How blogging helped me get my first data science job
- I wanted to become a data scientist since I learned that such a job exists.
- After four months, I limited the topics of my articles to data analytics and machine learning.
- I was training machine learning models, doing data analysis, and spending most of the day reading research papers.
- It was difficult to admit, but I learned that being a data scientist is not the perfect career path.
- I can train machine learning models, build ETL pipelines, write complex software, care about code quality, and plan the architecture of my software.
- If I started blogging regularly earlier, I wouldn’t need to write three times a week.
- I think that six months is the minimal amount of time you need to build a successful blog that boosts your career.
- I have created a free blogging course for aspiring data scientists.
Controversial UK porn block law scrapped for good after repeated delays
- The UK has scrapped a controversial plan to require age verification for accessing porn online, after repeated delays.
- Morgan didn’t elaborate further on what these proposals would entail.
- The proposed law would’ve forced pornography websites to age-verify internet users in the UK to ensure they are above the age of 18 before allowing access to pornographic material.
- But it hit a snag early this June for the third time, a month before it was due to be enforced on July 15, following concerns that watchers’ browsing habits could be tied to their identities and misused for large-scale data harvesting.
- Critics warned that those under 18 would be able to bypass the restriction using VPNs to disguise their locations, or switch to other social media platforms that host pornographic content, such as Reddit and Twitter, which aren’t covered by the law.
- If you like this kind of challenges please also check codeguppy.com and follow @codeguppy on Twitter.
- Have fun!
- Sore eyes?
- We're a place where coders share, stay up-to-date and grow their careers.
- We strive for transparency and don't collect excess data.
Breaking: Pound Collapses as Brexit Deal Crumbles in Flames
- Any hopes of securing a last-minute Brexit deal today went up in flames as the Northern Irish Democratic Union Party (DUP) said they couldn’t support Boris Johnson’s latest proposals “as it stands”.
- Boris Johnson returned to the EU in recent months to negotiate a fresh deal after former Prime Minster Theresa May failed to find a solution.
- But without a clear majority in parliament, Johnson needed the backing of 10 obscure Northern Irish MPs in the Democratic Union Party.
- Traders reacted immediately, sending the British pound tumbling 0.6% against the dollar in a matter of minutes.
- The likelihood of the UK crashing out the EU without a deal has now increased dramatically unless Boris Johnson can find a solution today that appeals to the EU and the DUP.
- Reach him at benjamin-brown.uk or on Twitter at _Ben_Brown.
- Email ben @ benjamin-brown.uk.
Zoho launches Catalyst, a new developer platform with a focus on microservices
- And today, it’s launching Catalyst, a cloud-based developer platform with a focus on microservices that it hopes can challenge those of many of its larger competitors.
- The company already offered a low-code tool for building business apps.
- What Catalyst does do is give advanced developers a platform to build, run and manage event-driven microservice-based applications that can, among other things, also tap into many of the tools that Zoho built for running its own applications, like a grammar checker for Zoho Writer, document previews for Zoho Drive or access to its Zia AI tools for OCR, sentiment analysis and predictions.
- The platform gives developers tools to orchestrate the various microservices, which obviously means it’ll make it easy to scale applications as needed, too.
- It also offers developers the ability to access data from Zoho’s own applications, as well as third-party tools, all backed by Zoho’s Unified Data Model, a relational datastore for server-side and client deployment.
Edge computing startup Pensando comes out of stealth mode with a total of $278 million in funding
- Pensando, an edge computing startup founded by former Cisco engineers, came out of stealth mode today with an announcement that it has raised a $145 million Series C.
- The company’s software and hardware technology, created to give data centers more of the flexibility of cloud computing servers, is being positioned as a competitor to Amazon Web Services Nitro.
- The round was led by Hewlett Packard Enterprise and Lightspeed Venture Partners and brings Pensando’s total raised so far to $278 million.
- HPE chief technology officer Mark Potter and Lightspeed Venture partner Barry Eggers will join Pensando’s board of directors.
- The startup claims its edge computing platform performs five to nine times better than AWS Nitro, in terms of productivity and scale.
- Pensando prepares data center infrastructure for edge computing, better equipping them to handle data from 5G, artificial intelligence and Internet of Things applications.
The Rise of Meta Learning
- This evolution from block orientation to solving a rubik’s cube is fueled by a Meta-Learning algorithm controlling the training data distribution in simulation, Automatic Domain Randomization (ADR).
- Although not as surprising as misclassifications do to hardly noticeable adversarial noise injections, Deep Convolutional Neural Networks will not generalize when trained on images in simulation (displayed below on the left) to real visual data (shown below on the right) without special modifications.
- Domain Randomization appears to be the key to bridging the Sim2Real gap, allowing Deep Neural Networks to generalize to real data when trained on simulation.
- It seems likely that the ideas of Automatic Domain Randomization would be improved with advanced search algorithms, i.e. something like the Population-Based Search proven useful in Data Augmentation by Researchers at UC Berkeley or AutoAugment from Google.
- Most data augmentation searches (even automatic domain randomization) is constrained to a set of transformations available to the meta-learning controller.
Let’s Get Back to Basics
- So it’s no surprise that there is a strong trend in the market to become data driven, in part because cloud technologies and the growing usage of SaaS make data more accessible than ever.
- I’m also seeing that while there’s more appetite than ever to be data-driven, it’s harder than ever to be good at it.
- In fact, I’d say there’s actually an opposite trend around the fundamentals of data (accuracy, quality, reliability, governance, access, protection, security) — these are all extremely challenging to get right, and getting worse to manage.
- Why is it that our standard for reliability is so high for everything we do in business — it’s part of what encourages this data driven trend in the first place!
Data Science and Healthcare
- I recently went to a Data & Healthcare meetup to see how a renowned cancer research institute, Memorial Sloan Kettering Cancer Center (MSK), applies and uses Data Science.
- It was very inspiring to hear that MSK has “a lot of data” and they’re exploring how Data Science can be used to be beneficial and impactful to provide the best patient centered experience.
- Another speaker from MSK stated that some providers don’t like to click on checkboxes to document their notes and that they prefer to write it out in prose.
- While I don’t believe that quote was necessarily addressed towards Data Science, it is inspiring and interesting to see how a company such as MSK promotes and shares their interest and excitement in the field of Data Science and analytics to ultimately provide the best patient-centered care.
6 Key Skills That Data Analysts Need to Master
- In addition to the skilled use of SQL statements, data analysts should also know about the storage and reading process of the database.
- The results of the data analysis are ultimately presented to others, and visualization skills are also a measure of the level of data analysts.
- If you want to know which tools you can learn, you can read this article 9 Data Visualization Tools That You Cannot Miss in 2019.
- Data analysts at this stage need to know how to use tools to process data, understand business scenarios, and analyze and solve basic problems.
- After that, if you want to delve into the technology and even develop in the direction of data scientists, you should learn the following skills.
- Machine learning algorithms are a class of algorithms that automatically analyze and obtain rules from data and use rules to predict unknown data.