Getting a job in data science is very competitive, so becoming a Chartered Data ScientistTM is always worth it. It doesn’t matter which career level a person chooses it, but it would help a data science professional in the long run.

Increased productivity: Data scientists are responsible for analyzing all kinds of data that can help an organization process large amounts of data faster. Better customer experience: Using data analytics processes, companies can offer personalized services to customers based on their preferences.

It’s like a career buffet that lets you customize your path according to your preferences. The versatility of data science skills is another advantage. Even if you don’t pursue a career as a data scientist, these skills will allow you to analyze and gain insights into a variety of roles and industries.

With the added fashionability of Data Science, it’s no shock that jobs like Data Scientist, Data Analyst, etc. are getting more in demand than ever. It appears that Data wisdom is the new buzz word and everybody in the tech sector is either formerly a data scientist or working on becoming a data scientist! 

 So there are further and further professionals exploring this field which means it’s getting more and more delicate to stand out in all the competition. Since numerous Data Scientists these days have no formal training and have learned Data Science on their own, there’s a demand for a standard that demonstrates the skill and experience of a Data Scientist and helps them stand out from the crowd. This is where the Chartered Data Scientist Program comes in! So let’s understand what’s a Chartered Data Scientist first and also we can concentrate on how to become one in this composition. 

What’s a Chartered Data Scientist? 

The Chartered Data Scientist Program is developed by the Association of Data Scientists which is a global professional body made up of Data Science and Machine Learning professionals. Since the Association of Data Scientists is devoted to the development and advancement of data wisdom, it has created the Chartered Data Scientist Program that will help you stand out in the crowd of data scientists. 

Still, this is an advertisement to all data associations and your fellow data scientists that you have a strong understanding of the advanced data wisdom and in-depth knowledge of applied colorful tools and chops needed in analytics, If you become a Chartered Data Scientist. This also means that you can forge further connections with other elite Chartered Data Scientists who are working in leading associations or involved in slice-edge invention at academic institutions. carrying the title of a Chartered Data Scientist allows you to demonstrate your mastery in data wisdom which will make you largely sought after by associations that want to move into the data age. It’ll give you an edge in your career development and ensure you stand a cut above the other data scientists. Now let’s see how you can become a Chartered Data Scientist to enhance your career. 

How to Become a Chartered Data Scientist? 

To become a Chartered Data Scientist, you need to pass a Data Wisdom test that tests you on the abecedarian tools and ways that are used in data wisdom along with the propositions governing them. The test is a computer-grounded, multiple-choice test with 150 questions of equal weightage and a time limit of 3 hours. To enroll in the test, you can register online via the Association of Data Scientists website. The Data Science test is available time- round and you need to pay USD 250 to appear in this test. But the plus point is that paying this important plutocrat also buys you a one-time class of the Association of Data Scientists! piecemeal from passing this test, you should also have a minimum of two times of full-time work experience in data wisdom or an affiliated field. However, you’re awarded with the minimal end score, you’re awarded the designation of a Chartered Data Scientist with a lifetime validity, If you have this experience and you pass the test. 

 The Chartered Data Scientist test has 10 different sections with the following motifs and weightage 

•  Section 1: Probability Proposition, Statistics, and Linear Algebra( Weightage is 12) 
•  Section 2: Data Engineering and Databases( Weightage is 8) 
•  Section 3: Exploratory Data Analysis( Weightage is 8) 
•  Section 4: Supervised literacy and Unsupervised literacy( Weightage is 15) 
•  Section 5: Neural Networks and Deep Literacy ( Weightage is 11) 
•  Section 6: Natural Language Processing( Weightage is 8) 
•  Section 7: Computer Vision( Weightage is 8) 
•  Section 8: Deployment and Model operation( Weightage is 8) 
•  Section 9: Python and R( Weightage is 10) 
•  Section 10: Business and data wisdom( Weightage is 12) 

 Who Should Consider Getting a Chartered Data Scientist? 

 Now that we’ve seen what a Chartered Data Scientist is, how do you decide whether you want to gain this qualification or not? It’ll be helpful to you anyway, but particularly useful if you fall into any of these three orders! 

• Data Scientists : Are you a data scientist, machine literacy mastermind, data critic, or any other professional relating to the data wisdom field who wants to expand their knowledge of data wisdom and ameliorate their skill set? If you are, also getting a Chartered Data Scientist is perfect for you! If you’re just beginning your career in data wisdom, also this test will force you to learn about all the aspects of Data Science and broaden your horizons. However, also his test will allow you to refresh your knowledge and understand the rearmost trends in Data Science If you’re an educated data scientist. It’ll also allow put a sanctioned stamp of confirmation to your knowledge. After completing the Chartered Data Scientist test, you can count yourself among elite data scientists with superior chops. 
• Scholar: Are you a pupil of Data Science who wants to test what you’ve learned and acquire indeed more knowledge in this field? If yes, also this Chartered Data Scientist test is a great value supporter for you! You can sit the test during your formal studies or after completing your studies. It’ll allow you to expand your knowledge base further than what you have learned in university and also help you learn further about other branches of Data Science. Also, the Chartered Data Scientist test is further practice- grounded than proposition- and it’ll demonstrate to your future employers that you’re a data scientist who can handle real-world problems. You’ll only become a Chartered Data Scientist after a minimum of two times of full-time work experience in data wisdom after passing this test but still, the fact that you passed the test will ameliorate your value in the job request. 
• Professional Carrier Changers : Are you in a profession that isn’t Data Science but you want to transition to data wisdom or any of its affiliated fields? also, the Chartered Data Scientist test is a veritably good option as it’ll demonstrate to your future employers that you’re complete in Data Science despite not having a background in this subject. As a professional from the Data Science field, this test will also familiarize you with all the aspects of Data Science and broaden your midairs. getting a Chartered Data Scientist will be a signal to unborn employers that you’re assiduity-ready and an expert in Data Science indeed if you have no formal education or former experience in this field. thus, this test will surely ameliorate your job prospects as you transition from a Data Science field to a Data Science professional. 

 Advantages of getting a Chartered Data Scientist 

 Now let’s check out some of the advantages of getting a Chartered Data Scientist 

• Global Recognition: The Chartered Data Scientist test tests you completely in colorful fields of data wisdom and makes sure that you have a strong understanding of the advanced data wisdom and in-depth knowledge of applied colorful tools and chops needed in analytics. thus, succeeding in this test and becoming a Chartered Data Scientist earns you global recognition because it proves your skill as a data scientist and demonstrates your fidelity to professional excellence to the whole world. 
•  Demonstration of Professional Experience : The designation of a Chartered Data Scientist is only awarded if you pass the Chartered Data Scientist test and accumulate at least two times of full-time work experience in data wisdom. Since the test is further practice- grounded than proposition- and you also have work experience, being a Chartered Data Scientist will demonstrate to your future employers that you’re a professional data scientist who can handle real-world problems. 
• Improved Employment openings : Getting a Chartered Data Scientist will demonstrate your experience and skill as a master in Data Science. This will elevate you to a group of elite Data Scientists which will also ameliorate your employment openings. Since employers know that you’re professed in colorful disciplines of Data Science and have real-world work experience, they will be much more likely to hire you than ever ahead. 
• Entry into an Elite Group : Since Data wisdom is getting more and more popular, there are numerous further Data Scientists and Judges in the request than ever ahead. This has resulted in stiff competition in the job request but getting a Chartered Data Scientist will give you an edge over the others. This will give you entry into an elite group with access to a network of like- inclined professionals that are all experts in Data Science. This will expand your connectivity and lead to numerous further job openings in the future. 
•  Acquiring Specialized Knowledge : The Chartered Data Scientist test contains questions from all Data Science branches similar to Linear Algebra, Exploratory Data Analysis, Supervised literacy and Unsupervised literacy Databases, Neural Networks and Deep literacy, Natural Language Processing, Computer Vision, etc. Studying all these motifs deeply will ensure that you acquire technical knowledge across the breadth of Data Science that will help you become an Expert Data Scientist. 

Take your chance to ride the surge of the data revolution! Every assiduity is spanning new heights by tapping into the power of data. Edge your chops and become a part of the hottest trend in the 21st century. 

Dive into the future of technology- explore the Complete Machine Literacy and Data Science Program by GeeksforGeeks and stay ahead of the wind. 

The post Exploring the Benefits of Becoming a Chartered Data Scientist first appeared on AITechTrend.

In the modern world of data science, SQL (Structured Query Language) is the cornerstone of data management and analysis. It is the common language for communicating with databases, getting data, and carrying out intricate analysis to produce useful insights. Being proficient with SQL is advantageous and necessary for data scientists who want to manage the complexities of large datasets and extract valuable insights in the era of data-driven decision-making. This post attempts to function as a thorough tutorial on SQL, including advanced methods, necessary resources, and opportunities for data scientists in the future.

Importance of SQL –

Structured Query Language, or SQL, is a fundamental component of contemporary data analysis and administration. Its importance stems from its capacity to manage, query, and analyse enormous amounts of data effectively kept in relational databases. By enabling companies and organisations to derive actionable insights from their data, SQL promotes strategic objectives and educates decision-making. SQL is the foundation of many systems and applications that are essential to today’s data-driven economy, ranging from providing business intelligence solutions to handling transactional records. Its adaptability, dependability, and performance make it a vital tool for developers, scientists, and data analysts alike, influencing how businesses use data to meet objectives and maintain their competitiveness in a market that is changing quickly.

Why is it important to master SQL?

Being proficient with SQL is essential in today’s data-driven environment for multiple reasons. First of all, relational databases—the foundation of data administration and storage for numerous businesses in a wide range of industries—interact with SQL primarily. Data scientists who are proficient in SQL are better equipped to extract, manipulate, and analyse data in an effective manner, which allows them to gain meaningful insights and make decisions based on evidence from data.

Second, data integration and interoperability depend on having a solid SQL foundation. Sophisticated systems and applications of today require the capacity to seamlessly integrate data from various sources. With SQL, businesses can query and combine data from many databases and platforms using a standard language, which streamlines data integration efforts and gives them a full understanding of operations. 

Furthermore, the development and upkeep of reliable data pipelines and ETL (Extract, Transform, Load) procedures depend heavily on SQL proficiency. Proficiency in SQL is crucial for the efficient design, optimisation, and automation of data operations, be it in the extraction of data from operational systems, its transformation into a readable format, or its entry into a data warehouse or analytics platform.

Additionally, maintaining data security, privacy, and regulatory compliance requires a strong grasp of SQL. Organisations may prove compliance with data protection laws like GDPR and HIPAA, secure sensitive data, and reduce the risk of data breaches by putting in place access restrictions, encryption methods, and SQL auditing procedures.

Here are the 8 resources that one requires to master SQL – 

1. Tutorials & Courses Online

There are several SQL courses available on online learning platforms like Coursera, Udemy, and edX, with options for students of all skill levels. Database administration principles, sophisticated querying strategies, and SQL fundamentals are all covered in these courses. Engaging courses on websites such as Codecademy offer practical experience, enabling students to hone their SQL abilities in an authentic database setting. A practical approach to utilising SQL for analytics and decision-making can also be learned through specific courses that concentrate on SQL for data science applications.

2. Textbooks and Records

Books like Alan Beaulieu’s “Learning SQL” and Philipp K. Janert’s “SQL for Data Scientists” provide in-depth explanations of SQL fundamentals and best practices. The official documentation provided by database providers, such as MySQL and PostgreSQL, is an invaluable tool for learning SQL syntax, capabilities, and functions. These resources help students build a solid foundation in SQL by offering thorough explanations, real-world examples, and best practices.

3. Community Engagement

Getting involved in online groups, including Stack Overflow and Reddit’s r/SQL, can help you solve common SQL problems and gain insightful knowledge. Learners can improve their comprehension of SQL ideas and problem-solving abilities by taking part in conversations and consulting with seasoned professionals. Furthermore, exchanging information and perspectives in community forums encourages teamwork and strengthens education among SQL community members.

4. Advanced Methods and Optimising Performance

Data scientists can become more proficient in SQL by learning about advanced SQL techniques including window functions, common table expressions (CTEs), and query optimisation methodologies. Gaining an understanding of data modelling, indexing, and database design principles is essential to enhancing SQL efficiency and performance. Data scientists can improve performance and optimise workflows for data analysis by streamlining SQL queries and database structures.

5. Combining business intelligence with data science

In data science workflows, SQL is essential for everything from preprocessing and data integration to reporting and model validation. Integrating data warehousing with business intelligence (BI) solutions allows data scientists to drive informed decision-making processes and extract insights. By utilising SQL for data analysis and visualisation, businesses can gain practical insights from sizable and intricate datasets, stimulating innovation and commercial expansion.

6. Prospects for the Future and Emerging Technologies 

SQL abilities will continue to be in high demand as long as data quantities continue to expand dramatically. The emergence of NoSQL databases, NewSQL technologies, and cloud-based data platforms offers data scientists fresh chances to advance their knowledge of SQL and adjust to changing paradigms in data administration. Furthermore, there are a lot of interesting opportunities for data-driven innovation and decision-making when SQL is integrated with cutting-edge technologies like artificial intelligence and machine learning.

7. Data Protection and Ethics:

Responsible data management necessitates adherence to compliance regulations and data governance principles. SQL assists in the implementation of auditing procedures, data security measures, and access restrictions to protect sensitive data and reduce the likelihood of data breaches. Organisations may create a safe and legal data environment and gain the trust of stakeholders and authorities by utilising SQL for data governance.

8. Ongoing Education and Career Advancement:

To remain current in the always-changing field of data science, data scientists must engage in continuous learning. Conferences, workshops, and online courses provide chances to learn new skills and remain current with business trends. Reading books, blogs, and research papers as part of a lifelong learning process broadens knowledge, and networking with peers and mentors offers insightful conversations and professional prospects. Adopting a mindset of perpetual learning guarantees that data scientists stay at the forefront of innovation and professional excellence.

To sum up, data scientists who want to succeed in the quickly developing discipline of data science must learn SQL. Through the use of a variety of resources, including books, online courses, community involvement, and sophisticated approaches, data scientists can establish a strong foundation in SQL and uncover new avenues for data-driven innovation. The future is bright for SQL expertise as the data landscape continues to change, providing data scientists with intriguing opportunities to influence the direction of data-driven decision-making. Accept the path of never-ending discovery and learning, and use SQL as your key to unlocking data science’s potential in the digital era.

The post 8 Resources for Data Scientists to Master SQL first appeared on AITechTrend.

Data Science is a branch that deals with the extraction of information from data. It has been an integral part of businesses at all levels. In the era of data-driven decision-making, data science has emerged as a field of immense significance. As data becomes the lifeline of businesses and industries, the demand for professionals who can extract insights from it is soaring. In today’s technology-driven world, data is like a raw diamond, and Data Science is the mining infrastructure set-up that makes the data useful for transforming the world. Without the presence of an enormous amount of data, self-regulating systems cannot be created. Data is the fuel that drives the industry. With the advent of Data Science,  industries can now make more informed decisions about their future growth. Industries require data for a competitive study and to grow in the market. Data Science is a rapidly growing field due to the increasing availability of data. With the advent of big data, organizations can now collect vast amounts of data from various sources. This data can be used to improve decision-making in all business areas, from marketing to product development.       

Scope of Data Science in India

Data science is a rapidly growing field in India, with an increasing demand for skilled professionals analyzing and interpreting large datasets. The scope for data science in India is vast, with opportunities in various industries, including finance, healthcare, e-commerce, and government organizations. Data Science aims to turn data into actionable insights that can be used to make better decisions.  As a result, the demand for data scientists is skyrocketing.  Data Science is one of the fastest-growing fields in India. Data Science job opportunities in India are increasing as organizations seek to harness the power of data to drive decision-making. Some of the most common job roles in data science include data analysts, data scientists, and big data engineers.  

Big data engineers build and maintain the infrastructure to store and process large amounts of data. With the rapid growth of data-driven businesses in India, these job roles are in high demand. Companies across all industries are looking for talented data scientists who can help them make sense of the huge amounts of data they collect daily. India is one of the best places to find these skilled professionals.   

Some of the top data science companies in India that are hiring data scientists include LinkedIn, Microsoft, Flipkart, Amazon, Ola, Airtel, and Tata Consultancy Services. These companies are all leaders in their respective industries and use data science to gain a competitive edge. For example, Flipkart is using data science to improve its customer targeting and product recommendations, while Ola is using it to optimize its pricing and demand prediction. 

The scope for data science in India is not limited to these industries, however. Data scientists are also in demand in sectors such as transportation, energy, and manufacturing. Additionally, the field of data science is constantly evolving, with new applications and techniques being developed all the time. This means that there will continue to be a wide range of opportunities for data scientists in India in the future. In recent years, India has witnessed remarkable growth in the data science industry. The proliferation of digital technologies, increasing internet penetration, and the government’s Digital India initiative have all contributed to this growth. The scope of data science in India and the need for IT professionals to upgrade their skills in the field of Data Science is increasing. There is a massive data revolution that has transformed the industries around the world. Data scientist jobs in India have immense potential. In India, the field of data science is still in its initial stages with scope for growth. With the advent of digitalization, the scope of data science in India has become more prominent than ever. Organizations are looking for ways to make better use of their data, creating a demand for data scientists.   

India is home to a large pool of talented engineers and scientists. Moreover, Indian organizations are increasingly recognizing the value of data science and are investing in building strong data science teams. Data science is a field that blends statistics, computer science, and domain knowledge. It is used to extract insights from data using techniques like Machine Learning and Artificial Intelligence. With the help of data science, industries can analyze trends in the market, make careful decisions, and analyze the various risks involved. Data Science has facilitated rapid growth in industries and has minimized their losses. It has led to a rise in demand for many data scientists. This has, therefore, contributed to having data science become a career of the future.  

In India, the field of Data Science is rapidly emerging. Various startups are focusing their businesses on various data science technologies. Major companies have shifted their base from traditional software development and consultancy towards Data Science. In India, there is a massive scope of Data Science. The role of a data scientist is set to grow even more in importance as data becomes increasingly crucial for businesses. As data grows larger, more complex, and more accessible through big data analytics tools, more data scientists will be required to make sense of this data. This increases data science scope in the future and makes it an incredibly lucrative career option. The irreplaceable demand for data analysis secures a niche for data scientists in businesses.  

Data science plays a hectic role in the business as it deals with the exploration, evaluation, modeling, and generation of meaningful insights from large datasets Data science is becoming mission-critical to many organizations. Therefore, the demand for data scientists is significantly spiking. Companies across domains need data science professionals by various means. Besides data scientists, there are various roles associated with the interdisciplinary field namely Data Engineers, Machine Learning Engineers, Data Architects, Big Data Engineers, etc. 

Here are the top 70 firms in India that can be considered ideal destinations for data scientists in 2024: 

• Tata Consultancy Services (TCS) 
• Infosys 
• Wipro 
• HCL Technologies 
• Tech Mahindra 
• Accenture 
• Capgemini 
• Cognizant 
• IBM 
• Microsoft 
• Amazon 
• Google 
• Adobe 
• Oracle 
• SAP 
• Uber 
• Ola 
• Zomato 

• Swiggy 
• Flipkart 
• Amazon 
• Paytm 
• PhonePe 
• BYJU’S 
• Unacademy 
• Vedantu 
• Reliance Jio 
• Airtel 
• Vodafone Idea 
• ICICI Bank 
• HDFC Bank 
• State Bank of India (SBI) 
• Axis Bank 
• Kotak Mahindra Bank 
• ICICI Prudential 
• LIC 
• HDFC Life 
• Max Life Insurance 

• Bajaj Allianz 
• Tata AIG 
• MakeMyTrip 
• Yatra 
• Cleartrip 
• Oyo Rooms 
• Treebo 
• Practo 
• 1mg 
• Policybazaar 
• BigBasket 
• Grofers 
• Nykaa 
• Lenskart 
• Pepperfry 
• Urban Ladder 
• Curefit 
• Cult.fit 
• CRED 
• Dream11 

• MPL 
• Nazara Technologies 
• Zomato 
• Swiggy 
• Dunzo 
• Delhivery 
• Ecom Express 
• Rivigo 
• BlackBuck 
• Udaan 
• Cars24 
• Ola Electric 

These companies span various sectors like IT services, consumer tech, e-commerce, fintech, ed-tech, health tech, logistics, and more. Many of them are leading tech firms or tech-enabled businesses that heavily rely on data science for products, services, and decision-making. They offer opportunities for data scientists to work on cutting-edge problems, large datasets, and impactful solutions. Data science has become a sought-after career opportunity in India. The opening for data science professionals in the country is getting more void as technological adoption increases. Remarkably, companies are also willing to pay high salaries for talented individuals. Big companies across diverse sectors, including technology, financial services, manufacturing, automotive, etc always welcome data scientists with open arms to drive innovation. The hunt for data scientists in India remains an endless tale.

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A workstation is a powerful microcomputer designed especially for scientific or engineering work. The workstation is intended primarily to be used by a single user, they are commonly connected to a local area network and run the multi-user operating system. The latest workstations are desktop computers with AMD/ NVIDIA GPUs to do high-performance computers on software programs such as video editing, 3D editing,  computer-aided designs, and rendering. Most of the current workstation market uses x86-64 microprocessors. Operating systems include Windows, FreeBSD, Linux distributions, macOS, and Solaris.  

Data is fundamentally changing the way companies do business, driving demand for data scientists as workflow complexity increases. Today’s data scientists need more than ordinary office computers. Almost everything they do is big: big data, enormous model repositories, a large assortment of tools, and insane levels of automation, while the cloud helps at a certain point in the process, data scientists first need a more agile, native computing solution wherever they’re working— a high-performance data science workstation.  

A workstation for a data scientist is a specialized computer setup designed to handle the demanding tasks involved in data analysis, machine learning, and other data science activities. 

A true data science workstation comes with essential software preinstalled and ready to go. Plus, it should be equipped with Windows Subsystem for Linux 2 (WSL 2),1 enabling data scientists to run both Windows and Linux, thereby eliminating the need for two separate computers or complicated workarounds. 

Data scientists rely on powerful workstations to tackle complex data analysis tasks efficiently.  

Need For a powerful workstation: 

1. Data scientists need powerful workstations to handle the immense computational requirements of analyzing large datasets, running complex algorithms, and conducting advanced data modeling and visualization tasks efficiently. 

2. Data scientists should consider factors such as processor speed, GPU capabilities, memory capacity, storage options, and overall system performance to ensure that the workstation can meet the demands of their data analysis workflows effectively. 
3. Many high-end workstations for data scientists offer customization options, allowing professionals to tailor the hardware components to their specific requirements, such as upgrading the processor, adding more memory, or increasing storage capacity. 
4. Data scientists can stay updated on future trends in workstation technology by following industry publications, attending conferences, participating in webinars, and engaging with online communities focused on data science and technology advancements.   

Key features of a data scientist’s workstation: 

1. High-Performance CPU: A powerful multicore processor (e.g., Intel Core i9 or AMD Ryzen 9) for parallel processing and efficient handling of complex computations. It must have a high core count (12+ cores) for parallel processing also it should support the latest high-end CPUs like Intel Xeon W or AMD Threadripper Pro.  

2. Sufficient RAM (Random Access Memory): A large amount of RAM (128GB – 1TB+) to handle large datasets and complex algorithms. It must also support the high-speed DDR4 or DDR5 RAM.  

3. Dedicated GPU (Graphics Processing Unit): A powerful GPU, such as an NVIDIA GeForce or Quadro card, for accelerating machine learning tasks using frameworks like TensorFlow or PyTorch. The GPUs should have a high CUDA core count and memory for deep learning/AI workloads.  

4. Storage:  The desktop should have fast and ample storage, including SSDs (Solid State Drives) for quick data access and faster loading times. It must have a High-capacity HDD (4TB+) for data storage. Also fast NVMe SSDs (1TB+) for active dataset and application storage. It should also support for RAID configurations. 

5. Data Storage Solutions: The desktop must have additional storage solutions for managing and storing large datasets, such as external hard drives or network-attached storage (NAS) systems. Multiple PCIe slots for adding GPUs, network cards, etc. is always a plus.  

6. Dual Monitors: Dual monitors or a large high-resolution display to facilitate multitasking and the simultaneous viewing of code, data, and visualizations is always in demand as it smoothens the working of the scientists and also provides a wider range of display of the data at a single glance. 

7. Comfortable Keyboard and Mouse: An ergonomic and comfortable keyboard and mouse for prolonged coding and analysis sessions is a must.  

8. High-Resolution Webcam and Microphone: A high-quality webcam and microphone for virtual meetings, collaboration, and communication with team members, cannot be skipped anyhow.  

9. Specialized Input Devices: The workstation must be well equipped with input devices like graphics tablets for data visualization or other specialized tools depending on the specific needs of the data scientist. 

10.  Expandability: The workstation must be Compatible with Linux distributions like Ubuntu and Windows 10/11 Pro.  

11. Software Ecosystem: The desktop must be certified for AI/ML frameworks like TensorFlow, PyTorch, etc. It must also be compatible with data science tools and IDEs to give a hassle-free performance.  

12. Software and Development Tools: Data science software tools such as Jupyter Notebooks, RStudio, or IDEs like PyCharm and Visual Studio Code. 

13.  Version Control System: Integration with version control systems like Git for tracking code changes and collaborating with other team members. 

14. Virtualization and Containerization Tools: Tools like Docker for creating and managing containers to ensure consistent environments across different stages of the data science workflow. 

15. Secure Network Connectivity: Secure network connections, especially if the data scientist is working with sensitive or confidential data. 

16. Cooling System: An effective cooling system to prevent overheating during resource-intensive tasks. 

17. Backup and Recovery Solutions: Regular backup solutions to safeguard important data and recovery options in case of hardware failures. 

 Based on the features and the requirements of the data scientists here are 6 of the best powerful workstation options for data scientists: 

1. Dell Precision 7920 Tower: The Dell Precision 7920 Workstation Tower is a new generation of dual-socket performance that features the latest Intel Xeon Scalable processor family with up to 28 cores per processor and support for up to 3TB of memory. With support for up to 750W of next-generation AMD Radeon Pro GPUs or high-performance NVIDIA Quadro graphics cards, this system is equipped to handle highly complex projects and it is one of the best options to handle all the complex data of the new era. 

Specifications: 

1. Up to Intel Xeon W-3275M CPU with 28 cores/56 threads 

2. Up to 3TB DDR4 RAM 

3. Support for multiple high-end NVIDIA Quadro or AMD Radeon Pro GPUs 

4. Ample storage and expansion options 

2. Lenovo Think Station P620: This is the 2^nd best options to serve as the workstation for the data scientists. Lenovo Think Station P620 is a professional-grade workstation computer.  It is designed for demanding data science, AI, and compute-intensive workloads. This desktop can provide a hassle free workstation to the data scientists as per their requirement.  

Specifications: 

1. AMD Threadripper Pro or Intel Xeon W CPUs, up to 64 cores 

2. Up to 2TB DDR4 RAM 

3. NVIDIA Quadro or AMD Radeon Pro GPUs 

4. Flexible storage and expansion capabilities 

5. 16 DIMM slots supporting up to 3200MHz memory speeds 

6. Support for up to 3 professional GPUs 

7. Support for M.2 NVMe SSDs and SATA HDDs/SSDs 

3. HP Z8 G4 Workstation: HP Z8 G4 Workstation was a high-end desktop computer designed for professional users in demanding fields such as content creation, engineering, and scientific research. The HP Z8 G4 is a powerful and highly configurable workstation designed for demanding workloads like data science, machine learning, and high-performance computing. This desktop is another good option for the new generation workload, as it can handle the complex workload very smoothly.  

Specifications: 

1. Intel Xeon W or Xeon Scalable CPUs, up to 56 cores 

2. Up to 3TB DDR4 RAM 

3. Support for up to 3 high-end GPUs 

4. Expansive storage options, including NVMe and RAID 

5. 24 DIMM slots supporting DDR4 ECC Registered memory 

6. Multiple storage bays for 3.5″ or 2.5″ SATA or SAS drives 

7. Optional M.2 PCIe NVMe SSDs for fast storage 

8. Certified to run various operating systems, including Windows and Linux 

4. Puget Systems Genesis Workstation: Puget Systems is a custom computer system builder that focuses on high-performance workstations tailored to the specific needs of its customers. The Puget Systems Genesis Workstation is one of their product offerings, designed for professional users in fields such as content creation, 3D rendering, scientific research, and other demanding applications. As this is a completely customizable computer thus, it can cater to almost all needs of the data scientists.  

Specifications: 

1. AMD Threadripper Pro or Intel Xeon W CPUs 

2. Up to 1TB DDR4 RAM 

3. Multiple GPU options, including NVIDIA RTX and AMD Radeon Pro 

4. Highly customizable and optimized for various workloads.  

5. Support for NVMe SSDs, SATA SSDs, and HDDs 

6. Hardware RAID options 

7. Up to 7 full-length, full-height PCIe slots 

8. Advanced liquid cooling for CPUs and GPUs. 

5. BOXX APEXX S3 Workstation: The BOXX APEXX S3 is a powerful workstation designed for demanding applications like rendering, simulation, deep learning, and data science. It offers high CPU core counts, massive memory capacity, multi-GPU support, fast storage, and advanced liquid cooling. BOXX also provides ISV certifications and benchmark optimizations. The BOXX APEXX S3 is a compact and high-performance workstation designed for professionals working in industries such as 3D content creation, CAD design, and other demanding applications.  

Specifications: 

1. Intel Xeon W or Xeon Scalable CPUs, up to 28 cores 

2. Up to 512GB DDR4 RAM 

3. Support for multiple high-end GPUs from NVIDIA and AMD 

4. Designed for rendering, simulation, and AI workloads 

5. Options for various storage configurations, including NVMe SSDs and SATA SSDs. 

6. Support for multiple drive bays. 

7. BOXX motherboards designed for performance and reliability. 

8. Multiple USB ports, including USB 3.2 Gen 2. 

9. Networking options, audio ports, and other standard connectivity features. 

10. Certified to run various operating systems, including Windows and Linux. 

6. Falcon Northwest Talon Workstation: Falcon Northwest is known for offering a high degree of customization, allowing users to tailor the workstation to their specific needs. Falcon Northwest is a boutique computer manufacturer known for building high-performance gaming PCs and workstations. The Talon is highly optimized and hand-crafted for maximum performance. It offers high core counts, massive RAM capacity, multi-GPU acceleration, high-speed storage, and meticulous liquid cooling. Falcon Northwest also provides specialized workstation consulting and optimization services. 

Specifications: 

1. Intel Xeon W or Xeon Scalable CPUs, up to 28 cores 

2. Up to 1TB DDR4 RAM 

3. NVIDIA Quadro or AMD Radeon Pro GPUs 

4. Highly customizable and optimized for various workloads. 

5. Support for Intel Xeon W or Xeon Scalable processors 

6. Overclocking support on select CPU models 

7. 7 PCIe slots (4×16, 2×8, 1×4) 

8. Additional M.2 NVMe and U.2 ports 

9. Dual 10GbE LAN ports 

10.  Multiple USB 3.2 Gen 2 Type-C and Type-A ports 

11. Thunderbolt 3 ports 

These workstations offer powerful CPU and GPU options, ample RAM, and expandable storage to handle demanding data science workloads like machine learning, deep learning, data mining, and scientific simulations. They come from reputable manufacturers and can be configured with top-end components suitable for data scientists. From quantum computing dreams to AI-driven innovations, the future of workstations for data scientists is packed with exciting possibilities. Investing in a top-end workstation tailored for data science can significantly enhance productivity and performance for professionals in this field. By carefully considering the key factors, exploring the best options, benchmarking performance, and staying informed about future trends, data scientists can make informed decisions when selecting the ideal workstation to support their data analysis endeavors. With the right workstation at their disposal, data scientists can unlock new levels of efficiency and effectiveness in handling complex data tasks. 

The post The 6 Best Top-End Options for Data Scientists    first appeared on AITechTrend.

Data engineers use their technical expertise to ensure the systems they build are secure, scalable, and reliable—meaning they can handle vast amounts of data and provide it in real time. Data engineering is a rapidly growing field with many lucrative job opportunities. In today’s fast-paced business landscape, the ability to efficiently design, build, and manage data pipelines is crucial for enterprises aiming to extract valuable insights and make data-driven decisions. Due to its instrumental role in transforming raw data into actionable intelligence, Data Engineering has emerged as a high-demand job. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig and many more. 

Key Responsibilities of a Data Engineer are: 

1. Obtain data from third-party providers with the help of robust API integrations. 

2. Build, Design, and maintain data architectures using a systematic approach that satisfies business needs. 

3. Create high-grade data products by coordinating with engineering, product, data scientists, and business teams. 

4. Develop optimized data pipelines and make sure they are executed with high performance. 

5. Track the latest developments in the domain of data infrastructure and analytical tools. 

6. Perform research to handle any problems faced while meeting the business objectives. 

7. Use the data efficiently and identify tasks that can be automated. 

8. Implement different methods to enhance data quality and reliability. 

Here is a list of the important skills for data engineers that one should possess to build a successful career in big data: 

1. SQL 

Data engineers use SQL for performing ETL tasks within a relational database. SQL is ideal for use when the destination and data source are the same type of database. Today, more and more cloud-based systems add SQL-like interfaces that allow you to use SQL. ETL is central to getting your data where you need it. Relational database management systems (RDBMS) remain the key to data discovery and reporting, regardless of their location. Traditional data transformation tools are still relevant today, while next-generation Kafka, cloud-based tools, and SQL are on the rise for 2024. Strong SQL skills allow using databases to construct data warehouses, integrating them with other tools, and analyzing that data for business purposes. There are several SQL types that data engineers might focus exclusively on at some point (Advanced Modelling, Big Data, etc.), but getting there requires learning the basics of this technology. 

2.  Machine Learning and AI 

A big data engineer should be familiar with Python’s libraries SciPy, NumPy, sci-kit learn, pandas, etc. They should also be familiar with the terminology and algorithms. Machine Learning is a big data analytics skill that is used to predict or process data through algorithms like Clustering, Classification, Regression, or Natural language processing. A big data engineer must understand the basic concept of machine learning. Machine learning is a subset of artificial intelligence. Data engineers typically require a functional knowledge of machine learning, which involves data modeling and statistical analysis.  

Applying this skill can help you better understand data scientists’ requirements and create relevant and usable solutions for them. 

3. Multi-Cloud computing 

A data engineer needs to have a thorough understanding of the underlying technologies that make up cloud computing. They would need to know their way around IaaS, PaaS, and SaaS implementation. Cloud computing refers to the provision of computing services over the Internet. These services include servers, storage, databases, networking, software, analytics, and intelligence, to help businesses innovate faster and more efficiently. Companies worldwide increasingly depend on the cloud for their computing power and data storage needs.  

As a result, they often require the services of data engineers who can use various cloud computing solutions on an organizational scale, such as SaaS, PaaS, and IaaS. Data engineering is all about designing, programming, and testing software, which is required for modern database solutions. This can be easier when you are using existing cloud services. The trend is to participate in multi-cloud over cloud technology and have a good understanding of the underlying technologies that make up cloud computing. Concepts of IaaS, PaaS, and SaaS are the trend, and big companies expect data engineers to have the relevant knowledge. 

4. NoSQL 

A data engineer should know how to work with key-value pairs and object formats like Avro, JSON, or Parquet in the open-source Apache-based or MongoDB and Cassandra. Big resources still manage file data hierarchically using Hadoop’s open-source ecosystem. The cloud could also be full of semi-structured or unstructured data with more than 225 no SQL schema data stores, which makes it one of the most important skills to be thorough with. Knowing how to work with key-value pairs and object formats is still necessary. NoSQL is a type of database management system (DBMS) that is designed to handle and store large volumes of unstructured and semi-structured data. Unlike traditional relational databases that use tables with pre-defined schemas to store data, NoSQL databases use flexible data models that can adapt to changes in data structures and are capable of scaling horizontally to handle growing amounts of data. NoSQL databases are often used in applications where there is a high volume of data that needs to be processed and analyzed in real-time, such as social media analytics, e-commerce, and gaming. They can also be used for other applications, such as content management systems, document management, and customer relationship management. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages, lack of ability to perform ad hoc joins across tables, lack of standardized interfaces, and huge previous investments in existing relational databases. Most NoSQL stores lack true ACID transactions, although a few databases have made them central to their designs. Examples of NoSQL include Apache River, BaseX, Ignite, Hazelcast, Coherence, and many more others.  

5 . Hyper Automation 

Hyperautomation focuses on improving the quality of work, increasing decision-making agility, and accelerating business processes. They require skills to run value-added tasks. Hyper automation is the concept of automating everything in an organization that can be automated. Organizations that adopt hyper automation aim to streamline processes across their business using artificial intelligence (AI), robotic process automation (RPA), and other technologies to run without human intervention.  

In addition to these technical skills, having a good understanding of data governance, and data security, and the ability to work in cross-functional teams will be invaluable for future data engineers. Continuously updating your knowledge and staying abreast of emerging technologies and trends is also vital to remain competitive in the rapidly evolving field of data engineering. The technical skills that are most in-demand for data engineers are constantly evolving, and it’s important to stay up-to-date and continually develop your skills in this exciting and rapidly growing field. The world is full of data, which is why the demand for data engineers is at an ever-increasing high. Society and industries of every kind depend on data to make critical decisions. A leading expert in the field can become a champion in the industry after acquiring relevant skills for data engineer and gaining hands-on experience. 

The post 5 Data Engineering Skills to Transform Your Career in 2024  first appeared on AITechTrend.

• The Power of Data Science in Market Analysis

Data science is revolutionizing market analysis by enabling the extraction of valuable insights from complex and diverse datasets. Through the application of machine learning algorithms, statistical models, and predictive analytics, data scientists can identify patterns, trends, and correlations within financial data, providing traders with a competitive edge in decision-making.

• Predictive Modelling for Enhanced Trading Strategies

Data science techniques facilitate the development of predictive models that can forecast stock prices, market trends, and volatility with a high degree of accuracy. By leveraging historical data and real-time market information, traders can employ sophisticated algorithms to anticipate market movements and optimize their trading strategies.

• Sentiment Analysis and Social Media Data

The integration of sentiment analysis and social media data into market analysis has become increasingly prevalent. Data science enables the extraction and analysis of market sentiment from social media platforms and news sources, allowing traders to gauge public perception and sentiment towards specific stocks and industries, thereby informing their trading decisions.

• Risk Management and Portfolio Optimization

Data science plays a pivotal role in risk management and portfolio optimization within stock trading. By employing quantitative models and risk analytics, businesses can construct diversified portfolios, identify potential risks, and optimize investment allocations based on historical performance and market dynamics.

• Algorithmic Trading and Automated Decision-Making

Algorithmic trading, in which automated computers execute transactions based on predetermined criteria and real-time market conditions, has become more popular thanks to data science. Businesses are able to create algorithmic trading strategies that take advantage of opportunities that arise quickly and market inefficiencies by applying machine learning and quantitative research.

• Regulatory Compliance and Fraud Detection

Data science and advanced analytics are instrumental in ensuring regulatory compliance and detecting fraudulent activities within stock trading. By leveraging data-driven surveillance and anomaly detection techniques, businesses can uphold regulatory standards and mitigate the risks associated with fraudulent trading practices.

Conclusion

The fusion of data analytics and data science with stock trading has redefined the dynamics of market analysis, empowering businesses to make data-driven decisions and capitalize on market opportunities with unprecedented precision. As thought leaders in the B2B sector, it is imperative to recognize the transformative potential of data science in stock trading and explore avenues for integrating advanced analytics solutions into trading practices.

Businesses can adopt data science to improve trading tactics, obtain a competitive edge, and reduce risks. This will help the industry evolve into one that is more informed and efficient for stock trading. The era of data-driven stock trading has arrived, and business-to-business companies can use data science to reach new heights of profitability in the financial markets.

The post From Data to Dollars: The Evolution of Market Analysis with Data Science in Stock Trading first appeared on AITechTrend.

Google Collab is a boon for coders as now they can do coding without buying a high-performance computer. This can be done as Google allows users to type code via the browser. They just need to sign in to their Google account. Google provides free access to GPUs and TPUs as users can access these through the cloud. Therefore no configuration is required and they can easily share their codes with their teammates. Collab is used by Google for its data analysis. Now, Google has released this to the public to make coding easy and cost-efficient. With this software, developers can easily achieve their machine-learning tasks. After its release, this is used by students who can’t afford heavy GPUs and also an opportunity to run their Data Science Experiments. Students can also access free resources provided by Google. But this software also has some flaws as it also pressures the processors and might affect the storage of your device. To make your work easier, here are five hacks that you can use to make your work efficient and hassle-free. Let’s take a look at these hacks

5 Efficient Hacks That Will Make Your Work Easy

Though Google Collab has made a revolutionary release this launch has affected developers in a different way as they might get storage issues or other issues that may slow their work or might even cause lag while coding. To make your computer efficient here are five hacks that will make your computer efficient. These five hacks are mentioned below.

• Increase RAM size of Google Colaboratory

After signing in to Google Collab you get 13 GB RAM. This RAM is enough but sometimes this RAM becomes less and causes lag while coding. But, Google has given a feature that can increase the RAM size of your site and help in efficient coding. You can increase your RAM size by following this simple workaround. Here is what to do:

1. First, crash Google Colab by typing this code by typing i = []

while(True):

    i.append(‘a’)

And then wait for Google Colab to crash.

1. This will prompt a box below on the screen with a message and an option of “ Get More RAM”

Source:Analytics Vidhya

2. Click on “ Get more RAM”

Source: Analytics Vidhya

3. Confirm the process by clicking on “Yes”

This will double the RAM size on your system and now you can efficiently continue your coding.

• Prevent Google Colab From Getting Disconnected

Google Colab disconnects the notebook if you keep your screen idle for more than 30 minutes. This is frustrating as you need to type the codes again if you have not saved your work. For this, you can use one trick that will solve this flaw and make your work easier. Let’s see what that trick is and also see the walkaround for the trick:

Source: Analytics Vidhya

1. Just open Google Chrome DevTools and then press F12 or Ctrl+Shift+I if you are using Linux and run the following Java Script.

function KeepClicking(){

console.log(“Clicking”);

document.querySelector(“colab-toolbar-button#connect”).click()

}setInterval(KeepClicking,60000)

Here 60000 means milliseconds so, 60000 means 60 seconds or 1 minute. This means that this command will automatically click the screen after every minute. This will make Google colab think that the screen is not idle and you can type your code and complete it anytime and save the whole code in one single folder.

• Save Your Snippets

For a beginner level developer remembering codes might be difficult and also this mistake can cost a lot as it may crash a program. But Google has saved you from this mistake as now, you can save your Syntax in your Notebook. This will help you remember the syntax and also keep your motivation to learn coding in data science alive. You can save your Syntax by following this simple method mentioned below:

1. Go to the “Tools” section and click on “Settings”.

Source: Analytics Vidhya

2. Paste the Snippet Notebook link in the “Custom Snippet Notebook URL” section.
3. Now, click on “Save”.

Source: Analytics Vidhaya

4. If you want to insert your snippet then you simply need to search by typing the name of the snippet.

• Activate GPU and TPU

Google Colab’s default hardware is CPU but if that’s not enough for you to work on then you can activate GPU or TPU to support more complex processes like deep learning and furthermore. This can be done by following these steps:

1. Click on the “Edit” section and then, select “Notebook Settings”.
2. From the drop-down menu select the available option and click save.

Source: Towards Data Science

This is how you can activate GPU and TPU on your computer and smoothly run other learning processes that need some high performance.

• Use Local Runtime

Source: Towards Data Science

Google Colab gives access to all the free GPU resources. But, if your computer has a GPU then you can use that too by changing to local runtime. This can be done by just clicking on “Connect” and then selecting “connect to local runtime”. Through this, you can run codes using your GPU and also access your local files without leaving the Colab notebook.

Download Kaggle Dataset into Colab

Source: Analytics Vidhaya

If you are using a computer with no GPU or less GPU then Kraggle is the solution as you can seamlessly download any dataset directly into Colab. For this, you can follow these steps:

1. After downloading the dataset from Kraggle.
2. Click on “Create New API Token”.

Source: Analytics Vidhaya

3. In your Drive create a folder named “Kraggle” and save the “Kraggle.json” in it.
4. Now, mount the drive in Colab Notebook change the config path to “Kraggle. jason” and change the current working directory by typing this

import os

os.environ[‘KAGGLE_CONFIG_DIR’] = “/content/drive/My Drive/Kaggle”

%cd /content/drive/MyDrive/Kaggle

1. Now, to copy the dataset that is to be downloaded you need to copy the command.
2. The API will be available under the “Data” section.
3. To download you need to run one of the following commands:

!Kaggle datasets download -d alexanderbader/forbes-billionaires-2021-30

!kaggle competitions download -c google-smartphone-decimeter-challenge

That’s it you can access the Kraggle data set directly from your cola notebook and proceed with your workings. There are other methods like uploading your code file to your drive to make your coding process smooth. You can also change the mode of Colab from different mode options available in the settings of the software. You can also change the background color of your dashboard from a light theme to a dark theme according to your working time and lighting conditions. There is a feature of Data Extension that can convert panda dataframes into interactive displays that are sorted, filtered, and examined. You can also have a comparison between the two notebooks and see if anything is missing or anything needs to be added. You can also visualize your data by using a feature called TensorBoard that visualizes data.

These are some of the hacks that you can try in order to work more efficiently using Google Colab and also practice coding without upgrading or using a high-performance device. This has a vast amount of features for data science students and professionals to make their work easy and smooth and also a wide area to access your codes and review them from any device. It has also made sharing your codes easy as one just has to click on share and select the recipient.   

Conclusion

In conclusion, Google Colab stands as a formidable ally for coders, offering a platform that transcends hardware limitations. The hacks provided here address common issues, ensuring a smoother coding experience. From boosting RAM to preventing disconnections, saving snippets, activating GPU/TPU, and utilizing local runtime, these strategies empower users to harness the full potential of Google Colab. As you implement these hacks, you’ll find your coding sessions becoming more efficient, productive, and enjoyable. Embrace the power of Google Colab and elevate your coding prowess without the need for expensive hardware upgrades.   

The post 5 Hacks to make Google Colab use Efficiently first appeared on AITechTrend.

Learning new software or skills for the betterment of your career has now become an essential process. This is for either gaining an edge over others or dealing with a new generation of team members. Cooperates require their employee to bring everything they have in their platter so that they know what they can do with their skills. They also require them to master new skills in no time so that can attain benefits from it. But, mastering a skill requires time and also correct guidance and approach towards it. There are numerous software available after offices have shifted to computers. Softwares that make work easier. To learn these software an employee has to be certified or go under on-the-job training. One such software is Tableau. Tableau is used by cooperates to scan large numbers of data and determine valuable information from it. Tableau has been in the market for decades and has clients like Amazon, Walmart, Adobe, and Cisco. It also has products like Desktop, Prep and Server that have helped its clients to decode data. To master such software takes time and luckily here is a list of five books that an analyst can read to achieve mastery in Tableau. So, let’s take a look at these books.

5 Must Read Books to Master Tableau

There are various books that claim to teach and guide analysts on how to use Tableau and decode even the most complex data structure in minutes. But, we have picked five of these books that are very good and have easy-to-understand language that may help an analyst to up their skill and also learn some new features of this amazing software. These books are best sellers and are widely read by analysts to understand the workings of Tableau. Let’s not waste much time and see these books.

Tableau Best Practices10.0 by Jenny Zhang

Source: Amazon

If you have used Tableau before then this book by Zhang is a good read as it has ample real-life problems that can help you learn new things about this software. This book helps if you spend most of your time data analyzing and visualizing. It also guides you on how to connect to a ton of variety of data from cloud or local servers and blend this data in a fast and efficient way and also perform complex calculations like LOD and Table calculations. The problems mentioned in the book also have a step-by-step guide given by Tableau experts. This book is very helpful for analysts who want to upgrade their skills in data analytics and also for data enthusiasts.

Learning Tableau 10 Second Edition by Joshua N. Milligan

Source:Amazon

This book by Joshua N. Milligan is also a good book for analysts. In this book, the author has made sure that he has written everything he knows about this software and also mentioned instructions related to the features. It has a dedicated guide from scratch that is how to make a pie chart, bar chart, and tree maps and also an installation guide to various tools that the software has to offer to its users. It also has detailed information on different techniques used to tackle different challenges. The book also deals with how to effectively use data for storytelling and also how to get insights from data that can help the business to flourish. This book is very helpful to learn how to manage data and also derive insightful information that can help make crucial decisions for business growth. This book is good for beginners and also advanced-level data analysts.

Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master by Ryan Sleeper

Source: Amazon

Ryan Sleeper is one of the most qualified Tableau consultants. In this book, he has given instructions about how Tableau works and has given numerous ways to derive insights from a large pile of data. This book is a good guide to understanding and working on Tableau. This book is as good as a manual for Tableau as it has everything an analyst should know while using Tableau and enjoy the full features of this software. It also has a step-by-step guide for every feature that is offered by Tableau for data analysis. This book also is a good read for people who want to become data analysts and want to learn this software and use it in the future.

Mastering Tableau by David Baldwin

Source: Amazon

David Baldwin is also a prolific writer who has written many books that have helped employees enhance their skills in business intelligence for almost 17 years. In this book, he has shared his experience while using Tableau. For this software, he has focused on Tableau training by shedding light on developing, BI solutions, Project management, technical writing, and web and graphic design. He has also written a detailed guide on the new features introduced by Tableau in its new version. i.e. 10.0. The features that are introduced in this version consist of creative use of different types of calculations like row-level, and aggregate-level, and how this software is able to solve complex data visualization challenges put to it. He also guides the reader about the tools offered by Tableau and helps them understand the tools of this software. The book has a systematic approach to training its reader to use Tableau as it starts from basic level training of features and then slowly moves towards advanced tools that include calculations, R integration parameters and sets and also data blending techniques.

Tableau 10: Business Intelligence Cookbook by Donabel Santos

Source: Amazon

This book is also a good pick for analysts and people who want to pursue a career in data analysis. This book also covers all practical cases but with a different approach. It has arranged cases from basic level to advanced level cases to make the readers understand each and every tool in Tableau and also ensure that the readers are getting practical experience too. The book also involves a step-by-step guide to creating basic and advanced charts and also an attempt to make the Tableau interface familiar to its readers. It also guides the readers on how to create effective dashboards and many other wonders about this software. As Santos itself is a data geek and has spent a lot of time around data she has tried to answer all the questions about Tableau in this book. She has also focused on the ratings of this book as the better the rating more it sells so this book is packed with some valuable tips and tricks that an analyst of any level can use and master this software. This book is very helpful to up your skills and learn new things about Tableau.

These are the top five books that are recommended to master Tableau in no time. But, reading and keeping it aside will not help as to master skills one needs to practice whatever they have learned and hone that skill with time. These books will give you information that you require but mastering Tableau is ultimately in your hands. If you keep practicing the tips and tricks given by these experts then you can master it and also get appreciation from your seniors and also have an edge over your peers. As one says perfect practice makes a man perfect. 

The post 5 Must Read Books for Mastering Tableau first appeared on AITechTrend.

NEW YORK, Jan. 2, 2024 /PRNewswire/ — Wavestone has published the results of its 12^th annual Data and AI Leadership Executive Survey of Fortune 1000 and Global data leadership.  This year, 95.3% of survey participants held a C-suite title or were their company’s corporate head of data and AI responsibilities, with 89.8% holding the title of Chief Data Officer (CDO) or Chief Data and Analytics Officer (CDAO) within their organization.  These executives held their positions during 2023 at over 100 Fortune 1000 and Global data leadership organizations.

This represents the 12^th annual edition of the Wavestone survey, which was first published in 2012 by NewVantage Partners (acquired by Wavestone in 2021) at the behest of a group of Fortune 1000 CIOs and data leaders who were looking to understand whether it was time to expand and accelerate data and analytics initiatives and investments.  The Data and AI Executive Leadership Survey has evolved over the past dozen years and is now widely recognized as the longest running survey of Fortune 1000 and global data, analytics, and AI leaders. 

Wavestone has published its 12th annual Data & AI Leadership Executive Survey of Fortune 1000 & Global data leaders.Post this

In the Foreword to this year’s survey, Randy Bean, Innovation Fellow at Wavestone and Founder of NewVantage Partners, and Thomas H. Davenport, author of the landmark study Competing on Analytics, write “The past year has been an extraordinary one in many respects, not the least of which is the amazing rise of Generative AI. That overshadows any other development in the data and technology domain, and in this 12^th annual survey from Wavestone (formerly NewVantage Partners), Generative AI has a strong influence. Generative AI seems to have catalyzed more positive change in organizations’ data and analytical cultures than in any time since the inception of this survey.”

Major findings of the 2024 Data and AI Leadership Executive Survey are: 

1. Leading companies continue investments in data and analytics with the expectation of delivering business value.
2. Companies see Generative AI as potentially the most transformative technology in a generation.
3. Companies believe the Chief Data Officer/Chief Data and Analytics Officer (CDO/CDAO) role is necessary, although turnover has been high and tenures short.
4. Companies recognize that integrating data and AI into traditional business processes and changing organizational culture requires time and commitment.
5. Companies believe data and AI safeguards and governance are essential, but much more needs to be done.

Among noteworthy results of the survey are:

• 87.9% of participants reported that investments in data and analytics are a top organizational priority.
  
• 62.3% of participants reported that investments in Generative AI are a top organizational priority.
  
• 89.6% of participants reported that investment in Generative AI is increasing within their organization.
  
• 79.4% of participants stated that Generative AI should be part of the Chief Data Officer/Chief Data and Analytics Officer (CDO/CDAO) function.
  
• 15.9% of participants stated that the industry has done enough to address data and AI ethics.

About Wavestone

Wavestone, a leading independent consultancy headquartered in France, and Q_PERIOR, a consulting leader in the Germany-Switzerland-Austria region, joined forces in 2023 to become the most trusted partner for critical transformations. Drawing on more than 5,500 employees across Europe, North America and Asia, the firm combines seamlessly first-class sector expertise with a 360° transformation portfolio of high-value consulting services.

SOURCE Wavestone

https://www.prnewswire.com/news-releases/wavestone-releases-2024-data-and-ai-leadership-executive-survey-302024534.html

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Introduction

Creating and training deep learning models often requires large amounts of labeled data. However, collecting and annotating real-world datasets can be time-consuming and expensive. Synthetic image datasets offer a solution to this problem by providing a way to generate large quantities of labeled data quickly and at low cost.

In this guide, we will explore how to generate realistic synthetic image datasets using Kubric, a powerful Python library for computer vision and image synthesis. We will cover the key concepts, techniques, and best practices to create high-quality synthetic datasets that can effectively train deep learning models.

Understanding Kubric

Kubric is an open-source library that makes it easy to synthesize and manipulate photorealistic images. It provides a wide range of functions and tools to generate synthetic data with control over various aspects such as lighting, camera parameters, textures, and object placement.

One of the key features of Kubric is its ability to render images using physically-based rendering (PBR) techniques. PBR ensures that the generated images accurately simulate real-world lighting and materials, resulting in highly realistic synthetic datasets.

Choosing a Domain and Purpose

Before generating synthetic images with Kubric, it is crucial to define the domain and purpose of the dataset. The domain refers to the specific area or subject matter that the images will represent, such as faces, objects, or scenes. The purpose determines the intended use of the dataset, whether it’s for object detection, semantic segmentation, or any other computer vision task.

Defining the domain and purpose helps in making informed decisions regarding the types of objects, backgrounds, and camera angles to include in the dataset. It also helps in setting the appropriate scene parameters and properties while generating the synthetic images.

Creating 3D Models and Assets

In order to generate realistic synthetic images, you need 3D models and assets that represent the objects of interest in the dataset. These models act as the building blocks for the scenes and images created by Kubric.

There are various ways to obtain 3D models and assets, such as downloading from online repositories or creating them from scratch using 3D modeling software. It is important to ensure that the models are accurate and realistic, as they directly impact the quality and authenticity of the synthetic images.

It is also advisable to have a diverse range of models and assets to include in the dataset, representing different variations, poses, and appearances of the objects. This helps in training the deep learning models to be robust and generalizable.

Defining Scene Parameters

Once you have the 3D models and assets, you need to define the scene parameters for generating the synthetic images. These parameters control various aspects of the scene, including lighting conditions, camera angles, object placements, and background settings.

Understanding the scene parameters and their impact on the final images is crucial for creating realistic datasets. For example, adjusting the lighting intensity and direction can affect the shadows and highlights in the images, while changing the camera parameters can impact the perspective and viewpoint.

Kubric provides functions and APIs to set and control these scene parameters programmatically. Experimentation and iteration are key to finding the right combination of parameters that generate realistic and diverse images.

Texturing and Material Properties

Texturing and material properties play a vital role in the visual realism of synthetic images. Kubric allows you to apply textures and define material properties for the 3D models used in the scenes. Textures can include color information, surface details, and patterns, while material properties define how light interacts with the surfaces of the objects.

By carefully choosing and applying textures and material properties, you can enhance the authenticity and believability of the synthetic images. Kubric provides tools to import and apply textures from external sources, as well as functions to modify and create new materials.

Randomization and Perturbation

To make the synthetic dataset more diverse and challenging, randomization and perturbation techniques are often applied. Randomization involves introducing variability, such as different object placements, lighting conditions, or camera angles, during the generation of each image.

Perturbation, on the other hand, involves introducing controlled variations to the scene and object properties. This can include modifying textures, changing object shapes or sizes, or adding simulated noise to the images. Perturbation helps in training the deep learning models to be robust to different conditions and variations.

Kubric provides built-in functions and utilities for randomization and perturbation, making it easy to introduce controlled variations into the synthetic datasets.

Quality Assessment and Validation

After generating the synthetic images using Kubric, it is important to assess their quality and validate their usefulness for the intended computer vision task. Quality assessment involves evaluating aspects such as visual realism, label accuracy, and dataset diversity.

Visual realism can be assessed by visually inspecting the synthetic images and comparing them with real-world examples. Label accuracy refers to the correctness of the annotations or ground truth labels associated with the synthetic images. Dataset diversity ensures that the generated images cover a wide range of variations and scenarios relevant to the computer vision task.

If any issues or shortcomings are identified during the quality assessment, it may require further iterations and adjustments in the scene parameters, models, or rendering settings to improve the dataset quality.

Conclusion

Generating realistic synthetic image datasets using Kubric can be a powerful and efficient way to train deep learning models. By carefully defining the domain, creating accurate 3D models, controlling scene parameters, applying textures and material properties, introducing randomization and perturbation, and evaluating the dataset’s quality, it is possible to create high-quality synthetic datasets that effectively simulate real-world conditions.

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