Data Science Dojo is offering Airbyte for FREE on Azure Marketplace packaged with a pre-configured web environment enabling you to quickly start the ELT process rather than spending time setting up the environment.
What is an ELT pipeline?
An ELT pipeline is a data pipeline that extracts (E) data from a source, loads (L) the data into a destination, and then transforms (T) data after it has been stored in the destination. The ELT process that is executed by an ELT pipeline is often used by the modern data stack to move data from across the enterprise into analytics systems.
In other words, in the ELT approach, the transformation (T) of the data is done at the destination after the data has been loaded. The raw data that contains the data from a source record is stored in the destination as a JSON blob.
Airbyte is conceptually composed of two parts: platform and connectors.
The platform provides all the horizontal services required to configure and run data movement operations, for example, the UI, configuration API, job scheduling, logging, alerting, etc., and is structured as a set of microservices.
Connectors are independent modules that push/pull data to/from sources and destinations. Connectors are built under the Airbyte specification, which describes the interface with which data can be moved between a source and a destination using Airbyte. Connectors are packaged as Docker images, which allows total flexibility over the technologies used to implement them.
Obstacles for data engineers & developers
Collection and maintenance of data from different sources is itself a hectic task for data engineers and developers. Building a custom ELT pipeline for all of the data sources is a nightmare on top that not only consumes a lot of time for the engineers but also costs a lot.
In this scenario, a unified environment to deal with the quick data ingestions from various sources to various destinations would be great to tackle the mentioned challenges.
Methodology of Airbyte
Airbyte leverages DBT (data build tool) to manage and create SQL code that is used for transforming raw data in the destination. This step is sometimes referred to as normalization. An abstracted view of the data processing flow is given in the following figure:
It is worth noting that the above illustration displays a core tenet of ELT philosophy, which is that data should be untouched as it moves through the extracting and loading stages so that the raw data is always available at the destination. Since an unmodified version of the data exists in the destination, it can be re-transformed in the future without the need for a resync of data from source systems.
Airbyte supports hundreds of data sources and destinations including:
Azure Event Hub
Other custom sources
By specifying credentials and adding extensions you can also ingest from and dump to:
Azure Data Lake
Google Cloud Storage
Amazon S3 & Kinesis
Other major features that Airbyte offers:
High extensibility: Use existing connectors to your needs or build a new one with ease.
Customization: Entirely customizable, starting with raw data or from some suggestion of normalized data.
Full-grade scheduler: Automate your replications with the frequency you need.
Real-time monitoring: Logs all the errors in full detail to help you understand better.
Incremental updates: Automated replications are based on incremental updates to reduce your data transfer costs.
Manual full refresh: Re-syncs all your data to start again whenever you want.
Debugging: Debug and Modify pipelines as you see fit, without waiting.
What does Data Science Dojo provide?
Airbyte instance packaged by Data Science Dojo serves as a pre-configured ELT pipeline that makes data integration pipelines a commodity without the burden of installation. It offers efficient data migration and supports a variety of data sources and destinations to ingest and dump data.
Features included in this offer:
Airbyte service that is easily accessible from the web and has a rich user interface.
Easy to operate and user-friendly.
Strong community support due to the open-source platform.
Free to use.
There are a ton of small services that aren’t supported on traditional data pipeline platforms. If you can’t import all your data, you may only have a partial picture of your business. Airbyte solves this problem through custom connectors that you can build for any platform and make them run quickly.
Install the Airbyte offer now from the Azure Marketplace by Data Science Dojo, your ideal companion in your journey to learn data science!
Click on the button below to head over to the Azure Marketplace and deploy Airbyte for FREE by clicking below:
In this blog post, the author introduces the new blog series about the titular three main disciplines or knowledge domains of software development, project management, and data science. Amidst the mercurial evolving global digital economy, how can job-seekers harness the lucrative value of those fields–esp. data science, vis-a-vis improving their employability?
To help us launch this blog series, I will gladly divulge two embarrassing truths. These are:
Despite my marked love of LinkedIn, and despite my decent / above-average levels of general knowledge, I cannot keep up with the ever-changing statistics or news reports vis-a-vis whether–at any given time, the global economy is favorable to job-seekers, or to employers, or is at equilibrium for all parties–i.e., governments, employers, and workers.
Despite having rightfully earned those fancy three letters after my name, as well as a post-graduate certificate from the U. New Mexico & DS-Dojo, I (used to think I) hate math, or I (used to think I) cannot learn math; not even if my life depended on it!
Following my undergraduate years of college algebra and basic discrete math–and despite my hatred of mathematics since 2nd grade (chief culprit: multiplication tables!), I had fallen in love (head-over-heels indeed!) with the interdisciplinary field of research methods. And sure, I had lucked out in my Masters (of Arts in Communication Studies) program, as I only had to take the qualitative methods course.
But our instructor couldn’t really teach us about interpretive methods, ethnography, and qualitative interviewing etc., without at least “touching” on quantitative interviewing/surveys, quantitative data-analysis–e.g. via word counts, content-analysis, etc.
Fast-forward; year: 2012. Place: Drexel University–in Philadelphia, for my Ph.D. program (in Communication, Culture, and Media). This time, I had to face the dreaded mathematics/statistics monster. And I did, but grudgingly.
Let’s just get this over with, I naively thought; after all, besides passing this pesky required pre-qualifying exam course, who needs stats?!
About software development:
Fast-forward again; year: 2020. Place(s): Union, NJ and Wenzhou, Zhejiang Province; Hays, KS; and Philadelphia all over again. Five years after earning the Ph.D., I had to reckon with an unfair job loss, and chaotic seesaw-moves between China and the USA, and Philadelphia and Kansas, etc.
But like many other folks who try this route, I soon came face-to-face with that oh-so-debilitative monster: self-doubt! No way, I thought. I’m NOT cut out to be a software-engineer! I thus dropped out of the bootcamp I had enrolled in and continued my search for a suitable “plan-B” career.
About project management:
Eventually (around mid/late-2021), I discovered the interdisciplinary field of project management. Simply defined (e.g. by Te Wu, 2020; link), project management is
“A time-limited, purpose-driven, and often unique endeavor to create an outcome, service, product, or deliverable.”
One can also break down the constituent conceptual parts of the field (e.g. as defined by Belinda Goodrich, 2021; link) as:
Project life cycle,
Professional responsibility / ethics.
Ah…yes! I had found my sweet spot, indeed. or, so I thought.
Eventually, I experienced a series of events that can be termed “slow-motion epiphanies” and hard truths. Among many, below are three prime examples.
Hard Truth 1: The quantifiability of life:
For instance, among other “random” models: one can generally presume–with about 95% certainty (ahem!)–that most of the phenomena we experience in life can be categorized under three broad classes:
Phenomena we can easily describe and order, using names (nominal variables);
Phenomena we can easily group or measure in discrete and evenly-spaced amounts (ordinal variables);
And phenomena that we can measure more accurately, and which: i)–is characterized by trait number two above, and ii)–has a true 0 (e.g., Wrench et Al; link).
Hard Truth 2: The probabilistic essence of life:
Regardless of our spiritual beliefs, or whether or not we hate math/science, etc., we can safely presume that the universe we live in is more or less a result of probabilistic processes (e.g., Feynman, 2013).
Hard truth 3: What was that? “Show you the money (!),” you demanded? Sure! But first, show me your quantitative literacy, and critical-thinking skills!
And finally, related to both the above realizations: while it is true indeed that there are no guarantees in life, we can nonetheless safely presume that professionals can improve their marketability by demonstrating their critical-thinking-, as well as quantitative literacy skills.
Bottomline; The value of data science:
Overall, the above three hard truths are prototypical examples of the underlying rationale(s) for this blog series. Each week, DS-Dojo will present our readers with some “food for thought” vis-a-vis how to harness the priceless value of data science and various other software-development and project-management skills / (sub-)topics.
No, dear reader; please do not be fooled by that “OmG, AI is replacing us (!)” fallacy. Regardless of how “awesome” all these new fancy AI tools are, the human touch is indispensable!
Bellevue, Washington (January 11, 2023) – The following statement was released today by Data Science Dojo, through its Marketing Manager Nathan Piccini, in response to questions about future in-person bootcamps:
In this blog, we will share the list of leading data science conferences across the world to be held in 2023. This will help you to learn and grow your career in data science, AI and machine learning.
1. AAAI Conference on Artificial Intelligence – Washington DC, United States
The AAAI Conference on Artificial Intelligence (AAAI) is a leading conference in the field of artificial intelligence research. It is held annually in Washington, DC and attracts researchers, practitioners, and students from around the world to present and discuss their latest work.
The conference features a wide range of topics within AI, including machine learning, natural language processing, computer vision, and robotics, as well as interdisciplinary areas such as AI and law, AI and education, and AI and the arts. It also includes tutorials, workshops, and invited talks by leading experts in the field. The conference is organized by the Association for the Advancement of Artificial Intelligence (AAAI), which is a non-profit organization dedicated to advancing AI research and education.
2. Women in Data Science (WiDS) – California, United States
Women in Data Science (WiDS) is an annual conference held at Stanford University, California, United States and other locations worldwide. The conference is focused on the representation, education, and achievements of women in the field of data science. WiDS is designed to inspire and educate data scientists worldwide, regardless of gender, and support women in the field.
The conference is a one-day technical conference that provides an opportunity to hear about the latest data science related research, and applications in various industries, as well as to network with other professionals in the field. The conference features keynote speakers, panel discussions, and technical presentations from prominent women in the field of data science. WiDS aims to promote gender diversity in the tech industry, and to support the career development of women in data science.
3. Gartner Data and Analytics Summit – Florida, United States
The Gartner Data and Analytics Summit is an annual conference that is held in Florida, United States. The conference is organized by Gartner, a leading research and advisory company, and is focused on the latest trends, strategies, and technologies in data and analytics.
The conference brings together business leaders, data analysts, and technology professionals to discuss the latest trends and innovations in data and analytics, and how they can be applied to drive business success.
The conference features keynote presentations, panel discussions, and breakout sessions on topics such as big data, data governance, data visualization, artificial intelligence, and machine learning. Attendees also have the opportunity to meet with leading vendors and solutions providers in the data and analytics space, and network with peers in the industry.
The Gartner Data and Analytics Summit is considered as a leading event for professionals in the data and analytics field.
4. ODSC East – Boston, United States
ODSC East is a conference on open-source data science and machine learning held annually in Boston, United States. The conference features keynote speeches, tutorials, and training sessions by leading experts in the field, as well as networking opportunities for attendees.
The conference covers a wide range of topics in data science, including machine learning, deep learning, big data, data visualization, and more. It is designed for data scientists, developers, researchers, and practitioners looking to stay up-to-date on the latest advancements in the field and learn new skills.
5. AI and Big Data Expo North America – California, United States
AI and Big Data Expo North America is a technology event that focuses on artificial intelligence (AI) and big data. The conference takes place annually in Santa Clara, California, United States. The event is for enterprise technology professionals seeking to explore the latest innovations, implementations, and strategies in AI and big data.
The event features keynote speeches, panel discussions, and networking opportunities for attendees to connect with leading experts and industry professionals. The conference covers a wide range of topics, including machine learning, deep learning, big data, data visualization, and more.
6. The Data Science Conference – Chicago, United States
The Data Science Conference is an annual data science conference held in Chicago, United States. The conference focuses on providing a space for analytics professionals to network and learn from one another without being prospected by vendors, sponsors, or recruiters.
The conference is by professionals for professionals and the material presented is substantial and relevant to the data science practitioner. It is the only sponsor-free, vendor-free, and recruiter-free data science conference℠. The conference covers a wide range of topics in data science, including artificial intelligence, machine learning, predictive modeling, data mining, data analytics and more.
7. Machine Learning Week – Las Vegas, United States
Machine Learning Week is a large conference that focuses on the commercial deployment of machine learning. It is set to take place in Las Vegas, United States, with the venue being the Red Rock Casino Resort Spa. The conference will have seven tracks of sessions, with six co-located conferences that attendees can register to attend: PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, PAW Climate and Deep Learning World.
8. International Conference on Mass Data Analysis of Images and Signals – New York, United States
The conference is not limited to these specific topics and welcomes research from other related fields as well. The conference has been held on a yearly basis
9. International Conference on Data Mining (ICDM) – New York, United States
The International Conference on Data Mining (ICDM) is an annual conference held in New York, United States that focuses on the latest research and developments in the field of data mining. The conference brings together researchers and practitioners from academia, industry, and government to present and discuss their latest research findings, ideas, and applications in data mining. The conference covers a wide range of topics, including machine learning, data mining, big data, data visualization, and more.
10. International Conference on Machine Learning and Data Mining (MLDM) – New York, United States
International Conference on Machine Learning and Data Mining (MLDM) is an annual conference held in New York, United States. The conference focuses on the latest research and developments in the field of machine learning and data mining. The conference brings together researchers and practitioners from academia, industry, and government to present and discuss their latest research findings, ideas, and applications in machine learning and data mining.
The conference covers a wide range of topics, including machine learning, data mining, big data, data visualization, and more. The conference is considered a premier forum for researchers and practitioners to share their latest research, ideas and development in machine learning and data mining and related areas.
11. AI in Healthcare Summit – Boston, United States
AI in Healthcare Summit is an annual event that takes place in Boston, United States. The summit focuses on showcasing the opportunities of advancing methods in AI and machine learning (ML) and their impact across healthcare and medicine. The event features a global line-up of experts who will present on the latest ML tools and techniques that are set to revolutionize healthcare applications, medicine and diagnostics. Attendees will have the opportunity to discover the AI methods and tools that are set to revolutionize healthcare, medicine and diagnostics, as well as industry applications and key insights.
12. Big Data and Analytics Summit – Ontario, Canada
The Big Data and Analytics Summit is an annual conference held in Ontario, Canada. The conference focuses on connecting analytics leaders to the latest innovations in big data and analytics as the world adapts to new business realities after the global pandemic. Businesses need to innovate in products, sales, marketing and operations and big data is now more critical than ever to make this happen and help organizations thrive in the future. The conference features leading industry experts who will discuss the latest trends exploding across the big data landscape, including security, architecture and transformation, cloud migration, governance, storage, AI and ML and so much more.
13. Deep Learning Summit – Montreal, Canada
The Deep Learning Summit is an annual conference held in Montreal, Canada. The conference focuses on providing attendees access to multiple stages to optimize cross-industry learnings and collaboration. Attendees can solve shared problems with like-minded attendees during round table discussions, Q&A sessions with speakers or schedule 1:1 meetings. The conference also provides an opportunity for attendees to connect with other attendees during and after the summit and build new collaborations through interactive networking sessions.
14. Enterprise AI Summit – Montreal, Canada
The Enterprise AI Summit is an annual conference that takes place in Montreal, Canada. The conference is organized by RE-WORK LTD, and it is scheduled for November 1-2, 2023. The conference will feature the Deep Learning Summit and Enterprise AI Summit as part of the Montreal AI Summit. The conference is an opportunity for attendees to learn about the latest advancements in AI and Machine Learning and how it can be applied in the enterprise. The conference is a 2-day event that features leading industry experts who will share their insights and experiences on AI and ML in the enterprise
15. Extraction and Knowledge Management Conference (EGC) – Lyon, France
The Extraction and Knowledge Management Conference (EGC) is an annual event that brings together researchers and practitioners from various disciplines related to data science and knowledge management. The conference will be held on the Berges du Rhône campus of the Université Lumière Lyon 2, from January 16 to 20, 2023. The conference provides a forum for researchers, students, and professionals to present their research results and exchange ideas and discuss future challenges in knowledge extraction and management.
16. Women in AI and Data Reception – London, United Kingdom
The Women in AI and Data Reception is an event organized by RE•WORK in London, United Kingdom that takes place on January 24th, 2023. The conference aims to bring together leading female experts in the field of artificial intelligence and machine learning to discuss the impact of this rapidly advancing technology on various sectors such as finance, retail, manufacturing, transport, healthcare and security. Attendees will have the opportunity to hear from these experts, establish new connections and network with peers
17. Chief Data and Analytics Officers (CDAO) – London, United Kingdom
The Chief Data and Analytics Officers (CDAO) conference is an annual event organized by Corinium Global Intelligence, which brings together senior leaders from the data and analytics space. The conference is focused on the acceleration of the adoption of data, analytics and AI in order to generate decision advantages across various industries. The conference will take place on September 13-14, 2023 in Washington D.C. and will include sessions on latest trends, strategies, and best practices for data and analytics, as well as networking opportunities for attendees.
18. International Conference on Pattern Recognition Applications and Methods (ICPRAM) – Lisbon, Portugal
The International Conference on Pattern Recognition Applications and Methods (ICPRAM) is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning. It will be held in Lisbon, Portugal and submissions for abstracts and doctoral consortium papers are due on January 2, 2023. Registration to ICPRAM also allows free access to the ICAART conference as a non-speaker. It is a annual event where researchers can exchange ideas and discuss future challenges in pattern recognition and machine learning
19. AI in Finance Summit – London, United Kingdom
The AI in Finance Summit, taking place in London, United Kingdom, is an event that brings together leaders in the financial industry to discuss the latest advancements and innovations in artificial intelligence and its applications in finance. Attendees will have the opportunity to hear from experts in the field, network with peers, and learn about the latest trends and technologies in AI and finance. The summit will cover topics such as investment, risk management, fraud detection, and more
20. The Martech Summit – Hong Kong
The Martech Summit is an event that brings together the best minds in marketing technology from a range of industries through a number of diverse formats and engaging events. The conference aims to bring together people in senior leadership roles, such as C-suites, Heads, and Directors, to learn and network with industry experts. The MarTech Summit series includes various formats such as The MarTech Summit, The Virtual MarTech Summit, Virtual MarTech Spotlight, and The MarTech Roundtable.
21. AI and Big Data Expo Europe – Amsterdam, Netherlands
The AI and Big Data Expo Europe is an event that takes place in Amsterdam, Netherlands. The event is scheduled to take place on September 26-27, 2023 at the RAI, Amsterdam. It is organized by Encore Media. The event will explore the latest innovations within AI and Big Data in 2023 and beyond, and covers the impact AI and Big Data technologies have on many industries including manufacturing, transport, supply chain, government, legal and more. The conference will also showcase next generation technologies and strategies from the world of Artificial Intelligence.
22. International Symposium on Artificial Intelligence and Robotics (ISAIR) – Beijing, China
The International Symposium on Artificial Intelligence and Robotics (ISAIR) is a platform for young researchers to share up-to-date scientific achievements in the field of Artificial Intelligence and Robotics. The conference is organized by the International Society for Artificial Intelligence and Robotics (ISAIR), IEEE Big Data TC, and SPIE. It aims to provide a comprehensive conference focused on the latest research in Artificial Intelligence, Robotics and Automation in Space.
23. The Martech Summit – Jakarta, Indonesia
The Martech Summit – Jakarta, Indonesia is a conference organized by BEETC Ltd that brings together the best minds in marketing technology from a range of industries through a number of diverse formats and engaging events. The conference aims to provide a platform for attendees to learn about the latest trends and innovations in marketing technology, with an agenda that includes panel discussions, keynote presentations, fireside chats, and more.
24. Web Search and Data Mining (WSDM) – Singapore
The 16th ACM International WSDM Conference will be held in Singapore on February 27 to March 3, 2023. The conference is a highly selective event that includes invited talks and refereed full papers. The conference focuses on publishing original and high-quality papers related to search and data mining on the Web. The conference is organized by the WSDM conference series and is a platform for researchers to share their latest scientific achievements in this field.
25. Machine Learning Developers Summit – Bangalore, India
The Machine Learning Developers Summit (MLDS) is a 2-day conference that focuses on machine learning innovation. Attendees will have direct access to top innovators from leading tech companies who will share their knowledge on the software architecture of ML systems, how to produce and deploy the latest ML frameworks, and solutions for business use cases. The conference is an opportunity for attendees to learn how machine learning can add potential to their business and gain best practices from cutting-edge presentations
CISO Malaysia 2023 is a conference designed for Chief Information Security Officers (CISOs), Chief Security Officers (CSOs), Directors, Heads, Managers of Cyber and Information Security, and cybersecurity practitioners from across sectors in Malaysia. The conference will be held on February 14, 2023 in Kuala Lumpur, Malaysia. It aims to provide a platform for attendees to get inspired, make new contacts and learn how to uplift their organizations security program to meet the requirements set by the government and citizens.
Which data science conferences would you like to participate in?
In conclusion, data science and AI conferences are an invaluable opportunity to stay up to date with the latest developments in the field, network with industry leaders and experts, and gain valuable insights and knowledge. These are some of the top conferences in the field and offer a wide range of topics and perspectives. Whether you are a researcher, practitioner, or student, these conferences are a valuable opportunity to further your understanding of data science and AI and advance your career.
Additionally, there are many other conferences out there that might be specific to a certain industry or region, it’s important to research and find the one that fits your interest and needs. Attending these conferences is a great way to stay ahead of the curve and make meaningful connections within the data science and AI community.
Data science myths are one of the main obstacles preventing newcomers from joining the field. In this blog, we bust some of the biggest myths shrouding the field.
The US Bureau of Labor Statistics predicts that data science jobs will grow up to 36% by 2031. There’s a clear market need for the field and its popularity only increases by the day. Despite the overwhelming interest data science has generated, there are many myths preventing new entry into the field.
Data science myths, at their heart, follow misconceptions about the field at large. So, let’s dive into unveiling these myths.
1. All data roles are identical
It’s a common data science myth that all data roles are the same. So, let’s distinguish between some common data roles – data engineer, data scientist, and data analyst. A data engineer focuses on implementing infrastructure for data acquisition and data transformation to ensure data availability to other roles.
A data analyst, however, uses data to report any observed trends and patterns to report. Using both the data and the analysis provided by a data engineer and a data analyst, a data scientist works on predictive modeling, distinguishing signals from noise, and deciphering causation from correlation.
Finally, these are not the only data roles. Other specialized roles such as data architects and business analysts also exist in the field. Hence, a variety of roles exist under the umbrella of data science, catering to a variety of individual skill sets and market needs.
2. Graduate studies are essential
Another myth preventing entry into the data science field is that you need a master’s or Ph.D. degree. This is also completely untrue.
In busting the last myth, we saw how data science is a diverse field welcoming various backgrounds and skill sets. As such, a Ph.D. or master’s degree is only valuable for specific data science roles. For instance, higher education is useful in pursuing research in data science.
However, if you’re interested in working on real-life complex data problems using data analytics methods such as deep learning, only knowledge of those methods is necessary. And so, rather than a master’s or Ph.D. degree, acquiring specific valuable skills can come in handier in kickstarting your data science career.
3. Data scientists will be replaced by artificial intelligence
As artificial intelligence advances, a common misconception arises that AI will replace all human intelligent labor. This misconception has also found its way into data science forming one of the most popular myths that AI will replace data scientists.
This is far from the truth because. Today’s AI systems, even the most advanced ones, require human guidance to work. Moreover, the results produced by them are only useful when analyzed and interpreted in the context of real-world phenomena, which requires human input.
So, even as data science methods head towards automation, it’s data scientists who shape the research questions, devise the analytic procedures to be followed, and lastly, interpret the results.
Being a data scientist does not translate into being an expert programmer! Programming tasks are only one component of the data science field, and these too, vary from one data science subfield to another.
For example, a business analyst would require a strong understanding of business, and familiarity with visualization tools, while minimal coding knowledge would suffice. At the same time, a machine learning engineer would require extensive knowledge of Python.
In conclusion, the extent of programming knowledge depends on where you want to work across the broad spectrum of the data science field.
5. Learning a tool is enough to become a data scientist
Knowing a particular programming language, or a data visualization tool is not all you need to become a data scientist. While familiarity with tools and programming languages certainly helps, this is not the foundation of what makes a data scientist.
So, what makes a good data science profile? That, really, is a combination of various skills, both technical and non-technical. On the technical end, there are mathematical concepts, algorithms, data structures, etc. While on the non-technical end there are business skills and understanding of various stakeholders in a particular situation.
To conclude, a tool can be an excellent way to implement data science skills. However, it isn’t what will teach you the foundations or the problem-solving aspect of data science.
6. Data scientists only work on predictive modeling
Another myth! Very few people would know that data scientists spend nearly 80% of their time on data cleaning and transforming before working on data modeling. In fact, bad data is the major cause of productivity levels not being up to par in data science companies. This requires significant focus on producing good quality data in the first place.
This is especially true when data scientists work on problems involving big data. These problems involve multiple steps of which data cleaning and transformations are key. Similarly, data from multiple sources and raw data can contain junk that needs to be carefully removed so that the model runs smoothly.
So, unless we find a quick-fix solution to data cleaning and transformation, it’s a total myth that data scientists only work on predictive modeling.
7. Transitioning to data science is impossible
Data science is a diverse and versatile field welcoming a multitude of background skill sets. While technical knowledge of algorithms, probability, calculus, and machine learning can be great, non-technical knowledge such as business skills or social sciences can also be useful for a data science career.
At its heart, data science involves complex problem solving involving multiple stakeholders. For a data-driven company, a data scientist from a purely technical background could be valuable but so could one from a business background who can better interpret results or shape research questions.
And so, it’s a total myth that transitioning to data science from another field is impossible.
In our current era, the terms “AI”, “ML”, “analytics”–etc., are indeed THE “buzzwords” du jour. And yes, these interdisciplinary subjects/topics are **very** important, given our ever-increasing computing capabilities, big-data systems, etc.
The problem, however, is that **very few** folks know how to teach these concepts! But to be fair, teaching in general–even for the easiest subjects–is hard. In any case, **this**–the ability to effectively teach the concepts of data-science–is the genius of DS-Dojo. Raja and his team make these concepts considerably easy to grasp and practice, giving students both a “big picture-,” as well as a minutiae-level understanding of many of the necessary details.
Still, a leery prospective student might wonder if the program is worth their time, effort, and financial resources. In the sections below, I attempt to address this concern, elaborating on some of the unique value propositions of DS-Dojo’s pedagogical methods.
The More Things Change…
Data Science enthusiasts today might not realize it, but many of the techniques–in their basic or other forms–have been around for decades. Thus, before diving into the details of data-science processes, students are reminded that long before the terms “big data,” AI/ML and others became popularized, various industries had all utilized techniques similar to many of today’s data-science models. These include (among others): insurance, search-engines, online shopping portals, and social networks.
This exposure helps Data-Science Dojo students consider the numerous creative ways of gathering and using big-data from various sources–i.e. directly from human activities or information, or from digital footprints or byproducts of our use of online technologies.
The big picture of the Data Science Bootcamp
As for the main curriculum contents, first, DS-Dojo students learn the basics of data exploration, processing/cleaning, and engineering. Students are also taught how to tell stories with data. After all, without predictive or prescriptive–and other–insights, big data is useless.
The bootcamp also stresses the importance of domain knowledge, and relatedly, an awareness of what precise data-points should be sought and analyzed. DS-Dojo also trains students to critically assess: why, and how should we classify data? Students also learn the typical data-collection, processing, and analysis pipeline, i.e.:
And finally, interpretation and evaluation.
However, any aspiring (good) data scientist should disabuse themselves of the notion that the process doesn’t present challenges. Au contraire, there are numerous challenges; e.g. (among others):
Complex and heterogeneous data
Data ownership and distribution,
Following the above coverage of the craft’s introductory processes and challenges, DS-Dojo students are then led earnestly into the deeper ends of data-science characteristics and features. For instance, vis-a-vis predictive analytics, how should a data-scientist decide when to use unsupervised learning, versus supervised learning? Among other considerations, practitioners can decide using the criteria listed below.
Unsupervised Learning…Vs. … >>
<< …Vs. …Supervised Learning
>> Target values unknown
>> Targets known
>> Training data unlabeled
>> Data labeled
>> Goal: discover information hidden in the data
>> Goal: Find a way to map attributes to target value(s)
Overall, the main domains covered by DS-Dojo’s data-science bootcamp curriculum are:
An introduction/overview of the field, including the above-described “big picture,” as well as visualization, and an emphasis on story-telling–or, stated differently, the retrieval of actual/real insights from data;
Overview of classification processes and tools
Applications of classification
Special topics–e.g., text-analysis
And “last but [certainly] not least,” big-data engineering and distribution systems.
In addition to the above-described advantageous traits, data-science enthusiasts, aspirants, and practitioners who join this program will be pleasantly surprised with the bootcamp’s de-emphasis on specific tools/approaches. In other words, instead of using doctrinaire approaches that favor only Python, or R, Azure, etc., DS-Dojo emphasizes the need for pragmatism; practitioners should embrace the variety of tools at our disposal.
“Whoo-Hoo! Yes, I’m a Data Scientist!”
By the end of the bootcamp, students might be tempted to adopt the above stance–i.e., as stated above (as this section’s title/subheading). But as a proud alumnus of the program, I would cautiously respond: “Maybe!” And if you have indeed mastered the concepts and tools, congratulations!
But strive to remember that the most passionate data-science practitioners possess a rather paradoxical trait: humility, and an openness to lifelong learning. As Raja Iqbal, CEO of DS-Dojo pointed out in one of the earlier lectures: The more I learn, the more I realize what I don’t know. Happy data-crunching!
Writing an SEO optimized blog is important because it can help increase the visibility of your blog on search engines, such as Google. When you use relevant keywords in your blog, it makes it easier for search engines to understand the content of your blog and to determine its relevance to specific search queries.
Consequently, your blog is more likely to rank higher on search engine results pages (SERPs), which can lead to more traffic and potential readers for your blog.
In addition to increasing the visibility of your blog, SEO optimization can also help to establish your blog as a credible and trustworthy source of information. By using relevant keywords and including external links to reputable sources, you can signal to search engines that your content is high-quality and valuable to readers.
5 things to consider for writing a top-performing blog
A successful blog reflects top-quality content and valuable information put together in coherent and comprehensible language to hook the readers.
The following key points can assist to strengthen your blog’s reputation and authority, resulting in more traffic and readers in the long haul.
1. Handpick topics from industry news and trends: One way to identify popular topics is to stay up to date on the latest developments in the data science and analytics industry. You can do this by reading industry news sources and following influencers on social media.
2. Use free – keyword research tools: Do not panic! You are not required to purchase any keyword tool to accomplish this step. Simply enter your potential blog topic on search engine such as Google and check out the top trending write-ups available online.
This helps you identify popular keywords related to data science and analytics. By analyzing search volume and competition for different keywords, you can get a sense of what topics are most in demand.
3. Look for the untapped information in the market: Another way to identify high-ranking blog topics is to look for areas where there is a lack of information or coverage. By filling these gaps, you can create content that is highly valuable and unique to your audience.
4. Understand the target audience: When selecting a topic, it’s also important to consider the interests and needs of your target audience. Check out the leading tech discussion forums and groups on Quora, LinkedIn, and Reddit to get familiar with the upcoming discussion ideas. What are they most interested in learning about? What questions do they have? By addressing these issues, you can create content that resonates with your readers.
5. Look into the leading industry websites: Finally, take a look at what other data science and analytics bloggers are writing about. From these acknowledged websites of the industry, you can get ideas for topics that help you identify areas where you can differentiate yourself from the competition
Recommended blog structure for SEO:
Overall, SEO optimization is a crucial aspect of blog writing that can help to increase the reach and impact of your content. The correct flow of your blog can increase your chances of gaining visibility and reaching a wider audience. Following are the step-by-step guidelines to write an SEO optimized blog on data science and analytics:
1. Choose relevant and targeted keywords:
Identify the keywords that are most relevant to your blog topic. Some of the popular keywords related to data science topics can be:
Business Intelligence (BI)
These are some of the keywords that are commonly searched by your target audience. Incorporate these keywords into your blog title, headings, and throughout the body of your post. Read the beginner’s guide to keyword research by Moz.
2. Use internal and external links:
Include internal links to other pages or blog posts on the website you are publishing your blog, and external links to reputable sources to support your content and improve its credibility.
3. Use header tags:
Use header tags (H1, H2, H3, etc.) to structure your blog post and signal to search engines the hierarchy of your content. Here is an example of a blog with the recommended header tags and blog structure:
H2: Linear Algebra and Optimization for Machine Learning
H2: The Hundred-Page Machine Learning Book
H2: R for everyone
H2: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
4. Use alt text for images:
Add alt text to your images to describe their content and improve the accessibility of your blog. Alt text is used to describe the content of an image on a web page. It is especially important for people who are using screen readers to access your website, as it provides a text-based description of the image for them.
Alt text is also used by search engines to understand the content of images and to determine the relevance of a web page to a specific search query.
5. Use a descriptive and keyword-rich URL:
Make sure your blog post URL accurately reflects the content of your post and includes your targeted keywords. For example, if the target keyword for your blog is data science books, then the URL must include the keyword in it such as “top-data-science-books“.
6. Write a compelling meta description:
The meta description is the brief summary that appears in the search results below your blog title. Use it to summarize the main points of your blog post and include your targeted keywords. For the blog topic: Top 6 data science books to learn in 2023, the meta description can be:
“Looking to up your data science game in 2023? Check out our list of the top 6 data science books to read this year. From foundational concepts to advanced techniques, these books cover a wide range of topics and will help you become a well-rounded data scientist.”
Share your data science insights with the world
If this blog helped you learn writing a search engine friendly blog, then without waiting a further, choose the topic of your choice and start writing. We offer a platform to industry experts and knowledge geeks to evoke their ideas and share them with a million plus community of data science enthusiasts across the globe.
Every eCommerce business depends on information to improve its sales. Data science can source, organize and visualize information. It also helps draw insights about customers, marketing channels, and competitors.
Every piece of information can serve different purposes. You can use data science to improve sales, customer service, user experience, marketing campaigns, purchase journeys, and more.
How to use Data Science to boost eCommerce sales
Sales in eCommerce depend on a variety of factors. You can use data to optimize each step in a customer’s journey to gain conversions and enhance revenue from each conversion.
Analyze Consumer Behavior
Data science can help you learn a lot about the consumer. Understanding consumer behavior is crucial for eCommerce businesses as it dictates the majority of their decisions.
Consumer behavior analysis is all about understanding the relationship between things you can do and customers’ reactions to them. This analysis requires data science as well as psychology. The end goal is not just understanding consumer behavior, but predicting it.
For example, if you have an eCommerce store for antique jewelry, you will want to understand what type of people buy antique jewelry, where they search for it, how they buy it, what information they seek before purchasing, what occasions they buy it for, and so on.
You can extract data on consumer behavior on your website, social media, search engines, and even other eCommerce websites. This data will help you understand customers and predict their behavior. This is crucial for audience segmentation.
Data science can help segment audiences based on demographics, characteristics, preferences, shopping patterns, spending habits, and more. You create different strategies to convert audiences of different segments.
Audience segments play a crucial role in designing purchase journeys, starting from awareness campaigns all the way to purchase and beyond.
Optimize digital marketing for better conversion
You need insights from data analytics to make important marketing decisions. Customer acquisition information can tell you where the majority of your audience comes from. You can also identify which sources give you maximum conversions.
You can then use data to improve the performance of your weak sources and reinforce the marketing efforts of high-performing sources. Either way, you can ensure that your marketing efforts are helping your bottom line.
Once you have locked down your channels of marketing, data science can help you improve results from marketing campaigns. You can learn what type of content or ads perform the best for your eCommerce website.
Data science will also tell you when the majority of your audience is online on the channel and how they interact with your content. Most marketers try to fight the algorithms to win. But with data science, you can uncover the secrets of social media algorithms to maximize your conversions.
Suggest products for upselling & cross-selling
Upselling & Cross-selling are some of the most common sales techniques employed by ecommerce platforms. Data science can help make them more effective. With Market Basket or Affinity Analysis, data scientists can identify relationships between different products.
By analyzing such information of past purchases and shopping patterns you can derive criteria for upselling and cross-selling. The average amount they spend on a particular type of product tells you how high you can upsell. If the data says that customers are more likely to purchase a particular brand, design, or color; you can upsell accordingly.
Similarly, you can offer relevant cross-selling suggestions based on customers’ data. Each product opens numerous cross-selling options.
Instead of offering general options, you can use data from various sources to offer targeted suggestions. You can give suggestions based on individual customers’ preferences. For instance, A customer is more likely to click on a suggestion saying “A Red Sweater to go with your Blue Jeans’ ‘ if their previous purchase shows an inclination for the color red.
This way data science can help increase probability of upsold & cross-sold purchases so that eCommerce businesses get more revenue from their customers.
Analyze consumer feedback
Consumers provide feedback in a variety of ways, some of which can only be understood by learning data science. It is not just about reviews and ratings. Customers speak about their experience through social media posts, social shares, and comments as well.
Feedback data can be extracted from several places and usually comes in large volumes. Data scientists use techniques like text analytics, computational linguistics, and natural language processing to analyze this data.
For instance, you can compare the percentage of positive words and negative words used in reviews to get a general idea about customer satisfaction.
But feedback analysis does not stop with language. Consumer feedback is also hidden in metrics like time spent on page, CTR, cart abandonment, clicks on page, heat maps and so on. Data on such sublime behaviors can tell you more about the customer’s experience with your eCommerce website than reviews, ratings and feedback forms.
This information helps you identify problem areas that cause your customers to turn away from a purchase.
Personalize customer experience
To create a personalized experience, you need information about the customer’s behavior, previous purchases, and social activity. This information is scattered across the web, and you need lessons in data science to bring it to one place. But, more importantly, data science helps you draw insights from information.
With this insight you can create different journeys for different customer segments. You utilize data points to map a sequence of options that would lead a customer to conversion. 80% customers are more likely to purchase if the eCommerce website offers a personalized experience.
For example: Your data analytics say that a particular customer has checked out hiking boots but has abandoned most purchases at the cart. Now you can focus on personalizing this customer’s experience by focusing on cart abandonment issues such as additional charges, postage shipping cost, payment options etc.
Several eCommerce websites use data to train their chatbots to serve as personal shopping assistants for their customers. These bots use different data points to give relevant shopping ideas.
You can also draw insights from data science to personalize offers, discounts, landing pages, product gallery, upselling suggestions, cross-selling ideas and more.
Use data science for decision making & automation
The information provided by data science serves as the foundation for decision-making for eCommerce businesses. In a competitive market, a key piece of information can help you outshine your competitors, gain more customers and provide a better customer experience.
As a beginner in data science, one of the hardest things is to land their first job and to build an impressive portfolio. We are all aware of the vicious cycle of not getting a job because of no experience, and no experience because of no job.
Most of us get stuck in this cycle either when we are starting our careers or when we are transitioning into another career. A career in data science is no different, but the question arises of how to break through this cycle and land your first job.
To answer this, Data Science Dojo collaborated with Avery Smith to conduct a webinar for every beginner in data science who is stepping into the real world. He discussed some useful tips to help data scientists build a data science portfolio.
Avery’s secret to breaking into the data science industry is through “Projects”, which you can create to show off your skills and knowledge in your next interview. In this session, Avery took us through the best practices for creating a project that makes you stand out and helps you land your dream job.
5 tips to create the best projects to improve portfolio
1. Choose the right topic
Choosing a topic that you can write passionately about is very important because that is the only way you will feel motivated to finish the project. If you are wondering where passion comes from, it could be something out of your hobbies or your next/dream job. The fun trick taught by Avery is to think about any hobby or industry you are passionate about.
Next, go to your LinkedIn job section and search for data-related roles in the fields you are interested in. After that, find a job or company that you would like to work in, and scroll down to look for the qualifications required for that job.
For instance, if the job requires SQL, Python, and Tableau skills, you should create a project that involves these three. You will also look at what the company does and its job requirements, to make your project as relevant as possible.
2. Get good data
If you have successfully decided on a topic to work on, now you must be thinking about where to find relevant data. There are four main ways of gathering data, as Avery pointed out:
Using an API
Collecting your data
These four ways are mentioned in order to increase the difficulty to get each and the more unique it is. Although downloading a CSV is easy, it’s not overly impressive. Collecting your own data is exceedingly difficult but is unique and will make a larger impact in showing off your skillset.
3. Decide on the type of project
There are three types of projects:
Skillshare- a few steps in Python or a SQL query or a graph in the dashboard. It’s not like a whole project but a section of the project.
Data Story- a whole paragraph with multiple lines of codes, and multiple graphs which is more like a complete article.
Product- a tool or app that you can give to someone, and they can use it.
The types are in order of increasing difficulty and impressiveness, skill share is easiest to do but not very impressive while on the other hand product is very difficult but highly impressive. In the webinar, Avery explained these using examples for each type of project.
4. Focus on visualization
Visualization is one of the easiest to do, looks impressive, and you can start it today. For beginners who feel like they are not ready to work on a big project, data visualization is something you can start working on day one. There are several tools and software available which are easy to learn and can help in creating amazing projects, you can learn more about visualization tips and techniques.
5. The best project is the one you can finish
Many data scientists have several projects that they started but never got the chance to finish. A very little-known fact is that these projects can become their marketeers by attracting recruiters and helping them land the right job. For that you need to get these projects out there, nothing is going to happen if keep them restricted to your computer.
For this reason, we need to finish and publish these projects. Avery’s advice has been to avoid the scenario where you have several unfinished projects and you decide to start another, the goal is to have published projects. To better understand it, Avery introduced us to the concept of Modular Projects.
What are modular projects
Avery explained the concept of modular projects with marathons. People who run a marathon don’t do it all at once. First, they run 5k, then 10k, maybe a half marathon, and probably then they can run a full marathon. Similarly for a project, don’t go for a marathon project off the start. Instead, start with 5k.
You can always imagine a marathon, but try to reach a 5k first, publish, and then move ahead for a 10k. The idea of a modular project is to pick a low finish line and work your way up.
In nutshell, Avery provided all beginners with a starting point to enter their careers and prove themselves. This is your sign to start building a project right away, considering all the tips and tricks given in the webinar.
Information experts and information researchers address two of the most sought-after, lucrative positions in 2022. The World Financial Gathering Fate of Occupations Report 2020 recorded these jobs as number one for expanding requests across businesses, followed promptly by artificial intelligence and AI subject matter experts and large information experts.
While there’s unquestionably a lot of interest in information experts, it may not be generally clear what the thing that matters is between an information examiner and an information researcher. The two jobs work with information, however, they do as such in various ways.
Information examiners and information researchers: What do they do?
One of the greatest contrasts between information examiners and researchers is how they manage information.
Information experts normally work with organized information to tackle unmistakable business issues utilizing devices like SQL, R, or Python programming dialects, information perception programming, and factual examination. Normal errands for an information examiner could include:
Teaming up with hierarchical pioneers to distinguish instructive requirements
Procuring information from essential and optional sources
Cleaning and rearranging information for examination
Examining informational indexes to recognize patterns and examples that can be converted into noteworthy bits of knowledge
straightforwardly introducing discoveries to illuminate information-driven choices
Understand more: How Does an Information Examiner Respond? A Lifelong Aide
Information researchers frequently manage the obscure by utilizing further developed information strategies to make expectations about what’s to come. They could mechanize their own AI calculations or plan prescient displaying processes that can deal with both organized and unstructured information. This job is for the most part thought to be a further developed variant of an information expert. Sometime in the future, today’s assignments could include:
Handling crude information
Planning prescient models and AI calculations to mine enormous informational indexes:
Creating apparatuses and cycles to screen and dissect information exactness
Building information perception apparatuses, dashboards, and reports
Composing projects to mechanize information assortment and handling
Information science versus examination: Instructive necessities
Most information expert jobs expect a four-year college education in a field like math, measurements, software engineering, or finances like usdt wallet. Information researchers (as well as many high-level information examiners) commonly have an expert’s or doctoral certificate in information science, data innovation, math, or measurements.
While a degree has been the essential way toward a profession in information, a few new choices are arising for those without a degree or experience. By procuring an Expert Endorsement in information examination from Google or IBM, both accessible on Coursera, you can fabricate the abilities important for a section-level job as an information examiner in under a half year of study.
Endless supply of the Google Declaration, you’ll approach an employing consortium of more than 130 organizations. If you’re simply beginning, functioning as an information expert initially can be an effective method for sending off a profession as an information researcher.
Information abilities for researchers and investigators:
Information researchers and information investigators both work with information, yet every job utilizes a somewhat unique arrangement of abilities and devices, just like using the best Italian adblocker. Numerous abilities associated with information science work off those information experts use. This is a gander at the way they look at it.
Information science is the space of study that deals with immense volumes of information utilizing present-day instruments and procedures to track down inconspicuous examples, determine significant data, and pursue business choices. Information science utilizes complex AI calculations to construct prescient models.
The information utilized for the examination can emerge from a wide range of sources and be introduced in different organizations.
Since it has become so obvious what information science is, how about we see the reason information science is vital for the present IT scene?
The information science lifecycle:
Since it has become so obvious what information science is, next up let us center around the information science lifecycle. Information science’s life cycle comprises five unmistakable stages, each with its errands:
The catch: Information Procurement, Information Passage, Signal Gathering, and Information Extraction. This stage includes gathering crude organized and unstructured information.
Keep up with: Information Warehousing, Information Purging, Information Organizing, Information Handling, and Information Engineering. This stage covers taking the crude information and placing it in a structure that can be utilized.
Process: Information Mining, Grouping/Order, Information Displaying, Information Outline. Information researchers take the pre-arranged information and analyze its examples, ranges, and inclinations to decide how helpful it will be in prescient examination.
Break down: Exploratory/Corroborative, Prescient Examination, Relapse, Text Mining, Subjective Investigation. Here is the genuine meat of the life cycle. This stage includes playing out the different investigations of the information.
Impart information announcing: Refers to information representation, business insight, and an independent direction. In this last step, examiners set up the examinations in effectively meaningful structures like outlines, charts, and reports.
In the past few years, the number of people entering the field of data science has increased drastically because of higher salaries, an increasing job market, and more demand.
Undoubtedly, there are unlimited programs to learn data science, several companies offering in-depth Data Science Bootcamp, and a ton of channels on YouTube that are covering data science content. The abundance of data science content can easily confuse one with where to begin or how to start their data science career.
To ease this data science journey for beginners, intermediate, or starters, we are going to list a couple of data science tutorials, crash courses, webinars, and videos. The aim of this blog is to help beginners navigate their data science path, and also help them to determine if data science is the most perfect career choice for them or not.
This talk will introduce you to what a typical data scientist’s job looks like. It will familiarize you with the day-to-day work that a data scientist does and differentiate between the different roles and responsibilities that data scientists have across companies.
This talk will help you understand what a typical day in the data scientist’s life looks like and assist you to decide if data science is the perfect choice for your career.
2. Data mining crash course
Data mining has become a vital part of data science and analytics in today’s world. And, if you planning to jumpstart your career in the field of data science, it is important for you to understand data mining. Data mining is a process of digging into different types of data and data sets to discover hidden connections between them.
The concept of data mining includes several steps that we are going to cover in this course. In this talk, we will cover how data mining is used in feature selection, connecting different data attributes, data aggregation, data exploration, and data transformation.
Additionally, we will cover the importance of checking data quality, reducing data noise, and visualizing the data to demonstrate the importance of good data.
3. Intro to data visualization with R & ggplot2
While tools like Excel, Power BI, and Tableau are often the go-to solutions for data visualizations, none of these tools can compete with R in terms of the sheer breadth of, and control over, crafted data visualizations. Thereby, it is important for one to learn about data visualization with R & ggplot2.
In this tutorial, you will get a brief introduction to data visualization with the ggplot2 package. The focus of the tutorial will be using ggplot2 to analyze your data visually with a specific focus on discovering the underlying signals/patterns of your business.
4. Crash course in data visualization: Tell a story with your data
Telling a story with your data is more important than ever. The best insights and machine learning models will not create an impact unless you are able to effectively communicate with your stakeholders. Hence, it is very important for a data scientist to have an in-depth understanding of data visualization.
In this course, we will cover chart theory and pair programs that will help us create a chart using Python, Pandas, and Plotly.
5. Feature engineering
To become a proficient data scientist, it is significant for one to learn about feature engineering. In this talk, we will cover ways to do feature engineering both with dplyr (“mutate” and “transmute”) and base R (“ifelse”). Additionally, we’ll go over four different ways to combine datasets.
With this talk, you will learn how to impute missing values as well as create new values based on existing columns.
6. Intro to machine learning with R & caret
The R programming language is experiencing rapid increases in popularity and wide adoption across industries. This popularity is due, in part, to R’s huge collection of open-source machine-learning algorithms. If you are a data scientist working with R, the caret package (short for Classification and Regression Training) is a must-have tool in your toolbelt.
In this talk, we will provide an introduction to the caret package. The focus of the talk will be using caret to implement some of the most common tasks of the data science project lifecycle and to illustrate incorporating caret into your daily work.
7. Building robust machine learning models
Modern machine learning libraries make the model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent.
Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Thereby, our aim here is to take you through the fundamentals of building robust machine-learning models.
8. Text analytics crash course with R
Industries across the globe deal with structured and unstructured data. To generate insights companies, work towards analyzing their text data. The data pipeline for transforming unstructured text into valuable insights consists of several steps that each data scientist must learn about.
This course will take you through the fundamentals of text analytics and teach you how to transform text data using different machine-learning models.
9. Translating data into effective decisions
As data scientists, we are constantly focused on learning new ML techniques and algorithms. However, in any company, value is created primarily by making decisions. Therefore, it is important for a data scientist to embrace uncertainty in a data-driven way.
In this talk, we present a systematic process where ML is an input to improve our ability to make better decisions, thereby taking us closer to the prescriptive ideal.
10. Data science job interviews
Once you are through your data science learning path, it is important to work on your data science interviews in order to uplift your career. In this talk, you will learn how to solve SQL, probability, ML, coding, and case interview questions that are asked by FAANG + Wall Street.
We will also share the contrarian job-hunting tips that can help you to find a job at Facebook, Google, or an ML startup.
Step up to the data science pathway today!
We hope that the aforementioned 12 talks assist you to get started with your data science learning path. If you are looking for a more detailed guide, then do check out our Data Science Roadmap.
This blog covers the top 8 data science use cases in the finance industry that can help them when dealing with large volumes of data.
The finance industry deals with large volumes of data. With the increase in data and accessibility of AI, financial institutions can’t ignore the benefits of data science. They have to use data science to improve their services and products. It helps them make better decisions about customer behavior, product development, marketing strategies, etc.
From using machine learning algorithms to Python for Data Science, there are several key methods of applications of data science in finance. Listed below are the top eight examples of data science being used in the finance industry.
1. Trend forecasting
Data science plays a significant role in helping financial analysts forecast trends. For instance, data science uses quantitative methods such as regression analysis and linear programming to analyze data. These methods can help extract hidden patterns or features from large amounts of data, making trend forecasting easier and more accurate for financial institutions
2. Fraud detection
Financial institutions can be vulnerable to fraud because of their high volume of transactions. In order to prevent losses caused by fraud, organizations must use different tools to track suspicious activities. These include statistical analysis, pattern recognition, and anomaly detection via machine/deep learning. By using these methods, organizations can identify patterns and anomalies in the data and determine whether or not there is fraudulent activity taking place.
For example, financial institutions often use historical transaction data to detect fraudulent behavior. So when banks detect inconsistencies in your transactions, they can take action to prevent further fraudulent activities from happening.
3. Market research
Tools such as CRM and social media dashboards use data science to help financial institutions connect with their customers. They provide information about their customers’ behavior so that they can make informed decisions when it comes to product development and pricing.
Remember that the finance industry is highly competitive and requires continuous innovation to stay ahead of the game. Data science initiatives, such as a Data Science Bootcamp or training program, can be highly effective in helping companies develop new products and services that meet market demands.
4. Investment management
Investment management is another area where data science plays an important role. Companies use data-driven approaches to optimize investment portfolios. They also use predictive models, such as financial forecasting, to estimate future returns based on past performance. Such predictions allow investors to maximize profits and minimize risks when it comes to investing. In addition to providing valuable insight into the future, data science also provides guidance on how to best allocate capital and reduce risk exposure.
5. Risk analysis
Risks are unavoidable in any organization. However, managing those risks requires understanding their nature and causes. In the finance industry, companies use data science methods such as risk assessment and analysis to protect themselves against potential losses.
For example, they can tell you which products are likely to fail, and which assets are most susceptible to theft and other types of loss. And when applied properly, these tools can help an organization improve security, efficiency, and profitability.
6. Task automation
One of the greatest challenges faced by many firms today is the need to scale up operations while maintaining efficiency. To do so, they must automate certain processes. One way to achieve this goal is through the use of data science. Data scientists can develop tools that improve existing workflows within the finance industry.
Examples of these tools include speech-to-text, image recognition, and natural language processing. The finance industry uses insights from data science to automate systems that eliminate human error and accelerate operational efficiency.
7. Customer service
It’s no surprise that customer satisfaction affects revenue growth. As a result, companies spend large amounts of money to ensure that their customers receive top-notch service. Data science initiatives can help financial services providers deliver a superior experience to their customers. Whether it’s improving customer support apps or streamlining internal communications, financial companies can leverage this technology to transform their operations.
For instance, financial institutions can track consumer behavior to provide better customer service. A company may use data analytics to identify the best time to contact consumers by analyzing their online behavior. Companies can also monitor social media conversations and other sources for signs of dissatisfaction regarding their services to improve customer satisfaction.
For certain financial institutions, the ability to scale up could mean the difference between success and failure. The good news is that data science offers solutions and insight that help companies identify what areas need to be scaled. These insights help them decide whether they should hire additional staff or invest in new equipment, among other things.
A good example of using data analytics for scalability is IBM’s HR Attrition Case Study. IBM, one of the world’s leading technology firms, has been able to use data science to solve its own scaling challenges by using it to analyze trends and predict future outcomes. This study shows how data scientists used predictive analytics to understand why employees quit their jobs at IBM.
Data science revolutionizing finance industry
There’s no doubt that data science will revolutionize almost all aspects of the financial industry. By using different data science tools and methods, financial companies can gain competitive advantages. The great thing about data science is that it can be learned through various methods.
Data science bootcamps, online courses, and books offer all the tools necessary to get started. As a result, anyone who works in finance—whether they are junior analysts or senior executives—can learn how to incorporate data science techniques in their industry.
This blog will discuss the strengths and limitations of Python and Julia to address a very common topic of debate; is Julia better than Python?
It is a high-level programming language that was designed in 2012, specifically for the Data Science and Machine Learning community. It was introduced as a mathematically oriented language and became popular for its speed and performance over other languages like Python and R.
Almost every introductory level course on Julia talks about its speed compared to Python, NumPy, and C, claiming that the performance of this language is as good as the speed of C. Also, it outperforms Python and NumPy but only by a margin. This leads to another debate; Will Julia conquer Python’s kingdom in Data Science?
To be able to address this question, let us dive deeper to compare several aspects of the two languages.
Popularity and community
Python has been operational for over 30 years and is one of the most popular programming languages right now with a large developer community offering solutions and help for potential problems. This makes Python much easier and more convenient to use than any other language.
Julia has a small but rapidly growing and active community. Even though the number of followers is constantly increasing for it; the majority of support is still provided by the writers themselves. It is expected that when the scope of this programming language expands outside of data science, the popularity of the language will increase.
It takes leverage upon other languages when it comes to its execution speed. It is a compiled language primarily written on its own base. Well-written code in it can be as fast as in C. It is an excellent solution for challenges related to data analysis and statistical computing.
Python is an interpreted language that is not famous for its speed. Self-implemented functions in Python can take a lot longer to compile as compared to Julia or C. Therefore, it uses libraries like NumPy, Sklearn, and TensorFlow to implement different functions and algorithms. These libraries provide implementations of algorithms that are much faster than Python but slower than Julia.
Python offers an extensive range of libraries that can be simply imported, and their functions can be used. Python is also supported by a large number of third-party libraries.
Julia does not have much in its library collection and the packages are not very well maintained. This makes some implementations like neural networks a bit tedious. Due to the lack of libraries, the scope of it is also limited, as many tasks like web development cannot be performed with this language yet. However, considering the expectations of the growing community, we can expect more developed and well-maintained libraries from it soon.
One of the most fascinating features of Julia is converting code from other programming languages to it. It is a very straightforward process that is widely supported.
In Python, code conversion is much more difficult than in Julia, but it is still possible. Julia’s code can be shared with Python using the module named “PyCall.”
Linear algebra (Data Science algorithms)
Julia was made with the intention of being used in statistics and machine learning. It offers various methods and algorithms for linear algebra. These methods are quite easy to implement, and their syntax is very similar to mathematical expressions.
Python does not have its own pre-defined methods for linear algebra, so users work through libraries, such as NumPy for such implementations. These implementations are, however, not as simple to use as in Julia.
Will Julia replace Python?
It would be too early to say that Julia will replace Python in Data Science. Both have their respective advantages. It depends on your use case and preference.
Python has built the trust of its community for years, and it is not an easy task for Julia to announce itself in that community. But it is not impossible either. As the community of this language grows, more support would be available for people. With the growth in resources, maybe in the near future, This language would be a new norm in Data Science.
There are several informative data science podcasts out there right now, giving you everything you need to stay up to date on what’s happening. We previously covered many of the best podcasts in this blog, but there are lots more that you should be checking out. Here are 10 more excellent podcasts to try out.
1. Analytics Power Hour
Every week hosts, Michael Helbling, Tin Wilson, and Moe Kiss cover a different analytics topic that you may want to know about. The show was founded on the premise that the best discussions always happen at drinks after a conference or show.
Recent episodes have covered topics like analytics job interviews, data as a product, and owning vs. helping in analytics. There are a lot to learn here, so they’re well worth a listen.
This podcast is hosted by DataCamp, and in it, you’ll get interviews with some of the top leaders in data. “These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too” says Graham Pierson, a tech writer at Ox Essays and UK Top Writers.
There are lots of episodes to dive into, such as ones on building talent strategy, what makes data training programs successful, and more.
3. Lex Fridman Podcast
If you want a bigger picture of data science, then listen to this show. The show doesn’t exclusively cover data science anymore, but there’s plenty here that will give you what you’re looking for.
You’ll find a broader view of data, covering how data fits in with our current worldview. There are interviews with data experts so you can get the best view of what’s happening in data right now.
4. The Artists of Data Science
This podcast is geared toward those who are looking to develop their career in data science. If you’re just starting, or are looking to move up the ladder, this is for you. There’s lots of highly useful info in the show that you can use to get ahead.
There are two types of episodes that the show releases. One is advice from experts, and the others are ‘happy hours, where you can send in your questions and get answers from professionals.
5. Not So Standard Deviations
This podcast comes from two experts in data science. Roger Peng is a professor of biostatistics at John Hopkins School of Public Health, and Hilary Parker is a data scientist at Stitch Fix. They cover all the latest industry news while bringing their own experience to the discussion.
Their recent episodes have covered subjects like QR codes, the basics of data science, and limited liability algorithms.
Released twice a month, this podcast will give you all the ins and outs of machine learning, showing you how this tech is used in real-life situations. That allows you to see how it’s being used to solve problems and create solutions that we couldn’t have before.
Recent episodes have covered high-stress scenarios, experience management, and autonomous checkouts.
7. In Machines We Trust
This is another podcast that covers machine learning. It describes itself as covering ‘the automation of everything, so if that’s something you’re interested in, you’ll want to make sure you tune in.
“You’ll get a sense of what machine learning is being used for right now, and how it impacts our daily lives,” says Yvonne Richards, a data science blogger at Paper Fellows and Boom Essays. The episodes are around 30 mins long each, so it won’t take long to listen and get the latest info that you’re looking for.
8. More or Less
This podcast covers the topic of statistics through noticeably short episodes, usually 8 minutes or less each. You’ll get episodes that cover everything you could ever want to know about statistics and how they work.
For example, you can find out how many swimming pools of vaccines would be needed to give everyone a dose, see the one in two cancers claim debunked, and how data science has doubled life expectancy.
9. Data Engineering Podcast
This show is for anyone who’s a data engineer or is hoping to become one in the future. You’ll find lots of useful info in the podcast, including the techniques they use, and the difficulties they face.
Ensure you listen to this show if you want to learn more about your role, as you’ll pick up a lot of helpful tips.
10. Data viz Today
This show doesn’t need a lot of commitment from you, as they release 30 min episodes monthly. The podcast covers data visualization, and how this helps to tell a story and get the most out of data no matter what industry you work in.
Share with us exciting Data Science podcasts
These are all great podcasts that you can check out to learn more about data science. If you want to know more, you can check out Data Science Dojo’s informative sessions on YouTube. If we missed any of your favorite podcasts, do share them with us in the comments!
These interviews cover the entire range of data as an industry, looking at its past, present, and future. The guests are from both the industry and academia sides of the data spectrum too, says Graham Pierson, a tech writer at Academized.
This blog explores the important steps one should follow in the data preprocessing stage such as eradicating duplicates, fixing structural errors, detecting, and handling outliers, type conversion, dealing with missing values, and data encoding.
What is data preprocessing
A common mistake that many novice data scientists make is that they skip through the data wrangling stage and dive right into the model-building phase, which in turn generates a poor-performing machine learning model.
This resembles a popular concept in the field of data science called GIGO (Garbage in Garbage Out). This concept means inferior quality data will always yield poor results irrespective of the model and optimization technique used.
Hence, an ample amount of time needs to be invested in ensuring the quality of the data is up to the standards. In fact, data scientists spend around 80% of their time just on the data pre-processing phase. But fret not, because we will investigate the various steps that you can follow to ensure that your data is preprocessed before stepping ahead in the data science pipeline.
Let’s look at the steps of data pre-processing to understand it better:
You may often encounter repeated entries in your dataset, which is not a good sign because duplicates are an extreme case of non-random sampling, and they tend to makethe model biased. Including repeated entries will lead to the model overfitting this subset of points and hence must be removed.
We will demonstrate this with the help of an example. Let’s say we had a movie data set as follows:
As we can see, the movie title: “The Dark Knight” is repeated at the 3rd index (fourth entry) in the data frame and needs to be taken care of.
Using the code below, we can remove the duplicate entries from the dataset based on the “Title” column and only keep the first occurrence of the entry.
Just by writing a few lines of code, you ensure your data is free from any duplicate entries. That’s how easy it is!
Fix structural errors:
Structural errors in a dataset refer to the entries that either have typos or inconsistent spellings:
Here you can easily spot the different typos and inconsistencies but what if the dataset was huge? You can check all the unique values and their corresponding occurrence using the following code:
Once you identify the entries to be fixed, simply replace the values with the correct version.
Voila! That is how you fix the structural errors.
Detecting and handling outliers:
Before we dive into detecting and handling outliers let’s discuss what an outlier is.
“Outlier is any value in a dataset that drastically deviates from the rest of the data points.”
Let’s say we have a dataset of a streaming service with the ages of users ranging from 18 to 60, but there exists a user whose age is registered as 200. This data point is an example of an outlier and can mess up our machine–learning model if not taken care of.
There are numerous techniques that can be employed to detect and remove outliers in a data set but the ones that I am going to discuss are:
If we use the describe function of pandas on the Age column, we can analyze the five number summary along with count, mean, and standard deviation of the specified column, then by using the domain specific knowledge like for the above instance we know that significantly large values of age can be a result of human error we can deduce that there are outliers in the dataset as the mean is 38.92 while the max value is 92.
As we have got some idea about what outliers are, let’s see some code in action to detect and remove the outliers
Box plots or also called “Box and Whiskers Plot” show the five number summary of the features under consideration and are an effective way of visualizing the outlier.
As we can see from the above figure, there are number of data points that are outliers. So now we move onto Z-Score, a method through which we are going to set the threshold and remove the outlier entries from our dataset.
A z-score determines the position of a data point in terms of its distance from the mean when measured in standard deviation units.
We first calculate the Z-score of the feature column:
The standard normal curve (Z-score) for a set of values represents 99.7% of the data points within the range of –3 and +3 scores, so in practice often the threshold is set to be 3 and anything beyond that is deemed an outlier and hence removed from the dataset if problematic or not a legitimate observation.
Type conversion refers to when certain columns are not of valid data type, for instance in the following data frame three out of four columns are of object data type:
Well, we don’t want that right? Because it would produce unexpected results and errors. We are going to convert Title and Director to string data types, and Duration_mins to integer data type.
Dealing With Missing Values:
Often, data set contains numerous missing values, which can be a problem. To name a few it can play a role in development of biased estimator, or it can decrease the representativeness of the sample under consideration.
Which brings us to the question of how to deal with them.
One thing you could do is simply drop them all. If you notice that index 5 has a few missing values, when the “dropna” command is implemented, it will drop that row from the dataset.
But what to do when you have a limited number of rows in a dataset? You could use different imputations methods such as the Measures of central tendencies to fill those empty cells.
The measures include:
Mean: The mean is the average of a data set. It is “sensitive” to outliers.
Median: The median is the middle of the set of numbers. It is resistant to outliers
Mode: The mode is the most common number in a data set.
It is better to use median instead of mean because of the property of not deviating drastically because of outliers. Allow me to elaborate this with an example
Notice how there is a documentary by the name “Hunger!” with “Duration_mins” equal to 6000 now observe the difference when I replace the missing value in the duration column with mean and with median.
If you search on the internet for the duration of movie “The Shining” you’ll find out it’s about 146 minutes so, isn’t 152 minutes much closer as compared to 1129 as calculated by mean?
A few other techniques to fill the missing values that you can explore are forward fill and backward fill.
Forward will work on the principle that the last valid value of a column is passed forward to the missing cell of the dataset.
Notice how 209 propagated forward.
Let’s observe backward fill too
From the above example you can clearly see that the value following the empty cell was propagated backwards to fill in that missing cell.
The final technique I’m going to show you is called linear interpolation. What we do is we take the mean of the values prior to and following the empty cell and use it to fill the missing value.
3104.5 is the mean of 209 and 6000. As you can see this technique is too affected by outliers.
That was a quick run-down on how to handle missing values, moving onto the next section.
Another core concept of data preprocessing is the feature scaling of your dataset. In simple terms feature scaling refers to the technique where you scale multiple (quantitative) columns of your dataset to a common scale.
Assume a banking dataset has a column of age which usually ranges from 18 to 60 and a column of balance which can range from 0 to 10000. If you observe, there is an enormous difference between the values each data point can assume, and machine learning model would be affected by the balance column and would assign higher weights to it as it would consider the higher magnitude of balance to carry more importance as compared to age which has relatively lower magnitude.
To rectify this, we use the following two methods:
Normalization fits the data between the range of [0,1] but sometimes [-1,1] too. It is affected by outliers in a dataset and is useful when you do not know about the distribution of dataset.
Standardization on the other hand is not bound to be within a certain range, it’s quite resistant to outliers and useful when the distribution is normal or Gaussian.
The last step of the data preprocessing stage is the data encoding. It is where you encode the categorical features (columns) of your dataset into numeric values.
There are many encoding techniques available but I’m just going to show you the implementation of one hot encoding (Pro-tip: You should use this when the order of the data does not matter).
For instance in the following example Gender column is nominal data meaning that the identification of your gender does not take precedence over other gender, to further clarify the concept let’s assume for the sake of argument we had a dataset of examination results of some high school class with a column of rank, the rank here is an example of ordinal data as it would follow certain order and higher-ranking students would take precedence over lower ranked ones.
If you notice in the above example, Gender column could assume one of the two options that were either male or female, what one hot encoder did was create the same number of columns as the number of options available, then for the row that had the associated possible value encoded it with one (why one? Well because one is the binary representation of true) otherwise zero (you guessed, zero represents false)
If you do wish to explore other techniques here is an excellent resource for this purpose:
It might have been a lot to take in, but you have now explored the crucial concept of data science that is data preprocessing.; Moreover, you are now equipped with the steps to curate your dataset in such a way that it would yield satisfactory results.
The journey to becoming a data scientist can seem daunting, but with the right mentorship you can learn it seamlessly and take on real world problems in no time, to embark on the journey of becoming a data scientist, enroll yourself in the Data Science bootcamp and grow your career.
Most people have heard the terms “data science” and “AI” at least once in their lives. Indeed, both of these are extremely important in the modern world as they are technologies that help us run quite a few of our industries.
But even though data science and Artificial Intelligence are somewhat related to one another, they are still very different. There are things they have in common which is why they are often used together, but it is crucial to understand their differences as well.
What is Data Science?
As the name suggests, data science is a field that involves studying and processing data in big quantities using a variety of technologies and techniques to detect patterns, make conclusions about the data, and help in the decision-making process. Essentially, it is an intersection of statistics and computer science largely used in business and different industries.
The standard data science lifecycle includes capturing data and then maintaining, processing, and analyzing it before finally communicating conclusions about it through reporting. This makes data science extremely important for analysis, prediction, decision-making, problem-solving, and many other purposes.
What is Artificial Intelligence?
Artificial Intelligence is the field that involves the simulation of human intelligence and the processes within it by machines and computer systems. Today, it is used in a wide variety of industries and allows our society to function as it currently does by using different AI-based technologies.
Some of the most common examples in action include machine learning, speech recognition, and search engine algorithms. While AI technologies are rapidly developing, there is still a lot of room for their growth and improvement. For instance, there is no powerful enough content generation tool that can write texts that are as good as those written by humans. Therefore, it is always preferred to hire an experienced writer to maintain the quality of work.
What is Machine Learning?
As mentioned above, machine learning is a type of AI-based technology that uses data to “learn” and improve specific tasks that a machine or system is programmed to perform. Though machine learning is seen as a part of the greater field of AI, its use of data puts it firmly at the intersection of data science and AI.
Similarities between Data Science and AI
By far the most important point of connection between data science and Artificial Intelligence is data. Without data, neither of the two fields would exist and the technologies within them would not be used so widely in all kinds of industries. In many cases, data scientists and AI specialists work together to create new technologies or improve old ones and find better ways to handle data.
As explained earlier, there is a lot of room for improvement when it comes to AI technologies. The same can be somewhat said about data science. That’s one of the reasons businesses still hire professionals to accomplish certain tasks like custom writing requirements, design requirements, and other administrative work.
Differences between Data Science and AI
There are quite a few differences between both. These include:
Purpose – It aims to analyze data to make conclusions, predictions, and decisions. Artificial Intelligence aims to enable computers and programs to perform complex processes in a similar way to how humans do.
Scope – This includes a variety of data-related operations such as data mining, cleansing, reporting, etc. It primarily focuses on machine learning, but there are other technologies involved too such as robotics, neural networks, etc.
Application – Both are used in almost every aspect of our lives, but while data science is predominantly present in business, marketing, and advertising, AI is used in automation, transport, manufacturing, and healthcare.
Examples of Data Science and Artificial Intelligence in use
To give you an even better idea of what data science and Artificial Intelligence are used for, here are some of the most interesting examples of their application in practice:
Analytics – Analyze customers to better understand the target audience and offer the kind of product or service that the audience is looking for.
Monitoring – Monitor the social media activity of specific types of users and analyze their behavior.
Recommendation – Recommend products and services to customers based on their customer profiles, buying behavior, etc.
Forecasting – Predict the weather based on a variety of factors and then use these predictions for better decision-making in the agricultural sector.
Communication – Provide high-quality customer service and support with the help of chatbots.
Automation – Automate processes in all kinds of industries from retail and manufacturing to email marketing and pop-up on-site optimization.
Diagnosing – Identify and predict diseases, give correct diagnoses, and personalize healthcare recommendations.
Transportation – Use self-driving cars to get where you need to go. Use self-navigating maps to travel.
Assistance – Get assistance from smart voice assistants that can schedule appointments, search for information online, make calls, play music, and more.
Filtering – Identify spam emails and automatically get them filtered into the spam folder.
Cleaning – Get your home cleaned by a smart vacuum cleaner that moves around on its own and cleans the floor for you.
Editing – Check texts for plagiarism and proofread and edit them by detecting grammatical, spelling, punctuation, and other linguistic mistakes.
It is not always easy to tell which of these examples is about data science and which one is about Artificial Intelligence because many of these applications use both of them. This way, it becomes even clearer just how much overlap there is between these two fields and the technologies that come from them.
What is your choice?
At the end of the day, data science and AI remain some of the most important technologies in our society and will likely help us invent more things and progress further. As a regular citizen, understanding the similarities and differences between the two will help you better understand how data science and Artificial Intelligence are used in almost all spheres of our lives.
In this blog, we will discuss how companies apply data science in business and use combinations of multiple disciplines such as statistics, data analysis, and machine learning to analyze data and extract knowledge.
If you are a beginner or a professional seeking to learn more about concepts like Machine Learning, Deep Learning, and Neural Networks, the overview of these videos will help you develop your basic understanding of Data Science.
Overview of the data science course for beginners
If you are an aspiring data scientist, it is essential for you to understand the business problem first. It allows you to set the right direction for your data science project to achieve business goals.
As you are assigned a data science project, you must assure yourself to gather relevant information around the scope of the project. For that you must perform three steps:
Ask relevant questions from the client
Understand the objectives of the project
Defines the problem that needs to be tackled
As you are now aware of the business problem, the next step is to perform data acquisition. Data is gathered from multiple sources such as:
1. Getting Started with Python and R for Data Science
Python is an open source, high-level, object-oriented programming language that is widely used for web development and data science. It is a perfect fit for data analysis and machine learning tasks, as it is easy to learn and offers a wide range of tools and features.
Python is a flexible language that can be used for a variety of tasks, including data analysis, programming, and web development. Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning.
Getting started with Python and R for Data Science
Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set. Python is an ideal language for data scientists who are looking to learn more about data analysis and machine learning. It is used to accomplish a variety of tasks, including data analysis, programming, and web development.
Python is an ideal tool for data scientists who are looking to learn more about data analysis and machine learning. Python is a great choice for beginners as well as experienced developers who are looking to expand their skill set.
2. Intro to Big Data, Data Science & Predictive Analytics
Big data is a term that has been around for a few years now, and it has become increasingly important for businesses to understand what it is and how it can be used. Big data is basically any data that is too large to be stored on a single computer or server and instead needs to be spread across many different computers and servers in order to be processed and analyzed.
The main benefits of big data are that it allows businesses to gain a greater understanding of their customers and the products they are interested in, which allows them to make better decisions about how to market and sell their products. In addition, big data also allows businesses to take advantage of artificial intelligence (AI) technology, which can allow them to make predictions about the future based on the data they are collecting.
The main areas that businesses need to be aware of when they start using big data are security and privacy. Big data can be extremely dangerous if it is not properly protected, as it can allow anyone with access to the data to see the information that is being collected. In addition, big data can also be extremely dangerous if it is not properly anonymized, as it can allow anyone with access to the data to see the information that is being collected.
One of the best ways to protect your data is by using encryption technology. Encryption allows you to hide your data from anyone who does not have access to it, so you can ensure that no one but you have access to your data. However, encryption does not protect
3. Intro to Azure ML & Cloud Computing
Cloud computing is a growing trend in IT that allows organizations to perform delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence. Cloud offers a number of benefits, including reduced costs and increased flexibility.
Organizations can take advantage of the power of the cloud to reduce their costs and increase flexibility, while still being able to stay up to date with new technology. In addition, organizations can take advantage of the flexibility offered by the cloud to quickly adopt new technologies and stay competitive.
In this intro to Azure Machine learning & Cloud Computing, we’ll cover some of the key benefits of using Azure and how it can help organizations get started with machine learning and cloud computing. We’ll also cover some of the key tools that are available in Azure to help you get started with your machine learning and cloud computing projects.
Start your Data Science journey today
If you are afraid of spending hundreds of dollars to enroll in a data science course, then direct yourself to the hundreds of free videos available online. Master your Data Science learning and step into the world of advanced technology.