Category Archives: Data Science
FS Insight Weekly Roadmap: Q4 Shows Promise After ‘Vortex of Pain’ – RealMoney
Ken Xuan, CFA, FRM
Head of Data Science Research
[Note: Fundstrat Head of Research Tom Lee was on a well-deserved vacation this week. In his absence, Head of Data Science Ken Xuan and the Fundstrat Team present their views on the week's developments.]
The most important data point over the past two days was Q2 GDP which came in at 2.1% vs 2.2% (expected). This lower than expected report partially caused a risk-on rally with the S&P 500 ending Thursday +0.59%. Still, markets are nervous about looming headline risks, notably a potential government shutdown over the next few weeks. Even though equities rallied the past two days, the equity put/call ratio surged to 1.41 Wednesday, underscoring the "nervousness" in markets. Forward 1M/3M returns are strong when such readings have occurred since 1995, so we see this as more constructive to markets than alarming. Still, the most important factor for markets in the coming months will arguably be the trajectory of inflation.
We believe the latest PCE data support our thesis that inflation remains on a glidepath lower. Although many cite the rise in headline (due to rise in gas prices) as a reason to be "wary" about the PCE release, Chair Powell himself has admitted that the short-term volatility of gasoline will not influence the Fed's actions.
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FS Insight Head of Technical Strategy
* Reversal likely could lead to a retest and minor break of lows next week.
* Treasury to Equity correlation remains quite strong.
* September's decline likely could lead to 4Q gains given seasonal tendencies.
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Head of Crypto Strategy
* On Thursday, equities and crypto markets rebounded, buoyed by anticipated SEC approval of ETH futures ETFs.
* While an ETH ETF should induce a short-term rally, its intermediate-term impact remains uncertain and will be monitored alongside other indicators (flows). These developments, in the context of a favorable Q4 macro-outlook, enhance the risk/reward profile for Ethereum-based assets like ETHE.
* Amid an upcoming government shutdown that has sped up ETH futures ETF approvals and delayed spot Bitcoin ETF decisions, the odds of a deferral until January for a BTC ETF have risen. However, a Q4 approval for spot Bitcoin ETFs remains our base case.
* Recent shifts in Bitcoin's correlation with macro variables and its outperformance compared to the Nasdaq 100 raise questions about whether crypto is signaling a local or longer-term bottom in asset prices.
* As the typically negative seasonal influences on crypto for August and September wane, we are approaching a period generally considered to be more bullish for crypto markets.
* Core Strategy - Despite soaring rates and volatile asset prices, we believe it's prudent to adopt a more constructive stance on crypto prices as we enter Q4. While we await confirmation from flows data, we think its right to start increasing risk exposure, particularly in the majors and Grayscale trusts, which continue to trade at a discount to NAV. We are reintroducing ETH L2 tokens to the Core Strategy allocation as well as a small allocation to SOL.
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FS Insight Washington Policy Strategist
* Speaker McCarthy fails to get Republican Continuing Resolution to keep the government open through the House, with 21 Republicans defying him.
* In the Senate, a bipartisan discussion for a CR continues, but House Republicans will demand immigration and border-protection provisions in exchange for their support.
* House and Senate leaders have told members to stay in DC for the weekend as the Sunday midnight deadline approaches.
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* The S&P 500 slipped to 4,288.05 this week, down 0.74%. The Nasdaq was nearly flat, closing at 13,219.32. Bitcoin climbed 2.36% to about 26,868.40.
* Despite high volume of noise, inflation remains the primary macro driver for markets.
* Government shutdown could put the Fed on an automatic "structural pause."
"Sometimes we stare so long at a door that is closing that we see too late the one that is open." ~ Alexander Graham Bell
Markets remain in what Fundstrat Head of Data Science Ken Xuan terms a "vortex of pain." We opened on Monday with markets continuing to digest the previous week's central bank actions (from the Fed, the Bank of England, and the Bank of Japan.)
Last week's FOMC meeting led to renewed fears among many market participants that the Fed would stay "higher for longer." A continued surge in yields followed, weighing on the stock market. Fundstrat's Head of Data Science, Ken Xuan, pointed out that the central bank's quantitative tightening might also have contributed to yields climbing: during the week of September 18, the Fed reduced its balance sheet by $75B -- the largest weekly decline since QT began. Part of this was achieved through a significant selloff in Treasuries, possibly depressing prices and boosting yields.
Regardless of the reason for surging yields, Head of Technical Strategy Mark Newton began his remarks at our weekly huddle by noting that, "The biggest thing you have to understand about the equity market right now is that rates and equities are very inversely correlated and with rates moving up equities do not respond well. We've pushed up to almost 4.75% in the 10-year and so that is certainly a source of concern for the equity market."
From a technical standpoint, however, Newton argued that "there are some reasons to think that these yields are just getting too stretched. So ideally, what's going to happen is that probably as of next week, we see yields start to break, markets bounce, but then yields sort of churn. Then eventually, we peak out between October or November with a larger pullback in rates happening in the next year."
He noted as an aside that, "There are some reasons to think [Treasuries] are pretty good value here. I like (TLT) and (IEF) -- even if you don't catch the exact low, they represent a great value here not only to make money on yield but also in price appreciation."
Returning to the stock market, Newton said that "Since September 1, momentum has turned negative. We are nearly oversold based on a lot of metrics, but we are nearing pretty good support which is right near the 4200 level. I don't think we're going to get down under that. To me, the risk-reward is increasingly good." He summed it up a bit more colorfully: "For those who like to buy and hold your nose, I think you'll be rewarded between now and year end, even if it's not right away."
Although Fundstrat Head of Research Tom Lee was out this week on a much-needed vacation, Xuan and the Fundstrat team assessed the week's data. They also concluded that our constructive base case for the rest of 2023 remains intact.
Data showed apparently conflicting views on the housing market this week. "We saw pretty weak data regarding pending home sales and new home sales this week," Xuan observed. But the July Case-Shiller Home Price Index came in hot at 0.87% versus expectations of 0.70%. This was the first positive reading after four straight negative months.
Xuan and the team found that this higher reading had more to do with the "base effects" of a methodology that excluded weak July 2022 data than with any strengthening of the housing market. We expect the same effect to cause the Case Shiller YoY figure to pivot higher in the next few months, but of the 20 cities that Case-Schiller tracks, eight of them (40%) have housing prices in outright deflation. This is double the 20% average (since 1988), Xuan pointed out, "so in our view, the housing market remains cool."
For stocks, "the battleground remains inflation," Xuan said. As we expected, Core PCE again showed declining inflation. YoY, Core PCE came in below 4% for the first time since 2021, and MoM came in below Street expectations at 0.1%, the lowest that figure has been since November 2020. This provided continued evidence of our assertion that inflation is on a glide path lower.
"Sentiment is actually now more bearish," Newton said. "It's taken almost two and a half months, but we've dropped from very bullish levels in July. Now we're extremely bearish on Fear and Greed. AAII is also now bearish by 13 percentage points."
Xuan also pointed out that hedge funds recently added the most short-exposure, on a week-over-week basis, since the Covid-19 pandemic -- a sign of weakening sentiment. Data from EPFR also shows equities had their largest outflows of 2023 last week.
To us, this bearish sentiment is a bullish sign.
"We're approaching seasonally a much better time," Newton told us. "When you look at the last four Septembers they were all down between 3.5 and 7%. But Q4 is anywhere up between 7% or higher. So we are coming into seasonally a very good time."
Xuan also pointed out that going back to 1950, when the S&P 500 is up >10% through July (as it was this year), but declines from July through September 23 (as it has this year), 4Q performance has always been strong -- eight out of eight times, with any average quarterly return of 7-8%. "That's a stunning 100% win ratio," he observed.
Washington Policy Strategist Tom Block has been writing about the possibility of a federal government shutdown ever since Kevin McCarthy won the role of Speaker of the House in January, and now the shutdown deadline is upon us.
Block collaborated with Ken Xuan and with Fundstrat Research Associate Alexandra Sinsheimer to look at the possible effects the shutdown might have on the market. The results are summarized in our Chart of the Week:
Of the 20 government shutdowns since 1960, the average shutdown lasted nine days, and markets on average were flat during those shutdowns. One month afterwards, markets on average were actually higher by 1.2%. The 1M median performance was +1.3%.
But there was one interesting implication. The Fed has long asserted that its policy decisions would be "data dependent." We contacted the Bureau of Labor Statistics to find out how a shutdown would affect their operations, and we were told in summary that during a shutdown, the BLS suspends data collection, processing, and dissemination. Once funding is restored, the BLS would resume normal operations and notify the public of changes to the economic releases.
Would it make sense for the Fed to make a policy decision with incomplete data? If it was sufficiently long, a government shutdown might automatically put the Fed on a "structural pause."
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FS Insight Weekly Roadmap: Q4 Shows Promise After 'Vortex of Pain' - RealMoney
NETL Scientist Participates in Research Experience in Carbon … – National Energy Technology Laboratory
An NETL researcher gathered invaluable knowledge and experience by participating in the annual Research Experience in Carbon Sequestration (RECS) program a carbon capture, utilization and storage (CCUS) education program designed to help graduate students and early career professionals expand their knowledge and grow a collaborative network.
Gail Choisser, an NETL geo-data scientist, was the latest Lab researcher to participate in the widely respected RECS program that was founded in 2004 by the U.S. Department of Energys (DOE) Office of Fossil Energy and Carbon Management (FECM) and NETL.
CCUS is a combination of technologies that capture, compress, transport, use and permanently store carbon dioxide (CO2) emissions from large, stationary energy and industrial facilities. The RECS program also addresses removal of CO2 from the atmosphere.
According to Choisser, the RECS program included interactive content on a range of CCUS topics and included site tours of a power plant specifically outfitted to integrate testing of carbon capture technologies, a coal mine, a CO2 capture facility and two injection wellheads as well as geology field exercises, live lectures, discussions and group projects.
Participants also toured the National Renewable Energy Labs (NREL) Energy System Integration Facility (ESIL), the ION Clean Energy Facility, the Global Thermostat Direct Air Capture Plant, and one of the NETL-supported CarbonSAFE storage sites in Gillette, Wyoming.
Some of the nations leading CCUS experts from DOE National Laboratories, the energy industry, CCUS project developers and academia provide valuable input for the program each year and lead key discussions of CCUS research, development and demonstration projects, commercial deployment trends, and policy and business impacts in the field.
More than 150 applicants sought to participate in the 2023 version of RECS. Choisser was selected as one of 31 participants who converged on NREL in Denver, Colorado, to participate in the program.
In addition to supporting RECS, NETL has a distinguished history with the program. Two NETL carbon storage researchers now serve as mentors to individuals who participate in the event.
Kelly Rose, Ph.D., NETLs Science-Based Artificial Intelligence and Machine Learning Institute (SAMI) director, serves as a RECS mentor.
With almost two decades of DOE and industry support, the CCUS industry plays a key role in reducing greenhouse gas emissions and initiating the shift to clean energy, Rose explained. By partnering with RECS, FECM and NETL are furthering the commitment to accelerating a safe, reliable and technology-informed CCUS commercial sector.
Ale Hakala, Ph.D., is a veteran speaker for RECS and currently serves as a senior fellow for geologic and environmental systems at NETL.
NETL is committed to the next generation of energy and environmental innovators, Hakala said. I found the RECS program to be very effective and Im excited to see the success of the program. RECS participants have been able to take the knowledge they gained in RECS and apply it to groundbreaking CCUS research and development.
RECS participants are graduate students or early career professionals who are based in the United States. RECS encourages people with backgrounds in geology, chemistry, hydrology, physics, engineering, natural sciences, and related fields to apply. Enrollment is limited and tuition is free.
NETL is a DOE national laboratory that drives innovation and delivers technological solutions for an environmentally sustainable and prosperous energy future. By using its world-class talent and research facilities, NETL is ensuring affordable, abundant, and reliable energy that drives a robust economy and national security, while developing technologies to manage carbon across the full life cycle, enabling environmental sustainability for all Americans.
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Getting Started with PyTorch in 5 Steps – KDnuggets
PyTorch is a popular open-source machine learning framework based on Python and optimized for GPU-accelerated computing. Originally developed by developed by Meta AI in 2016 and now part of the Linux Foundation, PyTorch has quickly become one of the most widely used frameworks for deep learning research and applications.
Unlike some other frameworks like TensorFlow, PyTorch uses dynamic computation graphs which allow for greater flexibility and debugging capabilities. The key benefits of PyTorch include:
PyTorch Lightning is a lightweight wrapper built on top of PyTorch that further simplifies the process of researcher workflow and model development. With Lightning, data scientists can focus more on designing models rather than boilerplate code. Key advantages of Lightning include:
By combining the power and flexibility of PyTorch with the high-level APIs of Lightning, developers can quickly build scalable deep learning systems and iterate faster.
To start using PyTorch and Lightning, you'll first need to install a few prerequisites:
It's recommended to use Anaconda for setting up a Python environment for data science and deep learning workloads. Follow the steps below:
Verify that PyTorch is installed correctly by running a quick test in Python:
This will print out a random 3x3 tensor, confirming PyTorch is working properly.
With PyTorch installed, we can now install Lightning using pip:
pip install lightning-ai
Let's confirm Lightning is set up correctly:
This should print out the version number, such as 0.6.0.
Now we're ready to start building deep learning models.
PyTorch uses tensors, similar to NumPy arrays, as its core data structure. Tensors can be operated on by GPUs and support automatic differentiation for building neural networks.
Let's define a simple neural network for image classification:
This defines a convolutional neural network with two convolutional layers and three fully connected layers for classifying 10 classes. The forward() method defines how data passes through the network.
We can now train this model on sample data using Lightning.
Lightning provides a LightningModule class to encapsulate PyTorch model code and the training loop boilerplate. Let's convert our model:
The training_step() defines the forward pass and loss calculation. We configure an Adam optimizer with learning rate 0.02.
Now we can train this model easily:
The Trainer handles the epoch looping, validation, logging automatically. We can evaluate the model on test data:
For comparison, here is the network and training loop code in pure PyTorch:
Lightning makes PyTorch model development incredibly fast and intuitive.
Lightning provides many built-in capabilities for hyperparameter tuning, preventing overfitting, and model management.
We can optimize hyperparameters like learning rate using Lightning's tuner module:
This performs a Bayesian search over the hyperparameter space.
Strategies like dropout layers and early stopping can reduce overfitting:
Lightning makes it simple to save and reload models:
This preserves the full model state and hyperparameters.
Both PyTorch and PyTorch Lightning are powerful libraries for deep learning, but they serve different purposes and offer unique features. While PyTorch provides the foundational blocks for designing and implementing deep learning models, PyTorch Lightning aims to simplify the repetitive parts of model training, thereby accelerating the development process.
Here is a summary of the key differences between PyTorch and PyTorch Lightning:
PyTorch is renowned for its flexibility, particularly with dynamic computation graphs, which is excellent for research and experimentation. However, this flexibility often comes at the cost of writing more boilerplate code, especially for the training loop, distributed training, and hyperparameter tuning. On the other hand, PyTorch Lightning abstracts away much of this boilerplate while still allowing full customization and access to the lower-level PyTorch APIs when needed.
If you're starting a project from scratch or conducting complex experiments, PyTorch Lightning can save you a lot of time. The LightningModule class streamlines the training process, automates logging, and even simplifies distributed training. This allows you to focus more on your model architecture and less on the repetitive aspects of model training and validation.
In summary, PyTorch offers more granular control and is excellent for researchers who need that level of detail. PyTorch Lightning, however, is designed to make the research-to-production cycle smoother and faster, without taking away the power and flexibility that PyTorch provides. Whether you choose PyTorch or PyTorch Lightning will depend on your specific needs, but the good news is that you can easily switch between the two or even use them in tandem for different parts of your project.
In this article, we covered the basics of using PyTorch and PyTorch Lightning for deep learning:
With these foundations you can start building and training advanced models like CNNs, RNNs, GANs and more. The active open source community also offers Lightning support and additions like Bolt, a component and optimization library.
Happy deep learning!
Matthew Mayo (@mattmayo13) holds a Master's degree in computer science and a graduate diploma in data mining. As Editor-in-Chief of KDnuggets, Matthew aims to make complex data science concepts accessible. His professional interests include natural language processing, machine learning algorithms, and exploring emerging AI. He is driven by a mission to democratize knowledge in the data science community. Matthew has been coding since he was 6 years old.
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CCPA/CPRA Data Mapping: The Why, What, and How – JD Supra
How often does the word right show up in the text of the CCPA/CPRA?
Over 100 times.
Out of all those references to rights, it doesnt seem that the rights of businesses are often discussed. In the CPRA, consumers get all the rights, while the word businesses are most associated with is responsibility.
Businesses that are subject to the CPRA have responsibilities to their consumersresponsibilities to manage the proliferation of personal data across their organization, responsibilities to respond to consumer requests, responsibilities to protect consumer data, and more.
The only way to attend to those responsibilities is to know where you collect personal data, where you process it, where its sent, whether or not its adequately protected, and whether or not it's being treated compliantly.
In essence, if your business is subject to the CPRA, then it is imperative that you map your data and data processing activities. Well explain why and how in this article.
Like most data privacy regulations, the CPRA does not directly require you to map your organizations data. However, if you knowingly refuse to map where, how, and why your organization processes personal information, then any violations that take place associated with unmapped (and therefore unknown) personal information under your control could be construed as negligence.
If you dont map your organizations personal data processing activities, how will you:
Moreover, the CPRA not only requires you to manage the personal information you collect, but it also creates the concept of sensitive personal information.
Sensitive personal information includes data with the potential to cause harm to the associated consumer if it should be left unprotected, such as their medical information, social security number, sexual identity, and more. In order to apply the higher level of protection required by the CPRA to this information, youll need to engage in sensitive data discovery to identify where it lives and flows in your organization.
How do you actually approach mapping your organizations data in the context of the CPRA? There are a few different strategies, each of which will suit different kinds of organizations.
For very small organizations or organizations who know they have only a handful of essential systems to map, the manual approach can work.
Under this approach, youll develop spreadsheets that log all relevant compliance information associated with a given store of personal information, such as who owns or controls the systems, where the data is sourced from, where it is sent to, and so on.
Once your spreadsheet library is complete, you can simply contact the system owner to carry out any requisite tasks, such as fulfilling DSARs and auditing contracts for data processing addenda.
It doesnt take much to see the flaws in this approach, however; if you have any more than a handful of systems that process personal data, then the task of creating and maintaining a spreadsheet-based data map quickly becomes untenable. In fact, the average company uses 130 different SaaS applicationsmany, if not most, of those systems will be handling consumer data in some fashion.
Thats treating each system as equal, too. In reality, some systems will contain more or less personal information, sensitive personal information, subsystems, connected vendors, and so on.
Some organizations may have data science resources in place, whether thats a team of experts, a homegrown solution, or an off-the-shelf business intelligence tool. These businesses are in a better position to map their organizations data for CPRA compliance than those relying on the manual approachbut there are still issues to overcome.
For one, multipurpose data science resources will be in high demand. After all, data science falls under the broader umbrella of business intelligencecompliance isnt typically thought of as a business intelligence activity.Although a data science asset will technically be faster at CPRA data mapping than a manual approach, you may have to wait a long time before its your turn.
Then, there is also the likelihood that a homegrown approach to CPRA data mapping will still require a great deal of manual effort. Data science experts arent data privacy and compliance experts after all; theyre data science experts. A privacy professional will need to review the output and fill in the metadata necessary to make your data map actionable from a compliance perspective.
Given how essential data mapping is to an effective privacy program, there are data mapping solutions designed specifically for data privacy and compliance professionals. Osano Data Mapping is one such example.
Rather than rely on manual discovery or require data science expertise, Osano Data Mapping quickly uncovers systems that contain personal information by integrating with your Single Sign On (SSO) provider.
Based on criteria like the number and types of data fields, vendor flows, and identities managed, Osano Data Mapping assigns systems a risk score that enables privacy professionals to prioritize by risk and effort. Any systems that live outside of your SSO can be easily mapped using an automated workflow that keeps external stakeholders alert to any outstanding tasks.
The benefit of using a privacy-focused solution like Osano for CPRA data mapping is twofold:
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Tredence bags 2 Gold at Brandon Hall Group Awards for Innovation … – PR Newswire
SAN JOSE, Calif., Sept. 28, 2023 /PRNewswire/ -- Tredence, a leading Data Science and Artificial Intelligence (AI) solutions company, announces its recent success in winning two esteemed awards at the 2023 Brandon Hall Group Human Capital Management (HCM) Excellence Awards. Theserecognitions were earned in the Learning & Development and Talent Management categories, highlighting Tredence's commitment to the advancement of a skilled and empowered workforce that drives excellence and innovation.
In the face of fierce competition, Tredence received the coveted gold award in the category of Best Advance in Machine Learning and AI in Learning & Development. This accomplishment underscores Tredence's steadfast commitment to leveraging artificial intelligence (AI) and machine learning to create transformative learning experiences that yield significant organizational results.
Additionally, Tredence has been honored with the Best Unique or Innovative Talent Management Program in the Talent Management category. This recognition highlights Tredence's adeptness in devising and implementing talent management strategies that foster individual growth and organizational success.
These awards are presented by the renowned Brandon Hall Group, which is dedicated to acknowledging exceptional achievements in Learning & Development, Talent Management, and various aspects of Human Capital Management. The awards celebrate organizations that exemplify outstanding practices, innovative strategies, and measurable accomplishments in workplace learning and talent enrichment.
Commenting on the recognition, Sumit Mehra, Chief Technology Officer at Tredence, said " At Tredence, we aspire to be a 'Learning First and Learning Always' organization. While we will continue to deliver AI solutions to customers as part of our core offerings, we also aim for our teams to immerse themselves in a culture of continuous learning. Over the last few years, we have deliberately invested in our training infrastructure, and this award is a testament to that strategy and the hard work of our L&D team. We are just getting started and hope that this is the first of many such recognitions for the team dedicated to transforming Tredence into a learning organization."
Rachel Cooke, Chief Operating Officer at Brandon Hall Groupand leader of the HCM Excellence Awards program, said, "Excellence Award winners are shown to be organizations that truly value their employees and invest in them through their human capital management programs. These HCM programs have been validated as best in class for business value and the impact on the employees themselves."
About Tredence:
Tredence is a global data science solutions provider focused on solving the last-mile problem in AI. The 'last mile' is the gap between insight creation and value realization. Tredence leverages strong domain expertise, data platforms & accelerators, and strategic partnerships to provide tailored, cutting-edge solutions to its clients. Tredence is 'Great Place to Work-Certified' and a 'Leader' in the Forrester Wave: Customer Analytics Services. Tredence is 2300-plus employees strong and headquartered in San Jose, with offices in Foster City, Chicago, London, Toronto, and Bangalore. It caters to the largest companies in retail, CPG, telecom, healthcare, travel, banking, and industrials as clients. For more information, please visit [www.tredence.com] and follow us on LinkedIn at Tredence.
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SOURCE Tredence Inc.
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Tredence bags 2 Gold at Brandon Hall Group Awards for Innovation ... - PR Newswire
Are data science certifications the gateway to competitive pay? – DataScienceCentral.com – Data Science Central
Working as a data scientist is the dream of many IT professionals these days. It is no secret that data science is a skyrocketing field attracting young professionals and inspiring many to switch careers to data science. On one front are young professionals who study their courses in colleges to pursue their dream of becoming data scientists and on the other are professionals seeking to enrol in short courses with computing, business analytics, and applied science skills that they already have to switch careers. But are these short courses and data science certifications worth the time and money? Lets find out.
Earning a data science certification amid demand for skilled data scientists is a good data science career move. Heres why:
Enrolling in a professional data science certificate offers a valuable opportunity to validate your expertise. These programs, often backed by respected reputed educational institutions, require rigorous examinations to assess your grasp of essential concepts, tools, and methods. Acquiring certification serves as tangible proof of your proficiency, elevating your credibility and setting you apart in a competitive job market.
A certification program offers chances to connect with peers, instructors, and industry experts. Building a network within the data science community can lead to valuable insights, job prospects, and collaborations. It is always good to have access to exclusive forums and alumni groups, that nurture a supportive network and enhance your learning and career development.
In the expansive realm of Data Science, there are various specializations, including data engineering, machine learning, data visualization, and more. To match your interests and career aspirations, select a certification program aligned with your desired specialization. By obtaining certification in a specific Data Science area, you demonstrate your dedication to becoming a specialist in that field, positioning yourself as a highly desirable professional in your chosen niche.
In todays data-driven world, organizations actively hunt for skilled data scientists capable of leveraging data for strategic advantage. Possessing a Data Science certification significantly boosts your career prospects and unlocks a plethora of job opportunities. Whether youre entering the Data Science field or aiming for career progression, certification becomes a pivotal asset in securing your desired position.
The high demand for professionals with comprehensive data science skills translates into attractive salaries. Obtaining a data science certification can significantly boost your income compared to non-certified peers. Additionally, with organizations increasingly embracing data-driven approaches, certified data scientists can expect enhanced job security and career stability.
Not every certification can be an ideal choice for a successful data science career. It is important to consider multiple factors before registering for one.
The U.S. Bureau of Labor Statistics anticipates a significant 27.9 percent surge in the demand for professionals with data science expertise by 2026. Obtaining a certification will not only enhance your skills but also equip you with the in-demand skills of the present moment.
You can visit the websites of the aforementioned certifications and check their curriculum, exam fees, and the duration required to complete the program. This will help you choose the certification that best suits your career requirements and your organizational goals.
Data science is a highly sought-after profession that can empower you to make critical business decisions. These certifications on the list will enable you to become a data science expert, as they are comprehensive programs that include a wide range of topics.
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10 Best Data Science Colleges in the USA – Analytics Insight
Explore the list of top 10 US colleges for data science that help shape the future of tech
The field of data science is at the forefront of the digital revolution, with a growing demand for skilled professionals who can extract valuable insights from vast datasets. Pursuing a degree in data science from a reputable institution can open doors to exciting career opportunities. This article will explore the ten best data science colleges in the United States, known for their exceptional programs, distinguished faculty, and cutting-edge research.
1. MIT: MIT is one of the worlds most prestigious and innovative universities. It offers a bachelors degree in data science, economics, and statistics, a pre-masters program in data science, and a masters degree in data analytics. MIT also has a dedicated Institute for Data, Systems, and Society that conducts interdisciplinary research on data science and its applications.
2. Harvard University: Harvard University is one of the worlds oldest and most renowned universities. It offers a masters degree in data science and a masters degree in health data science. Harvard also has a Data Science Initiative that fosters collaboration and innovation among faculty, students, and partners across various disciplines.
3. Columbia University: Columbia University is one of the leading research universities in the world. It offers a bachelors degree in data science and a masters in data science. Columbia also has a Data Science Institute that promotes education, research, and outreach on data science and its impact on society.
4. Johns Hopkins University: Johns Hopkins University is one of the top medical universities in the world. It offers a masters degree in data science that focuses on applying data science methods to health care problems. Johns Hopkins also has a Center for Data Science that supports interdisciplinary research and education on data science.
5. Northwestern University: Northwestern University is one of the top private universities in the world. It offers a masters degree in data science and a masters degree in analytics that emphasize both technical skills and business acumen. Northwestern also has a Center for Data Science that facilitates collaboration and innovation among faculty, students, and industry partners.
6. Yale University: Yale University offers a program in statistics and data science that teaches students how to perform practical statistical analysis using a variety of computational techniques, as well as how to visualize and explore data, find patterns and structures in data, and think and reason quantitatively about uncertainty.
7. University of UC Berkeley: Data science programs at UC Berkeley combine computational and inferential reasoning to reach conclusions on various real-world issues. The course equips you with the skills necessary to draw sound conclusions from contextualized data by utilizing your comprehension of statistical inference, computer techniques, data management techniques, domain knowledge, and theoryone of the top Data Science colleges in USA.
8. University of Texas at Austin: The data science curriculum at the University of Texas in Austin combines statistics, programming, and data analysis courses. Predictive modeling, artificial intelligence, and data mining are some of the specialization options available to students.
9. University of California, San Diego: A data science program that combines statistical modeling, data visualization, and data management is available from the University of California, San Diego. Via project-based courses and internships, the curriculum strongly emphasizes experiential learning.
10. Georgia Institute of Technology: The School of Computational Science and Engineering at Georgia Tech provides a data science degree that blends computational, statistical, and machine learning methods. Because of the programs interdisciplinary approach, students are prepared for various data-driven professions across industries.
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10 Best Data Science Colleges in the USA - Analytics Insight
Research partnership uses data science to look at household wealth … – University of WisconsinMilwaukee
MILWAUKEE_Home values appreciate more slowly for lower-income, minority and female homeowners. These were among the findings of a recent research project by a team from the University of Wisconsin-Milwaukee. The project was funded by the Mortgage Guaranty Insurance Corporation (MGIC).
The study used data science to find insights into what contributes to disparities in home values and how this impacts the accumulation of wealth that comes from owning a home.
More results from the project, which began last year, will be presented at 8:30 a.m., Tuesday, Sept. 12, at MGIC, 250 E Kilbourn Ave.
This research has produced findings we feel are actionable by the many public, private, non-profit and philanthropic stakeholders collectively focused on addressing equity in homeownership in Milwaukee, said Geoffrey Cooper, MGIC vice president of product development. It provides us a better understanding, specific to Milwaukee, of what moves the needle when it comes to building wealth through homeownership.
In this project, Bridging the Racial Disparity in Wealth Creation in Milwaukee,
UWM students and faculty created a data science method that examined the factors contributing to wealth creation through housing. It revealed inequities in the valuation of homes, and identified areas of policy interventions that could address them.
For many low- to middle-income households, homeownership is often their largest asset, said Purush Papatla, co-director of the Northwestern Mutual Data Science Institute and UWM professor of marketing. Appreciation in housing values is an important hedge against inflation and a primary source of wealth accumulation.
But how the values of homes are determined affects the amount of investment for the homeowner. For this project, researchers defined housing returns by an owners annual rate of return on home price growth or decline over time and also the resale value of a foreclosed home.
The research team created a machine-learning model called the Wealth Creation Index that uses data that tracks the wealth created by homeownership over time. The model separated data into the components that help or hinder valuation, providing a way to quantify social impact.
UWM faculty researchers on the team were Kundan Kishor, professor of economics; Rebecca Konkel, assistant professor of criminal justice; Jangsu Yoon, assistant professor of economics; and Tian Zhao, associate professor of computer science.
Research findings include:
The team found that homeownership is a better tool for wealth creation than renting even when the loss of wealth attributable to foreclosure is considered. Therefore, policy tools are needed to increase access to homeownership among lower-income, minorities and women.
Other recommendations included improving policies that support homeowners who are at risk of losing their homes to reduce foreclosure rates and policies that prevent widespread declines in property values.
For more information, contact Purush Papatla, papatla@uwm.edu, 414-229-4439.
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Data science on a global scale – Harvard School of Engineering and Applied Sciences
For Justin Xu, a third-year mechanical engineering concentrator at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), spending the summer working at the Harvard-China Project on Energy, Economy and Environment (known as the Harvard-China Project) gave him an opportunity to work with data on a large scale by studying the effect of climate change on global drought patterns at Hong Kong Baptist University. The project allowed him to explore a research area hed never before pursued while learning new, broadly applicable computational skills.
At Harvard, the extent that Id looked at climate science was a General Education course I took on natural disasters, Xu said. Admittedly, going in I didnt have that much experience in climate work, but what was more rewarding and valuable was the chance to work with that scale of data.
Based at SEAS and founded in 1993, the Harvard-China Project works with partner institutions in China to study and design effective policy to address the global challenges of climate change, air quality, energy systems, and economic development.The Harvard-China Project includes researchers from SEAS and a number of other schools, including the Harvard Faculty of Arts and Sciences, Harvard T.H. Chan School of Public Health, Harvard Graduate School of Design, and Harvard John F. Kennedy School of Government.
Xu, who is pursuing a secondary in government, is part of the Harvard Model United Nations, and last summer was a legislative intern in the U.S. Senate. Both the research and political elements of the Harvard-China Project drew him to the program.
For SEAS students, I know Im not alone in having interests both inside and outside of engineering, Xu said. During the school year, SEAS students have a very intense and focused course schedule. During the summer, this program is a great opportunity to explore outside of purely technical realms.
Xu split his time in Hong Kong between research in the lab and remote computation and frequently met with graduate and postdoctoral students working in the lab.
Working with grad students was great, and I think Harvard prepared me well for the rigorous academic culture, he said. For engineering students specifically, its a really rewarding experience because its different. You get to interact with people across disciplines, which is always a great experience.
Xu grew up in Delaware and hadnt visited his extended family in China in more than a decade. This was his first time living in Hong Kong.
Hong Kong is such a dense city, and living here provided an entirely new perspective on how city life works, he said. I grew up in a suburb, but even if you grew up in Manhattan, youd probably say the same thing about Hong Kong.
Xu went into the summer with some computational skills acquired through computer science and applied math courses hed already taken. His experience in the Harvard-China Project showed how useful those skills can be, even for students concentrating in other disciplines.
Even if the courses SEAS students take dont seem directly relevant, the general process of thinking about a challenge always is, he said. This type of engineering work is so broadly applicable across the world. It opened my mind to how interdisciplinary research areas in engineering, applied mathematics and applied sciences can be. Having an experience working with large batches of data is something you can do in mechanical engineering, climate science, and everything in between.
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Data science on a global scale - Harvard School of Engineering and Applied Sciences
OPINION: Data science courses must be part of what all students learn – The Hechinger Report
The calculator has replaced the slide rule. Latin is rarely offered in high school. Sentence diagramming has disappeared from most English classes.
Academic disciplines continually evolve to reflect the latest culture and technology. Why, then, are recent attempts to tinker with the high school math canon eliciting such a backlash? Students deserve a chance to learn up-to-date topics that reflect how mathematics is being used in many fields and industries.
Case in point: the debate over including data science courses as high school math options. Data science courses teach the use of statistical concepts and computer programming to investigate contemporary problems using real-world data sets.
The courses have been gaining in popularity, particularly with high school math teachers. They say the more relevant content offers a highly engaging entry point to STEM, especially for students who have been turned off by traditional math courses.
Others say that the courses are in fact detours away from STEM.
The high school teachers remain unconvinced. Its just been a pleasure to have an absence of hearing, How am I going to use this? or Why do I need to learn this? Lee Spivey, a math teacher from Merced County, told members of the California State Board of Education at their July meeting, before they voted to make California the 17th state to add data science to its curriculum.
This course transformed my teaching practices and transformed the lives of many students.Special education, English learners and calculus students worked side by side, Joy Straub, who taught a data science course in Oceanside for six years, told the board. Students who had a dislike for math suddenly were transformed into math lovers . . . skilled in statistical analysis, computer programming and critical thinking. I saw many students who never would have taken an AP math course take AP Statistics.
Despite the enthusiasm from teachers, some university STEM professors in California objected. Their vehement criticism focused on the fact that data science courses were proposed in the states math framework as alternatives to Algebra II. Faculty from both of the states public university systems went on record opposing the idea that students could take data science or statistics courses to meet university eligibility requirements instead of Algebra II. (They seemingly didnt realize that a 10-year-old policy already permitted students to take data science or statistics in lieu of Algebra II though that route is rarely utilized, at least among applicants to the University of California.)
Related: COLUMN: How can we improve math education in America? Help us count the ways
Algebra II, which covers topics such as exponential and logarithmic functions, is a typical university admission requirement. Twenty states consider Algebra II a high school graduation requirement, but about half of those allow for exceptions or alternative courses, according to a 2019 report, the most recent available.
Algebra II is traditionally considered a stepping-stone to calculus, which remains the key to the STEM kingdom. Many believe that bypassing the course risks prematurely closing off doors to STEM.
Critics, however, complain that the course is jammed with topics that are hard to justify as essential. How often do we use conic sections or synthetic division? Even content that is more important take exponential growth and the very concept of a function is often weighed down by tedious classroom teaching and rote learning.
At the same time, statistical reasoning and data fluency are becoming indispensable in the 21st century, regardless of profession. Digital technologies are changing everything from fitness training to personal investing. But many students are missing out on this essential learning because so many teachers feel ill-equipped to teach these topics, simply run out of time or bow to the perceived preferences of colleges.
Interestingly, both sides of the debate cite the importance of expanding access to STEM fields. The standoff reflects differing perspectives about how math is learned, including a tension between content coverage and conceptual understanding.
Algebra II defenders emphasize that the topics are foundational for STEM fields.
However, many students who take Algebra II dont learn much of the content. And even if students gain proficiency in Algebra II procedural skills, it doesnt necessarily improve their performance in subsequent college math courses. In college, two-thirds of high school calculus students retake calculus or take a prerequisite course.
Proponents of data science courses say not only is data competency essential to everyones future (and to STEM fields themselves) but that the greater relevance the courses provide can actually keep students interested and invested in STEM including in algebra.
Of course, good content and comprehension are both key to math learning. Ultimately, empirical research is needed to validate how well various paths prepare students for college and STEM success.
That is, states must analyze actual longitudinal data on student progress through different sequences to solve this math dilemma. Surely, both data science and algebra will have some role in the future likely with some archaic Algebra II content dropped, as proposed by the National Council of Teachers of Mathematics.
Though press coverage including of Californias recently approved math framework has emphasized the extremes of the debate, much work happening around the country exists in the more ambiguous middle.
Numerous efforts are underway to update Algebra II. Georgias modernized Algebra II course, for instance, incorporates data science concepts. The University of Texas Charles A. Dana Center also provides a model for such a course.
Related: TEACHER VOICE: Calculus is a roadblock for too many students; lets teach statistics instead
Other efforts focus on ensuring that data science courses teach some algebraic concepts. CourseKatas founders promote using data science courses to teach some basics of Algebra II. So does Bootstrap, a curriculum development project based at Brown University.
Even in California, where friction over how to fit data science into the mathematical canon has been especially public, most students who take the courses also take Algebra II. So do at least 99.8 percent of applicants to the UC system which may rise to 100 percent, if some faculty have their way in blocking statistics and data science courses from replacing Algebra II.
Such a decision might preserve coverage of traditional math content. But it would dodge the question of how to ensure that the next generation of students has the statistical and data fluency the 21st century demands. The California teachers are right: We cant defend teaching techniques like synthetic division when students finish high school unable to use data to understand the world around them.
Pamela Burdman is executive director of Just Equations, a California-based policy institute focused on the role of mathematics in education equity.
This story about data science courses was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechingers newsletter.
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OPINION: Data science courses must be part of what all students learn - The Hechinger Report