7 free learning resources to land top data science jobs

March 22, 2023 Coin Telegraph

Discover seven free resources to learn data science and land top jobs.

Data science is an exciting and rapidly growing field that involves extracting insights and knowledge from data. To land a top data science job, it is important to have a solid foundation in key data science skills, including programming, statistics, data manipulation and machine learning.

Fortunately, there are many free online learning resources available that can help you develop these skills and prepare for a career in data science. These resources include online learning platforms such as Coursera, edX and DataCamp, which offer a wide range of courses in data science and related fields.

Coursera

Data science and related subjects are covered in a variety of courses on the online learning platform Coursera. These courses frequently involve subjects such as machine learning, data analysis and statistics and are instructed by academics from prestigious universities.

Here are some examples of data science courses on Coursera:

Applied Data Science with Python Specialization: This specialization, offered by the University of Michigan, consists of five courses that cover the basics of data manipulation, analysis and visualization using Python.
Machine Learning by Andrew Ng: This course, offered by Stanford University, provides an introduction to machine learning, including topics such as linear regression, logistic regression, neural networks and clustering.
Data Science Methodology: This course, offered by IBM, covers the basics of data science, including data preparation, data cleaning and data exploration.
Statistics with R Specialization: This specialization, offered by Duke University, consists of four courses that cover statistical inference, regression modeling and machine learning using the R programming language.

I started my ML career 2019 with Coursera IBM Data Science courses @coursera with MS engineering background. Fascinating to learn daily of AI
— Risto Anton (@blogtheristo) March 17, 2023

One can apply for financial aid to earn these certifications for free. However, doing a course just for certification may not land a dream job in data science.

Kaggle

Kaggle is a platform for data science competitions that provides a wealth of resources for learning and practicing data science skills. One can refine their skills in data analysis, machine learning and other branches of data science by participating in the platform’s challenges and host of datasets.

Here are some examples of free courses available on Kaggle:

Python: This course covers the basics of Python programming, including data types, control structures, functions and modules.
Pandas: This course covers the basics of data manipulation using Pandas, including data cleaning, data merging and data reshaping.
Data Visualization: This course covers the basics of data visualization using Matplotlib and Seaborn, including scatter plots, line plots and bar plots.
Intro to Machine Learning: This course covers the basics of machine learning, including classification, regression and clustering.
Intermediate Machine Learning: This course covers more advanced topics in machine learning, including feature engineering, model selection and hyperparameter tuning.
SQL: This course covers the basics of SQL, including data querying, data filtering and data aggregation.
Deep Learning: This course covers the basics of deep learning, including neural networks, convolutional neural networks and recurrent neural networks.

Kaggle is a superb platform for Python & Machine Learning

Open this if you wish to use it to maximum potential ⏬⏬⏬
— Jaydeep (@_jaydeepkarale) March 15, 2023

edX

EdX is another online learning platform that offers courses in data science and related fields. Many of the courses on edX are taught by professors from top universities, and the platform offers both free and paid options for learning.

Some of the free courses on data science available on edX include:

Data Science Essentials: This course, offered by Microsoft, covers the basics of data science, including data exploration, data preparation and data visualization. It also covers key topics in machine learning, such as regression, classification and clustering.
Introduction to Python for Data Science: This course, offered by Microsoft, covers the basics of Python programming, including data types, control structures, functions and modules. It also covers key data science libraries in Python, such as Pandas, NumPy and Matplotlib.
Introduction to R for Data Science: This course, offered by Microsoft, covers the basics of R programming, including data types, control structures, functions and packages. It also covers key data science libraries in R, such as dplyr, ggplot2 and tidyr.

All of these courses are free to audit, meaning that you can access all the course materials and lectures without paying a fee. Nevertheless, there will be a cost if you wish to access further course features or receive a certificate of completion. A comprehensive selection of paid courses and programs in data science, machine learning and related topics are also available on edX in addition to these courses.

DataCamp

DataCamp is an online learning platform that offers courses in data science, machine learning and other related fields. The platform offers interactive coding challenges and projects that can help you build real-world skills in data science.

The following courses are available for free on DataCamp:

Introduction to Python: This course covers the basics of Python programming, including data types, control structures, functions and modules.
Introduction to R: This course covers the basics of R programming, including data types, control structures, functions and packages.
Introduction to SQL: This course covers the basics of SQL, including data querying, data filtering and data aggregation.
Data Manipulation with Pandas: This course covers the basics of data manipulation using Pandas, including data cleaning, data merging and data reshaping.
Importing Data in Python: This course covers the basics of importing data into Python, including reading files, connecting to databases and working with web APIs.

All of these courses are free and can be accessed through DataCamp’s online learning platform. In addition to these courses, DataCamp also offers a wide range of paid courses and projects that cover topics such as data visualization, machine learning and data engineering.

Udacity

Udacity is an online learning platform that offers courses in data science, machine learning and other related fields. The platform offers both free and paid courses, and many of the courses are taught by industry professionals.

Here are some examples of free courses on data science available on Udacity:

Introduction to Python Programming: This course covers the basics of Python programming, including data types, control structures, functions and modules. It also covers key data science libraries in Python, such as NumPy and Pandas.
SQL for Data Analysis: This course covers the basics of SQL, including data querying, data filtering and data aggregation. It also covers more advanced topics in SQL, such as joins and subqueries.
Intro to Data Science: This course covers the basics of data science, including data wrangling, exploratory data analysis and statistical inference. It also covers key machine-learning techniques, such as regression, classification and clustering.

MIT OpenCourseWare

MIT OpenCourseWare is an online repository of course materials from courses taught at the Massachusetts Institute of Technology. The platform offers a variety of courses in data science and related fields, and all of the materials are available for free.

Here are some of the free courses on data science available on MIT OpenCourseWare:

Introduction to Computer Science and Programming in Python: This course covers the basics of Python programming, including data types, control structures, functions and modules. It also covers key data science libraries in Python, such as NumPy, Pandas and Matplotlib.
Introduction to Probability and Statistics: This course covers the basics of probability theory and statistical inference, including probability distributions, hypothesis testing and confidence intervals.
Machine Learning with Large Datasets: This course covers the basics of machine learning, including linear regression, logistic regression and k-means clustering. It also covers techniques for working with large data sets, such as map-reduce and Hadoop.

GitHub

GitHub is a platform for sharing and collaborating on code, and it can be a valuable resource for learning data science skills. However, GitHub itself does not offer free courses. Instead, one can explore the many open-source data science projects that are hosted on GitHub to find out more about how data science is used in practical situations.

Scikit-learn is a popular Python library for machine learning, which provides a range of algorithms for tasks such as classification, regression and clustering, along with tools for data preprocessing, model selection and evaluation. The project is open-source and available on GitHub.

Please don't apply for senior dev roles unless your GitHub looks like this pic.twitter.com/6wptzkrMb2
— Nat Miletic (@natmiletic) February 27, 2023

Jupyter is an open-source web application for creating and sharing interactive notebooks. Jupyter notebooks provide a way to combine code, text and multimedia content in a single document, making it easy to explore and communicate data science results.

These are just a few examples of the many open-source data science projects available on GitHub. By exploring these projects and contributing to them, one can gain valuable experience with data science tools and techniques, while also building their portfolio and demonstrating their skills to potential employers.

5 high-paying careers in data science

February 8, 2023 Coin Telegraph

Big Data

Data science careers tend to have high salaries — often over six figures — as the demand for skilled professionals in this field continues to grow.

Data science plays a critical role in supporting decision-making processes by providing insights and recommendations based on data analysis. In order to create new products, services and procedures, businesses can use data science to gain a deeper understanding of consumer behavior, market trends and corporate performance.

By giving businesses a competitive edge in the market through better decision-making, increased consumer involvement and more efficient corporate processes, it enables companies to achieve a competitive advantage. The demand for data science experts is rising quickly, opening up new possibilities for development on both a personal and professional level.

Here are five high-paying careers in data science.

Data scientist

A data scientist is a specialist who draws conclusions and knowledge from both structured and unstructured data using scientific methods, processes, algorithms and systems. They create models and algorithms to categorize data, make predictions and find hidden patterns. Additionally, they clearly and effectively communicate their findings and outcomes to all relevant parties.

Data scientists have solid backgrounds in statistics, mathematics and computer science, as well as a practical understanding of the Python and R programming languages and expertise in dealing with sizable data sets. The position calls for a blend of technical and analytical abilities, as well as the capacity to explain complicated results to non-technical audiences.

A data scientist in the United States can expect to earn $121,169 per year, according to Glassdoor. Additionally, advantages like stock options, bonuses and profit-sharing are frequently included in remuneration packages for data scientists. However, a data scientist’s pay might vary significantly depending on a number of variables, including geography, industry, years of experience and educational background.

Machine learning engineer

A machine learning engineer is responsible for designing, building and deploying scalable machine learning models for real-world applications. They create and use algorithms to decipher complex data, interpret it and make predictions. In order to incorporate these models into a finished product, they also work with software engineers.

Typically, a machine learning engineer has a solid foundation in programming, computer science and mathematics. In the U.S., the average income for a machine learning engineer is $136,150, while top earners in big cities or those with substantial expertise may make considerably more.

Big data engineer

The architecture of a company’s big data infrastructure is created, built and maintained by big data engineers. They use a variety of big data technologies, including Hadoop, Spark and NoSQL databases, to design, build and manage the storage, processing and analysis of huge and complex data sets.

They also work along with data scientists, data analysts and software engineers to develop and implement big data solutions that satisfy an organization’s business needs. In the U.S., a data engineer can expect to make an average annual salary of $114,501.

Business intelligence manager

An organization’s decision-making processes are supported by data-driven solutions, which are developed and implemented under the direction of a business intelligence (BI) manager. They coordinate the implementation of BI tools and systems, create and prioritize business intelligence initiatives, and work in close collaboration with data analysts, data scientists and IT teams.

The data used in these solutions must be of a high standard, and BI managers must convey the findings and insights to senior leaders and stakeholders in order to inform business strategy. They are essential in creating and maintaining data governance and security rules that safeguard confidential corporate data. The salary range for a business intelligence manager in the U.S. normally ranges from $122,740 to $157,551. And the average compensation is $140,988 per annum.

Data analyst manager

A data analyst manager is responsible for leading a team of data analysts and overseeing the collection, analysis and interpretation of large and complex data sets. They develop and implement data analysis strategies, using various tools and technologies, to support decision-making processes and inform business strategy.

To make sure that data analysis initiatives are in line with company goals and objectives, data analyst managers closely collaborate with data scientists, business intelligence teams and senior management. They also play a crucial part in guaranteeing the accuracy and quality of the data used in analytic initiatives, as well as in conveying findings and suggestions to stakeholders. They could also be in charge of overseeing the allocation of resources and managing the budget for projects involving data analysis. In the U.S., a data analyst makes an average base salary of $66,859.

Human protocol introduces new blockchain coordination layer for data contribution

July 20, 2022 Coin Telegraph

Coin Telegraph

Users receive rewards for contributing data on the Human Protocol, which can be used an initial-point of learning for algorithms.

On Thursday, decentralized infrastructure project Human Protocol said it was launching a new blockchain coordination layer to handle routing functionality among third-party vendors to power data contribution on the network. The feature, known as Routing Protocol, sits atop Human to enable the discovery of network generators, fee agreements, consensus job standards, proof of balance, and governance support for network upgrades.

The Human protocol started via an on-chain bot blocker called hCaptcha that would reward users for solving CAPTCHAs and gradually became a broader solution for tokenizing contribution. Human expects the community-developed, open-source Routing Protocol to simplify the steps of operating a network entity such as an Exchange Oracle. This stems from Routing Protocol's ability to coordinate oracles, job exchanges, layer-one integrations for job listings, and workpool operators.

As an end goal, the Human network seeks to leverage the peer-to-peer consensus mechanism inherent in blockchain design to resolve automation tasks that cannot be performed without initial human assistance. One example of such a value proposition is in the realm of AI e-commerce marketing. Without an initial "training" data-set, a machine-learning algorithm cannot effectively suggest ads relevant to their shopping behavior to web users.

But by using the Human Protocol, network clients can post smart bounties for such consumer reviews and reward users for their input via the HMT token. The development team's vision is to create a decentralized platform for rewarding data suppliers to those demanding it. It seeks to meet the objective of facilitating direct, globally-mapped connections at the intersection of workers, companies, and machine learning, all at scale.

Human protocol introduces blockchain coordination layer for data contribution

July 20, 2022 Coin Telegraph

Coin Telegraph

Users receive rewards for contributing data on the Human Protocol, which can be used an initial-point of learning for algorithms.

Defying the bear market, this automated strategy is up 15% so far in 2022

January 28, 2022 Coin Telegraph

Even amid a protracted marketwide slump, profit opportunities for crypto traders are still out there.

Let’s be blunt: Being in a bear market sucks profoundly as a crypto trader. Most strategies that work when everything is green lead to losses. Growing the value of a portfolio takes twice as much work for half as much progress. The uncertainty over how long the market will remain down is exhausting. During these times, making use of every available tool that can enhance traders’ decision-making is key to success.

One such tool is the VORTECS™ Score, an algorithmic indicator available to the subscribers of Cointelegraph Markets Pro that is designed to use historical data on crypto assets’ performance to determine whether their current conditions are bullish, bearish or neutral.

The Score can be creatively used in an infinite number of ways, but one hypothetical strategy based on detecting the strongest historical analogies massively outperformed both Bitcoin (BTC), which has lost some 25% of its value during the first month of 2022, and the aggregate altcoin market, whose losses are comparable. This strategy, called “Buy 90/Sell 70,” yielded a 15% gain between Jan. 1 and Jan. 27.

What does Buy 90/Sell 70 mean?

The most important thing about VORTECS™ Score-based testing strategies is that they are not meant to be directly replicated by human traders. Rather, they serve as a tool to assess the overall efficiency of the model over a period of time.

Trades that inform this strategy occur on a server rather than an actual exchange. There can be dozens of them per day, and the testing portfolio gets rebalanced according to a formula after each trade. Still, the results that these tests generate can provide a compelling picture of the algorithm’s performance.

The way the indicator works is as follows: The higher the VORTECS™ Score, the more confident the model is that the observed conditions are bullish for a coin, based on historical precedent. Conventionally, a score of 80 is interpreted as high confidence in the outlook’s bullishness. Such scores are observed frequently, with around 50 instances in an average week.

Scores of 90 and above are much rarer; normally, there are just a few instances every week. What they indicate is that in the past, the observed setup of trading conditions reliably showed up before dramatic price spikes. The Buy 90/Sell 70 strategy means buying every asset whose VORTECS™ Score hits 90 and selling it once it drops below 70. If the testing algorithm already holds another asset at the time of the next 90 hit, the portfolio is rebalanced so that it holds all the qualifying assets in equal proportions.

How it has gone down in 2022

Throughout January 2022, a total of 18 crypto assets have achieved a VORTECS™ Score of 90. One of them was Voyager Token (VGX), pictured below, which hit the threshold on Jan. 25 against a price of $1.76 (red circle in the chart). Before the asset’s score went below 70, the price rose to $1.87. In the following hours, it went further up to $2.07, but that additional gain would not be accounted for in the 90/70 results.

*VORTECS™ Score (green/gray) vs. VGX price, Jan. 20–27. Source:* *Cointelegraph Markets Pro*.

The assets that hit the VORTECS™ Score of 90 tend to be more resilient than most other coins to the negative trends that exist in the wider market. Thanks to their extremely healthy individual conditions, these tokens delivered an average 5% gain within seven days of hitting the ultra-high score in 2021.

Of course, a strong VORTECS™ Score performance is never a guarantee of future price movement. All strategies based on buying at the score of 80, for example, yielded negative returns in the first weeks of 2022. However, the success of the 90/70 strategy shows that historical precedent can be extremely informative even amid a massive correction in the crypto market.

Cointelegraph is a publisher of financial information, not an investment adviser. We do not provide personalized or individualized investment advice. Cryptocurrencies are volatile investments and carry significant risk including the risk of permanent and total loss. Past performance is not indicative of future results. Figures and charts are correct at the time of writing or as otherwise specified. Live-tested strategies are not recommendations. Consult your financial adviser before making financial decisions.

IoTeX ‘MachineFi’ rebrand backs 200%+ rally to a new all-time high

November 16, 2021 Coin Telegraph

Altcoin Watch

IOTX price hits a new all-time high after the project shifts its focus to “MachineFi”, NFTs and decentralized finance.

In the past 30 years, the growth of the internet and digital technology has transformed the way the world operates and now artificial intelligence and machine learning continue to shift the balance of power away from physical labor and more toward a future filled with automation and smart technology.

IoTeX (IOTX), a blockchain-based project focused on Internet of Things (IoT) devices and the future of machine learning in the workforce, aims to design an open ecosystem that facilitates interaction between people and machines, and over the past month, the project's IOTX token has rallied more than 200%.

Data from Cointelegraph Markets Pro and TradingView shows that since hitting a low of $0.055 on Oct. 27, the price of IOTX has surged 377% to a new record high at $0.263 on Nov. 13 as its 24-hour trading volume spiked to a record $3.93 billion.

*IOTX/USDT 4-hour chart. Source: TradingView*

Three reasons for the breakout price action for IOTX are the rebranding of the project to focus on the rise of MachineFi, the addition of support from multiple protocols and exchanges, and the launch of nonfungible tokens (NFT) and decentralized finance (DeFi) projects on the IoTeX mainnet.

The rise of MachineFi

The biggest boost for the IoTeX ecosystem came early in November when the project announced that it was rebranding to focus on “The rise of MachineFi” as a way to further integrate machines, the Metaverse and the traditional workforce.

A new era of IoTeX is upon us.

MachineFi will connect the real world to the metaverse and unleash a new decentralized machine economy.

Join the revolution: https://t.co/HZfyxKBkde #MachineFi #Metaverse $IOTX pic.twitter.com/mHLodF7Gde
— IoTeX (@iotex_io) November 4, 2021

MachineFi follows the emergence of DeFi and more recently, GameFi, and is intended to deal with the confluence of smart devices, machines and finance through the integration of blockchain technology.

As the number of smart devices in use continues to rise, IoTeX aims to ensure security and trust between users and their devices, as well as create a simple interface where all devices can connect and be managed.

Ecosystem expansion and exchange listings

Another reason for the growing strength of IoTeX is the recent addition of new ecosystem partners, as well as listing on several cryptocurrency exchanges, which helped to increase the token's exposure to crypto traders.

This year, IOTX listed on ABRA, Crypto.com, LBank and Coinbase, and was added to Pokket, a passive yield-generating platform.

The project has also been busy establishing partnerships with other projects in the cryptocurrency sector, including integrations with Chainlink, the mobility as a service protocol MobiFi, Health Blocks, the Ethereum Push Notification Service, Travala and the blockchain payments provider NOWPayments.

DeFi and NFTs launch on the network

A third factor helping to drive the price of IOTX higher has been the expansion of the IoTeX ecosystem and the addition of multiple NFT and DeFi projects to the protocol.

DeFi is the largest sector of growth in the IoTeX ecosystem and some of the recent project launches include WOWSwap, the first leverage decentralized exchange (DEX) on IoTeX, Mimo DEX, and the cross-chain compatible Parrot Protocol, NAOS Finance and Firebird Finance.

On the NFT front, IOTX has benefited from the launch of Sota Finance, the first NFT marketplace on the IoTeX network, as well as the launch of multiple NFT projects including StarCrazy and PlaceWar.

VORTECS™ data from Cointelegraph Markets Pro began to detect a bullish outlook for IOTX on Oct. 20, prior to the recent price rise.

The VORTECS™ Score, exclusive to Cointelegraph, is an algorithmic comparison of historical and current market conditions derived from a combination of data points including market sentiment, trading volume, recent price movements and Twitter activity.

*VORTECS™ Score (green) vs. IOTX price. Source:* *Cointelegraph Markets Pro*

As seen in the chart above, the VORTECS™ Score for IOTX spiked into the green on Oct. 20 and reached a high of 70, around seven days before the price began to increase by 377% over the next three weeks.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph.com. Every investment and trading move involves risk, you should conduct your own research when making a decision.

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