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13 South African Data Science & Machine Learning Meetup Groups To Join

November 20, 2018 at 1:00 PM

Meetup (www.meetup.com) was founded in 2002 and is an online service used to organise groups that host in-person events for people with similar interests. A social networking tool, unlike any other, Meetup, has more than 35 million users in 180 countries. In South Africa, there are many meetups organised for various interests. Of course, our interests span data science, machine learning and AI, so we took the time to put together a (non-comprehensive) list of the groups who organise meetups that we (and hopefully you) find of interest. We hope you make it out to one of these groups meetups soon!

Meetup groups in Cape Town

R-Ladies Cape Town

Where: https://www.meetup.com/rladies-cape-town/

What they’re about: R-Ladies Cape Town is part of R-Ladies Global and was founded to celebrate and promote gender diversity in the R community.

This group is mainly aimed at female and female-identifying persons but is open to all. However, leadership roles remain reserved for female and female-identifying persons.

Frequency of meetups: Monthly

Number of members (at time of publishing): 395 members

Data Viz Meetup

Where: https://www.meetup.com/Data-Viz-Meetup/

What they’re about: The Data Viz Meetup is a community of data visualisation professionals in South Africa. The Meetup is the gathering of great minds! You will find designers, statisticians, analysts, programmers, data architects and all sorts of other amazing people from across the industry. We explore the full range of possibilities for how to convert raw data into visible, shareable insights.

Frequency of meetups: Monthly

Number of members (at time of publishing): 267 members

Cape Town Deep Learning

Where: https://www.meetup.com/Cape-Town-deep-learning/

What they’re about: This group is about exploring deep learning (Neural Networks in the field of A.I. / Machine Learning). People who have a background in Neural Networks, share their understanding of Deep Belief Networks, Share Articles, software, help configure open source code get everybody in this group up to date.

This meeting is not about: B.I., coding for the fun of it, general Machine Learning, Deep Learning is hard because the technology is bleeding edge and goes beyond standard Machine Learning and libraries that use the GPU are required.

Frequency of meetups: Infrequently

Number of members (at time of publishing): 377 members

Cape Town Data Science Meetup

Where: https://www.meetup.com/Cape-Town-Data-Science-Meetup/

What they’re about: This is the meetup group for enthusiasts of data science and applied machine learning. Our meetups are a healthy combination of working code and theory. We aim to not only focus on models but also on tools, implementation and production deployment (sometimes at large scale). We will sport speakers from both academic backgrounds as well as business. We hope to please a broad audience from academics to professionals and from enthusiasts to experienced practitioners.

We are always on the lookout for new topics and interesting speakers. If you have something interesting and sufficiently "data-sciency" to show and/or talk about, please let the organisers know.

Frequency of meetups: Infrequently

Number of members (at time of publishing): 831 members

Cape Town Machine Learning Meetup

Where: https://www.meetup.com/machinelearningcapetown/

What they’re about: This group is for anyone interested in Machine Learning. It is open to everyone, but some meetups will probably not be very interesting to those not interested in programming.

Frequency of meetups: Roughly every 2 months

Number of members (at time of publishing): 659 members

Johannesburg Meetups

Artificial Intelligence ZA

Where: https://www.meetup.com/ArtificialIntelligenceZA/

What they’re about: The Artificial Intelligence South Africa Meetup. Dedicated to helping the technical and business community learn more about AI and get hands-on experience with AI development, research, and execution. Sessions include talks by experienced people, tutorials for beginners, and discussions around pressing topics in AI. Beginners are welcome!

Frequency of meetups: Monthly

Number of members (at time of publishing): 2231 members

Joburg R Users Group (R Programming Language)

Where: https://www.meetup.com/Joburg-R-Users-Group/

What they’re about: We are the local R user group for Johannesburg, South Africa.

Check out our website at https://www.rusergroup.co.za/ and our GitHub repository at https://github.com/rusergroupcoza

What is R? R is a programming language for statistical computing, data analysis and visualisation of data. R is free and open source. Learn more at http://www.r-project.org/.

Our goals: Our purpose is to bring R users together to help each other with common problems, exchange knowledge and share experiences.

Secondly, this group intends to spread the use of R in South Africa and show others (also non-R users) the benefits of using R.

Frequency of meetups: 4 per year

Number of members (at time of publishing): 720 members

Gauteng Python Users Group

Where: https://www.meetup.com/Gauteng-Python-Users-Group/

What they’re about: We are a diverse group of (aspiring) Pythonistas from South Africa's Gauteng province. We host monthly meetups, on weekends/weekdays, where we connect with each other and discuss anything Python related.

Besides our monthly meetups, discussions happen in the GPUG Google Group (https://groups.google.com/forum/#%21forum/gpugsa) & Zatech-Slack (http://zatech.co.za)

Check out our site (https://gautengpug.github.io/) for announcements and blog posts.

Frequency of meetups: Monthly

Number of members (at time of publishing): 1403 members

Data Science Johannesburg

Where: https://www.meetup.com/Data-Science-Johannesburg/

What they’re about: In this meetup, we will discuss the tools, methods and technologies used by many startups and enterprises to analyse large-scale data (big data), obtain predictive and actionable insight, generate business value from data, and exploit business opportunities from data products.

We'll have data scientists talking about topics such as data analytics, distributed data analytics, data mining, machine learning, scalable data analytics, predictive analytics, statistical computing, applied statistics, R language, data visualisation, data science opportunities & challenges, data science learning & training.

Frequency of meetups: +/- 2 per month (sporadic)

Number of members (at time of publishing): 562 members

R-Ladies Johannesburg

Where: https://www.meetup.com/rladies-johannesburg/

What they’re about: R-Ladies Johannesburg welcomes members of all R proficiency levels, whether you're a new or aspiring R user, or an experienced R programmer interested in mentoring, networking & expert upskilling. Our non-profit, civil society community is designed to develop our members' R skills & knowledge through social, collaborative learning & sharing. Supporting minority identity access to STEM skills & careers, the Free Software Movement, and contributing to the global R community!

A local chapter of R-Ladies Global, R-Ladies Johannesburg exists to promote gender diversity in the R community worldwide. We are pro-actively inclusive of queer, trans, and all minority identities, with additional sensitivity to intersectional identities. Our priority is to provide a safe community space for anyone identifying as a minority gender who is interested in and/or working with R. As a founding principle, there is no cost or charge to participate in any of our R-Ladies communities around the world.

We are part of the Global R-Ladies group.

Frequency of meetups: Monthly

Number of members (at time of publishing): 157 members

Johannesburg Data Literacy Meetup

Where: https://www.meetup.com/Johannesburg-Data-Literacy-Meetup/

What they’re about: Anyone that is struggling with the interpretation of data and visualisations.

What is data literacy? Data literacy is the ability to read, work with, analyse and argue with data regardless of your role, skill level, or the BI tools you use. Improving data literacy hones your decision-making skills. You learn to ask the right questions of your data, interpret your findings and take informed action.

Frequency of meetups: TBD, this is a new group.

Number of members (at time of publishing): 53 members

Meetups in Durban

Open Data Durban Meetup

Where: https://www.meetup.com/Open-Data-Durban-Meetup/

What they’re about: This is a meetup for anyone interested in open data, data science, and data journalism in Durban, South Africa.

Open Data Durban is a civic technology lab that implements and advocates for open data, open government, and civic technology through projects, events, workshops, and dataquests. We work with civic technology partners such as Code for South Africa and code for Africa, as well as local partners and stakeholders such as eThekwini Municipality and Urban Earth.

Any level of technical ability is welcome; all that is required is a desire to see Durban Data liberated.

Frequency of meetups: Infrequently

Number of members (at time of publishing): 527 members

Durban Artificial Intelligence Meetup

Where: https://www.meetup.com/Durban-Artificial-Intelligence-Meetup/

What they’re about: This group is for anyone interested in Artificial Intelligence and Machine Learning. No previous experience required. I have started this group to get all Durban AI enthusiasts together, looking forward to meeting you.

Frequency of meetups: TBD, this is a new group.

Number of members (at time of publishing): 112 members

collection of data science and machine learning resources

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The 7 types of credit risk in SME lending

  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. If you are looking for a decision engine to manage your scorecards, policy rules and terms of business then take a look at our DecisionSmart business rules engine. AppSmart and DecisionSmart are part of Principa’s FinSmart Universe allowing for effective credit management across the customer life-cycle.   The different risk elements within a business credit application 1) Risk of proprietors For smaller organisations the risk of the business is inextricably linked to the financial well-being of the proprietors. How small is small? The rule of thumb is companies with up to two to three proprietors should have their proprietors assessed for risk too. This fits in with the SME segment. What data should be looked at? Generally in countries with mature credit bureaux, credit data is looked at including the score (there is normally a score cut-off) and then negative information such as the existence of judgements or defaults; these are typically used within policy rules. Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex.   4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. Small businesses may not be audited and thus the financials may be less trustworthy. Financial ratios can be divided into four categories: Profitability Leverage Coverage Liquidity Profitability can be further divided into margin ratios and return ratios. Lenders are frequently interested in gross profit margins; this is normally explicit on the income statement. The EBIDTA margin and operating profit margins are also used as well as return ratios such as return on assets, return on equity and risk-adjusted-returns. Leverage ratios are useful to lenders as they reflect the portion of the business that is financed by debt. Lower leverage ratios indicate stability. Leverage ratios assessed often incorporate debt-to-asset, debt-to-equity and asset-to-equity. Coverage ratios indicate the coverage that income or assets provide for the servicing of debt or interest expenses. The higher the coverage ratio the better it is for the lender. Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here.   5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. The loan/facility amount will of course change the ratios (mentioned in the last section) which could affect a positive/negative outcome. The outcome of the loan application is usually directly linked to a loan amount and any marked change to this loan amount would change the risk profile of the application.   6) Risk of industry The risk of an industry in which the SME operates can have a strong deterministic relationship with the entity being able to service the debt. Some lenders use this and those who do not normally identify this as a missing element in their risk assessment process. The identification of industry is always important. If you are in manufacturing, but your clients are the mines, then you are perhaps better identified as operating in mining as opposed to manufacturing. Most lenders who assess industry, will periodically rule out certain industries and perhaps also incorporate industry within their scorecard. Others take a more scientific approach. In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. If you would like to discuss your SME evaluation challenges or find out more about what we offer in credit management software (like AppSmart and DecisionSmart), get in touch with us here.

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