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Becoming a Data Engineer
To become a data engineer, there are various skills and software to learn, but the basics are the same!
Today, there are multiple data engineers’ certificates and courses that you can take.
Here are the free ones; they cover the basics, which are the most important part to grasp and understand, and later introduce specific Azure technologies.
They are free and will help you cover the basics and Get Started.
Later it will be easier for you to jump into any technology related to data engineering.
Free Courses to cover the Basics:
? Services for Data Engineer
? Store Data
? Work with Relational Data
? Work with NoSql solution
? Large scale data processing with Data Lakes
? Implement a Data Streaming Solution with Azure Streaming Analytics
? Implement a Data Warehouse
Each module will take you about 4 hours to complete. If you are completing 1 every weekend, it will take you only 7 weekends.
After it, you will be ready to take the certification exam.
the certificate is not free, but you can apply for a scholarship through the link.
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Now, after you finish understanding everything, you can start diving into any technology related to data!
I hope this helps you get started and get a Data Engineer role fast!
Have concerns? would you like to chat? send me a private message on twitter.
Don’t forget to give us your ? !
Becoming a Data Engineer was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
Via https://becominghuman.ai/becoming-a-data-engineer-b9fd3343b153?source=rss—-5e5bef33608a—4
source https://365datascience.weebly.com/the-best-data-science-blog-2020/becoming-a-data-engineer
Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science
Data science is ever-evolving, so mastering its foundational technical and soft skills will help you be successful in a career as a Data Scientist, as well as pursue advance concepts, such as deep learning and artificial intelligence.
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Understanding Transformers the Data Science Way
Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.
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365 Data Use Cases: Product Development with Tina Huang
Hi! My name is Tina Huang and I’m a data scientist at a FAANG company. I hold a Bachelor’s degree at the University of Toronto where I studied pharmacology. Subsequently, I worked in bioinformatics for a year and then did my Master’s in computer science (MCIT) at the University of Pennsylvania. My experience includes an internship at Goldman Sachs where I did some machine learning work before I took up my current data science job in tech.
I am super excited to join the 365 Data Use Cases series, and in this post, I will share insights about my favorite data use case: product development.
What Is Product Development?
Product development is a huge and complex set of processes that rests on many moving parts, lots of hypotheses testing, and ultimately many decisions. The end goal is to create and grow a product that users love.
You can also check out our video on the topic below or scroll down to keep on reading.
Think about some of your favorite apps today: Instagram, Uber, Facebook, and YouTube. Instagram has an infinite scroll, and Uber has different options such as UberXL and Uber Pool.
Nowadays, we take it for granted that these features exist, but the integrity of those products is, in fact, a labor of love. It requires countless decisions to get them right, many of which have not been as straightforward at first. It’s the complexity of product development, with all of its moving parts, that makes data immensely powerful.
What Is the Product Development Process?
Data Needs in Product Development
In product development, we do lots of opportunity sizing to decide which features to build and what opportunities to invest in. This often involves analyzing similar products to compare how they performed. We can have either structured data (e.g. 1-5-star ratings) or unstructured data (such as social media reviews).
We also do experiments to test out the features we build and see how users respond to them. The data here can also be structured or unstructured. Ideally, we also want lots of data because this will give us more confidence in our results.
Once we have the data, we begin to tease out the high-impact insights. Remember, it’s the problem that defines the tools we use! As a general rule of thumb, we start from the simplest issue and gradually increase in complexity.
Types of Analysis: Traditional, Machine Learning, NLP, and Deep Learning
In the first place, we have traditional analysis. It involves lots of hypothesis testing and statistics which 365 Data Science also has a wonderful course on. Typically, it calls for a great deal of data heavy-lifting and data visualizations with SQL and Python or R.
A number of things can be done with traditional analysis, but it is machine learning that has taken an extremely solid place in product development. In practice, we use machine learning in different conjunctures. However, one of my favorites is using supervised learning techniques, such as random forest, SVM, and XGBoost, to discover which features contribute the most to the success of a product. These models are easy to implement as well as really helpful in deciding what to build and how much to invest in it.
Natural Language Processing (NLP) is another technique that has a well-deserved position in product development.
Finally, unsupervised clustering and more complex techniques, such as deep learning, also have their place. All in the name of developing an amazing product!
Why Is Product Development Important?
Product development is, by far, my favorite data science use case for two main reasons.
First, I like it because of how powerful (almost magical) it is in driving decisions. Here, data really gets to shine, in that it’s both the source of truth and the driver of insights. You’ll be surprised how much value there is in a quick XGBoost model, for example.
Second, data-driven product development meshes very well with my own philosophy, commonly known as the 80/20 rule, where 80% of the results come from just about 20% of inputs. In product development, 80% of the success of a product is determined by 20% of its features. Basically, you minimize effort and maximize outcomes. That’s exactly why choosing the right feature to build is so important!
I hope you’ve enjoyed reading this article. If you’d like to learn more about data science, transitioning into computer science, and software engineering, you can also subscribe to my YouTube channel. And if you’re new to data science, check out the 365 Intro to Data and Data Science course.
The post 365 Data Use Cases: Product Development with Tina Huang appeared first on 365 Data Science.
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Top KDnuggets tweets Sep 23-29: An Introduction to #AI updated for 2020; Master using Pandas for time series analysis
An Introduction to #AI – updated for 2020; Free From MIT: Intro to Computer Science and Programming in Python; The Most Complete Guide to #PyTorch for Data Scientists; (Good) Data Cleaning is just reusable Data Transformations
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How Machine Learning will Transform Cybersecurity
The Role of Machine Learning in Cybersecurity
As digital technology progresses, there has been a growing number of cyberattacks in the last couple of years. By the first half of 2019, 4.1 billion records were exposed due to data breaches. That number is multiplying every day as more people and businesses move online.
Cyberattacks are critical for every organization as they can bring the entire organization to a standstill. In such scenarios, security is a necessity for all organizations. Machine Learning in cybersecurity is going to be a crucial player in securing businesses from threats and vulnerabilities.
There are countless volumes of data uploaded on the internet every day. Machine Learning algorithms that can secure this data are now becoming popular as more and more organizations recognize their potential in threat detection.
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In the digital world, Machine Learning network security is of utmost importance as most of the cyber-attacks take place through network phishing and other similar activities. DDoS and DoS attacks are a few examples of cyber threats that expose a wide array of information to hackers and cyber criminals.
Source: https://infotechlead.com/security/can-machine-learning-cybersecurity-50357
Why Machine Learning in Cybersecurity
With the advancements that Machine Learning in cybersecurity has seen over the years, it has proven itself to be a promising method of tackling cyber attacks. Machine Learning security assists in the development of a safer and automated digital space. It offers a structure to organizations for protecting their data through threats and vulnerabilities.
Today, Machine Learning is one of the most important technologies that your organization can implement. Read this article to learn why we say that.
Here are a few reasons how Machine Learning cyber security can safeguard organizations:
Analyze patterns of threats
- Given a sufficient amount of data, Machine Learning is excellent at analyzing patterns of threats that lead to cyberattacks. Machine Learning solutions run through the entire gamut of cyber attacks that took place in the past. It can analyze the patterns and identify exactly how the attacks were executed.
- This is the most valuable benefit of cyber security Machine learning. It assists organizations in keeping a comprehensive analysis of patterns that lead to a security vulnerability.
- On the other hand, Machine Learning and cybersecurity get stronger together when the algorithms frequently run through the data to ensure that only secure protocols are followed for safeguarding the data.
Anomaly detection
- Machine Learning security offers the advantage of detecting anomalies in the system and identify changing behaviour. If there are any modifications in the network configurations, the Machine Learning algorithm can recognize it as an anomaly.
- Once it detects the anomaly, the system can alert the IT personnel about the behaviour changes. It will allow them to proactively respond to any cyberattacks and give them an upper hand while managing data security.
- Since Machine Learning in cybersecurity can run through massive volumes of data within a couple of seconds, it can inform you about any anomalies in real-time. It improves the response time of the cybersecurity team drastically and allows preventing vulnerable attacks.
Automate regular checkups
- Most of the time, the cybersecurity team has to manually check for security vulnerabilities by conducting a regular checkup of the system through fixed parameters. In such circumstances Machine Learning for cybersecurity can be of great assistance.
- Machine Learning security can automatically run test cases on massive amounts of data for conducting a regular checkup of the system. By entering some fixed parameters and triggering the algorithm, organizations can automate their routine checkups that take place at regular intervals.
- It offers more time to the cybersecurity and IT team to work hand-in-hand and create a quick response system for any attacks that may occur. Apart from that, cyber security Machine Learning offers them more time to focus on critical issues related to digital data and resource management.
Effective and In-expensive
- It doesn’t help if your organization is often struggling to keep up with cybersecurity. That is riskier to the system than the attack itself. Poor management of data can also expose the records to security risks, leading to cyber attacks and vulnerabilities in the system.
- Machine Learning and cyber security are effective when algorithms can properly organize the unstructured data and detect patterns from them. It’s about manipulating these datasets to identify how and where a cyber attack can take place. It is more effective than IT personnel trying to manage threats through manual methods.
- On the other hand, cybersecurity Machine Learning is relatively inexpensive that manual protection of data. You need to install the AI-based algorithm once in your system, and it will learn by itself every time. You don’t need to add functionalities as the Machine is capable of making its own selection of threats that can threaten the system.
Early threat Detection: The Best Solution for Cyber Security
Arguably, early threat detection is one of the best solutions that Machine Learning can offer for assisting in cybersecurity. The algorithms can be trained to detect threats and anomalies in the system, which stray away from usual behaviour.
By identifying the threat early, Machine Learning security can send notifications to the engineers and alert them about the attacks. With the help of classification algorithms, Machine Learning models can classify what type of cyber attack is taking place. It will help the team to be better prepared and respond to it.
Source: https://fadeevab.com/ai-and-machine-learning-in-cybersecurity-simply-explained/
Reports by Kaspersky suggest that DDoS attacks rose by 33% in the third quarter of 2019, and every breach cost the organization an average of $138,000 in damages. While this has led to a growth in the cybersecurity solutions market, custom Machine Learning solutions for cybersecurity present a real opportunity to stay ahead of cybercrime.
Another way to secure the system from cyber attacks is to understand an internal vulnerability in the system — centralization of data for connected systems. When an attack happens, the entire system is at risk, and all the data can be breached.
Combined with Blockchain, Machine Learning for cybersecurity will enable the creation of peer-to-peer networks that are more secure. The attackers will not be able to gain command of the system due to the P2P nature of the blockchain system.
Blockchain is still in its early stages. However, it has emerged as one of the most secure ways to conduct transactions and protect your data. With remote work culture coming into action, there’s a high need for secure systems where organizations can connect without any risk of cybercrime.
However, Machine Learning does possess a few challenges for businesses while implementation. This blog shows how you can solve them.
Take the assistance of Machine Learning Experts for your Business
You cannot ignore the importance of Machine Learning development — especially in today’s world, when digital transformation has become a necessity for organizations to succeed. Whether it’s an application that assists in customer service or a custom solution that helps in cybersecurity, Machine Learning is now an inevitable resource that you should implement as soon as possible.
Protecting your organization from cyber threats is an important aspect of being a digital organization. You should hire an expert Machine Learning services company like BoTree Technologies for custom solutions that can help you make better digital decisions for your business.
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How Machine Learning will Transform Cybersecurity was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
AI in Healthcare: A review of innovative startups
The AI innovation in healthcare has been overwhelming with the Global Healthcare AI Market accounting for $0.95 billion in 2017, and is expected to reach $19.25 billion by 2026. What drives this vibrant growth?
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GPT-f Neural Network That Generates Theorem Proofs
GPT-f is a language model that has been trained to generate theorem proofs
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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5 Game-Changing Computer Vision Applications in Sports
On the one hand, sports are an engaging type of entertainment that connects different cultures around the world. On the other hand, many types of sports are often complex multidimensional systems that incorporate a plethora of data points that make one team or athlete better than the other. AI-enabled technologies like computer vision are poised to have an impact on all parts of this equation.
Let’s see how the use of computer vision software in sports is changing fans’ viewing experience and impacts the ways athletes analyze their performance.
Improving Analytics
In many team sports, in-depth analytics enable teams to have the edge in strategy, scouting, and performance benchmarking. Conventional game statistics that provide dry summaries of goals, assists, and shots are not sufficient to understand the performance on a granular level. With AI and computer vision, we can now understand the root cause of a game outcome, be it a specific player’s condition or move.
One of the first companies that started to dig deeper into AI-enabled analytics is Sportlogiq. Headquartered in Montreal and founded by former Olympic skater Craig Buntin and Ph.D. graduates in computer vision and machine learning, Sportlogiq helps hockey, soccer, and football teams make smarter decisions by utilizing deeper insights.
The company uses computer vision-enabled cameras that analyze specific game events and nuances like players’ movements, ball trajectories, shots, and passes. Then this data is converted into meaningful reports for teams’ coaches, commentators, scouts, analysts, and even sports betting companies. Sportlogiq’s value is indisputable as now its technology is used by more than 60 teams in professional hockey, football and soccer teams.
Ensuring Racing Safety
With an average speed of 200 mph, at least one fatal accident during the NASCAR season shouldn’t come as a surprise. Every driver that participates in the competition fully understands the risk they are taking. However, getting in an accident because of a malfunctioning car is a whole other story. Ford in collaboration with Argo AI has managed to create a deep learning neural network that helps increase NASCAR safety measures.
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The reason for Ford’s foray into the AI world was originally all about self-driving cars. During development, the team noticed that its algorithm was able to effectively identify the specific models of cars, especially when it came to blurred images. Given that NASCAR pushes the limit in terms of maximum speeds, this capability of the algorithm has proven to be especially relevant. The network can now quickly detect cars with malfunctions, which allows them to prevent serious accidents and reduce risks.
Making Smarter Scouting Decisions
Besides intuition, the major factor of sports scouting success is making sense of lavish amounts of data presented to analytics departments. Last year, the Orlando Magic turned to sports analytics and technology company STATS to find the next big NBA star. The AutoStats software collects players’ data directly from video footage with the help of AI and computer vision.
Capturing and analyzing players’ data from cameras is not new in big sports. However, previously, it was only possible with special in-venue cameras installed on the premises of official NBA courts and few colleges, which made analysis limited. AutoSats can now source players’ data from any recorded game, which significantly expands the analytics capabilities and makes the scouting process much less complex and more reliable.
The global player tracking market is expected to grow at a CAGR of 24.9% during the next five years. A few months ago, Boston Celtics Executive Director of Performance Phil Coles said that he believes there is a ‘significant place’ for AI and data in the NBA. However, it’s too early to make any definite statements about these technologies and their impact on scouting outcomes as only the Orlando Magic has truly committed so far.
Enhancing Shooting Accuracy
Given that the NBA’s shooting accuracy percentages haven’t been improving for the past twenty years, Alan Marty, the Noah Shooting System’s founder, has recognized that the players might need an AI-assisted boost for improving their shots.
The Noah System uses small computer vision-enabled cameras and sensors that are installed in the rafters above the baskets and track dozens of shooting analytics including players’ locations on the court, ball rotation speed, shot’s arc and shot depth. The Noah Shooting System gives instant audio feedback, allowing players to correct their shots during training in real time. For example, if the System is set to assess the player’s shooting arc, it might say just ‘39’, given that 45 degrees is the optimal number.
With over 100 thousand shots recorded across many courts every day, the System is continuously improving, allowing coaches and players to understand shooting on a granular level. The System now not only records ball trajectory data but also analyzes the players’ shooting biomechanics. Currently, the Noah System has been adopted by nearly half of the NBA teams and by about 30% of the NCAA. With teams reporting significant improvements, it’s only a matter of time when the Noah Shooting System becomes a standard tool in the professional basketball.
Improving Strategy and Viewing Experience
When it comes to professional leagues, basketball is much more about strategy and team play than the right shooting technique. Second Spectrum, an Official Tracking Provider for the NBA, uses computer vision, AR, and players’ historic data to superimpose graphics and statistics, like the probability for a player to make a successful shot. Second Spectrum’s Court Vision makes basketball broadcasts much more engaging for fans and provides comprehensive analytics for teams on the court.
With such advanced technologies disrupting strategy-heavy games like baseball and basketball, there is that nagging question: will the competition between teams reduce to the competition between algorithms?
Well, while the best chess player in the world is currently a computer, this won’t ever happen in games where the human factor is critical. AI algorithms can help coaches adjust the strategy but will never be able to motivate the players or predict Curry’s every long-range three-pointer. There is simply too much randomness in athletic sports for AI algorithms to understand.
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5 Game-Changing Computer Vision Applications in Sports was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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