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Predictive Analytics

Ruhma Khawaja author
Ruhma Khawaja
| September 8

Artificial Intelligence (AI) and Predictive Analytics are revolutionizing the way engineers approach their work. This article explores the fascinating applications of AI and Predictive Analytics in the field of engineering. We’ll dive into the core concepts of AI, with a special focus on Machine Learning and Deep Learning, highlighting their essential distinctions.

By the end of this journey, you’ll have a clear understanding of how Deep Learning utilizes historical data to make precise forecasts, ultimately saving valuable time and resources.

Predictive analytics and AI
Predictive analytics and AI

Different Approaches to Analytics

In the realm of analytics, there are diverse strategies: descriptive, diagnostic, predictive, and prescriptive. Descriptive analytics involves summarizing historical data to extract insights into past events. Diagnostic analytics goes further, aiming to uncover the root causes behind these events. In engineering, predictive analytics takes center stage, allowing professionals to forecast future outcomes, greatly assisting in product design and maintenance. Lastly, prescriptive analytics recommends actions to optimize results.


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AI: Empowering Engineers

Artificial Intelligence isn’t about replacing engineers; it’s about empowering them. AI provides engineers with a powerful toolset to make more informed decisions and enhance their interactions with the digital world. It serves as a collaborative partner, amplifying human capabilities rather than supplanting them.

AI and Predictive Analytics: Bridging the Gap

AI and Predictive Analytics are two intertwined yet distinct fields. AI encompasses the creation of intelligent machines capable of autonomous decision-making, while Predictive Analytics relies on data, statistics, and machine learning to forecast future events accurately. Predictive Analytics thrives on historical patterns to predict forthcoming outcomes.


Read more –> Data Science vs AI – What is 2023 demand for?


Navigating Engineering with AI

Before AI’s advent, engineers employed predictive analytics tools grounded in their expertise and mathematical models. While these tools were effective, they demanded significant time and computational resources.

However, with the introduction of Deep Learning in 2018, predictive analytics in engineering underwent a transformative revolution. Deep Learning, an AI subset, quickly analyzes vast datasets, delivering results in seconds. It replaces complex algorithms with neural networks, streamlining and accelerating the predictive process.

The Role of Data Analysts

Data analysts play a pivotal role in predictive analytics. They are the ones who spot trends and construct models that predict future outcomes based on historical data. Their expertise in deciphering data patterns is indispensable in making accurate forecasts.

Machine Learning and Deep Learning: The Power Duo

Machine Learning (ML) and Deep Learning (DL) are two critical branches of AI that bring exceptional capabilities to predictive analytics. ML encompasses a range of algorithms that enable computers to learn from data without explicit programming. DL, on the other hand, focuses on training deep neural networks to process complex, unstructured data with remarkable precision.

Turbocharging Predictive Analytics with AI

The integration of AI into predictive analytics turbocharges the process, dramatically reducing processing time. This empowerment equips design teams with the ability to explore a wider range of variations, optimizing their products and processes.

In the domain of heat exchanger applications, AI, particularly the NCS AI model, showcases its prowess. It accurately predicts efficiency, temperature, and pressure drop, elevating the efficiency of heat exchanger design through generative design techniques.






Predictive Analytics


Artificial Intelligence

Definition Uses historical data to identify patterns and predict future outcomes. Uses machine learning to learn from data and make decisions without being explicitly programmed.
Goals To predict future events and trends. To automate tasks, improve decision-making, and create new products and services.
Techniques Uses statistical models, machine learning algorithms, and data mining. Uses deep learning, natural language processing, and computer vision.
Applications Customer behavior analysis, fraud detection, risk assessment, and inventory management. Self-driving cars, medical diagnosis, and product recommendations.
Advantages Can be used to make predictions about complex systems. Can learn from large amounts of data and make decisions that are more accurate than humans.
Disadvantages Can be biased by the data it is trained on. Can be expensive to develop and deploy.
Maturity Well-established and widely used. Still emerging, but growing rapidly.

Realizing the Potential: A Use Case

  1. Healthcare:
    • AI aids medical professionals by prioritizing and triaging patients based on real-time data.
    • It supports early disease diagnosis by analyzing medical history and statistical data.
    • Medical imaging powered by AI helps visualize the body for quicker and more accurate diagnoses.
  2. Customer Service:
    • AI-driven smart call routing minimizes wait times and ensures customers’ concerns are directed to the right agents.
    • Online chatbots, powered by AI, handle common customer inquiries efficiently.
    • Smart Analytics tools provide real-time insights for faster decision-making.
  3. Finance:
    • AI assists in fraud detection by monitoring financial behavior patterns and identifying anomalies.
    • Expense management systems use AI for categorizing expenses, aiding tracking and future projections.
    • Automated billing streamlines financial processes, saving time and ensuring accuracy.

Machine Learning (ML):


  1. Social Media Moderation:
    • ML algorithms help social media platforms flag and identify posts violating community standards, though manual review is often required.
  2. Email Automation:
    • Email providers employ ML to detect and filter spam, ensuring cleaner inboxes.
  3. Facial Recognition:
    • ML algorithms recognize facial patterns for tasks like device unlocking and photo tagging.

Predictive Analytics:


  1. Predictive Maintenance:
    • Predictive analytics anticipates equipment failures, allowing for proactive maintenance and cost savings.
  2. Risk Modeling:
    • It uses historical data to identify potential business risks, aiding in risk mitigation and informed decision-making.
  3. Next Best Action:
    • Predictive analytics analyzes customer behavior data to recommend the best ways to interact with customers, optimizing timing and channels.

Business Benefits:

The combination of AI, ML, and predictive analytics offers businesses the capability to:

  • Make informed decisions.
  • Streamline operations.
  • Improve customer service.
  • Prevent costly equipment breakdowns.
  • Mitigate risks.
  • Optimize customer interactions.
  • Enhance overall decision-making through clear analytics and future predictions.

These technologies empower businesses to navigate the complex landscape of data and derive actionable insights for growth and efficiency.

Enhancing Supply Chain Efficiency with Predictive Analytics and AI

The convergence of predictive analytics and AI holds the key to improving supply chain forecast accuracy, especially in the wake of the pandemic. Real-time data access is critical for every resource in today’s dynamic environment. Consider the example of the plastic supply chain, which can be disrupted by shortages of essential raw materials due to unforeseen events like natural disasters or shipping delays. AI systems can proactively identify potential disruptions, enabling more informed decision-making.

AI is poised to become a $309 billion industry by 2026, and 44% of executives have reported reduced operational costs through AI implementation. Let’s delve deeper into how AI can enhance predictive analytics within the supply chain:

1. Inventory Management:

Even prior to the pandemic, inventory mismanagement led to significant financial losses due to overstocking and understocking. The lack of real-time inventory visibility exacerbated these issues. When you combine real-time data with AI, you move beyond basic reordering.

Technologies like Internet of Things (IoT) devices in warehouses offer real-time alerts for low inventory levels, allowing for proactive restocking. Over time, AI-driven solutions can analyze data and recognize patterns, facilitating more efficient inventory planning.

To kickstart this process, a robust data collection strategy is essential. From basic barcode scanning to advanced warehouse automation technologies, capturing comprehensive data points is vital. When every barcode scan and related data is fed into an AI-powered analytics engine, you gain insights into inventory movement patterns, sales trends, and workforce optimization possibilities.

2. Delivery Optimization:

Predictive analytics has been employed to optimize trucking routes and ensure timely deliveries. However, unexpected events such as accidents, traffic congestion, or severe weather can disrupt supply chain operations. This is where analytics and AI shine.

By analyzing these unforeseen events, AI can provide insights for future preparedness and decision-making. Route optimization software, integrated with AI, enables real-time rerouting based on historical data. AI algorithms can predict optimal delivery times, potential delays, and other transportation factors.

IoT devices on trucks collect real-time sensor data, allowing for further optimization. They can detect cargo shifts, load imbalances, and abrupt stops, offering valuable insights to enhance operational efficiency.

Turning Data into Actionable Insights

The pandemic underscored the potency of predictive analytics combined with AI. Data collection is a cornerstone of supply chain management, but its true value lies in transforming it into predictive, actionable insights. To embark on this journey, a well-thought-out plan and organizational buy-in are essential for capturing data points and deploying the appropriate technology to fully leverage predictive analytics with AI.

Wrapping Up

AI and Predictive Analytics are ushering in a new era of engineering, where precision, efficiency, and informed decision-making reign supreme. Engineers no longer need extensive data science training to excel in their roles. These technologies empower them to navigate the complex world of product design and decision-making with confidence and agility. As the future unfolds, the possibilities for engineers are limitless, thanks to the dynamic duo of AI and Predictive Analytics.


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Ali Haider - Marketing manager
Ali Haider Shalwani
| October 20

In this blog, we will have a look at the list of free Data Science crash courses to help you succeed in Data Science


With more and more people entering the field, data science and data engineering are surely amongst the topmost emerging areas of work in the 21st century. Higher salaries, perks, benefits, and demand has made it a field of interest for 1000s of people.  


While a good chunk of students is opting for data science in their undergraduate and graduate programs, there are people who are opting for different Data Science Bootcamps to get started with the field.   


However, enrolling instantly in an expensive undergraduate, master’s, or data science Bootcamp might not be the correct choice for one to go with. An individual would want to explore more within the scope of data science before switching fields or making the final call. Hence, below we present a list of free data science crash courses that an individual can go through before choosing their career path.   

Data Science crash courses
                                                                              Free Data Science crash courses – Data Science Dojo



If you are completely new to data science or planning to switch your career, our Data Science Practicum Program should be able to help you.   


Likewise, data science is an emerging field. Just a single program or bootcamp cannot help you to excel within the domain of data science, engineering, and analytics. You will have to keep learning and update your skillsets with short courses like Python for Data Science to remain competitive in the job market. This list of free crash courses can help you acquire a number of skills like Power BI, SQL, MLOps, and many others.   


Set of Data Science free crash courses 


So, if you are the one who is already in a data science career or the one who is planning to make a transition, this set of free data science crash courses can help you all out in every possible way. Check them out:  

1. SQL crash course for beginners:

This crash course can help beginners with no previous experience in SQL. By the end of this course, you will understand the difference between SQL and NoSQL, what is a database, the differentiation between MySQL, Oracle, PostgreSQL, SQL Server, and SQLite, how to find data in a database by writing a SQL query, and much more. 




2. Python crash course for Excel users:

This course can assist all Excel users with no prior knowledge for Python. In this course, you will understand how Python is different from Excel as an open-source software tool, navigation & execution of codes in Jupyter Notebook, implementing useful packages for data analytics, and translating common Excel concepts such as cells, ranges, and tables to Python equivalents. 




3. Redis crash course for Artificial Intelligence and Machine Learning:

If you have no experience with Redis, then this crash course is for you. This course covers the difference between Redis and SQL databases, key machine learning concepts and use cases Redis enables, data types and structures that can be stored in Redis, Redis as an online feature store, and Redis as a vector database for embeddings & neural search. 




4. MLOps crash course for beginners:

Do you have the basic knowledge of developing machine learning models in a Jupyter notebook setting? Then this course is a perfect fit for you. We will cover what is MLOps and machine learning pipelines, why is MLOps important, how to create and deploy a fully reproducible MLOps pipeline from scratch, and Learn the basics of continuous training, drift detection, alerts, and model deployment. 




5. Crash course on Naïve Bayes classification:

Need an introduction to Naïve Bayes Classification? Then this short course will take you through the theory and coding examples. With this course, you should be able to acquire a strong understanding of this technique. 



6. Crash course in modern Data Warehousing using Snowflake platform:

With this crash course, you can get started with the new generation of data warehouse i.e. Snowflake. We will discuss Snowflake architecture, its user interface, and the data caching feature of Snowflake. We have also included a lot of instructor-led demos to provide you with a pragmatic experience regarding the Snowflake Platform. 


7. Crash course in Data Visualization:

This crash course is planned for intermediate users with previous experience in python. In this session, introduce chart theory, outline data to visual representations, get access to a Google Colab Notebook that you’re able to code your own interactive charts with, transform data to be ingested by pandas and plotly, and customize your chart with options & properties to make it unique for your use case. 




8. Power BI crash course for beginners:

With this crash course, get started with Microsoft’s Power BI. We will walk you through how to prepare your data, analyze it and build insightful visualizations on the interactive data visualization software Power BI Desktop. By the end of the course, you will know the basics of importing data into Power BI, carrying out exploratory data analysis, cleaning, manipulating, and aggregating data, and building insightful visualizations with Power BI. 




You can also get an in-depth Introduction to Power BI with our live-instructor-led training. Do check it out.   


9. Crash course on designing a dashboard in Tableau:

This crash course is intended for beginners. In this course, you will know what is Tableau, how to design a basic dashboard in tableau, how to include a bar chart in your dashboard, and how to create a map in tableau.   




10. Crash course in Predictive Analytics:

The uncertainty after Covid-19 has made it difficult for companies to thrive but data and analytics helped companies survive it. Companies need to work proactively with predictive and prescriptive analytics to optimize their operations and compete in a changing world. This crash course will provide an in-depth overview of predictive analytics.  



11. Crash course on Transfer Learning:

In this course, we will discuss the idea of transfer learning, learn how deep learning models communicate with each other, explore the real-world applications of transfer learning, and compare transfer learning with a human’s continuous growth model.  


Need help with your data science career? This Data Science Roadmap can navigate your way.   

12. R and Python- the best of both worlds:

One of the common data science arguments has been what language to learn, R and Python. This argument has led to a language rivalry between R and Python. The purpose of this course is to take through the main defining features of both languages and how they compare different workflows in data science and data types. We will also show what methods are available for combining both in the same workspace and demonstrate this with a case study.  




Want to learn more about free Data Science crash courses? 


Only a top few popular data science crash courses are listed here, however, these might not be sufficient enough to sustain in such a competitive environment. If you are in a search for more data science crash courses, then make sure to go through this list of free data science courses.   


If you are absolutely new to data science, then I can assure you that  our YouTube channel  can navigate your journey, do check it out!  


CTA - Data Science courses

Data Science Dojo
Nathan Piccini
| January 28

If you’ve ever been to a conference, you know how much there is to learn. Which data science conferences will you attend? 

2019’s coming to an end. Check out my list of 8 Data Science Conferences To Attend In 2020.

Data Science Dojo’s sponsoring Kaggle Days Tokyo! Kagglers of all levels are welcome to join Grandmasters, Masters, and other data science experts to grow their skills at the first global series of offline events for Kagglers.

Have you missed these fun data science conferences?

Most, if not all, industries will have major conferences for members to attend. They consist of educational workshops, networking opportunities, and industry leader keynotes for attendees to absorb.  Did I mention conferences are fun? For those of you who have attended a conference in the past, you know meeting new people and learning in a new environment is often invigorating.

Especially for a data enthusiast.

Data science may be a relatively new industry, but conferences have still managed to bring together industry leaders around the world. from North America to Asia, you can find a data science conference at almost any time of the year. Like a data science BootCamp, the hardest part is narrowing down which one(s) to attend.

Strata data science conference

If you’re looking for a conference where “cutting-edge science and new business fundamentals intersect – and merge” look no further than the Strata Data Conference. A collaboration between O’Reilly and Cloudera, Strata offers an insider experience to develop new skills as well as learn about the future of data science. Strata has three locations set for 2019.

Dates: March 25 – 28, 2019

LocationSan Francisco, CA

VenueMoscone Center

Dates: April 29 – May 2, 2019

LocationLondon, UK

VenueExCel London

Dates: September 23 – 26, 2019

LocationNew York, NY

VenueJavits Center

Predictive analytics innovation summit

The Predictive Analytics Innovation Summit combines keynote presentations with interactive breakout sessions and open discussion to create an interactive experience. With confirmed speakers from Facebook, eBay, and Uber, you will be learning and networking with the best the industry has to offer.

Dates: April 29 – 30, 2019

Location: San Diego, CA

VenueMarriott Marquis and Marina

Enterprise data world

Enterprise Data World’s (EWS) 23rd annual conference speaks for itself. 6 total days, three of which are dedicated to tutorials and workshops, are packed with data professionals to educate the world on data management.

Dates: March 17 – 22, 2019

Location: Boston, MA

VenueSheraton Boston Hotel

Artificial intelligence conference

Presented by O’Reilly and Intel AI, the AI conference is an event you won’t want to miss out on. Featured speakers from TwitterPrinceton University, and Columbia University are sure to pique your curiosity and the New York location.  Speakers at the other locations have yet to be announced.

Dates: April 15 – 18, 2019

LocationNew York, NY

VenueHilton Midtown

Dates: June 18 – 21, 2019

Location: Beijing, China

Venue: Beijing International Hotel Conference Center

Dates: September 9 – 12, 2019

LocationSan Jose, CA

VenueSan Jose McEnery Convention Center

Dates: October 14 – 17, 2019

LocationLondon, UK

VenueHilton London Metropole


In its third year, “DataFest will showcase Scotland’s leading role in data science and artificial intelligence on the international stage,” creating a networking platform for local and international talent to interact with each other. A two-week data festival focusing on making data a community effort, DataFest asks you use #datatogether in all your social posts to emphasize that fact. DataFest consists of six events across Scotland:

EventData Summit

Dates: March 21 – 22, 2019

Location: Edinburgh, UK

VenueThe Assembly Rooms

EventData Talent

Dates: March 19, 2019

Location: Glasgow, UK

VenueHilton Hotel

EventFringe Events

Dates: March 11 – 22, 2019

Location: Across Scotland


EventData Tech

Dates: March 14, 2019

Location: Edinburgh, UK

Venue: TBD

Event: Women in Data Science

Dates: March 11, 2019

Location: Edinburgh, UK

Venue: National Museum of Scotland

Event: Executive Dinner

Dates: N/A

Location: N/A

Venue: N/A

Open data science conference

As one of the largest gatherings of professional data scientists, the Open Data Science Conference (ODSC) hosted 12,000 attendees across 3 conferences in 2017. ODSC’s goal is to bring together the people who “are working on shaping the present and future of AI and data science”.  This year, ODSC will be hosting four major conferences across Asia, North America, and Europe.

Dates: April 30 – May 3, 2019

LocationBoston, MA

VenueHynes Convention Center

Dates: August 7 – 10, 2019

LocationBengaluru, India

VenueSheraton Grand Bangalore Hotel

Dates: November 19 – 22, 2019

LocationLondon, UK

VenueHotel Novotel London West

Dates: October 29 – November 1st, 2019

LocationSan Francisco, CA

VenueHyatt Regency San Francisco Airport

Predictive analytics world

2019 is already looking very different for Predictive Analytics World (PAW). Being called Mega-PAW, there will only be one mega-conference made up of 5 industry-specific events (called conferences). Throughout 5-days, 5 conferences on BusinessFinanceDeep LearningHealthcare, and Industry 4.0 will take place with 160 speakers and more than 150 sessions.

Dates: June 16 – 20, 2019

Location: Las Vegas, NV

VenueCaesars Palace

Other conferences to consider

Data Science Dojo
Nathan Piccini
| December 17

North America (the USA and Canada) has a crazy amount of data science conferences to choose from. Here are 6 conferences you should consider attending.

There is an abundance of events to attend so make sure to do your homework. I’ve also included resources at the end of the article if you’d like to look at other options.

Maybe North America isn’t the region you’re interested in? Don’t worry, I’ve also written about conferences to attend in other regions of the world:

  • Asia
  • Europe (in progress)
  • Latin and South America (in progress)
  • The Middle East and Africa (in progress)

1.  Women in analytics

This conference was also featured in my list for 2020, so it’s no surprise that I also included it here.

Women in Analytics (WIA) is all about providing “visibility to the women making an impact in the analytics space and provide a platform for them to lead the conversations around the advancements of analytical research, development, and application,”. The conference has grown every year since its founding in 2016, now taking 3 days to accommodate all the speakers. People of all genders are welcome to join.

Date: June 3-5, 2020

Location: Columbus, OH, USA

VenueGreater Columbus Convention Center

2. The data science conference

Reverberating from my blog post on data science conferences to attend in 2020, I would highly suggest attending The Data Science Conference.

Vendor-free, sponsor-free, and recruiter-free, this conference isn’t trying to sell you anything. Organizers are just trying to bring together the best data science minds to help attendees learn and grow the field. Obviously, if you’re attending a conference to simply network you should pass on this event, but if you want to learn in a distraction-free environment you should highly consider attending.

Date: May 14-15, 2020

Location: Chicago

VenueGleacher Center

3. TDWI Las Vegas

What happens in Vegas stays in Vegas…Okay, that’s completely untrue because you’re going to take away a lot from Transforming Data With Intelligence (TDWI) in Las Vegas.

TDWI is one of the leading data science conferences in North America. Even if you have to travel, you should consider attending. TDWI prides itself in offering full and half-day courses, rather than the traditional 45 to 60-minute sessions, and that’s what really sets this conference apart for me.

You actually get time to pick the brain of the instructor rather than running through a session and quickly forgetting what it was about because the next one has already started. TDWI also has a Sell Your Boss template to help you get those work days off for professional development.

Date: February 9-14, 2020

Location: Las Vegas

VenueCaesars Palace

4. Predictive Analytics World (PAW) Las Vegas

PAW typically comes to Las Vegas every year and doesn’t really need much explaining. The conference is one of the leaders in data science, machine learning, and AI coverage and is also one of the most widely attended. Formerly known as Mega-Paw and being rebranded as Machine Learning Week, you can expect the same great conference.

Machine Learning Week is technically 5 conferences and 10 workshops with 160 speakers and 150+ sessions packed into 1 week. They sell passes for all 5 days or as little as 2. It depends on what you want to get out of the week. Business, Healthcare, Finance, Industry 4.0 (IoT), and Deep Learning are the five conferences taking place during the week.

Keynotes from GM, Lyft, Google, Kennesaw State University, Manulife, and Fidelity Investments highlight the list of speakers at ML Week. If you can’t attend all 5 days, make sure to find the conference that most applies to your industry (or future industry) to get the most out of it.

Date: May 31 – June 4, 2020

Location: Las Vegas

VenueCaesars Palace

5. ODSC East 2020

The Open Data Science Conference (ODSC) is one of the best in the world. It’s 5 days of learning, networking, developing, and building that leave you exhausted at the end of each day. It includes 8 focus areas spread across 5 days, with 3 of those days strictly for training.

The best conferences don’t earn their title without incredible content. Some of the speakers at ODSC East are from some of the most renowned universities and organizations. Google, Harvard Medical School, Bloomberg, and MIT-IBM Watson AI Lab all have representatives speaking/instructing during the conference.

The most difficult part of attending will be choosing who’s talk you should attend. That would require a whole different blog post, so I won’t get into it.

Date: April 13-17, 2020

Location: Boston, MA, USA

VenueBoston Hynes Convention Center

6. Big data and analytics Summit Canada

What I love about the Big Data and Analytics Summit is that it’s not as big as some of the conferences previously mentioned. It won’t be as chaotic, you won’t feel overwhelmed, and you’ll get more networking time with speakers and peers.

The other great thing is the list of speakers still comes from reputable companies in the field. Representatives from McDonald’s, Coca-Cola, UPS, and Sephora will all be in attendance at the 2020 event. One speaker that I would actually like to see is  Sunanda Parthasarthy, Associate Director of  Data Science & Algorithms at Wayfair.

Sunanda is joined by Eugene Wen and Niraj Krishna for a panel titled Secure Buy-in for your Big Data Initiatives in a Data-Driven Culture. They’ll be giving tips on visualizations, value propositions, and talking about their experience. This conference looks to give you a real-world outlook on big data and data-driven cultures.

Date: February 12-13, 2020

Location: Toronto, ON

Venue: Hyatt Regency Toronto Hotel

Resource of other conferences

Also, If you want to learn data science for professional development without all the distractions from a conference, consider attending a data science Bootcamp taught by Data Science Dojo.

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