Such programs provide students with the opportunity to learn these skills. This article discusses the top technology courses expected to be in demand in 2023, including their importance, applications, and their job prospects.

Data Science and Analytics

As data has become the major source of revenue for most organizations, the ability to extract some important insights from a large set of data is a skill that is in great demand in the modern corporate environment. 

Students who take courses in Data Science and Analytics gain the knowledge and skills necessary to collect, analyze, and interpret complex data sets, which enables them to make judgments based on the data. Students who take courses in Data Science and Analytics also gain the ability to make decisions which are totally based upon the data.

Data science, machine learning, &  statistical analysis professionals will gain huge attraction in the job market in 2023 as businesses continue to leverage data. These are the kinds of skills that are valuable in a wide range of industries, including banking & financials, healthcare, and even marketing and e-commerce. People who are in possession of these skills have multiple job alternatives available to them, including the possibility of working as data scientists, data analysts, or Artificial intelligence specialists.

Artificial Intelligence and Machine Learning

AI and ML’s dominance in technology is changing several business areas. AI algorithms and ML models allow computers to learn, predict, and act without human intervention. AI and machine learning expertise are in demand as more firms utilize AI-powered solutions.

AI and ML graduates will have more job opportunities in 2023. AI/ML experts will shape the future in many ways. Driverless cars, intelligent chatbots and virtual assistants, and firm operations optimization are these methods. Data scientists, artificial intelligence engineers, and machine learning engineers can use these skills. A few examples.

Security for Computer Networks and Ethical Breaking 

There will be an increase in need for employees competent in cybersecurity as the digital ecosystem continues to undergo change. As a result of an increase in the amount of cyber threats and data breaches, there is a strong demand for people who are able to protect sensitive information, secure networks, and identify vulnerabilities. These skills are in great demand. As a result of this, there has been a notable increase in the number of people interested in taking courses that concentrate on computer security and moral hacking.

Experts in the field of cybersecurity will continue to be in great demand across a wide range of industries, including the public sector, the healthcare business, the financial sector, and the technology sector. Participating in instructional programs on network security, ethical hacking, incident response, and risk management will help people protect their digital assets. To name just a few, some of the job titles that are commonly linked with the topic of cybersecurity include security analyst, ethical hacker, cybersecurity consultant, and chief information security officer (CISO).

Cloud computing and the practice of “DevOps”

Cloud computing has dramatically transformed the ways in which businesses not only store their data but also analyze it and obtain access to it. As more and more businesses shift their infrastructure to the cloud, there has been an explosion in the demand for individuals who have expertise in cloud computing as well as DevOps, which stands for development and operations.

The attendees of these classes will walk away with an understanding of cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), among others. Courses in DevOps place an emphasis not only on the integration of software development and operations, but also on the importance of continuous delivery, automated workflows, and collaboration.

In 2023, IT employees will need cloud computing and DevOps skills. These abilities can lead to careers as cloud architects, DevOps engineers, and SREs. Building, implementing, and handling cloud-based infrastructure and improving software development processes will be crucial skills in the future.

Internet of Things (IOT)

In past years, there has been a discernible increase in growth for a concept that is known as the Internet of Things (IoT), and its potential keeps expanding. In addition to this, the Internet of Things has the potential to continue growing. Internet of Things (IoT) courses give students an in-depth analysis of the interconnected network of software, sensors, and gadgets. Students will now have the opportunity to create and implement their very own Internet of Things solutions as a result of this.

As connected devices expand, IoT experts will be in demand. The healthcare, manufacturing, transportation, and smart city industries will need experts in Internet of Things system design, development, and management. IoT architects, developers, and solutions engineers might pursue appealing job paths. A few examples.

Is it time for you to reach new professional heights? 

You won’t be able to compete successfully in today’s rapidly changing job market without the skills and information that NearLearn will teach you how to acquire. If you are interested in acquiring new skills, changing careers, or enhancing the ones you currently have, we offer a program that will cater to your specific requirements.

The post Which technology is in demand in 2023? appeared first on .

The constant upswing in the hiring craze has escalated emerging job seekers to comprehend the prominence of acquiring such skills. As the domain promises a fascinating career progression for beginners as well as working professionals, learning these skills would help you earn a six-digit figure salary package.

Top 10 Data Science Skills That Will Land You A 6 Digit Job

1. Become a Pro in Statistics and Probability 

To develop top-notch mathematical models, aspiring job-seekers should have a critical understanding of topics such as statistics and probability. These are the scientific tools that help the transformation of raw and unstructured data into exemplary conclusions.

Experts believe that it is essential to discern the potential of these topics since Machine Learning and Algorithm configuration are integral parts of Data Science jobs.

2. Expertising the Programming Skills

Data science requires a strong understanding of critical coding. In order to thrive in this domain, aspirants need to become proficient in programming languages. Different programming languages such as Python, R, and Javascript are broadly exploited in creating comprehensive data science models.

3. Command over Automated Analytics Tools

Your competence to exploit Automated Analytics Tools is one of the prominent data science skills that improvise constantly. This allows techies to utilize the results of their data science mechanisms and explore the data. The several processes that computerize data differ in perplexity. The aspirant needs to understand the data analytics tools such as Whatagraph, Darwin, DataRobot, Datapine, and SAS Visual forecasting to get command over Automated analytics.

4. Data Visualisation

Data Visualisation can help aspiring data scientists how to bridge data with the ultimate consumers effectively. However, the skill is one of the most critical aspects of data analysis. It is essential to impart information or data in a way that is both comprehensible and pleasing to the eyes. These skills further help to convey stories by illustrating data in a form of easier-to-comprehend outliers and trends.

5. Good at Data Wrangling

Data wrangling has emerged as one of the most prominent concepts of data science. By mastering the skill, aspiring data scientists will be able to eliminate corrupted data and categorize it accordingly. Further, the data could be exploited for several analytical objectives. 

6. Proficiency in Software Engineering Principles

Data scientist professionals need to have a thorough knowledge of software engineering principles. They should have expertise in creating top-quality code that will ease the process during production. In addition, the concept helps aspirants with comprehensive information about the fundamental mechanism of software development data types, compilers, projects, and more. 

7. Pro in AI and Machine Learning Skills

Mastering these skills will help an aspiring data scientist’s job easier. Artificial Intelligence and Machine learning models are broadly exploited in various industries and encourage data scientists to work effectively and quickly. Nevertheless, the greatest challenge would be to figure out the right data prior to developing an AI model to carry out human tasks.

8. Strong Data Intuition

This is probably one of the most prominent data scientist skills. The aptitude of accessing lucrative data insights makes professionals more efficient at their work. However, one can gain these skills with experience, and boot camps or workshops are a great way to master them.

9. Great Business Acumen

Along with strong Technical skills, data scientist professionals must possess great business acumen. With the help of strong business acumen, professionals can easily discern the potential challenges, which might hamper the progress of the organization.

10. Ability to handle Unstructured and Large datasets

Aspiring Data Scientists are required to possess great experience with handling Unstructured and large datasets that have been received from various sources and channels. The main responsibility would be to create the data through analysis, and with the expanding amounts of data, jobseekers should understand how to pragmatically handle a massive chunk of datasets and organize them for extracting important insights. 

The post Top 10 Data Science Skills That Will Transform Your Career In 2022!  appeared first on .

Before you ring up the curtain on this rapidly growing career path, the first thing is you should be confident in your skill set. However, if you are a hardcore technologist and a coder, who is interested in how we visualize data and communicate with the data, you will find several opportunities within a single industry.  

Your resume plays an essential role in fetching the best data science job. Your resume should present all the relevant skill-set in front of the employer. Have a mention the projects that you have handled with brevity and clarity. As a whole, your resume should be crisp and clear enough to impress the employer. However, you need to keep your resume updated so that there will be more chances of getting an opportunity. 

5 Tips to Find the Right Data Science job?

A little experience could be advantageous

Securing a data science job as a fresher is not an easy task. Many aspirants carry technical and communication skills, but no prior work experience prohibits them from finding a well-paid data science job. When a candidate starts looking for a job, one has to seek relevant projects to work on concurrently. Your work experience will be considered, and your portfolio will be valued.

Channelized networking through multiple hiring platforms 

There are many networking applications to get connected to employers. For instance, LinkedIn is one of the best applications to seek a job and highlight your skill set. If you have any plans to use LinkedIn to network for a job, start building your relationship with employers. It is up to you how you start building connections. Attending conferences and meetings is a top-notch approach to perfectly channelling your networking. 

Don’t give upon data roles opportunity

Many aspirants only aim for data science roles and they hesitate to take on data-related job roles. We recommend aspirants who are looking for Data science job opportunities to take up data roles if it comes first. A data scientist role includes working with data, so it will be easy to move into a data science job later or other data-relevant jobs. 

Data-related jobs help in enhancing your domain knowledge. It will boost the data skills that are required to get into data scientist job roles. 

Try for emerging companies

The actual part of the job search is sending an application to the right company where you can transform your career. Usually, aspirants run behind well-established companies. But the fact is companies conduct a well-organised hiring process making it hard to clear the rounds for the aspirants with no relevant experience. 

However, to enhance your skill-set, you can begin your career in an emerging company. There are numerous advantages of starting your career in emerging companies because you can easily communicate with the founders of the company and your skills and contributions will be much more visible valued and appreciated. 

Apply for multiple companies

Once you become an expert in a programming language and understand the various machine learning algorithms, it is recommended to send as many applications as you can to multiple companies. Make sure that every application explains your skill set.

Read: Top 20 Frequently Asked Data Science Interview Questions 2022

The post A Comprehensive Guide to Find A Right Data Science Job appeared first on .

After some basic Data Science interview questions, we have included some technical and Data analysis questions that further help you crack an interview. 

Most Asked Data Science Interview Questions

1. What is Data Science?  How it is different from Big Data? 

Data Science is an interdisciplinary field that blends several tools, algorithms, and machine learning principles to with the aim to find common patterns and assemble realistic insights from the raw data using mathematical and statistical approach is called Data Science.

How Data Science is different from Big Data?

2. List the major differences between Supervised and Unsupervised Learning? 

Read: Who is a Data Scientist, a Data Analyst and a Data Engineer

3. How Data Analytics is different from Data Science? 

• Data Science is responsible for transforming data with the help of various technical analysis approaches to exploit required insights using which data analyst employ to thier different business solutions. 
• Data Analytics involves the task of examining the existing hypothesis and information and helps in answering the questions to provide effective business related decision making process.

4. Mention some of the techniques used for sampling. 

It is highly challenging task to conduct Data analysis on a whole volume of data at a time specifically when it includes larger datasets. 

It becomes essential to collect some data samples that could be used for illustrating the whole population and later carry out analysis on it.  

Notably, there are two different methods of sampling techniques based on the utilization of statistical models.

1. Probability Sampling Techniques: 

• Clustered sampling.
• Simple random sampling. 
• Stratified sampling. 

2. Non-probability Sampling Techniques: 

• Quota sampling.
• Snowball sampling.
• Convenience sampling etc. 

5. Brief the steps involved in making a decision tree.

Making decision tree includes the following steps: 

1. Get the list of entire dataset as input which are helpful for making a decision tree. 

2. Evaluate entropy of the target variable and predictor attributes. 

3. Evaluate the information gain of total attributes. 

4. Select the attribute along with the highest information gain as the root node. 

5. Reiterate the same approach on each branch until the decision node of every branch is concluded. 

6. How Data Scientists check for data quality? 

Some of the terms utilized to check data quality: 

Integrity. 

Uniqueness. 

Accuracy. 

Consistency. 

Completeness. 

7. Explain in brief about Hadoop.

Hadoop is a an open-source processing platform that handles data processing and storage for big data applications built on pooled systems.

Hadoop handles the task of splitting files into separate large blocks and directs them across nodes in a cluster. It then shifts a packs of code to nodes to execute the data in parallel. 

8. What is the abbreviation of ‘fsck’?

‘fsck’ abbreviation stands for ‘file system check’. It performs handling the task of searching for possible errors in the file. 

9. What are the conditions for Overfitting and Underfitting?

Overfitting: The Overfitting model process the simple training data. Incase any new data employed as input to the model, it fails to give any output. These conditions result owing to low bias and high variance in the  model. Decision trees are more vulnerable to overfitting. 

Underfitting: In underfitting, the model will be so simple that it is fails to find out the exact relationship in the data, and hence it does not execute well on the test data. This can take place due to high bias and low variance. Linear regression is more vulnerable to underfitting. 

10. Explain about Recommender systems? 

Recommender systems are a subdivision of information filtering systems, utilized to analyse how consumers would rate particular objects such as music, movies and more. 

Recommender systems filter large filter huge chunk of information based on the data fecilitated by a user and other factors, and they also manage user’s preference and interest. 

11. Explain differences between wide and long data formats.

Categorical data are always grouped in a wide format. 

The long format is in which there are a number of instances with several instances with many variables and subject variables. 

12. How much data is required to get a valid outcome?

All the industries are different and evaluate in different ways. Thus, they never have enough data. The amount of data which is essential depends on the approaches users use to have an best chance of procuring vital results.

13. Explain Eigen values and Eigen vectors.

Eigenvectors are known as unit vectors or colomn vectors whose length to magnitude ratio is 1.

Eigenvalues are coefficients that are implied on eigenvectors that assign these vectors different values for length or magnitude. 

14. Explain about power analaysis. 

Power analysis enables the determination of the sample size essential to find out an effect of a given siz with a assigned degree of confidence. 

15. Explain logistic regression. Mention any example related to logistic regression. 

Logisti regression is also called as the logit model. It is a approach to forecast outcome from a linear combination of variables.

For instance, let’s say that we would like to forecast the outcome of elections for a specific political leader. Therefore, we need to search whether this politician has the potential to win the election or not. Hence, the outcome would be binary that is win (1) or loss (0). 

16. Exaplain Linear Regression. Mention some of the disadvantages of the linear model.

Linear regression is an approach in which the score of variable Y is calculated with the help of a predictor variable X. Y is known as the criterion variable. 

Some of the disadvantages of Linear Regression are: 

• The assumption of linearity of errors is a major setback. 
• Overfitting problems are present which are difficult to solve. 

17. Explain Random forest model and steps to build it.

A random forest is created with the help of many number of decision trees. If you distribute the data into several different packages and build a decision tree in each of the different groups of data, the Random forest includes all those trees together. 

Steps to create a random forest model:

1. Randomly choose ‘k’ features from the sum of ‘m’ features provided k<<m.

2. Out of the ‘k’ features, predict the node D with the help of split point. 

3. Use the best split to divide the node into daughter nodes. 

4. Reiterate the steps two and three until leaf nodes are conirmed. 

5. Build the Random forest model by reiterating the steps one to four for ‘n’ times to build ‘n’ number of trees. 

18. Explain in brief about Neural Network Fundamentals. 

A Neural network is an artificial representation of the human brain that attempts to simulate its learning process. The neural network understands the patterns from the data and utilizes the the information that it acquires to predict the output of new data, with no human assistance. 

19. Explain about auto-encoders.

Auto-encoders are called as learning networks. They ensure minimum possible errors while transforming inputs into outputs. Therefore, Auto-encoders tries to confirm if required output is equal or as close as to input. 

20. Explain root cause analysis? 

Root cause analysis initially designed with  motive to analyse industrial accidents. It is basically a problem-solving method utilized for isolating the root causes of problems or faults. 

Read: Mandatory Skills to Become Data Scientist

The post Top 20 Frequently Asked Data Science Interview Questions 2022 appeared first on .

Don’t Panic! Consciously Work on Below Roadmap! 

The burgeoning job profile Data Science is awaiting you with an ocean of opportunities. Let me preface this majority of the job seekers ignore the profile just because it’s a challenging career. But believe me, if you are enthusiastic about solving puzzles and have a creative mindset it’s one of the exciting job profiles. 

This article will guide you on the path to reaching your career goals. If you follow the roadmap below, you will thank yourself later when you’re enjoying the fruits of your efforts. 

Read: Mandatory Skills to Become Data Scientist

What Big Tech Companies Are Expecting From Upcoming Data Scientists? 

Data Science has been high in demand over the past couple of years, as the world’s most dominant and big tech firms set out the blueprint to optimise the power of data-driven strategies. The salary packages of this job profile reflect the demand. 

Data Science has been high in demand over the past couple of years, as the world’s most dominant and big tech firms set out the blueprint to optimise the power of data-driven strategies. Data science has an average salary of Rs 10.8 lakh per annum. 

1. Pay Dedication to Quality. 

Big tech firms receive applications from aspirants with advanced degrees, what they actually or equally lookout is interest and knowledge in the subject. 

For instance, solid technical knowledge is necessary if you would like to work at Netflix. Data Scientists need to be more creative in analyzing the data to accomplish better productivity in business outcomes. Especially, some other job profiles such as data roles expect candidates to be expertise in entertainment studio production and entertainment. 

          In another reputed firm Meta, data scientists are required to prove experience with gauging 

          the success of product efforts, including the aptness to forecast important product metrics 

          to enhance trends. 

          If you’re facing rejection due to a lack of skillset, hunt for opportunities. Get in touch with  

          startups and complete an internship. Once you receive the tag of experience you will get 

          exposure to working at higher job profiles in big tech companies.

2. Companies lookout for dynamic Data Scientists, who can Connect Data to Big Picture. 

Big tech firms such as Amazon Web Services (AWS), Meta, and Netflix expect data scientists to share relevant information and ideas actively with other associates and non-technical clients. 

The job profile has been evolving at a brisk phase, so tech firms hunt for aspirants with the potential to achieve productivity. Hence, data scientists are expected to grow and thrive in this booming technology acquiring knowledge about new topics constantly.

A Roadmap For All the Aspiring Data Scientists! 

Big Tech firms will hire you, either if you have learnt skills via course or completed higher studies in the domain. 

1. Earn a Data Science Degree: 

Big technology companies ensure you have quality knowledge to contribute your ideas in a creative way. If you don’t have a related bachelor’s or master’s degree, a Data Science course will certainly help you to get through the job in the desired company. 

2. Learn! Learn! Learn! Mastering Relevant Skills Is Important.

Try to polish your hard data skills by taking up an online course or registering in a relevant Bootcamp.

Expertise in Programming Languages, Data visualization, Machine Learning, Big Data, and Communication to earn the desired salary package. 

3. Work in entry-level data analytics job: 

There are various ways to get certified as a  Data Scientist, and an entry-level job as a data analytics engineer would be a smart choice. As you become more proficient in relevant skills, it would become easy to work your way up to earn the tag of a data scientist. 

Collectively, a piece of fundamental knowledge in mathematical concepts backing machine learning and data science models is necessary along with programming knowledge, especially expertise in R and Python is essential to get through the job. 

Read: 7 Factors Companies Look For When Hiring A Data Scientist

The post A Roadmap to Become A Data Scientist At A Big Tech Company! appeared first on .

Dear Learners in this blog we are going to tell you the top 7 professional data science certifications courses that you can pursue in 2022. The demand graph for certified data scientists is rapidly growing day by day. The responsibility of a data scientist is to prepare data, analyze the data process the data, and perform the advanced data analysis, and reveal the pattern.

Lets us first understand the life cycle of data science. Data science basically depends upon common techniques. Which are down below:

First step: Capture: on this stage, their data is in the form of a row structured or unstructured. So in this stage data is scraped from the device or system in r4eal time. 

Second step: The second step is to prepare the data and maintain the data. At this stage, data is transformed into a row to its correct needed format. This transformation is required for analytics, deep learning, and machine learning. At this stage cleaning, duplicating, and reformatting of data are being done.  

Third stage: On this stage determine the suitable data for use for analysis, machine learning, and deep learning algorithms to determine the category and pattern values with the data.

Fourth step: the fourth stage is analysis, on this stage discovery takes place. In this stage, data scientists perform statical analysis.

Fifth step: fifth and final stage is communication, in this stage insights are presented in the form of reports or any other form. Insights make it easier for a businessman to take decisions.

Read: Machine Learning Engineer vs-Data Scientist a Career Comparison

Responsibilities of a Data Scientist

As a data scientist, you will have a lot of responsibilities mentioned down below.

Acquire the data, clear and process the data, store and integrate the data, analyze the data in the initial stage, choose the correct data algorithms, apply the right data science techniques, improve the result, make the change and adjust according to the feedback, again repeat the process to solve new problems.

After the course the common data science job titles

1. Data engineer
2. Data architects
3. Data scientist
4. Data analyst
5. Business intelligence specialists

Salary of a Data Scientist

The salary of a data scientist is depended upon the experience and also how much knowledge you have. The salary of a data scientist will grow with time. A report by IBM says that the data scientist jobs are growing by 30%. A data scientist fresher can get around $500,000 per anum.

Now lets us know the top 7 professionals data science certificates for 2022

No1. (CAP) Certified Analytics Professional: The Certified Analytics Professional (CAP) certification is a credible, independent validation of critical technical expertise and related soft skills, possessed by adept analytics and data science professionals, and valued by analytics-oriented organizations. Best for 2022.

No2. (CCP) Cloudera Certified Professional Data engineer: the next one is the Cloudera Certified Professional certificate. This certificate adds value to me as a SQL developer. CCP helps you to pull & generate reports from the Cloudera CDH environment. This is done with the help of impala and hive.  Best for 2022.

No3. Data science for human reports: Data Science has found its way through specific domains of organizational functions. The Certified Data Scientist-HR curriculum primarily focuses on the deployment of data science in HR functions. The NearLearn-accredited certification is widely recognized and plays a vital role in meeting long-term career goals.  Best for 2022.

No4. Data science for operations: the role of this certificate we can see after the deployment on operations tasks. The NearLearn-accredited certification is widely recognized and plays a vital role in meeting long-term career goals. Best for 2022.

No5. Certified Data Scientist (CDS): This is another popular course in the field of data science. This course is designed to level high. The main concepts of this course are to cover all aspects of data science. The NearLearn-accredited certification is widely recognized and plays a vital role in meeting long-term career goals. Best for 2022.

No6. (DSF) Data science Foundation: this is another high-level data science course best for 2022. This course is also designed for covering the core concepts of data science. On this certification course, concepts are Machine Learning, Statics, Programming, data skills are covered. This course is also going to be the best course for 2022.

No7. (DSF) Data science for finance: the DSF Data science for finance course is specially designed for finance functions. This is also will be a great course for 2022.

You can also pursue this course if you want to deploy in finance functions

Final words: These are the top 7 certifications for 2022 you can do. Why these courses are better we already discussed in the above paragraphs. So in this last section, we just want to tell you that you can choose any of these data science certificates if you want to become a part of this data science industry as a data scientist. Wish you good luck in your future.  

The post Top 7 professional data science Certificates for 2022 appeared first on .

The challenges connected with getting and assuring the consistency of the data, as well as your choice of machine learning models and associated analysis, all benefit from the collaboration of team members with diverse skill sets.

This article discusses the three primary sorts of teams, who make up each one, and how they operate.

Let’s begin with the most lonesome of data science SEO professionals: the team of one. 

The Lone Data Science SEO Expert

In both small and large organizations, the one-person team is frequently the reality.

There are numerous individuals out there that are capable of managing both the SEO and data functions independently.

The lone data science SEO specialist can be broadly defined as an SEO expert who has chosen to pursue advanced computer science studies to concentrate on the more technical aspects of SEO.

They are proficient in at least one programming language (e.g., R or Python) and are proficient in the usage of machine learning methods.

They are actively monitoring Google improvements such as Rankbrain, BERT, and MUM since Google’s algorithms have included an increasing amount of machine learning and artificial intelligence.

These professionals must be proficient in automating SEO operations to grow their efforts.

This may involve the following: Automatic indexing of newly created URLs in Bing.

• Sitemaps with the new URLs created for Google.
• Generating text using GPT models.
• All SEO reports have anomalies.
• Long-tail traffic forecasting.

At my company, we discuss these SEO use examples via a Jupyter Notebook.

However, they can be automated to run every day using Papermill or DeepNote (which now has an automatic mode for launching Jupyter Notebooks).

If you want to diversify your skillset and increase your professional value, there are fantastic training courses available for SEO enthusiasts interested in learning data science – and vice versa, for data scientists interested in learning SEO.

The only constraint is your willingness to master new skills.

Some prefer to work alone; after all, it eliminates any bureaucracy or politics that may exist (but are not required) in larger teams.

However, as the French proverb states, “alone we travel faster; together we travel farther.”

Even if projects are completed fast, they may not be as effective as they could have been with a more diverse set of abilities and expertise.

Now, let’s move on from the lone SEO to two-person teams. 

The MVT for Data Science SEO

You may already be familiar with the term MVP, which stands for Minimum Viable Product.

This style is often used in agile methodologies, where the project begins with a prototype and evolves over one to three weeks.

The MVT is the team’s equivalent.

This team structure can assist in mitigating project risks and expenses while bringing more different perspectives to the table.

Are you looking for an easy way to generate compelling content on the go?

Verify the SEO friendliness, readability, and consistency of your material.

Increase traffic and engagement.

Today, test the SEO Writing Assistant.

It entails assembling a team of two people with complementary skill sets — typically an SEO specialist who also knows machine learning methods and a developer who tests ideas.

The team is constituted for a specified period, often around six weeks.

Consider content classification for an e-commerce site. Often, one person may evaluate several methods and adopt the most efficient one.

However, an MVT might do more complicated tests concurrently with multiple models — for example, preserving the most often occurring classification while adding image categorization.

This can be accomplished automatically using any of the pre-existing templates.

Current technology enables accurate findings to reach 95% of the time, at which point the granularity of the results becomes relevant.

PapersWithCode.com can assist you in staying current with the state of technology in each field (for example, text generation), while also providing the source code.

For example, OpenAI’s GPT-3 may be used for prescriptive SEO to recommend text summarizing, text production, and picture generating operations that are all of the high quality. 

The Data Science Search Engine Optimization Task Force

For a moment, let’s travel back in time with me and examine one of the greatest partnerships of all time: The A-Team.

Each member of this legendary team played a critical part, and as a result, they excelled at each of their collective assignments.

Regrettably, there were no episodes devoted to SEO.

However, what may the composition of your data science SEO task force look like?

You will undoubtedly require the assistance of an SEO professional, as well as a data scientist and a developer.

This team will manage the project, prepare the data, and apply the machine learning algorithms together.

The SEO specialist is best equipped to act as a project manager and manage communication with management and external stakeholders.

(In larger organizations, the team manager and project leader may have separate duties.)

Several examples of projects for which this type of team might be responsible are as follows:

• Establishing a data warehouse for the enterprise (an out-of-the-box data warehouse that merges business, market share-of-voice, technical, and content data).
• Detection and erasure of “zombie” pages.
• New query detection.
• Forecasting traffic/profits in response to specific activities. 

Compliance with SEO Standards for Data

Naturally, teams want tools to maximize their efforts.

This takes us to the concept of SEO-compliant data management software.

Three criteria, in my opinion, must be strictly followed here to prevent utilising black-box tools that provide results without disclosing their approaches and algorithms.

1. Access to documentation that discusses the machine learning model’s techniques and parameters in detail.

2. The capacity to replicate the results on a different dataset to evaluate the methodology.

This does not imply software emulation; rather, the difficulties are with the performance, security, reliability, and industrialization of machine learning models, not with the model or approach it.

3. The tool must have been developed scientifically, describing the background, the aims, the methods used, and the final results.

Data SEO is a scientific method to search engine optimization that is based on data analysis and the use of data science in decision-making.

It is possible to implement data science methodologies regardless of your budget. The current trend is for data scientists’ notions to become more accessible to anyone interested in the discipline.

It is now up to you to take responsibility for your data science projects by assembling the appropriate team and capabilities.

To the success of your data science SEO efforts! 

Read: What is Off Page SEO and On Page SEO? Know Its working, Importance and Benefits in Detail

The post Three Different Types of Data Science SEO Teams and How They Operate appeared first on .

Professional courses and certifications have many advantages. They can help you in:

• representative your skills and gaining trustworthiness
• Enhancing technical knowledge and up skilling
• Qualifying for senior-level jobs
• Staying abreast of the latest trends
• Acquiring experience and hands-on training

Top Trending Online Courses 

1. Data Science

Study programs in data science classically focus on big data analytics, data visualization, statistics, and prognostic analytics. The curriculum is designed for individuals pursuing technical job positions, such as data scientists, data analysts, business analysts, and machine learning engineers.

Whether you are a fresher, a marketing professional, or a software engineer, data science knowledge can advance your career. 

2. Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning  are the exciting technologies of the 21st century. AI and ML certifications can help boost the careers of IT professionals who have a background in mathematical or statistical fields.

Such programs cover various programming languages, tools, and libraries to equip students with the required competencies. Examples of the technologies taught include Python, MySQL, AWS, Docker, Kubernetes, Keras, REST-API, etc. Moreover, learning opportunities would expand when the syllabus is delivered via videos and real-life industry projects. 

3. Big Data

Big data certifications can help you go after more diverse roles than specific data science jobs. You can opt for different specializations depending on your background and career aspirations. Generally, learning tracks of big data certifications include:

• Business Analytics
• Data Engineer
• Natural Language Processing
• Deep Learning 
• Business Intelligence

4. Business Intelligence

BI professionals put their arithmetical ability to work and solve real-world business problems. Their insights can translate into actionable metrics, leading to changes in planning, operations, product development, and strategic management.

Therefore, their primary responsibility is to maximize the use of data in an organization to direct it on the path of successful performance. Training in business intelligence can help IT professionals build aptitude in:

• Data mining and analytics
• Data visualization
• Management reporting
• Using Excel, SQL, R, Python, Spark, Hadoop, etc.

5. Cloud Computing

Cloud computing is one of the top IT fields that is experiencing an emerging trend in 2020. As organizations around the world go after dynamic and scalable applications, the demand for cloud services is only going to rise.

While making the shift, companies encounter many technical challenges. So, having qualified people with domain expertise would be necessary. It is already a top IT job globally. Software developers can go for specialized programs in cloud computing to learn the following things:

• Understanding of cloud migration and multi-cloud environments
• Knowledge of serverless architecture
• Cloud security skills
• Building cloud-native technologies, virtual machines, microservices, etc. 

6. Project Management

Project management is one of the trending courses 2020 has generated interest in, for both employers and aspirants. Project managers ensure that work assignments are delivered within time and budget constraints. They assign tasks to the concerned members, set deadlines, and drive the project team to achieve the desired goals. 

7. Software Development

The primary duties of a software development job include designing, managing, and testing software applications. Being successful in this field requires efficiency in writing and implementing code. To build the necessary proficiencies, you can undertake training in Java programming, C programming, automation testing, SQL database, among other topics. Here are some of the job titles associated with this IT career:

• Computer systems analyst
• IT Coordinator
• Network administrator
• Systems architect
• Computer and information research scientist
• Junior software engineer
• Senior programmer
• Web developer

8. DevOps

As the title suggests, DevOps combines the Development and Operations arms of software applications. In today’s fast-paced marketplace, tech companies need to stay ahead of their competitors. And DevOps enables agile methods in software development. A DevOps-oriented study program would emphasize the following things:

• Agile methodologies for integration and deployment
• Creation and management of software lifecycle
• Continuous deployment through CI/CD practices and many more. 

To gain an in-depth understanding of the mentioned concepts, you can enroll in the specialized DevOps course, earning a software development diploma from IIIT-B and a data science certification from NearLearn. 

9. Digital Marketing

Digital marketing is an exciting subject for professionals like brand managers, sales personnel, entrepreneurs, and marketers. With increasing internet penetration and online activity, the scope of traditional marketing has also expanded. Digital marketing encompasses topics like Search Engine Optimization (SEO), content marketing, social media, and marketing analytics. 

Some popular examples include PG Certification in Digital Marketing and Communication on NearLearn. This online course is offered by MICA, a leading higher education institution in India. 

10. Blockchain

Blockchain is a rapidly growing discipline capable of bringing about significant transformations in the fields of real estate, healthcare, finance, insurance, among several others. The syllabus of blockchain development certifications comprises Ethereum, Smart Contracts, Hyperledger, Composer, Javascript, NodeJS, Solidity, etc. A background in engineering, mathematics, or computer science is a prerequisite for this stream. 

Blockchain is touted to be the disruptive technology that will dominate industries beyond 2020. Having a working knowledge of blockchain can prepare you for software development jobs and assist you in getting an accelerated start in business development, engineering, and operations. 

The post Top 10 Trending Online Courses in 2021 [For Both Students & Working Professionals] appeared first on .

Although, because of the disaster, the market currently gets tougher to be able to set it up again with more men force as they are doing previous. So, it might possible that you have to prepare yourself mentally for the long run hiring journey and many rejections in along the way.

Below I give 8 unique ideas for your data science portfolio with attached reference articles from where you will get the insights of how to get started with any particular idea.

1. Sentiment analysis for depression based on social media posts

This topic is so responsive to be considered nowadays and in urgent need to do something about it. There are more than 264 million individuals worldwide who are suffering from depression. Depression is the main reason of disability worldwide and is a important supporter of the overall global load of disease, and nearly 800,000 individuals consistently bite the dust because of suicide every year

Internet-based life gives the main edge chance to change early melancholy mediation services, especially in youthful grown-ups. Consistently, roughly 6,000 Tweets are tweeted on Twitter, which relates to more than 350,000 tweets sent for each moment, 500 million tweets for every day, and around 200 billion tweets for each year.

As indicated by the Pew Research Center, 72% of the public uses some sort of internet-based life. Datasets released from social networks are important to numerous fields, for example, human science and brain research. But the supports from a specialized point of view are a long way from enough, and explicit methodologies are desperately out of luck.

2. Sports match video to text summarization using neural network

So this project idea is basically based on getting a exact summary out of sports match videos. There are sports websites that tell about highlights of the match. Various models have been proposed for the task of extractive text summarization, but neural networks do the best job. As a rule, summarization alludes to introducing information in a brief structure, concentrating on parts that convey facts and information, while safeguarding the importance.

Automatically creating an outline of a game video gives rise to the challenge of unique charming minutes or highlights of a game.

So, one can attain that using some deep learning techniques like 3D-CNN, RNN , LSTM, and also through machine learningalgorithms by dividing the video into different sections and then applying SVM NN and k-means algorithms.

3. Handwritten equation solver using CNN

Among all the issues, handwritten mathematical expression recognition is one of the confusing issues in the region of computer vision research. You can train a handwritten equation solver by handwritten digits and mathematical symbols using Convolution Neural Network (CNN) with some image processing techniques. Developing such a system requires training our machines with data, making it capable at learning and making the required forecast.

4. Business meeting summary generation using NLP

Ever got stuck in a situation where everyone wants to see a summary and not the full report? Well, I faced it during my school and college days, where we spent a lot of time preparing a whole report, but the teacher only has time to read the summary.

Summarization has risen as an inevitably helpful way to tackle the issue of data over-burden. Extracting information from conversations can be of very good profitable and educational value. This can be done by feature capture of the statistical, linguistic, and sentimental aspects with the dialogue structure of the conversation.

Manually changing the report to a summed up form is too time taking, isn’t that so? But one can rely on Natural Language Processing techniques to achieve that.

Text summarization using deep learning can understand the context of the whole text. Isn’t it a dream comes true for all of us who need to come up with a quick summary of a document!

5. Facial recognition to detect mood and suggest songs accordingly

The human face is an important part of an individual’s body, and it mainly plays a significant role in knowing a person’s state of mind. This eliminates the dull and tedious task of manually dividing or grouping songs into various records and helps in generating an appropriate playlist based on an individual’s moving features.

Computer vision is an interdisciplinary field that helps conveys a high-level understanding of digital images or videos to computers. Computer vision components can be used to determine the user’s emotion through facial expressions.

6. Finding out habitable exo-planet from images captured by space vehicles like Kepler

In the most recent decade, over a million stars were monitored to recognize transiting planets. Manual understanding of potential explanted candidates is labor-intensive and subject to human mistake, the consequences of which are hard to evaluate. Convolution neural networks are fit for identifying Earth-like explants in noisy time-series data with more prominent precision than a least-squares strategy.

7. Image regeneration for old damaged reel picture

I know how time-consuming and sore it is to get back your old damaged photo in the original form as it was previous. So, this can be done using deep learning by finding all the image defects, and using in painting algorithms, so that one can easily find out the defects based on the pixel values around them to restore and colorize the old photos.

8. Music generation using deep learning

Music is an variety of tones of various frequencies. So, automatic music generation is a process of composing a short piece of music with the least human arbitration. Recently, deep learning engineering has become the cutting edge for programmed music generation.

Conclusion

I know that it’s a real struggle to build up a cool data science portfolio. But with such a collection that I have provided above, you can make above-average development in that field. The collection is new, which gives the chance for research purposes too. So, researchers in Data Science can also choose these ideas to work on so that their research would be a great help for Data Scientists to start with the project.

So, I will not only recommend this for newbies in the data science area but also senior data scientists. It will open many new paths during your career, not only because of the projects but also through the newly gained network.

These ideas show you the wide range of possibilities and give you the ideas to think out of the box.

So, basically, I enjoy doing such projects that give us a way to gain huge knowledge in a way and let us explore the unexplored dimensions. That is our main focus when dedicating time to such projects.

Original. Reposted with permission.

The post 8 AI Machine Learning Projects To Make Your Portfolio Stand Out appeared first on .

Well, there is a good cause to get confused as both these technologies drop in the same domain. Yet, both these technologies have specific meaning and application of their own with a few overlaps. 

Data Science and its Scope

 Data Science is a stream of learning with a broad range of data systems and processes. The general aim of Data Science is to maintain data sets and get meaning from them. Data tools, algorithms, tools, and principles are used to increase insights from random data sets. Digitalization has taken the world by tempest.

This has resulted in the creation and collection of a huge amount of data. With so much data everywhere, it becomes difficult to store, manage, and monitor it. The ever-growing data sets are managed by using data warehousing and data modeling. The analysis and information collected by the application of Data Science are used to assist decision-makers in reaching business goals.

Predictive Analysis

It is the branch of data analytics used by data scientists to predict future business events. In this data analytics life cycle, a data scientist uses many techniques, including data mining, statistics, modeling, Machine Learning, and artificial intelligence (AI). These technologies help them to get insights from the given data and make predictions about the future.

Prescriptive Analysis

It is a branch of data analytics used by data scientists to set down a set of actions based on predictive analytics, which are most likely to succeed. It uses the insights/ inferences from the prognostic model and helps companies by providing the best possible ways to attain business goals. It automates a complex decision and provides updated recommendations.

Machine Learning and its Various Components

Machine Learning is a part of artificial intelligence. Machine Learning is a technology, which means that machines can learn and get better automatically from experience. This technology is primarily about independent learning methods for machines, so they don’t have to be programmed for incessant improvement. 

Machine Learning means analyzing data to recognize patterns and set up logical reasoning based on inferences. The four dangerous components of Machine Learning are supervised Machine Learning, unsupervised Machine Learning, semi-supervised Machine Learning, and reinforcement Machine Learning. 

Supervised Machine Learning

Supervised Machine Learning creates a model that predicts based on proof during uncertainty. It takes a recognized set of input data and a recognized set of output data. Based on the behavior of these historical data sets, it instructs a model to produce logical predictions for the response to unrecognized data. They play a very important role in mapping the input-output pair. Learn more about types of supervised machine learning.

Unsupervised Machine Learning

As the name says, it is a Machine Learning process that requires minimum to no human attempt. Unsupervised Machine Learning algorithms use unspecified or non-labelled parameters to find out patterns and trends. These algorithms use clusters, anomaly detection, neural networks, and more. Learn more about unsupervised machine learning.

Semi-supervised Machine Learning

It is a mixture of supervised and unsupervised Machine Learning. It utilizes classified as well as unspecified data to derive more precise insights. It is considered to be a cost-efficient solution when labeling or classify data is an luxurious procedure.

Reinforcement Machine Learning

If you have ever played Mario, then you must know that you have already knowledgeable the plunder of strengthening Machine Learning. Reinforcement Machine Learning helps in understanding the best likely way to attain an complicated objective after multiple steps.

What is the difference between Machine Learning and Data Science?

Data Science and Machine Learning are two different domains of technology. They both work on different aspects of a business. Data Science uses data to help companies in understanding the trends and forecast behaviors. Machine Learning enables devices to self-learn and executes various tasks.

Since these both technologies are unified, a basic knowledge of both is necessary to apply any of them for business growth and development. Data Science is already an essential part of almost all the companies, while demand for Machine Learning is rising at a fast pace. Both technologies are going to be highly pertinent and useful for companies in the coming future. 

Both the technologies and skills are highly in insisting. Many young professional are keen on learning these skills. They get confused between wide ranges of courses offered by a variety of Machine Learning & Data Science training institutes. It is vital to understand and analyze your current skill set to decide which skill can propel your career upwards with NearLearn.

The post Machine Learning & Data Science Life Cycle: What’s the Difference? appeared first on .