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Today, Python has become one of the most favored programming languages among developers across the globe — from process automation to scripting to web development to machine learning — it’s used everywhere. Before we delve deeper to understand why Python is steadily becoming a great choice among machine learning professionals, let’s have a quick look at where actually the study of algorithms helps in.

Perhaps you already know that artificial intelligence (AI) stands for any intelligence demonstrated by a machine in order to obtain an optimal solution. Machine learning, which is a part of the broad category of data science, is what takes the solution further by using algorithms that finally helps in making informed decisions.

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In the context of information technology, we can see that companies are increasingly investing strategically into resource pools associated with machine learning. And professionals are talking about Python that has become a juggernaut already. But why Python, especially when there’re lots of other programming languages?

1- The need for a good language

Standard expertise in and familiarity with a robust programming language is almost imperative for machine learning professionals. Unless you’re a researcher working purely on some complex algorithm, you’re expected to use the existing machine learning algorithms mostly and apply them in resolving problems. And this requires a programming hat for you to put on.

2- The equation between machine learning and Python

Apart from enjoying huge popularity in different areas of software development, Python has obtained a leading position in the machine learningdomain today. The combination of simplicity, shorter development time, and consistent syntax make Python well-suited for projects in the field of machine learning. Let’s take a deeper look into these factors.

3- Extensive set of libraries

Libraries refer to sets of functions and routines, which are written in a given language. A solid set of libraries eliminates the need for developers to rewrite many lines of code when performing complex tasks. As machine learning largely encompasses mathematical optimization, probability, and statistics, extensive Python libraries help mathematicians/researchers to perform study easily.

Here’re some of the most commonly used fundamental Python libraries in machine learning.

  • Pandas: Developed upon a NumPy (Numerical Python) array, Pandas offers fast execution speed and various data engineering features. This is the most popular library in the Python ecosystem for performing general-purpose data analysis. Some of its data engineering features include finding and filling missing data, selecting subsets of data, combing multiple datasets together, reshaping data into different forms, reading/writing different data formats, as well as calculating down columns and across rows, among others.
  • NumPy: It’s the fundamental package needed for high-performance data analysis and scientific computing in the Python ecosystem. Higher-level tools like scikit-learn and Pandas are developed based on this foundation. Having many NumPy operations implemented in C, the package is absolutely fast, which is an invaluable advantage for today’s machine learning
  • Scikit-learn: One of the most popular machine learning libraries, scikit-learn supports an array of supervised as well as unsupervised algorithms like decision trees, linear and logistic regression, k-means, and clustering, among others. Two fundamental libraries of Python namely NumPy and SciPy are the basis of Scikit-learn. It makes implementation of tasks like feature selection, transforming data, ensemble methods etc possible within a few lines.
  • Seaborn and Matplotlib: Both data visualization and storytelling are critical for any machine learning professional as they often need to carry out an exploratory analysis of datasets before deciding to apply a specific machine learning algorithm. Seaborn is an excellent visualization library aimed at statistical plotting. On top of Matplotlib, it offers an API together with defining simple high-level functions for general statistical plot types. It also integrates with functionality offered by Pandas. Matplotlib is the most commonly used 2D Python visualization library. Presence of a great array of interfaces and commands allows professionals to develop publication-quality graphics from the data.

4- Simplicity

Python is highly acclaimed for its readable, concise code. It’s perhaps the best when it comes to simplicity and ease of use, especially for novice developers. Multi-stage workflows and extremely complex algorithms are two pillars of machine learning, and less intricacies of coding allow professionals to focus more on finding solutions to problems, and attaining the goals of a project.

In addition, when it comes to collaborative coding or machine learning projects changing hands between teams, easy readability of codes plays a hugely advantageous role in business life. It becomes even more important if the project comes with a great deal of third-party components or custom business logic. Simple syntax of Python helps in faster development compared to many other programming languages, allowing the developers to test algorithms quickly without having to implement them.

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5- Great support

Python is completely open source and is supported by a great community. It offers a great array of resources that are capable of enabling developers to work faster. In addition, presence of a huge and active community of developers can help in any and every single stage of a development cycle.

6- Flexibility

Flexibility is another core advantage offered by Python. Additionally, it’s perfect for linking different data structures and works as an ideal backend. A majority of code can also be checked in the IDE, especially for developers who’re struggling between different algorithms.

7- Less amount of code

Machine learning hugely encompasses algorithms, and Python makes it simpler for developers in testing. It comes with the potential of implementing the same logic with as less as one-fifth of code required in other OOP (object-oriented programming) languages. In addition, Python’s integrated approach lets developers to check code methodology.

These are the key factors that smoothen the working process, making Python one of the hottest languages today.

8- Machine learning with Python

We have already seen Python’s rich features that make the programming language one of the most common backbones of machine learning. If you’re a beginner in this field and plan to proceed in your career with the help of Python, here’re some simple yet highly beneficial steps to attain future of image.

9- Brush up fundamental Python skills

You have to have some basic knowledge of Python in order to use it for machine learning. Anaconda is the version of Python that is supported by all commonly used OSs like Windows, Linux etc. It offers a complete package for machine learning that includes scikit-learn, matplotlib and NumPy. If you don’t have any prior knowledge of programming, there’re lots of online resources, books etc that can help you obtain the fundamental knowledge.

10- Basic machine learning skills

Before diving deeper into the process, it’s imperative to have a robust grasp of fundamental machine learning skills. There’re plenty of online courses that can help you to gain adequate knowledge of machine learning before working with various algorithms. Additionally, when people take advantage ofdata science bootcamp, they may learn machine learning skills easily.

11- Getting started

Assuming you’ve learned the basics of Python and machine learning, it’s time to use the scikit-learn library to implement machine learning algorithms. Try to obtain significant amount of hands-on experience in the library by working on sample projects.

12- Explore the algorithms

Once you’ve obtained enough knowledge of scikit-learn, it’s time to move toward advanced levels where you should explore different popular machine learning algorithms. Some of these common algorithms include linear progression, logistic progression, k-means clustering etc.

13- Moving forward

At this stage, you should try to explore some advanced machine learning topics with Python. Some useful techniques which you should try to master include Dimensionality Reduction, Kaggle Titanic Competition, Support Vector Machines etc.

14- Getting into deep learning

Being the key developmental element of neural network, deep learning plays a significant role in machine learning. It works as the fundamental block for a diverse range of technologies used in different industries. Neural networks for machine learning can be developed using Python. There’re two deep learning libraries namely Theano and Caffe that come with Python.

Final takeaway

Despite the apparent maturity and age of machine learning, it’s perhaps the best time to learn it, mainly because of its practical uses. And Python is probably the best programming language that can help you excel in your career in this field. With a robust understanding of fundamental machine learning and Python skills, you should be all set to dive deeper. Just remember the fact that as with learning any skill, the more you work with it, the better you become. So, practice diverse types of algorithms and try to work with different datasets to obtain a solid understanding of machine learning using Python, and to enhance your overall problem-solving skills in event space.

MINDROME

Also remember that there’re lots of advanced nuances and steps that you’ll be encountering as you progress. But this post should serve as a good foundation to make you familiar with the relation between machine learning and Python, and help you think through the key factors that will let you proceed further in the future.

If you’re interested to take it forward, just do an online research and you’ll find several low-cost machine learning training courses using Python available in the market. It’s strongly advisable not to trust blindly any course/certification provider’s word. Instead, you should take a closer look and see whether you actually find it worth investing your effort and time on. People give heed to find the course because every course isn’t Magnimind.

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Why Learning Python Is Important For Machine Learning Aspirants? 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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French startup Stanley Robotics showed off its self-driving parking robot at Lyon-Saint-ExupĂ©ry airport today. While I couldn’t be there in person, the service is going live by the end of March 2019. And here’s what it looks like.

The startup has been working on a robot called Stan. These giant robots can literally pick up your car at the entrance of a gigantic parking lot and then park it for you. You might think that parking isn’t that hard, but it makes a lot of sense when you think about airport parking lots.

Those parking lots have become one of the most lucrative businesses for airport companies. But many airports don’t have a ton of space. They keep adding new terminals and it is becoming increasingly complicated to build more parking lots.

That’s why Stanley Robotics can turn existing parking lots into automated parking areas. It’s more efficient as you don’t need space to circulate between all parking spaces. According to the startup, you can create 50 percent more spaces in the same surface area.

If you’re traveling for a few months, Stan robots can put your car in a corner and park a few cars in front of your car. Stan robots will make your car accessible shortly before you land. This way, it’s transparent for the end user.

At Vinci’s Lyon airport, there will be 500 parking spaces dedicated to Stanley Robotics. Four robots will work day in, day out to move cars around the parking lot. But Vinci and Stanley Robotics already plan to expand this system to up to 6,000 spaces in total.

According to the airport website, booking a parking space for a week on the normal P5 parking lot costs €50.40. It costs €52.20 if you want a space on P5+, the parking lot managed by Stanley Robotics.

Self-driving cars are not there yet because the road is so unpredictable. But Stanley Robotics has removed all the unpredictable elements. You can’t walk on the parking lot. You just interact with a garage at the gate of the parking. After the door is closed, the startup controls the environment from start to finish.

Now, let’s see if Vinci Airports plans to expand its partnership with Stanley Robotics to other airports around the world.

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Noble.AI, an SF/French AI company that claims to accelerate decision making in R&D, has raised a new round of funding from Solvay Ventures, the VC arm of a large chemical company, Solvay SA. Although the round was undisclosed, TechCrunch understands it to be a second seed round, and we know the company has closed a total of $8.6 million to date.

Solvay was previously an early customer of the platform, prior to this investment. The joint announcement was made at the Hello Tomorrow conference in Paris this week.

As a chemical company, Solvay’s research arm generates huge volumes of data from various sources, which is part of the reason for the investment, confirmed the firm. Noble.AI’s “Universal Ingestion Engine” and “Intelligent Recommendation Engine” claim to enable the creation of high-quality data assets for these kinds of big data sets that can later be turned into recommendations for decision making inside these large businesses.

Founder and CEO of Noble.AI, Dr. Matthew C. Levy, said he is “enthusiastic to see what unfolds in its next phase, tackling the most important and high-value problems in chemistry” via the partnership with Solvay.

“Noble.AI has the potential to be a real game changer for Solvay in the way it enables us to utilize data from our 150-year history with new AI tools, resulting in a unique lever to accelerate our innovation,” said StĂ©phane Roussel, Solvay Ventures’ managing director.

Prime Movers led a seed round in Noble.AI in late 2018, which was never previously disclosed to the press. Solvay Ventures is now leading this second seed round.

The move comes in the context of booming corporate R&D spending, which in 2018 reached $782 billion among the top 1,000 companies, representing a 14 percent increase relative to 2017 and the largest figure deployed to R&D ever. However, R&D in corporates lags behind the startup world, so these strategic investments seem to be picking up pace.

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According to a report from job site Indeed, machine learning engineer is the best job of 2019 due to growing demand and high salaries.

The career boasts a current average salary of $146,085 with a growth rate of 344 percent last year.

Tech-related jobs, in general, continue to be winners. Indeed set out to find the top 25 jobs for 2019 in their report and nine of them are comprised of tech roles.

Roles such as software developer continue to rank highly due to a high number of job openings, but machine learning engineer roles claim the number one spot due to higher salaries and faster growth.

A second AI-related job sits just outside the top 10. At number 13, ‘Computer Vision Engineer’ has a higher average base salary ($158,303)  than a machine learning engineer, but is ranked lower due to slower growth (116%).

Here’s the full list of top jobs in Indeed’s report:

Due to the increasing use of AI in companies’ operations, the report expects this growth to continue accelerating in the coming years.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post Surprise! Machine learning jobs are high-paying and in-demand appeared first on AI News.

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At I/O this year, Google displayed its vision for a more ubiquitous and conversational way of interacting with technology. Its Assistant is chattier, answering natural language queries with a more human voice, and it’s found its way into several new Google products: the messenger Allo and the Echo-like speaker Home. Both are areas where other companies have a lead, but Google’s strength in AI gave these services some nice twists, doing things like automatically generating surprisingly specific reactions to photos. (More)

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Google’s Assistant, Amazon’s Alexa, Apple’s Siri– the conversational user interfaces and voice assistants have completely changed the way we interact with computing devices. With our computational capability, we have taught the machines to interact with us through voice, and over the years our ability to design languages has advanced and so have our interfaces.

Technology has taken a giant leap from when Graphical User Interface (GUI) was introduced to simple touch interfaces for smartphones that made computers accessible on handsets, to the present day voice interfaces like Alexa and Siri that are hugely impacting our lives. An early 2018 study by Spiceworks on AI chatbots and intelligent assistants showed that 24% of large businesses and 16% of small businesses have already adopted such technologies while a combined 27% are expected to adopt in the next 12 months. The study also found out that Cortana, Siri and Google Assistant are the widely-used intelligent assistants in the workplace

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The Growth of Voice Assistants

With the accelerated pace at which AI and cloud technology are advancing, anyone who is smart with technology can build an AI system, which was only a dream a few years back. Over a period of time, with the increased use of voice interfaces, more data will be gathered enabling algorithms to work better, thereby improving the speech recognition accuracy. Soon we’ll have devices interacting with us.

Gartner predicted in April 2015 that by the end of 2018, 30 percent of all interactions with technology would be through “conversations” with smart machines, and many of them by voice.

According to ComScore, “By 2020, 50% of all searches will be carried out via voice.”

Further, the report by Tractica, the market intelligence firm says that the users for virtual digital assistants will grow from a mere 390 million worldwide users in 2015 to 1.8 billion by the end of 2021. While the revenue is forecasted to grow from $1.6 billion in 2015 to $15.8 billion in 2021.

With more and more industries starting to use speech recognition technology, the market share of the technology is estimated to grow at 35% rate annually between 2016 and 2024, according to Global Market Insights, Inc.

For voice AI to succeed, technology has to continually improve and evolve. Industries such as hospitality, healthcare, automotive, etc. will greatly benefit from voice AI in the years to come.

We have already started seeing the benefits. With improved speech recognition, faster Internet speed, cloud computing, and tech-savvy smartphone user has led to the first generation of virtual personal assistants.

The five drivers for voice AI include:

  • Recognizing the language and user-specific vocabulary
  • Identifying and getting used to accents
  • Understanding the intent that allows you to convert speech into action
  • Understanding and translating actions into a user’s surroundings or workflow
  • Providing control around data security, ownership, and privacy

Impact of Voice AI in 2020

Advanced voice AI technology will soon be all-pervasive, as intelligent user interface technology will seamlessly integrate into daily life and eventually will affect almost all enterprises.

We have already seen its impact on customer service. A lot of customer service functions have been already automated using the evolving NLP in the internet of things (IoT). Chatbots are increasingly being used to interact with customers. The value proposition is that it saves a lot of time, manpower and is also productive.

The voice assistants are very useful and are can increasingly make our lives better, smarter and more connected. Today you can get the weather forecast, reminders, news for the day, unlock cars and homes, and get your schedule update. A voice assistant can send messages, update shopping list, and make calls, set appointments and more. They can initiate or complete many other jobs.

The next paradigm shift would be self-learning computing. Rather than learning complex interfaces, enterprises will voice-enable their interfaces, enabling users to interact more freely and give more flexible commands.

You won’t just have self-driving cars, cashier less stores, automated restaurants — there will be Voice-AI web everywhere, integrated with everything. You’ll have the next generation growing up with Voice-AI and voice controlled bots.

Voice is the interface for connected homes. It will control your home devices, including thermostat, lighting, sound system, appliances, and alarm systems.

Major cars manufacturers will adopt intelligent, voice-driven systems for entertainment and location-based search. AV entertainment systems will depend on spoken voice for content discovery to match different ways users think about content.

Voice-controlled wearable devices will be popular at workplaces, such as hospitals, warehouses, laboratories, and factory floors.

With the voice AI technology growing exponentially, it is becoming extremely popular, with industries competing to be a part of it. The voice AI is going to be a game changer since the Internet and Web in terms of how consumers interact with services.

Going forward both voice and speech recognition is going to take a giant leap in both functionality and intelligence. The biggest plus will be people moving away from the screen. This is going to take time, but the beginning has already been made, which is exciting.

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Voice AI: 2020 and Beyond 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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Research into artificial intelligence (AI) has experienced a surge in the last few decades. This was built largely on pioneering results from the 60’s and 70’s, including the utilization of advanced neural networks (NNs). It has even taken inspiration from biological behavior for methods like fuzzy logic or genetic algorithms (GAs).

A major area where AI has taken off is in transportation. Media hype has covered quite a bit of recent advances, like self-driving Ubers or Tesla’s new semi-autonomous trucks, but what else lies ahead? How will AI impact this industry?

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How AI can help transportation:

Transportation problems arise when system behavior is too difficult to model according to a predictable pattern, affected by things like traffic, human errors, or accidents. In such cases, the unpredictability can be aided by AI.

AI uses observed data to make or even predict decisions appropriately. NNs and GAs are perfect AI methods to deal with these types of unpredictability. AI has been in development and implemented in a variety of ways. Some examples are given below:

  • Improvement of Public Safety: Safety of citizens when traveling by public transport in urban areas is improved by tracking crime data in real time. This will also enable the police to increase their efficiency by patrolling and keeping their citizens safe.
  • Corporate Decision Making: The road freight transport system can utilize accurate prediction methods to forecast their volume using AI methods, which simplifies transportation company planning. Additionally, several decision-making tools for transport can be designed and run by AI. This will affect investments made by companies in the future in a productive way.
  • Autonomous Vehicles: Self-driven cars and trucks have been of high interest in the last several years. In the commercial sector, Uber and Elon Musk have produced self-driving trucks to reduce the number of accidents on highways and increase productivity.
  • Traffic Patterns: Transport is greatly affected by traffic flow. Traffic congestion in the US costs around $50 billion per year. If this data is adapted for traffic management via AI, it will allow streamlined traffic patterns and a significant reduction in congestion. Several similar systems are already in place. For example, smarter traffic light algorithms and real time tracking can control higher and lower traffic patterns effectively. This can also be applied to public transport for optimal scheduling and routing.
  • Pedestrian Safety: The use of AI to predict the paths of pedestrians and cyclists will decrease traffic accidents and injuries allowing for more diverse transportation usage and an overall reduction in emissions.

The impact of AI in transport:

Benefits

In October 2016, Uber announced a driverless truck made by Otto that successfully drove 120 miles at 55 mph without any issues. Additionally, Daimler trucks has produced an 18-wheeler semi-autonomous truck with an auto-pilot system.

Costs of labor in this sector will continually decrease with increased use of AI, providing higher profits for industry players. The issue of long driving hours and stopping for a break will no longer be a concern with fully automated fleets.

Beyond straightforward labor costs, safety and traffic accidents will be majorly affected by AI. The number of accidents involving truck drivers at night is a large issue and can be significantly improved with the use of smart unmanned vehicles. The personnel and financial costs of these accidents are quite substantial. Auto-pilot or complete unmanned vehicles can allow the driver to have a snooze without causing severe accidents. Some AI trucks even have a special feature of predicting accidents as well as health issues of people around the truck like detecting a heart attack and alerting the emergency servicesautomatically with the location and details of diagnosis.

Drawbacks

Automated trucking has sparked a hot debate among 3.5 million truck drivers in the US alone. Developments would mean autonomous trucks, ships, aircraft or trains slated for the future, along with any future vehicles becoming completely unmanned. Job flow is thus a major concern for truck drivers, taxi drivers, and other members of the industry. Social experts have argued that job skills can be shifted or evolved to other sectors, but tensions remain high.

Implementation around the world presents another major issue. Undeveloped and third world countries face enormous challenges in utilizing these solutions, as their infrastructure is not as stable or capable of providing maintenance and repairs. It will be a long time before AI can become a reality there.

Increasing focus on AI also presents a dilemma for transport companies: transport costs contribute to the company turnover by 3–10%. This makes it a very important factor in corporate economies as a whole. All existing businesses will need to engage in, develop, and implement AI technologies to remain a competitor in the transportation industry. This affects transportation logistics as well, as it is used in the supply chain of operations and manufacturing and even predicting the time and total cost of the entire process.

The future:

By 2020, it is estimated that there will be 10 million self-driving vehicles and more than 250 million smart cars on the road. Tesla, BMW, and Mercedes have already launched their autonomous cars, and they have proven to be very successful.

Experts like Elon Musk and Stephen Hawking predict that AI can be a grey area when the root of AI decisions cannot be comprehended by humans. Stephen Hawking warned at the Web Summit tech conference in Lisbon that:

“Unless we learn how to prepare for, and avoid, the potential risks, AI could be the worst event in the history of our civilization. It brings dangers, like powerful autonomous weapons, or new ways for the few to oppress the many.”

Despite this, we can gain tremendous productivity improvements in several industrial areas. Interstate driving in the US and delivering products to customers will become an easy task for companies, thereby increasing their profits.

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How will AI impact the transportation industry? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

In the past few years, the field of data science has grown exponentially. In today’s information-driven world, data is playing a crucial role in every industry — from cybersecurity, healthcare, online retail, banking and insurance, to digital marketing, SEO and several others. No wonder why businesses have started relying on data heavily. And this triggers a boom in diverse job openings related to data science. Among all these positions, perhaps the most overlapping two are that of a data scientist and a data analyst. There’re many who get confused between these two titles and some of them even think that data scientist is just another glammed up word for data analyst.

While the prefix of these titles may lead many to believe that professionals holding these titles carry out the same functions, it isn’t really so. The job descriptions may look somewhat similar, but there’re key differences between the careers. In this post, we’re going to highlight the individual aspects of both data scientist and data analyst and how they’re related to each other.

1- Difference by definition

A data scientist refers to a professional who analyzes massive sets of data from a business standpoint and is responsible for predicting potential trends, exploring disconnected and disparate data sources, and identifying better ways to analyze information in order to help businesses make accurate and informed decisions.

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A data analyst focuses on collecting, processing, and obtaining statistical information out of the existing datasets. They focus on developing methods to gather, process, and organize data to reveal actionable insights for present issues, and establishing the best way to demonstrate this data. Put simply, a data analyst is directed toward solving problems that can obstruct immediate improvements.

2- Difference by responsibilities

A data scientist and a data analyst may share similar job responsibilities to some extent, but some notable differences do exist. Let’s take a look at them.

Data scientist:

  • Cleansing and processing of data
  • Developing machine learning models and new analytical methods
  • Finding new features by exploring the value of data
  • correlating disparate datasets
  • Identifying new business questions which can add value
  • Data visualization and storytelling
  • Identifying the root issues of an outcome

From Data Analyst to Data Scientist

Data analyst:

  • Analyzing and mining business data to discover patterns and identify correlations from different data points
  • Implementing new metrics for identifying not so clearly understood business parts
  • Coordinating with the engineering team to collect incremental new data
  • Mapping and tracing the data from different systems to find out solutions to a given business problem
  • Applying statistical analysis
  • Designing and creating data reports to help stakeholders make better decisions
  • Identifying partialities in data acquisition and data quality issues

3- Difference by skill sets

While both data scientist and data analyst positions require solid knowledge of mathematics together with knowledge of software engineering, understanding of algorithms and good communication skills, their actual skill sets differ significantly.

Data scientist skills:

  • Programming languages like R, Python, SAS, SQL, Hive, Pig, MatLab, Spark, Scala etc
  • Data visualization and storytelling
  • Distributed computer frameworks such as Hadoop
  • Machine learning and deep statistical insights
  • Business acumen

Data analyst skills:

  • Programming languages like JavaScript, HTML etc
  • Data visualization tools such as Tableau
  • Data storing and retrieving tools and skills
  • Robust exposure to SQL and analytics
  • Spreadsheet tools

Connection Between Data Science, ML And AI - Magnimind Academy

I wrote about that here.

4- Difference by pay packet

Data scientists earn substantially more money than data analysts. On an average, the starting base salary of a data scientist is around $110,000 while for a data analyst, it stays around $65,000. However, the salary of the latter depends on the type of the analyst they’re — market research analyst, financial analyst, or operations analyst, among others. Learning data scienceis the first step for these jobs.

5- Difference by job roles

Both the groups are divided further based on their job roles.

  • Data scientists are offered job roles like data developers, data researchers, data business and data creative people
  • Data analysts are categorized into roles like database administrators, data architects, operations and analytics engineers

Which career is best for you — Data scientist or data analyst?

When you need to determine whether a data scientist or a data analyst career path would be the best for you, how will you proceed? We’ve already talked about the skills that are required to excel in both the positions, but there’re some other key factors that you should consider when choosing one of these two. These include your personal interests, your preferred career path, and your background. When you select the ones, you may know data science in 6 weeks.

1- Personal interests matter a lot

Do you have a keen interest in statistics and numbers? Or, is it computer science and business that keep you excited?

While a data scientist needs to have solid understanding of computer science, statistics, and mathematics, he/she also needs to have good business acumen. Apart from having robust presentation and communication skills, you need to be able to find opportunities, risks and trends in the data if your aim is to become a data scientist. In addition, communicating the findings in easy-to-understand formats should be one your key fortes.

On the other hand, work of a data analyst heavily encompasses programming, statistics, and numbers. They almost exclusively work in databases to reveal data points from complicated and sometimes, disparate sources. Also, a robust understanding of the industry they’re working in is something crucial for a data analyst.

2- Career path holds its fair share too

Where do you want to see yourself in the distant future? Apart from job responsibilities, as the level of values added by data scientists and data analysts differ significantly, so do their compensations.

Data scientists, who’re typically graduate degree holders, usually have advanced skillsets and come with more working experience. They are generally considered to be more senior that data analysts. As a result, data scientists receive healthier pay packets than data analysis professionals. And they can earn a yearly compensation between $110,000 and $163,500.

This compensation range comes down to $77,500 and $118,750 for data analysts. However, as their work encompasses databases mainly, they can increase their seniority and in turn, compensation by learning programming skills that are considered crucial in the domain. Once a data analyst gains substantial experience and acquires an advanced degree, he/she can easily move into better positions with increased compensations.

3- Don’t ignore your background

Though the positions of data scientists and data analysts may look somewhat similar, it’s the background, in terms of both educational and professional, that acts as one of the key factors when it comes to choosing one of them.

For a data scientist, a PhD or Master’s degree in mathematics, computer science, or statistics is desired. Add to it the desired professional experiences like working in statistical computer languages, working with data mining and statistical techniques, creating and working with data architectures, 5 to 7 years of experience in building statistical models and manipulating datasets, experience in using web services, and experience in working with distributed computing/data tools, among others.

At their core, most data analysts require a degree in statistics, mathematics, or business with an analytical bend of mind. Desired experiences usually include working with languages like Python, R etc, and working in agile development methodology etc.

Both the positions of data scientist and data analyst are considered highly coveted in today’s job landscape. You can certainly go for either one. Just be sure to consider the above factors to excel in your chosen trajectory.

Where the roles intersect

Though data scientists and data analysts aren’t two interchangeable roles, they hold a fundamental overlapping point — both of them draw insights from data. In the business acumen context, data scientists hold a richer skillset and have a deeper familiarity with advanced statistical modeling, Hadoop, machine learning than their counterparts in the data analysis domain. However, both professionals are capable of transforming data into insightful answers needed by business owners to take informed and better decisions. But the difference lies in their approaches and in the answers. Typically, a data scientist can help a business by formulating new questions that help it drive forward while a data analyst is able to answer critical business questions.

Final thoughts

Today, there’re lots of ways to become a data science professional, but the ideal move should be solidifying your educational background first, in terms of obtaining a Master’s or Bachelor’s degree. And then, there’re other ways that can help you sharpen your data science skills. Ideally, before you dive into a higher-education program, you should try to figure out the industry you’ll be working in to identify the most critical software, skills, and tools.

Whether you’ll be working as a data scientist or a data analyst, some business domain expertise will be required that will vary based on the industry. For instance, if you’re working in marketing, education, or business, you’ll require a different skillset than if you work in science, healthcare, or government. Once you’ve chalked out your desired industry needs, just do some research and you’ll find an array of professional development courses, bootcamps and online classes that can help you learn and hone the requisite skills. Apart from these, there’re data science certifications available as well that can strengthen your resume and in turn, help you get a healthier pay packet. Data science bootcamp in Bay Area might provide these options.

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How to Become a Data Scientist? - Magnimind Academy

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Difference Between a Data Scientist and a Data Analyst 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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For Ben Dias, head of advanced analytics and data science at Royal Mail, there are three non-negotiables when it comes to looking for prospective data scientists. Candidates need to want to learn, and be able to pick things up quickly, be able to program, and have a deep understanding of mathematics.

The latter is not overly surprising given Dias (left) describes himself as a ‘computational mathematician’ and that his undergraduate studies were in mathematics and astronomy. Yet the evident mix between maths and coding cannot be overemphasised. “If you don’t know the underlying mathematics that underpins an algorithm, you could apply it to the wrong data and if you make business decisions on that, you could kill the business,” he tells AI News.

“If you apply an algorithm to data, it’ll give you an answer,” he adds, laughing. “I’m always looking for people who have a solid maths background who can program – they don’t have to be software engineers – and they want to learn. I can teach them to talk to the business, I can teach them everything else, but if they don’t have those three it’s very difficult.”

Dias has been busy at Royal Mail since joining in 2017, building up a team of 25 data scientists and analysts. It was an opportunity he had been looking for, and Royal Mail gave him it on the condition of it being a blank canvas to work with.

“They had set everything up nicely – there was a platform we could work on,” he says. “It wasn’t perfect but they had done a lot of the groundwork, and they said ‘you’re the expert coming in, you tell us what you need and we’ll give it to you.’ They stayed true to their word, which was brilliant.”

The team works on initiatives split into three broad areas; revenue protection and recovery, customer experience, and operations. The latter is of particular interest because it covers both vehicles – optimising schedules, maintenance, streamlining deliveries – and personnel. The algorithms for internal use evidently make sense. These include predicting sick absences, as well as correlating a link between doing too much overtime and becoming ill. When an employee gets a certain amount of overtime, the model gets flagged.

It’s important here to determine between the B2B side and the end customer who receives their post, thanks to the data science team, in a more seamless fashion. “For the business customer it will be recommended systems, customer segmentation, churn models, that kind of thing,” says Dias. “For the end customer it will be things like the estimated delivery window for parcels. We’ve built an algorithm for that.”

In terms of the sheer amount of data available, then the numbers are unsurprisingly vast; billions of parcels and letters to more than 29 million addresses, collecting from more than 100,000 post boxes and 10,000 post offices. The overall amount encompasses B2B customers, marketing and operations data, as well as Internet of Things (IoT) data coming from tracking devices. “It’s a lot of data, covering a lot of asepcts of the company, and it’s all available to us which is great,” says Dias. “It’s not always in one place and not always instantly available, but all we have to do is ask.”

The result is what Dias describes as ‘about £50 million of opportunities created’ in just the first year of building out the team, with many initiatives now moving out of the pilot stage. This journey of ‘from zero to data science’ is going to be explored further when Dias takes to the stage at AI & Big Data Expo Global on April 26 in London.

“A lot of people are wondering how to set up a data science function or team and, because I’ve managed to do it in two years, delivered stuff while building the team out, I thought it’d be useful to share what I did and the things that worked, and the things that didn’t work, to help others get there as well,” he says.

“It’s important for us to deliver fast because the hype cycle is coming to an end – and when people hire they want impact immediately.”

You can find out more about the session here.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

The post Ben Dias, Royal Mail: On building out data science teams and ensuring deep mathematical understanding appeared first on AI News.

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Artificial intelligence is no longer just in the realm of science fiction. While we have been long promised AI that will enhance our day-to-day lives, the fact is that it is already here and doing just that. Though AI capabilities like that depicted film, television, and video games is still quite a ways off, the AI that does exist improves the world around us nevertheless.

AI has been a major driving force in business innovation over the last few years, and while the layman may never know it, AI has already affected their life in some way or another. One way that artificial intelligence and machine learning has an enormous impact on society is through the influence of the credit card industry. Behind the scenes, AI is working to make credit cards safer and more user friendly. More than that, it is shaping up to change the way we interact with money at large.

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Plastic Is King

In the past, cash has always been regarded as king. Cash has always been a safe, reliable, foolproof way to pay for goods and services. However, in this modern day and age it is the credit card that reigns supreme. Card and mobile payments are quickly becoming the new norm as the technologically fast-paced world continues to develop and spread.

Credit cards and mobile payments are currently on pace to dominate the business world in the near future. In 2015, nearly 5 percent of the United States’ GDP was transacted through credit cards, and while the number of U.S. households with credit cards is steadily increasing, the number of households with revolving debt is in decline. Despite the increasing number of those with credit cards around the world, advancements in credit card security have actually significantly reduced instances of credit card fraud.

In the United Kingdom in 2017, consumers paid with cards a whopping 13.2 billion times and made transactions using contactless payments 5.6 billion times. Nearly two-thirds of people living in the U.K. now use contactless payments, and for the first time in history cash payments have been eclipsed by card and mobile payments. This trend doesn’t show any sign of slowing, which means businesses worldwide are making moves to cater to this new mobile and plastic first economy.

AI Is Everywhere

AI is already being used in the healthcare world to predict risk factors for developing certain diseases, providing support for treatment methods, and even aiding in the discovery of new drugs. The ability of AI to improve diagnostics and predict certain illnesses is invaluable to the medical field, and in many ways the future of medicine lays within AI.

The inexorable march of AI-powered technological advancement permeates nearly every facet of the modern world, and it can yield tangible results in the business world. AI is shaping the future of business by streamlining manufacturing and day-to-day operations, finding and filling information gaps, increasing a project managers’ ability to assess project goals, and reduce the overall workload on employees from the factory floor to the board room. In recent years, business owners have even begun utilizing automation software to complete taxes, a traditionally intensive and stressful process. AI, simply put, makes business just that much easier to conduct.

Apart from increasing the ease with which business can be conducted on multiple levels, AI also has the potential to increase overall profits for a company. Companies are using AI to drive higher sales and and improve the user experience by identifying patterns in data sets that can help businesses provide a unique and personalized experience. AI has even been used to develop a visual search engine, allowing consumers to quickly discover what something is when they see it out in public. This is all moving consumers directly toward products, decreasing the need for advertisements and education in marketing.

Innovation Is Constant

AI and machine learning has actually already been a part of the credit card industry for decades, and it is only getting smarter by the day. Whether it is using algorithms to target new potential customers or recommending ways to redeem loyalty points, AI has been integral to the rise of the credit card as a main form of payment. AI can review spending data for millions of individual users in order to target them with offers with high specificity and accuracy.

However, AI does so much more than that. AI actually has the potential to reshape how humans get loans and may even one day render credit scores obsolete. AI can parse unconventional data points to evaluate creditworthiness of individuals in emerging markets in Asia, Africa, and Latin America, opening up these areas for quality loans that have a high likelihood of repayment.

Even credit card fraud can be stymied by AI, as artificial intelligences are being trained to suss out current fraud techniques and will eventually be able to predict future fraud techniques before they are put into practice. This is beneficial to all sides, as consumers will be protected from fraud while lenders have to deal with less fraudulent activity on their end as well. It isn’t entirely unlikely that AI will one day completely wipe out credit card fraud, improving on an already increasingly safe world of digital transactions.

Artificial intelligence is here to stay, and while some might view it as a gimmick or fear it as a potential threat, AI is often misrepresented and misunderstood. Whether it is helping us solve medical mysteries or protecting us from fraud, AI has been an incredible boon to humanity since its inception. There is only upward mobility from here, and AI has the momentum to achieve some truly amazing things within our lifetime.

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