(testing signal)

Tag: dataanalysis

Stem and Leaf Plot

Stem and leaf plots display the shape and spread of a continuous data distribution. These graphs are similar to histograms, but instead of using bars, they show digits. It’s a particularly valuable tool during exploratory data analysis. They can help you identify the central tendency, variability, and skewness of your distribution. Additionally, they can help you find outliers. Stem and leaf plots are also known as stemplots.
Stem and leaf plots have one advantage over histograms because…… Read more...

Use These Unique Data Sets to Sharpen Your Data Science Skills

Want to get your hands on some real-world data sets right now? Kick off your bootcamp prep with this list of hot-button data sets curated to help you hone different data science skills.

Sponsored Post.

Want to warm up your data science skills before jumping into a bootcamp program? Aspiring data scientists can practice key techniques like data cleaning, data analysis, data visualization and even machine learning with free, publicly available data sets. Hands-on data science exploration is one of the most effective ways to prepare for a data science bootcamp. In addition to learning more about your strengths, interests, and the skills you’ll need to grow, you’ll also gain experience working with the intricacies and idiosyncrasies of real-world data.

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Data Analysis Using Scala – KDnuggets

By Roman Zykov, Founder/Data Scientist @ TopDataLab

It is very important to choose the right tool for data analysis. On the Kaggle.com forums, where international Data Science competitions are held, people often ask which tool is better. R and Python are at the top of the list. In this article we will tell you about an alternative stack of data analysis technologies, based on Scala programming language and Spark distributed computing platform.

How did we come up with it? At Retail Rocket we do a lot of machine learning on very large data sets. We used to use a bunch of IPython + Pyhs2 (hive driver for Python) + Pandas + Sklearn to develop prototypes.

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Why Do You Need Data Matching ?

Enterprises need data for making informed decisions, interacting with customers and vendors, and analyze results. Trusted data helps overcome fraud challenges and enables organizations to comply with regulations. High-quality data about key business entities provides the growth funnel for a successful enterprise.

Clean and duplicate free customer records enable efficient sales and marketing and help the organization to grow. Imagine reaching out to the same customer multiple times only because of multiple entries in the system. This is expensive and time consuming for the sales and support staff, troublesome for the data analyst, cumbersome for the BI developer and frustrating for the customer.

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Data Analyst vs Business Analyst Salary

  1. Introduction
  2. Data Analyst
  3. Business Analyst
  4. Summary
  5. References

Note:

This article is the fifth, a part of a continuing series on reported salaries between popular data/tech roles. I will link the other four at the end of this article.

Like I have said for the previous articles in this series, this article aims not to compare roles as if one deserves more money or not, but is instead a guide allowing professionals in these two fields to assess against their current or expected salary. Keep in mind that these salary values are more general and no one website can be enough to assess your worth.

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New Study uses Federated Learning to Predict Covid-19 Outcomes

  • New study used Federated Learning to predict severity of Covid-19 for E.R. patients.
  • Significant improvements seen in central vs. local models.
  • Model slated for use in production in the near future.

Many ethical and legal challenges surround COVID-19 data analysis, including data ownership, data security, and privacy issues. As a result, healthcare providers have typically preferred models validated on their own data. However, this limits the scope of analysis that can be performed, often resulting in AI models that lack diversity, suffer from overfitting, and demonstrate poor generalization. One recent study titled Federated learning for predicting clinical outcomes in patients with COVID-19, published in September 15 issue of Nature Medicine [1], offered a solution to these problems: Federated Learning (FL).

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PathQL: Intelligently finding knowledge as a path through a maze of facts

PathQL simplifies finding paths through the maze of facts within a KnowledgeGraph. Used within IntelligentGraph scripts it allows data analysis to be embedded within the graph, rather than requiring graph data to be exported to an analysis engine. Used with IntelligentGraph Jupyter Notebooks it provides powerful data analytics

I would suggest that Google does not have its own intelligence. If I search for, say, ‘Arnold Schwarzenegger and Harvard’, Google will only suggest documents that contain BOTH Arnold Schwarzenegger and Harvard. I might be lucky that someone has digested these facts and produced a single web page with the knowledge I want.

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The Importance of Data Analysis in Problem Solving

Once in the program that I was working on had a lot of delays in the schedule. People were on a long vacation, and the program was delayed and in a catch-up mode to recover the lost time.

At that crucial time when IT teams were working hard, the business came back saying that because of priority activities that laid in front of them, there will be even more delay of 2 months.

This was a problem. A big problem. When there is a delay in the project, there is an associated cost. Who will bear it? Will you attribute that to the stakeholders causing it?

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Let’s Learn from the StackOverflow Survey

An exploratory data analysis with pandas, seaborn, and sklearn

What are the most popular IDEs? What aspects do software developers pay attention to when applying for a new job? What determines people’s job satisfaction? To answer these questions, I dug into the 2017 Stack Overflow survey and discovered some interesting results.

I chose the 2017 survey instead of the latest one because it contains more relevant numerical information. The survey was sent out to over 60k developers around the world through sites related to Stack Overflow, and the response rate was around 57%. Keeping various selection biases in mind, I first performed some data visualizations to gain some insights.

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Top Data Analyst Skills

  1. Introduction
  2. SQL
  3. Spreadsheets
  4. Critical Thinking
  5. Statistical Programming Languages
  6. Data Visualization
  7. Summary
  8. References

Data analysts can expect to be able to perform several different functions, ranging from quick tasks in an hour, to tasks spanned out over a month. With that being said, there are also skills that vary in technicality, as well as some soft skills, like communication, and all of these skills are incredibly important. In this article, I will discuss my opinion of these top five data analyst skills defined by Indeed [2], in addition to some other skills that I found particularly interesting. As a note, this article is intended for people who are looking to become a data analyst and want to know about the expected skills, as well as people who are generally interested in knowing the top skills for data analysts (if you are a seasoned data analyst, you most likely already know about all of these skills).

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Discount brings Spark to genomic data analysis on Zeppelin

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Data Scientist vs Data Analyst Best Practices

  1. Introduction
  2. Data Scientist
  3. Data Analyst
  4. Summary
  5. References

As someone who has worked in both professions, I have learned some best practices, processes, or tricks that have helped me to perform my job better. Working as a data scientist and data analyst have some similarities, as well as clear differences, which can relate to best practices too. In this article, I will be highlighting three best practices examples for each position. With that being said, keep on reading if you would like to learn a little about some of what I have learned so that you can apply it moving forward in your career, as well as if you are interested in hearing some of the best practices of each role in general.

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7 Differences Between a Data Analyst and a Data Scientist

By Zulie Rane, Freelance Writer and Coding Enthusiast

Spoiler: there’s a $30k difference.



What are the benefits of becoming a data analyst vs a data scientist? What is the main difference between a data analyst and a data scientist? Are they the same job? Which job, data analyst or data scientist, pays a higher salary? How do you science data anyway?

Most people have heard of the job data scientist ever since the Harvard Business Review called it the sexiest job of the decade. Data analysts received no such claim.

(If you are or want to become a data analyst, I still think it’s a sexy job.)

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Never Skip This Step in Your Exploratory Data Analysis (EDA)!

DATA SCIENCE. STATISTICS. EDA. PYTHON.

How descriptive statistics alone can mislead you

If you are new to data science and have taken a course to do preliminary data analysis, chances are one of the first steps taught into doing exploratory data analysis (EDA) is to view the summary / descriptive statistics. But what do we really intend to accomplish with this step?

Summary statistics are important because they tell you two things about your data that are important for modeling: location and scale parameters. Location parameters, in statistics, refer to the mean. Knowing this lets you know if your data is normal and whether there is potential skewness to help in modeling decisions.

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CDC Seeks Data Science Training to Upskill Workforce

The Center for Disease Control’s (CDC) National Center for Injury Prevention and Control (NCIP) is searching for data science training to help meet the organization’s Data Science Strategy.

In a sources sought notice on Sam.gov, NCIP said the training will be for its Division of Injury Prevention’s Data Analytics Branch, specifically the Data Science Team, and NCIP staff scientists. The CDC said the Data Science Team will be receiving advanced training, and the staff scientists receiving more general training.

The NCIP’s Division of Injury Prevention’s Data Analytics Branch (DAB) is tasked with conducting methodologic research and data analysis, addressing existing data issues and new data challenges (big, complex, non-traditional data), and developing and disseminating data, tools, and applications.

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Book published on data science

Data science is an emerging field of study that can offer higher-paid employment opportunities, said American College director M. Davamani Christober at the launch of his book, Concepts of Data Science, Using R on Friday.

The book deals with the fundamentals of data science, an area of ​​study that derives practical knowledge from structured and unstructured data, and the importance of the R language in data analysis. Mr. Christober said: “Since I am a mathematician, I was fascinated by data science and its applications. During the pandemic, I decided to explore its different facets through books, but I couldn’t discover many books by Indian or Tamil authors.

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Book on data science released

Data science is an emerging field of study that can provide employment opportunities with higher salaries, said Principal of The American College M. Davamani Christober, during the launch of his book ‘Concepts of Data Science, Using R’ on Friday.

The book which deals with the basics of data science, a field of study that acquires insights from structured and unstructured data for practical uses, and the significance of the R language in data analysis. Mr. Christober said, “Since I am a mathematician, I was fascinated by data science and its applications. During the pandemic, I decided to explore its different facets through books, but I was not able to spot many books from Indian or Tamil authors.

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7 data science use cases for business

Data science is a powerful tool that can be used in many different ways. The data it generates can help you make better decisions on everything from marketing to product development. You can use it for forecasting, predicting outcomes, and optimizing outputs. It can also be used as a competitive edge over your competition.

To avoid being left behind, it’s time to take your business into the future with data science. With these 7 data science use cases, you’ll be able to see how data analysis can help you make your business more profitable and competitive.

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​How Data Science and BI Is Revolutionizing the Sports Industry with Power BI

Nowadays, data has become very important in all industries, and that is why thousands of companies hailing from multiple sectors are resorting to data analytics tools. Using BI and data analysis tools can prove to be helpful for businesses in any sector, as it is. Even the sports sector can benefit a lot from the implementation of proper BI solutions and technologies. Businesses hailing from this sector can gain from hiring the right Power BI consulting services.

Why is using BI tools in the sports sector necessary?

These days, sports are not just about physical games. On the contrary, it is more like a numbers game.

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What is the Significance of Time-Weighted Averages in Data Analysis

Time-weighted averages are a way to get an unbiased average when you are working with irregularly sampled data. Time-series data comes at you fast, sometimes generating millions of data points per second. TimescaleDB is a petabyte-scale, completely free relational database for time-series. Using time_weight and other hyperfunctions, you can download and install the timescaledb_toolkit extension on GitHub, after which you’ll be able to use TimewiseDB.

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David Kohn Hacker Noon profile picture

@davidkohnDavid Kohn

Software engineer at Timescale. Former battery guy turned database guy. Enjoys teaching and pottery.

David Kohn Hacker Noon profile picture

by David Kohn @davidkohn. Software engineer at Timescale. Former battery guy turned database guy.
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Data Visualization In Excel Using Python

Using ExcelWriter for Creating Visualizations in Excel by Python Code

Excel is widely used for data analysis and has a lot of functionalities for analyzing, manipulating, visualizing, etc. Using excel should be one of the main skills required for a Data Analyst, Product Analyst, and Business Analyst. It helps in understanding the data and how we can use it for generating useful insights.

Python is also widely used for Data Analysis purposes and also overcomes the drawbacks of Excel. With a little knowledge of Python, we can enhance our Data Analysis skills and also generate more useful insights.

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Data Scientist vs Data Analyst Salary

What are the differences between these two popular tech roles?

Photo by Ryan Quintal on Unsplash [1].
  1. Introduction
  2. Data Scientist
  3. Data Analyst
  4. Summary
  5. References

To some, these roles can seem very similar, while for others, they can seem vastly different; the same can be said for their respective salaries. There are a lot of factors that go into a salary like seniority, location, education, skills, negotiation, industry, company, and more. So, when factoring in these characteristics, you can see quite the range of salary per role. Knowing what you have to offer is important as that can ultimately decide what your final salary is heading into a job from the job interviewing process.

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CDC Center Wants Data Science Training For Everyone, Including New Data Science Team

A data-heavy research arm of the Centers for Disease Control and Prevention wants the entire organization to get better at data collection and analysis, whether employees’ degrees are in biological sciences or data science.

The CDC’s National Center for Injury Prevention and Control, or NCIPC, includes a Data Analytics Branch charged with conducting “methodologic research and data analysis, addresses existing data issues and new data challenges—big, complex, non-traditional data—and develops and disseminates data, tools, and applications,” according to a request for quotes posted to SAM.gov. “It offers expertise in statistics, economic analysis, programming and data science to NCIPC and other partners.

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Data Analyst Offers 15 Reasons Extraterrestrials Aren’t Seen

Data analyst Yung Lin Ma offers fifteen reasons, including some new to us. He begins by observing,

There are about 1 billion stars that can produce an environment similar to the Earth. The environment of the earth does not necessarily have life, and this ratio is lower than 1 in 10,000. The reasoning is that at least in our galaxy, there should be 100,000 civilizations. Then why haven’t we seen even any single one civilization?

Yung Lin Ma, “15 Reasons Why We Can’t See Aliens” at Medium (July 14, 2020)

So, it’s an active question.

Of his fifteen reasons, here are three:

3.

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What You Don’t Learn About Data in School

Show Directional Results

I was once asked to help marketing set up an A/B test to evaluate the effectiveness of their email series aimed at converting users to start a trial and become paying members. I was pulled away to work on other projects and we had to wait until a marketing data analyst was hired before the A/B test could be evaluated. This is when we discovered the test hadn’t been set up correctly after it had already been running for 6 months. The control and test group proportions weren’t a 50/50 split as we had originally intended.

If this had been a class on A/B testing, you would’ve received perfect test data with the proper 50/50 split, enough users in the sample size, and proceeded to evaluate statistical significance.

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