The world collectively has been bracing for a change in the job landscape. Driven largely by the emergence of new technologies like data science and artificial intelligence (AI), these changes have already made some jobs redundant. To add to this uncertainty, the catastrophic economic impact of the Covid-19 pandemic has brought in an urgency to upskill oneself to adapt to changing scenarios.
While the prognosis does not look good, this could also create the demand for jobs in the field of business analytics. This indicates that heavily investing in data science and AI skills today could mean the difference between you being employed or not tomorrow.
By adding more skills to your arsenal today, you can build your core competencies in areas that will be relevant once these turbulent times pass over. This includes sharpening your understanding of business numbers and analysing consumer demands – two domains which businesses will heavily invest in very soon.
But motivation alone will not help. You need to first filter through the clutter of online courses that the internet is saturated with. Secondly, you need to create a study plan that ensures that you successfully complete these courses.
We have a solution.
Developed with the objective of providing you a comprehensive understanding of key concepts that are tailored to align with the jobs of the future, we are launching a series of special short-term courses. These courses will not only help you upskill yourself, it will also ensure that you complete these courses in a matter of a few days.
These short-term courses will have similar content as regular ones, but packed in a more efficient way. Whether you are looking for courses in business analytics, applied AI, or data analytics, these should hold you in good stead for jobs of the future.
About The Course: This all-encompassing data analytics certification course is tailor-made for analytics beginners. It would cover key concepts around data mining, and statistical and predictive modelling skills, and is curated for candidates who have no prior knowledge about data analytics tools. What is more, the inclusion of popular data visualization tool Tableau makes it one of the best courses available on the subject today. Additionally, it also puts an emphasis on widely used analytics tools like R, SQL and Excel, making this course truly unique.
Duration: While the original data analytics course this short-term course is developed from includes 180 hours of content and demands an average of 10-15 hours of weekly online classes and self-study. This course will enable you to acquire the same skills, but within a shorter period of time.
Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background in engineering, finance, math, and business management. It will also be a useful skill-building course for candidates who want to target job profiles based around R programming, statistical analysis, Excel-VBA or tableau-based BI analyst profiles.
About the course: Adapted to greatly help candidates when searching for data science roles, this certification covers all that they need to know on the subject using Python as the programming language. While other languages like R are also commonly used today, Python has emerged as one of the more popular options within the data science universe.
This ‘Python for Data Science’ course will make you proficient in defly handling and visualizing data, and also covers statistical modelling and operations with NumPy. It also integrates these with practical examples and case studies, making it a unique online training data science course in Python.
Duration of the course: While the original data science course this short-term course is developed from includes 220 hours of content and demands an average of 15-20 hours of weekly online classes and self-study, this course will enable you to acquire the same skills, but within a shorter period of time.
Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background of working with data analysis and visualization techniques. It will also help people who want to undergo Python training with advanced analytics skills to help them jumpstart a career in data science.
About this course: This course delves into the applications of AI using ML and is tailor-made for candidates looking to start their journey in the field of data science. It will cover tools and libraries like Python, Numpy, Pandas, Scikit-Learn, NLTK, TextBlob, PyTorch, TensorFlow, and Keras, among others.
Thus, after successful completion of this Applied AI course, you will not only be proficient in the theoretical aspects of AI and ML, but will also develop a nuanced understanding of its industry applications.
Duration of the course: While the original ML and AI course this short-term course is developed from includes 280 hours of content and demands an average of 8-10 hours of weekly self-study, this Applied AI course will enable you to acquire the same skills, but within a shorter period of time.
Target Group: While anyone with an interest in analytics can pursue this course, it is especially targeted at candidates with a background in engineering, finance, math, statistics, and business management. It will also help people who want to acquire AI and machine learning skills to head start their career in the field of data science.
While the Covid-19 pandemic has witnessed a partial – or even complete – lockdown at several places across the globe, people have been reorienting their lives indoors. But with no end in sight, it necessitates that professionals turn these circumstances into opportunities to upskill.
Given an oncoming recession and economic downturn, it behoves them to adapt to these changes to remain employable in such competitive times. In this setting, Covid-19 could emerge as a tipping point for learning, with virtual learning offering the perfect opportunity to self-learn.
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Big Data Analytics in Automotive Market 2018: Global Industry Insights by Global Players, Regional Segmentation, Growth, Applications, Major Drivers, Value and Foreseen till 2024
The report provides both quantitative and qualitative information of global Big Data Analytics in Automotive market for period of 2018 to 2025. As per the analysis provided in the report, the global market of Big Data Analytics in Automotive is estimated to growth at a CAGR of _% during the forecast period 2018 to 2025 and is expected to rise to USD _ million/billion by the end of year 2025. In the year 2016, the global Big Data Analytics in Automotive market was valued at USD _ million/billion.
This research report based on ‘ Big Data Analytics in Automotive market’ and available with Market Study Report includes latest and upcoming industry trends in addition to the global spectrum of the ‘ Big Data Analytics in Automotive market’ that includes numerous regions. Likewise, the report also expands on intricate details pertaining to contributions by key players, demand and supply analysis as well as market share growth of the Big Data Analytics in Automotive industry.
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Big Data Analytics in Automotive Market Overview:
The Research projects that the Big Data Analytics in Automotive market size will grow from in 2018 to by 2024, at an estimated CAGR of XX%. The base year considered for the study is 2018, and the market size is projected from 2018 to 2024.
The report on the Big Data Analytics in Automotive market provides a bird’s eye view of the current proceeding within the Big Data Analytics in Automotive market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Big Data Analytics in Automotive market and offers a clear assessment of the projected market fluctuations during the forecast period. The different factors that are likely to impact the overall dynamics of the Big Data Analytics in Automotive market over the forecast period (2019-2029) including the current trends, growth opportunities, restraining factors, and more are discussed in detail in the market study.
Leading manufacturers of Big Data Analytics in Automotive Market:
The key players covered in this study
Advanced Micro Devices
Big Cloud Analytics
Market segment by Type, the product can be split into
Market segment by Application, split into
Manufacturing & Supply Chain
After-Sales, Warranty & Dealer Management
Connected Vehicles & Intelligent Transportation
Marketing, Sales & Other Applications
Market segment by Regions/Countries, this report covers
Central & South America
The study objectives of this report are:
To analyze global Big Data Analytics in Automotive status, future forecast, growth opportunity, key market and key players.
To present the Big Data Analytics in Automotive development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.
To strategically profile the key players and comprehensively analyze their development plan and strategies.
To define, describe and forecast the market by type, market and key regions.
In this study, the years considered to estimate the market size of Big Data Analytics in Automotive are as follows:
History Year: 2015-2019
Base Year: 2019
Estimated Year: 2020
Forecast Year 2020 to 2026
For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.
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Some important highlights from the report include:
- The report offers a precise analysis of the product range of the Big Data Analytics in Automotive market, meticulously segmented into applications
- Key details concerning production volume and price trends have been provided.
- The report also covers the market share accumulated by each product in the Big Data Analytics in Automotive market, along with production growth.
- The report provides a brief summary of the Big Data Analytics in Automotive application spectrum that is mainly segmented into Industrial Applications
- Extensive details pertaining to the market share garnered by each application, as well as the details of the estimated growth rate and product consumption to be accounted for by each application have been provided.
- The report also covers the industry concentration rate with reference to raw materials.
- The relevant price and sales in the Big Data Analytics in Automotive market together with the foreseeable growth trends for the Big Data Analytics in Automotive market is included in the report.
- The study offers a thorough evaluation of the marketing strategy portfolio, comprising several marketing channels which manufacturers deploy to endorse their products.
- The report also suggests considerable data with reference to the marketing channel development trends and market position. Concerning market position, the report reflects on aspects such as branding, target clientele and pricing strategies.
- The numerous distributors who belong to the major suppliers, supply chain and the ever-changing price patterns of raw material have been highlighted in the report.
- An idea of the manufacturing cost along with a detailed mention of the labor costs is included in the report.
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The Questions Answered by Big Data Analytics in Automotive Market Report:
- What are the Key Manufacturers, raw material suppliers, equipment suppliers, end users, traders And distributors in Big Data Analytics in Automotive Market ?
- What are Growth factors influencing Big Data Analytics in Automotive Market Growth?
- What are production processes, major issues, and solutions to mitigate the development risk?
- What is the Contribution from Regional Manufacturers?
- What are the Key Market segment, market potential, influential trends, and the challenges that the market is facing?
And Many More….
Netflix’s famous algorithm challenge awarded a million dollars to the best algorithm for predicting user ratings for films. But did you know that the winning algorithm was never implemented into a functional model?
Netflix reported that the results of the algorithm just didn’t seem to justify the engineering effort needed to bring them to a production environment. That’s one of the big problems with machine learning.
At your company, you can create the most elegant machine learning model anyone has ever seen. It just won’t matter if you never deploy and operationalize it. That’s no easy feat, which is why we’re presenting you with seven machine learning best practices.
At the most recent Data and Analytics Summit, we caught up with Charlie Berger, Senior Director of Product Management for Data Mining and Advanced Analytics to find out more. This is article is based on what he had to say.
Putting your model into practice might take longer than you think. A TDWI report found that 28% of respondents took three to five months to put their model into operational use. And almost 15% needed longer than nine months.
So what can you do to start deploying your machine learning faster?
We’ve laid out our tips here:
1. Don’t Forget to Actually Get Started
In the following points, we’re going to give you a list of different ways to ensure your machine learning models are used in the best way. But we’re starting out with the most important point of all.
The truth is that at this point in machine learning, many people never get started at all. This happens for many reasons. The technology is complicated, the buy-in perhaps isn’t there, or people are just trying too hard to get everything e-x-a-c-t-l-y right. So here’s Charlie’s recommendation:
Get started, even if you know that you’ll have to rebuild the model once a month. The learning you gain from this will be invaluable.
2. Start with a Business Problem Statement and Establish the Right Success Metrics
Starting with a business problem is a common machine learning best practice. But it’s common precisely because it’s so essential and yet many people de-prioritize it.
Think about this quote, “If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and 5 minutes thinking about solutions.”
Now be sure that you’re applying it to your machine learning scenarios. Below, we have a list of poorly defined problem statements and examples of ways to define them in a more specific way.
Think about what your definition of profitability is. For example, we recently talked to a nation-wide chain of fast-casual restaurants that wanted to look at increasing their soft drinks sales. In that case, we had to consider carefully the implications of defining the basket. Is the transaction a single meal, or six meals for a family? This matters because it affects how you will display the results. You’ll have to think about how to approach the problem and ultimately operationalize it.
Beyond establishing success metrics, you need to establish the right ones. Metrics will help you establish progress, but does improving the metric actually improve the end user experience? For example, your traditional accuracy measures might encompass precision and square error. But if you’re trying to create a model that measures price optimization for airlines, that doesn’t matter if your cost per purchase and overall purchases isn’t going up.
3. Don’t Move Your Data – Move the Algorithms
The Achilles heel in predictive modeling is that it’s a 2-step process. First you build the model, generally on sample data that can run in numbers ranging from the hundreds to the millions. And then, once the predictive model is built, data scientists have to apply it. However, much of that data resides in a database somewhere.
Let’s say you want data on all of the people in the US. There are 360 million people in the US—where does that data reside? Probably in a database somewhere.
Where does your predictive model reside?
What usually happens is that people will take all of their data out of database so they can run their equations with their model. Then they’ll have to import the results back into the database to make those predictions. And that process takes hours and hours and days and days, thus reducing the efficacy of the models you’ve built.
However, growing your equations from inside the database has significant advantages. Running the equations through the kernel of the database takes a few seconds, versus the hours it would take to export your data. Then, the database can do all of your math too and build it inside the database. This means one world for the data scientist and the database administrator.
By keeping your data within your database and Hadoop or object storage, you can build models and score within the database, and use R packages with data-parallel invocations. This allows you to eliminate data duplications and separate analytical servers (by not moving data) and allows you to to score models, embed data prep, build models, and prepare data in just hours.
4. Assemble the Right Data
As James Taylor with Neil Raden wrote in Smart Enough Systems, cataloging everything you have and deciding what data is important is the wrong way to go about things. The right way is to work backward from the solution, define the problem explicitly, and map out the data needed to populate the investigation and models.
And then, it’s time for some collaboration with other teams.
Here’s where you can potentially start to get bogged down. So we will refer to point number 1, which says, “Don’t forget to actually get started.” At the same time, assembling the right data is very important to your success.
For you to figure out the right data to use to populate your investigation and models, you will want to talk to people in the three major areas of business domain, information technology, and data analysts.
Business domain—these are the people who know the business.
- Marketing and sales
- Customer service
Information technology—the people who have access to data.
- Database administrators
Data Analysts—people who know the business.
- Data miners
- Data scientists
You need the active participation. Without it, you’ll get comments like:
- These leads are no good
- That data is old
- This model isn’t accurate enough
- Why didn’t you use this data?
You’ve heard it all before.
5. Create New Derived Variables
You may think, I have all this data already at my fingertips. What more do I need?
But creating new derived variables can help you gain much more insightful information. For example, you might be trying to predict the amount of newspapers and magazines sold the next day. Here’s the information you already have:
- Brick-and-mortar store or kiosk
- Sell lottery tickets?
- Amount of the current lottery prize
Sure, you can make a guess based off that information. But if you’re able to first compare the amount of the current lottery prize versus the typical prize amounts, and then compare that derived variable against the variables you already have, you’ll have a much more accurate answer.
6. Consider the Issues and Test Before Launch
Ideally, you should be able to A/B test with two or more models when you start out. Not only will you know how you’re doing it right, but you’ll also be able to feel more confident knowing that you’re doing it right.
But going further than thorough testing, you should also have a plan in place for when things go wrong. For example, your metrics start dropping. There are several things that will go into this. You’ll need an alert of some sort to ensure that this can be looked into ASAP. And when a VP comes into your office wanting to know what happened, you’re going to have to explain what happened to someone who likely doesn’t have an engineering background.
Then of course, there are the issues you need to plan for before launch. Complying with regulations is one of them. For example, let’s say you’re applying for an auto loan and are denied credit. Under the new regulations of GDPR, you have the right to know why. Of course, one of the problems with machine learning is that it can seem like a black box and even the engineers/data scientists can’t say why certain decisions have been made. However, certain companies will help you by ensuring your algorithms will give a prediction detail.
7. Deploy and Automate Enterprise-Wide
Once you deploy, it’s best to go beyond the data analyst or data scientist.
What we mean by that is, always, always think about how you can distribute predictions and actionable insights throughout the enterprise. It’s where the data is and when it’s available that makes it valuable; not the fact that it exists. You don’t want to be the one sitting in the ivory tower, occasionally sprinkling insights. You want to be everywhere, with everyone asking for more insights—in short, you want to make sure you’re indispensable and extremely valuable.
Given that we all only have so much time, it’s easiest if you can automate this. Create dashboards. Incorporate these insights into enterprise applications. See if you can become a part of customer touch points, like an ATM recognizing that a customer regularly withdraws $100 every Friday night and likes $500 after every payday.
Here are the core ingredients of good machine learning. You need good data, or you’re nowhere. You need to put it somewhere like a database or object storage. You need deep knowledge of the data and what to do with it, whether it’s creating new derived variables or the right algorithms to make use of them. Then you need to actually put them to work and get great insights and spread them across the information.
The hardest part of this is launching your machine learning project. We hope that by creating this article, we’ve helped you out with the steps to success. If you have any other questions or you’d like to see our machine learning software, feel free to contact us.