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.
Provide your comments below
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.
Get Free Sample PDF (including COVID19 Impact Analysis, full TOC, Tables and Figures) of Market Report @ https://www.researchmoz.com/enquiry.php?type=S&repid=2636782&source=atm
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.
Do You Have Any Query Or Specific Requirement? Ask to Our Industry [email protected]https://www.researchmoz.com/enquiry.php?type=E&repid=2636782&source=atm
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.
You can Buy This Report from Here @ https://www.researchmoz.com/checkout?rep_id=2636782&licType=S&source=atm
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….
A data lake is an absolutely vital piece of today’s big data business environment. A single company may have incoming data from a huge variety of sources, and having a means to handle all of that is essential. For example, your business might be compiling data from places as diverse as your social media feed, your app’s metrics, your internal HR tracking, your website analytics, and your marketing campaigns. A data lake can help you get your arms around all of that, funneling those sources into a single consolidated repository of raw data.
But what can you do with that data once it’s all been brought into a data lake? The truth is that putting everything into a large repository is only part of the equation. While it’s possible to pull data from there for further analysis, a data lake without any integrated tools remains functional but cumbersome, even clunky.
On the other hand, when a data lake integrates with the right tools, the entire user experience opens up. The result is streamlined access to data while minimizing errors during export and ingestion. In fact, integrated tools do more than just make things faster and easier. By expediting automation, the door opens to exciting new insights, allowing for new perspectives and new discoveries that can maximize the potential of your business.
To get there, you’ll need to put the right pieces in place. Here are four essential tools to integrate into your data lake experience.
Never miss an update about big data! Subscribe to the Big Data Blog to receive the latest posts straight to your inbox!
Even if your data sources are vetted, secured, and organized, the sheer volume of data makes it unruly. As a data lake tends to be a repository for raw data—which includes unstructured items such as MP3 files, video files, and emails, in addition to structured items such as form data—much of the incoming data across various sources can only be natively organized so far. While it can be easy to set up a known data source for, say, form data into a repository dedicated to the fields related to that format, other data (such as images) arrives with limited discoverability.
Machine learning can help accelerate the processing of this data. With machine learning, data is organized and made more accessible through various processes, including:
In processed datasets, machine learning can use historical data and results to identify patterns and insights ahead of time, flagging them for further examination and analysis.
With raw data, machine learning can analyze usage patterns and historical metadata assignments to begin implementing metadata automatically for faster discovery.
The latter point requires the use of a data catalog tool, which leads us to the next point.
Simply put, a data catalog is a tool that integrates into any data repository for metadata management and assignment. Products like Oracle Cloud Infrastructure Data Catalog are a critical element of data processing. With a data catalog, raw data can be assigned technical, operational, and business metadata. These are defined as:
- Technical metadata: Used in the storage and structure of the data in a database or system
- Business metadata: Contributed by users as annotations or business context
- Operational metadata: Created from the processing and accessing of data, which indicates data freshness and data usage, and connects everything together in a meaningful way
By implementing metadata, raw data can be made much more accessible. This accelerates organization, preparation, and discoverability for all users without any need to dig into the technical details of raw data within the data lake.
A data lake acts as a middleman between data sources and tools, storing the data until it is called for by data scientists and business users. When analytics and other tools exist separate from the data lake, that adds further steps for additional preparation and formatting, exporting to CSV or other standardized formats, and then importing into the analytics platform. Sometimes, this also includes additional configuration once inside the analytics platform for usability. The cumulative effect of all these steps creates a drag on the overall analysis process, and while having all the data within the data lake is certainly a help, this lack of connectivity creates significant hurdles within a workflow.
Thus, the ideal way to allow all users within an organization to swiftly access data is to use analytics tools that seamlessly integrate with your data lake. Doing so removes unnecessary manual steps for data preparation and ingestion. This really comes into play when experimenting with variability in datasets; rather than having to pull a new dataset every time you experiment with different variables, integrated tools allow this to be done in real time (or near-real time). Not only does this make things easier, this flexibility opens the door to new levels of insight as it allows for previously unavailable experimentation.
Integrated Graph Analytics
In recent years, data analysts have started to take advantage of graph analytics—that is, a newer form of data analysis that creates insights based on relationships between data points. For those new to the concept, graph analytics considers individual data points similar to dots in a bubble—each data point is a dot, and graph analytics allows you to examine the relationship between data by identifying volume of related connections, proximity, strength of connection, and other factors.
This is a powerful tool that can be used for new types of analysis in datasets with the need to examine relationships between data points. Graph analytics often works with a graph database itself or through a separate graph analytics tool. As with traditional analytics, any sort of extra data exporting/ingesting can slow down the process or create data inaccuracies depending on the level of manual involvement. To get the most out of your data lake, integrating cutting-edge tools such as graph analytics means giving data scientists the means to produce insights as they see fit.
Why Oracle Big Data Service?
Oracle Big Data Service is a powerful Hadoop-based data lake solution that delivers all of the needs and capabilities required in a big data world:
- Integration: Oracle Big Data Service is built on Oracle Cloud Infrastructure and integrates seamlessly into related services and features such as Oracle Analytics Cloud and Oracle Cloud Infrastructure Data Catalog.
- Comprehensive software stack: Oracle Big Data Service comes with key big data software: Oracle Machine Learning for Spark, Oracle Spatial Analysis, Oracle Graph Analysis, and much more.
- Provisioning: Deploying a fully configured version of Cloudera Enterprise, Oracle Big Data Service easily configures and scales up as needed.
- Secure and highly available: With built-in high availability and security measures, Oracle Big Data Service integrates and executes this in a single click.