365 Data Science

Machine Learning Experiment Tracking

Why is experiment tracking so important for doing real world machine learning?

Originally from KDnuggets https://ift.tt/30aIi3w

source https://365datascience.weebly.com/the-best-data-science-blog-2020/machine-learning-experiment-tracking

Data Scientist is better than Financial Analyst Data Analyst and Research Analyst

Though there are many options to take your pick from, most aspirants have their eyes fixated on the coveted job of data scientists.

If you have an analytical bend of mind, you may consider a career in the field of data science. Though there are many options to take your pick from, most aspirants have their eyes fixated on the coveted job of data scientists. But who exactly are data scientists? You may call them a new class of analytical data expert, who possess the technical skills necessary to solve complex problems.

At the same time, these people also have the inquisitiveness to search for problems that need to be solved. Thus, when defining a data scientist, you can say it’s someone who blends some expertise of a computer scientist, a mathematician, and a trend-spotter. What’s the beauty of the field of data science is that it has numerous applications in various domains. Thus, you will find that there’s a high demand for data scientists in both the business domain and the field of IT, which make these professionals highly sought-after and well-paid.

No wonder why a majority of aspirants looking to make a mark in the field of data science aim to become data scientists.

2 Immersive Topics: Data Science and Artificial Intelligence

1- Who’s a data scientist?

Even a decade ago, data scientists weren’t a hot property that they have become today. Perhaps the change in their fortunes signals how times have changed. This could be attributed to the massive amount of data that’s getting generated almost every second today. And with the emergence of big data, companies and businesses too have changed their views on how they see data and even the ways they can leverage the pile of data that they have been sitting upon for quite some time now. After all, the data businesses collect these days or the ones their existing and potential customers willingly share via their website, special campaigns, social media accounts, etc. make up a bulky mass of unstructured information.

No business worth its salt can ignore or forget this data anymore as it’s nothing short of a virtual gold mine that can bring several benefits their way and give their revenue a significant boost. But it will happen only when someone digs into this pile of massive data and discovers business insights that no one considered looking for before. And this is where the data scientist comes into the picture.

As these people have an intense intellectual curiosity and are deep thinkers, they interpret this data and try to draw useful insights from them. From making new discoveries and asking new questions to learning new things, data scientists are driven their originality and creativity to solve complex problems and indulge in their curiosity constantly. Thus, data scientists don’t just make an observation with the complex reads from data. Rather, they seek to uncover the “truth” that lies hidden underneath the surface. For these professionals, problem-solving isn’t merely a task. Rather, it’s an intellectually-stimulating trip to find a solution.

Thus, you will find data scientists designing and building new procedures for data modeling, and production utilizing algorithms, prototypes, predictive models, custom analysis etc to decode data and gather useful insights, which are then presented to tell a story to the stakeholders.

These decision-makers can then use this insight to make data-driven, timely decisions that will help them take on challenges, if any, be better prepared to stay ahead of the competition, and even improve their bottom-line significantly. No wonder why Harvard Business Review called data scientist the 21st century’s sexiest job.

Now that you have an overview of who a data scientist is and what kind of role he/she plays, let’s try to find what makes these professionals better than others working with data such as data analysts, financial analysts, and research analysts. But before we do that, it’s important to take a closer look at what roles these professionals play.

2- Who’s a data analyst?

Along with data scientists, data analysts too are in high demand. Together, they are often called as DSA (data science and analytics) job. According to Forbes, DSA job listings are expected to grow by almost 364,000 listings to touch the mark of approximately 2,720,000. Just like data scientist posts, those for data analysts too aren’t the easiest positions to fill. Perhaps this explains what Forbes says about DSA jobs — they remain open for an average of 45 days, which is five days more than the market average.

The role of a data analyst is the one that people often confuse with a data scientist. It’s true that professionals in both these roles have a similarity as they work with data. Yet, the main difference between them arises based on what they do with the data.

A data analyst’s primary role is to assemble, categorize, and study data to offer business insight. Typically, data analysts are concerned with cleansing, aggregating, managing, and abstracting data in addition to conducting a variety of analytical studies on that data. Here’s a peek into some of these responsibilities that would help you know what they actually mean:

Cleansing: This refers to the procedure of checking data accuracy and quality by recognizing and then removing biased or incorrect data from a database.
Aggregating: This is the compilation of information from multiple data sources to organize combined datasets for data processing.
Managing: This involves planning data processes as well as executing and maintaining them to ensure safe storage of data and information assets.
Abstracting: It’s the process of removing a dataset’s characteristics to reduce it to a set of essential characteristics, which in turn would help in more efficient data processing.

By leveraging additional software engineering and ML (machine learning) skills, a data scientist builds upon a data analyst’s core competencies. Thus, you will find data scientists actively exploring unique ways to use existing and new algorithmic, statistical, predictive, artificial intelligence (AI), and machine learning (ML) tools and techniques to discover valuable and significant patterns in data and convert these into information for the company or organization.

Perhaps you now understand that though the two job titles and even some roles may be deceptively similar, being a data scientist is better than being a data analyst.

Data-Driven Interviewing: Hiring a Data Scientist

3- Who’s a financial analyst?

A financial analyst is responsible for collecting and organizing financial information followed by its analysis after which S/he would create presentations and offer recommendations, which will get shared with a company’s clients or the stakeholders.

The primary responsibility of financial analysts is to create financial models that can forecast the result of specific business decisions. To do this the right way, these professionals need to collect a large pile of financial data while also considering factors such as earlier transactions having a similar nature, financial market trends, etc. Based on where a financial analyst works, his/her role can differ a lot. For example, a financial analyst working in an investment bank will be much more focused on helping with deals and mergers while the one working for an insurance company would be more concerned with the risks involved in different lines of insurance, how they would affect premiums, etc.

The difference between a financial analyst and a data analyst is that the former works with large number of datasets that come from a wide range of sources such as customers, operations, safety etc. and analyzing them and turning them into recommendations and takeaways for management or clients.

The main difference between a financial analyst and a data analyst is that while the former only works with financial and accounting figures, the latter works with a wide variety of numbers from diverse industries.

4- Who’s a research analyst?

This is a professional who is responsible for researching, examining, interpreting and presenting data related to operations, markets, economics, accounting/finance, customers, and other information related to the field s/he works in. Typically, a research analyst is extremely analytical, quantitative, and logical apart from being adept in handling data.

Almost every industry engages research analysts though they are more commonly found in some specific industries like the financial services industry, retail industry, etc. A broad job category is covered by the research analysts, especially in the areas of operations research, market research, and industry research.

The role of an operations research analyst is to study particular aspects of an organization’s business processes and work out means to improve them. For a market research analyst, the responsibilities include studying the markets to help businesses understand what kind of demand exists for services or products. For an industry analyst, the job entails researching on a specific company or specific industry in addition to keeping track of new developments and trends in an industry. In brief, the key responsibility of a research analyst is to research and find ways to improve the operations of the business or company s/he is involved with.

Thus, the role of a research analyst is limited as compared to that of a data analyst. And since it’s better to be a data scientist than a data analyst, you can infer that the post of a data scientist is much more coveted than that of a research analyst.

— @Edhabit

Data Scientist is better than Financial Analyst, Data Analyst and Research Analyst – Magnimind Academy

Don’t forget to give us your ? !

Data Scientist is better than Financial Analyst, Data Analyst and Research 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.

Via https://becominghuman.ai/data-scientist-is-better-than-financial-analyst-data-analyst-and-research-analyst-ac5a1f3b20cb?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/data-scientist-is-better-than-financial-analyst-data-analyst-and-research-analyst

Facial Recognition Technologies in the Wild: A Call for a Federal Office

We human beings are social creature, of course, facial recognition is going to be big and there are going to be a lot of different…

Continue reading on Becoming Human: Artificial Intelligence Magazine »

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source https://365datascience.weebly.com/the-best-data-science-blog-2020/facial-recognition-technologies-in-the-wild-a-call-for-a-federal-office

Creating an Image Classifier using Create ML (and how it works)

Artificial intelligence is everywhere these days. It’s used in recommendations, online searching, and healthcare. AI even drives the $127 billion autonomous vehicle market!!

Artificial Intelligence can become confusing since it has a lot of different fields, such as machine learning. Machine learning is responsible for most AI applications. Machine learning is used in applications like Siri, Alexa, Facebook, Twitter, Netflix, and Youtube. For this project, I used machine learning to help me create an Image classifier app.

So, what is Machine Learning exactly?

Machine Learning is an application that provides the system with the ability to learn and improve from experience without being explicitly programmed. The primary objective of machine learning is to allow computers to learn automatically without human intervention. Machine learning collects a bunch of data, such as what you clicked on and watched, then it finds a pattern from the data. It trains itself to make better recommendations based on your past actions and behaviors. There are many types of machine learning algorithms, but the two most common are supervised and unsupervised learning.

Supervised Learning

Supervised Learning uses data that has been labeled to create a function and test on new data that has been checked. The goal of supervised learning is to create predictions with already categorized data. An example would be creating models to predict how well students will do on a test based on their environmental and demographic factors.

Unsupervised Learning

Unsupervised Learning finds hidden patterns in large amounts of data. The goal of unsupervised learning is to identify patterns within a set of data. For example, imagine you were given a bunch of images of random balls like baseballs, volleyballs, soccer balls, basketballs, kickballs, etc. all of them have different colors, sizes, etc. Then, you are asked to put them into groups. This is what an unsupervised machine learning algorithm does. The system doesn’t get the right input, but it explores the data and draws inferences from datasets to describes hidden struggle cures from unlabeled data.

Here’s what I did and how I did it

I created an app that uses supervised learning to classify images. The app can either take a picture or upload one from your photo library, and the machine learning algorithm will classify it.

The app has 3 main parts:

— A place to either take a photo or select from your photo library

— A place to display the Image

— A place for the model to classify the image

Training the model

To show I create the model, I’m going to use classifying flowers as an example.

To train the model, I used Create ML. It works by applying machine learning algorithms into the training data to help create predictions to use. Create ML uses a transfer algorithm. This means that it uses another algorithm to develop its own models. Although it never explicitly states which model it uses, I would assume it’s similar to a supervised machine learning algorithm, since its goal is to classify the data instead of finding the pattern in the data.

Tada!!… The finished product (and takeaways)

Now, I’ve built a simple image classifier (yay 🙂 ). This project helped me learn what machine learning is and how I could use it. Before doing this, I didn’t know much about machine learning algorithms or how they worked. Now I understand more about the different types of machine learning.

In the end, I learned was to build my own image classifier that uses supervised learning

Link to my model: https://github.com/AssiHann21/AnImageClassifier

Don’t forget to give us your ? !

Creating an Image Classifier using Create ML (and how it works) 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/creating-an-image-classifier-using-create-ml-and-how-it-works-4c0fea6ed78a?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/creating-an-image-classifier-using-create-ml-and-how-it-works

5 Essential Papers on AI Training Data

Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers.

Originally from KDnuggets https://ift.tt/3dEgIji

source https://365datascience.weebly.com/the-best-data-science-blog-2020/5-essential-papers-on-ai-training-data

Skills to Build for Data Engineering

This article jumps into the latest skill set observations in the Data Engineering Job Market which could definitely add a boost to your existing career or assist you in starting off your Data Engineering journey.

Originally from KDnuggets https://ift.tt/2AyhvDz

source https://365datascience.weebly.com/the-best-data-science-blog-2020/skills-to-build-for-data-engineering

Top KDnuggets tweets May 27 Jun 02: Deep Learning for Coders with fastai and PyTorch: The Free eBook

Also: Machine Learning from First Principles; The Best NLP with Deep Learning Course is Free; Top Stories, May 25-31: Python For Everybody: The Free eBook; Interactive Machine Learning Experiments.

Originally from KDnuggets https://ift.tt/307bEQy

source https://365datascience.weebly.com/the-best-data-science-blog-2020/top-kdnuggets-tweets-may-27-jun-02-deep-learning-for-coders-with-fastai-and-pytorch-the-free-ebook