That Aren't Data Scientists by @metabrown312 via @Forbes

Here are twenty careers that use data and analytics skills but are not a Data Scientist.

Great article. So it could be that you might aspire to being a Data Scientist, but these careers may be more in your reach - definitely worth considering even if they are a bit off the wall.

Please note this is a 3 page article and you will need to switch off your ad blocker to access it.

18 data sources for investigative journalists by Mădălina Ciobanu via @journalismnews

Government websites can be a starting point for many journalists investigating issues in the public interest, such as local planning and development, or spending. There are also many other data sources available that are often the result of other investigations and projects journalists and news organisations have worked on, or that have been compiled by civic groups and non-profits, which other reporters can use.

Some great sources that could also be used to provide test data for any practising you would like to do with any data science concepts or techniques.

5 Tips How to Write a Data Analysis Plan by Janet Anthony via @Analyticbridge

With a data analysis plan, you know what you’re going to do when you actually sit down to do the analysis of the data you’ve gathered.

Great article and definitely worth a read. Maybe if we all did a plan we would get everything right first time and work so much more efficiently?

Conversion rates — you are (most likely) computing them wrong by/via @fulhack

I brilliant article by Erik Bernhardsson about how to correctly calculate conversion rates - especially when you are looking at them over time.

Something we should all read and understand.  Essential reading.

The Value of Exploratory Data Analysis by Chloe Mawer via @kdnuggets

In this post, the author will give a high level overview of what exploratory data analysis (EDA) typically entails and then describe three of the major ways EDA is critical to successfully model and interpret its results.

This is crucial and it's important that it is done properly.

Historians versus futurists – the battle of analysts? by David Cokins via @infomgmt

Some analysts dig deep into historical information to glean insights once hidden. Others are obsessed with predictive analytics and Big Data to foresee the future.

I really like this opinion piece and think it is worth thinking about the two faces of analysis and what you are currently doing.

Battling superbugs with Big Data by Shruti Sharma via @livemint

Antibiotics that once cured ailments across the spectrum are now turning into a potential source of prolonged illness, disability and death. India, sitting at the cusp of a digital revolution, is well placed to address the antibiotic resistance problem.

This sounds like a great use of Big Data and I really hope they can get something useful out of their analysis.