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Learn Python Programming and Conduct Real-World Financial Analysis in Python – Complete Python TrainingWhat you’ll learn
Requirements
Description Do you want to learn how to use Python in a working environment? Are you a young professional interested in a career in Data Science? Would you like to explore how Python can be applied in the world of Finance and solve portfolio optimization problems? If so, then this is the right course for you! We are proud to present Python for Finance: Investment Fundamentals and Data Analytics – oneof the mostinteresting and complete courses we have created so far. It took our team slightly over four months to create this course, but now, it is ready and waiting for you. An exciting journey from Beginner to Pro. If you are a complete beginner and you know nothing about coding, don’t worry! We start from the very basics. The first part of the course is ideal for beginners and people who want to brush up on their Python skills. And then, once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks. Finance Fundamentals. And it gets even better! The Finance block of this course will teach you in-demand real-world skills employers are looking for. To be a high-paid programmer, you will have to specialize in a particular area of interest. In this course, we will focus on Finance, covering many tools and techniques used by finance professionals daily:
Everything is included! All these topics are first explained in theory and then applied in practice using Python. Is there a better way to reinforce what you have learned in the first part of the course? This course is great, even if you are an experienced programmer, as we will teach you a great deal about the finance theory and mechanics you will need if you start working in a finance context. Teaching is our passion. Everything we teach is explained in the best way possible. Plain and clear English, relevant examples and time-efficient videos. Don’t forget to check some of our sample videos to see how easy they are to understand. If you have questions, contact us! We enjoy communicating with our students and take pride in responding within the 1 business day. Our goal is to create high-end materials that are fun, exciting, career-enhancing, and rewarding. What makes this course different from the rest of the Programming and Finance courses out there?
Please don’t forget that the course comes with Udemy’s 30-day unconditional, money-back-in-full guarantee. And why not give such a guarantee, when we are convinced the course will provide a ton of value for you? Just subscribe to this course! If you don’t acquire these skills now, you will miss an opportunity to separate yourself from the others. Don’t risk your future success! Let’s start learning together now!Who this course is for:
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Momentum trading is based around the logic that if a predominant trend is already visible in the market, then that trend is plausibly going to continue at least until signals begin to come in that it has ended.The idea with momentum trading is that if a certain asset has been moving primarily in one direction for, say, several months, then we can safely assume this trend will continue, at least until data starts to show otherwise. Therefore, the plan will be to buy on every dip and lock in profits on every pump, or vice versa if shorting. Of course, traders need to be aware of when a market shows signs of trend reversals, or else this same strategy could begin to turn around pretty fast. It should also be noted that traders shouldn’t set strategies that try to buy and sell on the actual lows and highs, or what is called “catching the knife,” but rather lock in profits and buy back in at levels that are reasonably safe. Algorithmic trading is ideal for this, as users can simply set percentages they feel comfortable with and let the code do the rest. This technique on its own, however, can be ineffective if a market is moving sideways or so volatile that a clear trend has not emerged. One excellent indicator for watching trends is moving averages. Just as they sound, a moving average is a line on a price chart that shows the average price for an asset over x amount of days (or hours, weeks, months, etc.). Often, amounts like 50, 100 or 200 are used, but different strategies look at different time periods in order to make their trade predictions. Generally, a trend is thought of as strong when it stays well above or below a moving average — and weak when it approaches or crosses over the MA line. In addition, MAs based upon longer time periods are generally given a lot more weight than one that only watches, say, the last 100 hours or a similar timeframe. The post What is momentum trading? appeared first on Data Science PR.
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Created in 2009 by a four-person team and unveiled to the public in 2012, Julia is meant to address the shortcomings in Python and other languages and applications used for scientific computing and data processing. “We are greedy,” they wrote.Here are some of the ways Julia implements those aspirations:
Source: InfoWorld The post What is the Julia language? appeared first on Data Science PR.
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Financial Modeling in Excel that would allow you to walk into a job and be a rockstar from day one!What you’ll learn
Requirements
Description **Updated for June 2020!** Do you want to learn how to use Excel in a real working environment? Are you about to graduate from university and start looking for your first job? Are you a young professional looking to establish yourself in your new position? Would you like to become your team’s go-to person when it comes to Financial Modeling in Excel? If you answered yes to any of these, then this is the right course for you! Join over 119,002 successful students taking this course! The instructor of this course has extensive experience in Financial Modeling:
Learn the subtleties of Financial Modeling from someone who has walked the same path. Beat the learning curve and stand out from your colleagues. A comprehensive guide to Financial Modeling in Excel:
What we offer:
By completing this course, you will:
About the course:
Make an investment that will be highly rewarded in career prospects, positive feedback, and personal growth. This course is suitable for graduates who aspire to become investment bankers as it includes a well-structured DCF model and goes through the theoretical concepts behind it. Moreover, it will encourage you to be more confident when coping with daily tasks and will give you the edge when the firm must decide whether to take you on for a full-time position. People with basic knowledge of Excel who go through the course will dramatically increase their Excel skills. Go ahead and subscribe to this course! If you don’t acquire these skills now, you will miss an opportunity to separate yourself from others. Don’t risk your future success! Let’s start learning together now! Who this course is for:
Join now!The post Beginner to Pro in Excel: Financial Modeling and Valuation appeared first on Data Science PR.
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Data scientists and ML engineers can now speedup their applications using the power of FPGA accelerators from their browser.FPGAs are adaptable hardware platforms that can offer great performance, low-latency and reduced OpEx for applications like machine learning, video processing, quantitative finance, etc. However, the easy and efficient deployment from users with no prior knowledge on FPGA was challenging. InAccel, a pioneer on application acceleration, makes accessible the power of FPGA acceleration from your browser. Data scientists and ML engineers can now easily deploy and manage FPGAs, speeding up compute-intense workloads and reduce total cost of ownership with zero code changes.
Through the JupyterHub integration, users can now enjoy all the benefits that JupyterHub provide such as easy access to computational environment for instant execution of Jupyter notebooks. At the same time, users can now enjoy the benefits of FPGAs such as lower-latency, lower execution time and much higher performance without any prior-knowledge of FPGAs. InAccel’s framework allows the use of Xilinx’s Vitis Open-Source optimized libraries or 3rd party IP cores (for machine learning, data analytics, genomics, compression, encryption and computer vision applications.) The Accelerated Machine Learning Platform provided by InAccel’s FPGA orchestrator can be used either on-prem or on cloud. That way, users can enjoy the simplicity of the Jupyter notebooks and at the same time experience significant speedups on their applications. Users can test for free the available libraries on the InAccel cluster using the following link: https://inaccel.com/accelerated-data-science/ The platform is available for demonstration purposes. Multiple users may access the available cluster with the 2 Alveo cards. Source: Inside Big Data The post Accelerated Machine Learning from Your Browser in 2020 appeared first on Data Science PR.
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What if I told you that you could learn over 24 different tech skills, worth thousands of dollars in real-world value, for a fraction of the cost? Specifically 94% off what they originally cost?Well, that’s EXACTLY what’s happening inside the 2020 version of the Ultimate Tech Career Toolbox. After many long days and nights of negotiating and planning, we’ve been able to get 27 expert technical course creators to say “yes,” and help make that vision of mine a reality. I’m so excited to share what we’ve put together. In this post, you’ll find a detailed breakdown of every single product you’ll get in this year’s toolbox. If you went out and purchased all these products on your own, it would cost $4,206! Here’s the catch…this deal is so good that it’s only going to be available for five days: Monday, June 22nd to Friday, June 26th. Keep reading to see everything you’ll get inside! Sign up now: https://bit.ly/toolbox2020![]() All The Products Inside the 2020 Ultimate Tech Career Toolbox
The post 33 Different Products to Level Up Your Tech Skills in 2020 appeared first on Data Science PR.
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The ecommerce industry has greatly benefited from the explosion of customer-centric data analytics. Industry leaders have been able to leverage it to great effect, fueling their various marketing, advertising and website content personalization efforts, yielding game-changing sales conversion lift. Contemporary online shopping companies have even been rampantly gathering, tracking, and even buying data regarding individuals’ shopping intent, cross-platform browsing behavior and owned media interactions to compile dynamic, frighteningly detailed customer profiles. These profiles are then used to create targeted marketing campaigns that can effectively hook and reel in customers. One only need to look at how Facebook Ads and Amazon product recommendations seem to accurately coincide with a person’s needs and wants to see how effective and even eerie these methods can be. However, the consumer backlash against these practices has been building momentum. An increasing number of users have now become more discerning of how their personal data is being used. In addition, privacy regulations such as the GDPR and CCPA now have stringent provisions on how companies can gather, store and use consumer data, making it difficult to now use these “intrusive” marketing methods. Third-party cookies are now being phased out, and web browsers that block all data collection are rising in popularity. As a result, merchants are now at a critical juncture where they have to find a balance between maximizing their data investments and still effectively engaging customers while making sure that consumer privacy is respected. WHY DATA PRIVACY BECOME AN ISSUE IN THE FIRST PLACEData privacy regulations are now regarded as a critical safeguard to public good and a pillar of an individual’s rights in the digital age. A great deal of this trend can be attributed to the number of headline-grabbing episodes of data blunders involving businesses, such as:
HOW ECOMMERCE BENEFITS FROM CUSTOMER DATAEcommerce companies use data to drive personalization and targeted advertising and marketing to boost their conversion and sales in a highly competitive market. In fact, 40% of executives report that their personalization efforts have had a direct impact on maximizing sales, basket size, and profits in direct-to-consumer channels such as ecommerce. Data also helps businesses identify certain groups of people that are most likely to buy their products. As such, they will be able to target their marketing to interested customers and won’t waste resources on groups that are less likely to buy from their brand. In addition, companies can also track consumer activities to provide them with better customer experience and recommend products and services based on their context. Around 75% of today’s buyers expect businesses to anticipate their needs and make relevant suggestions. For example, online fashion retailer ASOS has a dynamic interface that changes based on user browsing and search history. If a user has previously searched for men’s clothing, for instance, the user will automatically be redirected to the men’s section the next time they browse to ASOS’s homepage. COMPLIANT DATA USE IS JUST TABLE STAKESThe emergence of data privacy regulations has prompted many businesses to rethink their data efforts as these laws carry the real threat of litigation and very hefty fines. And given the importance of data for a business’ success, they have little choice but to adjust their methods and adopt tools that make their data efforts compliant. But compliance with regulations is just the beginning. Nowadays, it seems that additional regulations are constantly under deliberation at trade organizations and legislative bodies around the world. It’s also possible to remain compliant while still freaking out your customers, so ecommerce data managers would do well to always err on the side of less intrusiveness rather than more. Seeking permission from visitors and providing transparency into data use policies are key in all this. This means that companies must always ask for consent before they collect user data. Websites must now contain a cookie permission notice that explains what data will be collected and for what purposes. It’s important that customers have a choice to accept or decline. Companies even have to keep records of which customers have actually given consent. Likewise, customers must also be given the option to opt in or opt out of promotional materials. They must first affirm that they’re willing to receive marketing emails before organizations can send them one. In addition, companies must only ask for relevant information and be transparent in the data they collect in their sign-up forms. Under privacy laws, consumers can also withdraw their consent, ask for their data records, and request that their histories be deleted. Companies must provide their customers with options to unsubscribe from mailing lists, deny access to data collection, and delete their histories and records on a website. Businesses must also be able to provide a copy of collected consumer data that includes information about which parties the data was shared with and the purpose of the collection – within 30 days after the request was made. Achieving compliance is no easy feat. Fortunately, the emergence of compliance tools and platforms can help companies implement all these necessary changes to their channels. Other compliance applications can even automatically generate data reports to help companies fulfill data requests from users. Privacy regulations also make companies more accountable for the protection of consumer data. The adoption of security solutions such as antivirus apps, firewalls and access controls can safeguard sensitive information from being intercepted. Overall, companies must also review the impression that their user experiences create. Customers must feel safe throughout their journey and that they aren’t creeped out by any of the website or application’s behavior at all. User testing and feedback are critical in building such experiences. FINDING THE RIGHT BALANCEBeing respectful of people’s privacy is indeed a matter of doing the right thing and regulatory compliance, but it’s also understandable that business leaders would aim to optimize interfaces and paid media activity for sales conversions. They are running businesses after all. Ecommerce companies need to perform their due diligence to remain compliant while using careful personalization to drive their sales. Ultimately, the most important thing is that your customers always feel that their privacy is being respected, that you’re looking after their best interests, and no lines are being crossed. Source: DataConomy The post How eCommerce Companies Can Be Less Intrusive When Using Customer Data in 2020? appeared first on Data Science PR.
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. In this deep learning with TensorFlow tutorial, we will introduce TensorFlow, the second machine learning framework created by Google. The post Introduction to TensorFlow Syntax in 20 Minutes appeared first on Data Science PR.
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Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, deep learning and big data. Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies. Here is an essential list of tips to kick start your data science career:
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We talk about an alternative way of getting into data science by becoming a data architect!More specifically, we’ll look at who the data architect is, what they do, how they fare in terms of salaries, and what skills and academic background you need to become one. Who is the data architect exactly?If you’ve seen the 1999 cult movie The Matrix, you probably recognize the Architect as the creator of the utopian world for human minds to inhabit. Much like their blockbuster counterpart, data architects create the database from scratch. They design the way data will be retrieved, processed, and consumed. Data architects are technical experts who adapt dataflow management and data storage strategy to a wide range of businesses and solutions. They’re in charge of continually improving the way data is collected and stored. In addition, data architects control access to data. So, all you corporate spies out there – now you know who to look for. Data architects are also responsible for design patterns, data modeling, service-oriented integration, and business intelligence domains. They often partner with fellow data scientists and IT guys to reach the company’s data strategy goals. A data architect constantly seeks out innovations to provide improved data quality and reporting, eliminate redundancies, and provide better data collection sources, methods, and tools… The post How to Become a Data Architect in 2020? appeared first on Data Science PR. |
Data ScienceData Science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data Science is related to data mining, deep learning and big data. Archives |