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SEO How-to, Part 5: Analyzing Keyword Data



Keyword research is a search engine optimization tactic to identify the words and phrases of consumers who are looking for the types of products and services that your company sells.

This post is the fourth installment in my “SEO How-to” series, following:

Think of keyword research in terms of supply and demand. Every word on your site represents keyword supply. The words that people input into Google and other search engines represent the demand. Keyword research is the process of determining the demand to adjust your supply.

In this post, I’ll cover four steps: seeding keyword research, collecting the data, organizing the data in my free keyword analysis template, and analyzing it to understand intent and demand. 

Seeding Keyword Research

The tools for collecting keyword research rely heavily on a seed list — your choice of words that consumers search on. A comprehensive list of seeds yields the most data. A weak list generates fewer keyword themes and often leads to poor optimization decisions.

To brainstorm a seed list, start with your site’s navigation. An ecommerce site that’s organized in a logical, hierarchical manner will contain navigational options that cover all of its products. Your keyword research will tell you whether those products coincide with what people search for.

List of all of the product categories — copy and paste directly from your menus. Then add synonyms and the types and styles of those items.

For example, a site that prints pictures on mugs, wall art, and greeting cards would include synonyms such as “photos” and “images” as well as popular product attributes such a size, occasion, and style.

Don’t be afraid to generate a long keyword seed list. It may look daunting, but the process of assembling the keywords can go surprisingly quickly once you get started, and the resulting data is pure gold.

You can also enter the URL for any page in Google Keyword Planner, and it will fetch the relevant keywords. Hence, identify pages that rank well for the keywords you need to rank for, and then note those URLs in your seed list.

Collecting Keyword Data

Next, push the sets of seed words into the input field of your keyword tool to understand the search demand of each. The process is simple and tedious.

Copy a set of seeds from your list and paste them into the tool. Wait for it to finish processing, export the resulting data, and repeat until you’ve entered all of the seeds.

When you finish passing the seeds through the tool and exporting the data, you’ll have a mass of .csv or Excel files. Merging these manually would take forever. On a PC, use the command line in your operating system to merge .csv files into a single file. Do the same thing on a Mac in Terminal.

Populate the Keyword Template

Now you have a lot of raw keyword data. But it has little value unless you organize it for analysis.

That’s where my free keyword analysis template — shown below — comes into play. It aggregates in Google Sheets the keyword demand for one-, two-, and three-word combinations, and displays that demand alongside data from:

  • Google Search Console for an approximation of organic search volume by keyword combination.
  • Google Ads for estimates on conversion potential by keyword combination.
Use this template in Google Sheets to make sense of your keyword data.

Use this template in Google Sheets to make sense of your keyword data.

To use the template, copy the Google Sheet, or download it as an Excel file. (Excel is your best bet if you have more than 20,000 rows of data.)

Paste your keyword data into the “Google Keyword Planner” tab. For a different keyword tool, just make sure that the keywords are in column A and the number of monthly searches ends up in column C. Both columns are referenced from the primary “Keyword Analysis” tab.

Next, remove the duplicate rows in the keyword data. In Excel on a PC or Mac, go to the “Data” tab and choose “Remove Duplicates.” You can do the same thing using Numbers on a Mac.

Your keyword data will likely contain many irrelevant words and phrases. Sort the keyword data by the number of searches, from highest to lowest. Review the top 300 or so (or until the number of searches drops below a level you find valuable) and remove the unrelated rows.

The tool may identify keywords that drive many relevant searches but weren’t in your original seed list. If so, consider running those back through the tool, as well.

Also, download data sets from Google Search Console and Google Ads. Keyword volume is calculated as a monthly number. Thus the other three tabs should each contain data for a full month.

For reference, I’ve included a tab for Google Analytics data for traffic from organic search (Acquisition > All Traffic > Channels > Organic Search), though I’ve excluded that data in the “Keyword Analysis” tab since Google Analytics reports traffic for URLs but not for organic keywords. It consequently can’t be aggregated with the other sources.

Analyzing Keyword Data

As you prune the irrelevant keywords from your dataset, you will likely notice patterns in individual keywords. Now it’s time to quantify those broad patterns and compare them to understand the value of the overall keyword theme.

Start entering seeds into the columns A through C, as shown below. As you enter words in those columns, the formulas in the template calculate the keyword demand (Keyword Planner), current organic search performance (Search Console), and potential conversions (Ads).

Enter the words into columns A through C. The template will aggregate data from those columns.

Enter the words into columns A through C. The template will aggregate data from those columns.

Scan the words to find patterns in keyword usage. Notice how the demand and performance can be radically different for slight variations when aggregated. For example, the monthly search volume for keywords containing both “gift” and “picture” is 121,460 whereas the volume for keywords containing both “present” and “picture” is just 19,280.

Think of the analysis as an exploratory process. You can always add words or remove them if the ones you start with don’t work out. You’ll have it right when the amount of keyword demand is consistent with the total demand in the “Google Keyword Planner” tab.

Above all, listen to the data and look for trends with an open mind. Don’t attempt to prove the validity of your site’s existing structure.


Big Data

9 Outstanding Reasons to Learn Python for Finance



9 Outstanding Reasons to Learn Python for Finance

Is Python good for learning finance and working in the financial world? The answer is not only a resounding YES, but yes for nine very good reasons. This article gets into the details behind why Python is a must-know programing language for anyone who wants to work in the financial sector.

By Zulie Rane, Freelance Writer and Coding Enthusiast


If you’re thinking about dipping your toe into the finance sector for your career and you stumble across this article, you may be wondering, “How can Python help in finance?”

You, like me, may be surprised to learn that you should learn to code altogether – and even more surprised to learn that the best language for finance is a popular data science language, Python. Learning financial programming with Python is becoming a requirement.

Finance and banking have a reputation for very high salaries, so the job field attracts a large number of applicants. If you’re one of them, you should know Python is hugely popular for finance — and still growing in popularity. Python is widely used in risk management, the creation of trading bots, quantitative finance for analyzing big financial data, and more.

Here are nine answers to “Should I learn Python for finance?”

1. Fast, Simple & Clear

If you’ve read any other coding listicle, you’ll already know the reasons Python rules in general so I’ll save you time and condense them into a single bullet: Python is fast for both beginner and experienced coders to learn. It’s easy to read as it has a clear and simple syntax. It’s quick to deploy – you can build working code in just a few lines.

It’s a great idea to learn Python for finance because of the same reasons that make Python great for anything else. If you want to analyze financial data with Python, you’ll be pleased to hear it’s a breeze to start.

2. Shortcuts Already Exist

Python as a language is bolstered by libraries, AKA reusable chunks of code someone else already built for you. Beyond being simple to pick up, there’s a robust framework of open-source, third-party libraries that are useful in all sorts of situations but specifically for finance.

Libraries like Scikit and Pybrain are especially useful in the finance world. Some people consider the Python finance libraries “shortcuts” already since you don’t have to code that functionality from scratch. I’d agree. Financial data analysis with Python is a lot quicker with these libraries.

Scipy, NumPy, Matplotlib, and a range of other add-ons make it easier than ever to perform and visualize financial calculations and algorithms. Importantly, these help you not only work with the data, but more easily communicate the results to key decision-makers.

3. Chock Full of Resources

The really great thing about the use of Python in finance is that despite being a relatively new use of the language, there are already a ton of resources that can help you get started and smooth your way in. This is because Python has been in use generally for decades. A lot of the same resources that made Python great for data science can make your finance experience better too.

Beyond the open-source libraries, there are entire Python courses and textbooks and tutorials dedicated to helping you pick up the basics specifically of Python for finance.

If you want to learn Python for finance, you won’t have to rely on generic courses and tutorials – there is plenty of content geared around how to learn Python in a finance context.

4. Great Community

If the libraries, textbooks, and tutorials aren’t quite enough for you to find your feet with Python for finance, there’s a vast community of Pythonistas in general. You may be excited to learn that there is specifically a community for finance-focused Pythonistas, too.

If you’re stumped on how to learn Python for finance, worry no longer. You can find Github projects dedicated to easy Python for finance tutorials, Slack groups devoted to finance and coding, an active finance contingent in the r/learnpython subreddit, and of course StackOverflow is full of helpful folks who have been where you are and are willing to help you wade through your finance-related Python questions.

While I’ve found Pythonistas in general to be a friendly, helpful, enthusiastic bunch, it looks like the same is true of the finance-focused Python community too.

5. It’s the MVP

While I do consider Python to be the most valuable player (if coding languages were in sports leagues), I’m talking about minimum viable products in this section. The finance sector prizes agility and responsiveness as it’s customer-facing.

In finance, programmers should be able to quickly create a prototype that can be tested. A company can use an MVP to determine market fit and get the idea off the ground that way. Python is a dream language to spin up quick MVPs for testing.

If your finance company is a startup, Python is valuable because it helps developers create products quickly and flexibly without wasting resources. If it’s an established corporation, Python is valuable because it increases agility and helps encourage innovation, which is critical for bigger, slower companies. Either way, it’s a great tool for your team.

In sum, Python lets finance coders build a minimum viable product and iterate from there.

6. A Bridge Between Two Worlds

Python is better known for data science but it serves as a great bridge between economics and data science (also known as the finance area).

To quote an expert, “It’s just much easier to integrate the work of economists into Python-based platforms. Tools like SciPy, NumPy or Matplotlib allow one to perform sophisticated financial calculations and display the results in a very approachable manner,” says Jakub Protasiewicz, an engineering manager with over ten years of IT experience.

Economists use Python for their economic analysis due to its simplicity and flatter learning curve.

7. You’re In Good Company

Python has heavy buy-in from significant finance corporations. Lots of finance companies are built on Python like Venmo, Stripe, and Robinhood for a lot of the same reasons I recommend it to finance workers in this very article.

Beyond just using Python as a framework for finance companies, some organizations go further: Citibank offers Python coding lessons to banking analysts and traders so they can make use of Python’s incredible data science toolset, for example.

If you want to learn Python programming for finance, it should be clear that you’re not alone: plenty of companies have done the very same thing.

8. Here Today, Still Here Tomorrow

There’s nothing worse than dedicating time to learning a coding language for a job and then discovering that language is becoming obsolete, or is being replaced by an up-and-comer.

In the finance world, you will never have that problem with Python. It’s been around for three decades; plenty of finance companies have already invested in building infrastructure and long-term projects with Python, and most tellingly, there are new applications of Python for finance coming out every year, like Dash for cryptocurrency as you’ll learn in section nine.

Python will still be useful for financial analysis in a decade or two. Using Python for financial analysis is a safe bet.

9. Python Cryptocurrency

What can you do with Python in finance? Dig into cryptocurrency. I was actually surprised to learn that Python is becoming a key player in the cryptocurrency space. I had a quick look on (a list of all libraries available for Python) and found 327 packages that deal with cryptocurrency and blockchains.

Products like Dash, Anaconda and CryptoSignal have taken advantage of how easy it is to use Python to retrieve and analyze cryptocurrency pricing. If you’re invested in the cryptocurrency scene, Python is a must for you.

Python and Finance Are a Match Made in Heaven

How important is Python in finance? I’d go so far as to say it’s a critical language to learn. It’s no longer enough to rely on existing products that are built on Python for financial companies and jobs – more and more, employees are expected to know Python for finance in everyday use.

Python use in finance is practically unlimited, and I expect that as time goes on and more finance workers learn Python, that number will only grow. If you were also unclear on if Python is good for finance, hopefully this article has cleared up the answer for you.

Want to Learn Python For a Future Finance Role?

If you’re looking to get started with Python, we built our Python Fundamentals course to take you from novice to intermediate level, prepping you to take on more advanced topics like data science for financial data.

If you’re more advanced, we also have Big O Data Structures and Advanced Algorithms to learn key computer science concepts while interactively coding along with each lesson’s challenge, putting your newly-gained skilled into action.

Bio: Zulie Rane is a freelance writer and coding enthusiast.

Original. Reposted with permission.


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Blockchain News

One River Asset Management Closes $41M Funding Round Led by Goldman Sachs



One River Asset Management, one of the leading institutional asset managers in the cryptocurrency ecosystem, has completed a $41 million Series A funding round led by Coinbase Ventures and Goldman Sachs Group Inc, amongst others.

As noted by the firm, the funds will help deepen its ties with financial and digital industry leaders and provide capital to accelerate One River Digital’s ongoing development further. 

The One River Asset Management’s funding and its high-profile participants, including the two Wall Street giants named above, showcases the growing influx of mainstream players into the digital currency industry. With many ways to embrace crypto-related investments, active backing players in the space through venture capital funding has proven to be a very safe bet for many, including Goldman Sachs.

“We are thrilled to have the support of these five new strategic investors. Each institution is a leader of their specific category of finance, bringing with them unique experiences, connectivity, and capabilities,” said Sebastian Pedro Bea, President of One River Digital. “We are already collaborating to develop and distribute an expanding range of institutional digital asset strategies that best meet the needs of our global clients.”

One River Asset Management made headlines back in March when it employed former US Securities and Exchange Commission (SEC) boss Jay Clayton as one of its advisors. The aim was to get a headstart in navigating the regulatory landscape as the company seeks to roll out a number of products to increase its market share.

A number of digital currency firms and projects have raised funds successfully this year. Amongst the most notable ones include the $100 million Series C round announced by Singapore-based Digital financial service platform Matrixport and Avalanche’s recent $230 million raised from a group of investors led by Polychain and Three Arrows Capital, amongst others. 

Image source: Shutterstock
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Source: https://Blockchain.News/news/one-river-asset-management-closes-41m-funding-round-led-by-goldman-sachs

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ACCA to hold career fair for finance professionals on Sept 29



With the objective of lending support to present and future members in terms of employability, the Association of Chartered Certified Accountants (ACCA) is organising a Virtual Careers Fair. This is an enhanced version of the Fair that the global body for professional accountants had organised successfully in 2020.

The Fair is a platform that connects employers with professionally-qualified finance talent.

Md. Sajid Khan, head of international development, ACCA says, ‘This mobile-friendly, immersive Virtual Career Fair managed by ACCA is a platform for employers to browse resumés from thousands of potential applicants. Organisations can also dynamically post their vacancies on ACCA Careers and interact with forward-thinking accountancy and finance professionals.’

The improved version of the Fair this year offers greater access to candidates as well as the technology to make real-time connections via video chat with a vast array of ACCA’s future-ready finance professionals. Scheduled to be held on September 29, 2021, the Fair will witness participation from prospective students and those completing ACCA studies, as well as experienced professionals along with thousands of ACCA members and affiliates.

Employers can review the CVs and resumés available on the platform, before the event, and shortlist candidates. Thererafter, they can book on-the-day appointments to interact with the candidates and discuss potential roles and expectations.

Organisations will get a chance to showcase themselves to a wide audience in a live environment. Employers can also list their live vacancies, for free, on the ACCA Careers website, using the intuitive guide. This will give them access to ACCA’s resume/CV database, so that they can make event-day appointments with potential candidates.

Helen Brand OBE, chief executive, ACCA is confident of “ACCA’s strength as a super connector in the accountancy and finance profession across the world” and encourages employers and job seekers to “explore all the available resources we have created for you” and “look to build on or even begin, your career in accountancy and finance.’

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SDP/SI Provides a Drop-In Motor Solution for Robotics Applications



New Series of Frameless Motors Now Available

Offering an assortment of motion control solutions that ensure accuracy and dependability the frameless motors provide an additional option to those in the robotics, industrial automation, and medical industries.

Rapid growth in the industrial automation market is producing an urgent need for premium quality parts and services. Stock Drive Products/Sterling Instrument (SDP/SI), a leader in providing mechanical and electromechanical based design, engineering, and precision manufacturing for critical motion and power transmission applications is launching a series of Frameless Motors as a drop-in solution for robotics applications.

Designed to be pressed into a machine’s housing, the SDP/SI NH1-D series Frameless Brushless Motor s provide a compact, lightweight, and powerful motor solution. Available in standard sizes, 35 mm, 52 mm, 64 mm, 77 mm, and 100 mm, the frameless motors are machine wound with bondable magnet wire for superior dependability. Each motor features a large inner diameter rotor permitting easy cable management.

With their compact size, the NH1-D series frameless motors fit easily into smaller machines requiring precision, high efficiency, low inertia, and high torque density. “Rated for continuous operation the frameless brushless DC motors are an ideal solution for many applications including the replacement of heavier, traditional motors by eliminating components, reducing torsional losses, decreasing weight, system inertia, and size envelope while providing maximum speed control,” said Jacques Lemire, Business Unit Director, Motors & Motion Control. “Offering an assortment of motion control solutions that ensure accuracy and dependability the frameless motors provide an additional option to those in the robotics, industrial automation, and medical industries.”

SDP/SI products are available with a wide range of standard features for easy integration intended for robotics and industrial automation applications. To cover any system requirements SDP/SI offers Integrated Motor Drive Controllers, DC Motors, Gearheads, AGV Gear Motors, and a wide range of AGV accessories: such as optical flow sensor, magnetic track following sensor, and controller network communication options.

About Stock Drive Products/Sterling Instrument (SDP/SI) a Designatronics company

SDP/SI, ISO 9001:2015 + AS9100D certified, offers custom mechanical-based design, engineering, and manufacturing services for critical motion control and small power transmission applications, including aerospace, medical, defense, robotics, recreational, and industrial automation. Over 87,000 standard inch and metric small mechanical components are available for fast turnaround. SDP/SI specializes in high-quality machined parts, molded components, synchronous belt drives, precision gears, and subassemblies. For more information go to:

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