AI Monks Response:

Business analytics has opened doors to a whole new world and raised decision making to a radically different stratum. Today, any organization makes an informed decision by slicing, dicing, and dissecting the data. But will this analysis hold any value if we completely ignore the business aspect of the problem at hand? We don’t think so. Analytics consulting is a functional role that aims to maintain a balance between business and hardcore analytics with an intention to deliver value-added analytical solutions.

Let us understand the role of an analytics consultant.

Usually, when we talk about consultants, the first thing that strikes our mind is an external expert who visits a company to solve a specific current or potential problem. And they leave the company(clients, as they are called) once they have done the task. However, there is another role that may not be very explicit, but plays a somewhat similar role; an internal consultant.
The external and internal consultants are substantially similar in the kind of work they do. Whereas, the significant difference lies in the way the company engages or hires them.
External consultants are usually employed for specific projects; while, the internal consultants are full-time employees.
Apart from how the company hires the external and internal consultants, almost all other functions stay the same.

Globally, corporate spending on analytics consulting had seen a surge of $43 billion in 2017. Companies evenly split the investment between developing in-house capabilities and spending on external consultants. With 91% of executives convinced that analytics have already generated substantial value for their firm, we could expect a rise in both.

Let’s talk about some of the projects that consultants work on:

1. Automating reports or developing dashboards from scratch for a company using tools such as Tableau, PowerBI, Qlikview or custom-dashboards using scripting languages such as Python
2. Identify avenues to improve logistics performance of a logistics company (or logistics unit of a manufacturing or retail company)
3. Reduce the inventory held by a retail chain
4. Analyze the campaign performance of a company
5. Reduce the customer churn for a telecom client
6. Identifying the target customer personas/segments
7. Reduce the uninstall rate for app-driven companies
8. Cannibalization effect on sales of one store due to the opening of another store
9. Impact of A/B tests
10. Analyze the results of sampling or experimental designs

We can notice through the list mentioned above that all these projects start with a robust descriptive and diagnostic analysis.

• The diagnostic analysis helps in identifying the root causes of areas where the company is not doing good.
• Whereas, the descriptive analysis helps in identifying and assessing the performance of the company.
• Once you understand the overall context and underlying problem, you should move to predictive analysis.

Hence, for anyone aspiring to become an analytics consultant, it is essential to develop a strong understanding of descriptive and diagnostic analysis. Having identified the problem, the next step is to identify and provide a solution to the company. Now, this solution can come in the form of process changes or by building a predictive model that can help the company understand the potential situations that can arise beforehand.

Therefore, it becomes essential for an analytics consultant to have a basic understanding of statistics and predictive modeling techniques. As a consultant, you may not be required to go very deep into statistical analysis or in-depth mathematics of predictive models, but it’s helpful to know the basic principles.

Analytics Consultant Skills

Let’s further understand what skills an analytics consultant will require:

Soft Skills

• Ability to breakdown the business problem into small problems
• Ability to read and comprehend data
• Good communication skills
• Great presentation skills (Powerpoint)

Technical Skills

• SQL
• Any visualization tool such as Tableau, PowerBI, Qlikview or any other
• Any ETL tool such as Alteryx
• Excel

Programming Language

• R or Python

Statistical Concepts

• Sampling
• Hypothesis testing
• Data distribution
• ANOVA
• Different types of data

Read: Impact of Recession on Data Science Industry

How can you acquire these skills?

1. Soft Skills

Developing soft skills is more like running a marathon and not sprint. “Practice makes perfect” is not just a saying. You can develop these skills with practice over a period of time. The best source to develop these skills is case studies. Practice case studies as much as possible.

• Form a group with friends, pick up case studies from whatever source you can, and get into it.
• Try to solve them end-to-end as if you were presenting to an audience.
• Right from breaking down the problem statement, analyzing data to creating presentations – do all of that.
• Find yourself a mentor who can guide you in improving your analysis and presentation.
• Present the analysis to your mentor as if you were presenting to the CEO of your company. That will prepare you for real-world scenarios.

If you are keen on improving your ability to read and comprehend data quickly, read as much as possible. Understand what are the different KPIs companies track regularly. Learn about different use cases that different companies work on. This will help you in developing data understanding.

2. Technical Skills

Understanding of technical skills is vital because you want to work on data eventually. From extracting to cleaning to manipulation to analysis, you should have expertise in all four areas. In a lot of scenarios, you will be given access to SQL systems where you will have to query data yourself; hence SQL becomes essential. DO NOT disregard SQL – it’s one of the most important skills if you want to work in the world of data.

• Alteryx, as an ETL tool, can help you carry out basic data transformation and data manipulation easily. Knowledge of a visualization tool can help you analyze data quickly and present it beautifully and insightfully.
• Learn any one of two languages (R or Python). They help significantly in building predictive models and much more complex data analysis where your other options may not work out. Hence they come very handily.
• Basic statistical concepts can help you in developing an understanding of the data collection process. Additionally, it can help you work on different types of datasets, such as sample data, survey data, experimental data, etc. Hence, learning these basic statistical concepts is essential.

3. Latest Happenings

Additionally, staying updated with the latest happenings in the industry would help you stay ahead of the game.

Conclusion

Summing up, we could ascertain that Analytics consulting is one of the most financially rewarding and coveted profiles today. Analytics consultants are in hugely sought after by global giants like Accenture, HP, Dell, BNY Mellon etc. Sound domain knowledge, rich experience and exceptional communication skills paves the way for it.

Read AI Bytes by AI Monks on stay on top of analytics and data science industry insights.

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Recession. How scary can this word be? People around the world dread this term; they relate recession to mass layoffs. The global pandemic has hit the economy worldwide hard, which has led to recession. People are losing jobs, and those who have not are just waiting for it to happen to them too. These are tough times, and people in the data science and analytics industry are worried if they will get affected also?

In the past few years, ‘data science,’ ‘machine learning,’ ‘artificial intelligence’ have become buzz words. Every company has been hiring and investing in building data science teams. Just before the pandemic, it had started to look that the field is invincible.

Is it?

Is data science really immune to recession?

Well, the truth is that this crisis will take everyone down with it.

The data science and analytics fields are not immune to recession. Like any other industry, recession will have an impact on data science field as well. Can any industry or organization really escape from the clutches of recession? Howsoever hard it may be, one thing we have to accept is that data science and analytics as a profession is not invincible.

1. How will companies deal with investments during the recession?

Business leaders face a hard time managing their finances during the recession. They have to figure out:

⦁ where to invest money
⦁ where to make budget cuts
⦁ which part of the business is dispensable, and,
⦁ what part a company just cannot do without?

Companies have to turn to cost-cutting during a recession. Since data scientists and analysts are costly, businesses who are unaware of their benefits, cut them off first. These companies don’t know how to leverage data science and analytics to its best use. They turn to intuition and gut feeling to make business decisions. How reliable they are, only time will tell.

Whereas companies who know how to utilize the data scientists and analytics teams optimally, know a sure shot way to deal with whatever may come their way. They have a higher chance of survival as compared to those who shun data scientists and analysts during these times.

In a nutshell, it’s more dependent on the company’s vision than a data science as a profession. If a company has thought-through thoroughly and has a clear vision of how and where to leverage the data science team, chances of survival are significantly higher.

If you have limited money to invest, you will always invest in a known territory.

2. Once you’re in a data-driven culture, the data science team will be as important as any other, if not more

Companies that relied on data-driven culture pre-pandemic will continue to rely on it during the recession too. A data scientist or analyst’s value to the overall development of the business is not clearly visible as it doesn’t directly affect the decision making. It is tough to measure a data scientist’s contribution to the firm. But, to guide a company smoothly through the recession, it would need prescriptive analysis and insights generated by the data science team.

An analytically mature organization would appreciate a data scientist or analyst even more during the recession. In these hard times, data scientists are going to help organizations optimize their cost using sophisticated models and algorithms. Marketing budgets are usually the first ones to face the axe during any such situation. In a situation where pressure on the organization is high to sustain and grow, if possible, data science can help optimize the spending on different channels and improve customer acquisition.

3. Digital transformation, catalyzed by remote working, to create more analytics jobs

Given that more and more companies are going digital and looking for tech-based solutions, data will grow faster than anticipated. With the increase in data generation, there will also be an increase in the requirement of people to analyze, monetize, and optimize it. Data Science and Analytics isn’t a wave that will pass; it is here to stay. Data Scientists and analysts help businesses stay afloat and mitigate through uncertain environments. We don’t expect the buzz around analytics and data science abilities to go bust that soon. In fact, we believe the field to be resilient and pick up the pace soon as the market conditions improve. With other sectors widely recognizing the benefits of data science and analytics, we can only imagine how they will leverage it.

4. Transition to full-stack data scientist

Recession and slow-down in the economy will result in tighter budgets for all the teams, not just data science. In such a scenario, expectations from data scientists will evolve and they would be expected to carry out much than just building models. Data scientists will have to evolve from ‘just data scientists’ to ‘full-stack data scientists’ where they are involved in end-to-end building and deployment of models. Similar was the case with developers a few years ago, companies wanted to have ‘full-stack engineers’ rather than just ‘back-end engineers’ or ‘front-end engineers’ . But this does not mean every one will be expected to be a full-stack engineer. Organizations which are mature and have developed data-driven culture will still value data scientists for their core value generation.

In conclusion, we could say that Data Science and Analytics aren’t immune to recession, but they are comparatively more resilient than other areas. In fact, the crisis has allowed these people to showcase their skills and make the companies realize the impact they have on a company’s functioning. This will increase the demand for data scientists and analysts going forward. recession will pass, but the need for a data scientist or analyst will continue, though it might be slower than before.

Read AI Bytes by AI Monks on stay on top of analytics and data science industry insights.

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Having a mentor is extremely crucial in today’s ever-evolving world. It’s super hard for students to know the analytics field just from online research. Only someone working in the analytics field can give a clear picture of the job market. Also, from a student’s perspective, there are so many courses to choose from, and one small mistake could end up changing your future. So it would be best to have someone who can guide you to make the right career choice. “Who,” you ask? A mentor. That’s right.

3 Key principles: how to choose mentor for your analytics mentorship

1. Mentor ≠ Career Counselor

A mentor has a personal interest and a stake in your success, whereas a career counselor gives advice based on neutral principles and doesn’t have a stake in the candidate’s success.

2. Fresh Perspective

A mentor possesses a view different from that of the candidate. He has experience and insight and has walked a similar path that you aim to walk on.

3. No conflict of interest

A mentor guides you to benefit you in the best way possible. Their sole plan is to help you in every way possible.

Why do you need an analytics mentor?

Analytics career scope is at an all-time high and growing with countries fast-tracking their digital and automation initiatives. Often aspirants plan the next move of their analytics journey by researching online, talking to their friends, or looking at other professionals’ journeys; such strategies seldom deliver excellent results. The chances of facing failure are a lot higher. Your friends might not be able to guide you as well as a mentor can because they are at the same crossroads as you and lack the years-long industrial experience that a mentor possesses. A mentor steered path might be the best way to achieve your goal.

The demand for data science and analytics professionals is exceedingly high than their availability. A mentor-steered career path helps you stand out from the crowd.

A mentor is like a helmsman who steers you through your entire analytics journey, ensuring you arrive at your destination port, i.e., finding the right analytics course and job.

1. Mentor steers you in the right direction:

A mentor reads you, understands you, and then guides you based on your skills. They help you create a plan to achieve your goals in a set time frame. A mentor, having seen the evolution of the landscape, is best suited to guide you through this life-changing journey.

2. Mentor assesses your progress:

You could work diligently and yet fail to see successful outcomes. If you don’t want to be the Jack from Jack of all trades and master of none, then just cramming new things won’t help you, you might need to master specific skills.

3. A mentor is a motivational force:

A data analyst usually puts all their efforts into gaining a competitive advantage and yet facing setbacks in their journey. In situations like these, mentees need a mentor’s guidance to find a solution, overcome any challenges that come their way, and keep going with utmost enthusiasm.

4. Mentors act as connectors:

A mentee can benefit from a well-connected mentor. Expanding connections in analytics field is significant for learning from various experts of different techniques.

5. You can bounce ideas and thoughts with your mentor:

What language will work best on a project? How should you approach the given case study? Should you learn additional software for your target job? Which analytics job role suits your education, aptitude, and skills? You can always share and discuss these ideas with your mentor.

6. Mentors are advisors to look up to:

Mentors are qualified expert professionals who have walked the lanes where you want to enter. You look up to them for advice, facilitation, and guidance.

7. Mentors act as incubators of ideas:

Mentors possess in-depth industry knowledge and work with students and mid-career professionals from various backgrounds and with varying aptitudes. They have the innate ability to spot an analytics gem, guide you, and make your analytics journey considerably smoother.

Lord Ram had Maharishi Vashishtha, Pandavas had Guru Dronacharya. You need a Guru, too. You would agree with us that having a mentor is essential as they can assist fast track your success. But how can you find the best career sensei to guide you take off on your analytics career? That’s where AI Monks comes to connect you with the trusted go-to person to address your career-specific needs. AI Monks presents its Analytics Mentorship Program, an online platform that helps you connect with the experienced data science and analytics industry veteran, who will guide you through every step of your journey.

AI Monks takes pride in being your analytics guide, opening the door to the world of data science and analytics. Our analytics mentorship program will help you craft your profile for the analytics job market and guide you in the transition from the Non-Analytics field to the Analytics field.

Pursue your dream career with AI Monks.

Check out our Expert Mentorship Programs. For any query, reach us at info@aimonks.com

The post Why Investing In An Analytics Mentorship Program is Crucial? appeared first on AI MONKS.

Do you often find yourself wondering how you’re going to pursue a course in analytics? Or how to get that analytics job you’ve been long thinking about, amplifying your skills and fast-tracking your career growth? Jumping right into the theory won’t help you achieve your goal.

Don’t you wish there was someone who could guide you to enhance your professional journey?

Well, what if we tell you that someone can help you get where you desire to reach? Your own Sensei, who would guide you, help you pinpoint your mistakes, and work on them with you till you are prepared to enter the real world.

How did the term “mentor” originate?

The term mentor comes from the character “Mentor” in Homer’s epic tale, The Odyssey. Odysseus, the king of Ithaca, had a trusted friend named Mentor. When Odysseus fought in the Trojan War, Mentor served as friend and counsel to Odysseus’ son. Because of Mentor’s relationship with Telemachus, Latin, and other languages, including English, use the term for someone who imparts knowledge to and shares the experience with a less-experienced colleague.

A mentor:

You have to listen to their advice and follow it even if you don’t believe in it. You have to trust that the Mentor has your best interest in their heart.

The Three C’s of Successful Mentoring:

Counsel: A good mentor provides you with counsel, opportunities, and advice to develop your skills. 

Candor: The best mentors care enough to be critics and provide a candid view of what you need to know.

Confidence: A great mentor gives you courage. They know your strengths, weaknesses, and know what ticks you off and still believe in you.

Over the last two decades, many organizations have included workplace mentoring to help freshers cross-over or build skills in a specific area. According to the Association for Talent Development (ATD), 71 percent of Fortune 500 companies have formal mentorship programs. After benefitting various other industries, mentorship programs are finally gaining momentum in the data analytics field. Data analytics aspirants or people who hope to make a career transition should get in touch with senior analytics industry veterans. They can give them one-on-one guidance about courses and career advice.

Analytics is a fast-evolving field. Almost every organization is adapting techniques anchored around emerging technologies. With this, the demand for data science and analytics jobs is growing exponentially. Hence, data science and analytics enthusiasts are either up-skilling or switching careers to be ahead of this game. Staying in the know is very difficult with the ever-changing trends, especially for people who are relatively new to the field.

You may also like 6 Resume Mistakes That Could Make Or Break Your Career

Why should you opt for an analytics mentorship program?

Gone are the days when having a degree in an analytics course and basic knowledge of Python could help you land a job in the analytics industry. Today the market is flooding with novices straight out of college looking for a job. What does candidate ‘A’ have that hundreds of others don’t? Why did ‘A’ get a job while others had to go back to applying for a job somewhere else? ‘A’ probably hired a mentor who guided him to use his skills to impress the recruiters and stand out from the crowd. That’s right.

1. A mentorship program enables students to experience the world of analytics beforehand, hence preparing him for the real world.

2. The program helps students understand how to begin their analytics journey and gain valuable tips.

3. The program prepares students for the analytics role, and teach them what might be expected from them once they land the job.

4. A mentorship program can give candidates a definitive edge over hundreds of newbies.

5. A mentorship program provides guidance and input to help candidates level up and improve candidates’ scope of scoring a relevant position in the data analytics field.

6.  Having a mentor can help you gain more credibility and traction in the analytics job market.

People often consider self-learning to be the most effective form of learning. But it takes support from an experienced mentor to level-up from being a newbie to becoming industry-ready. One should seek help from mentors to hit fewer roadblocks in their analytics career. A mentoring program’s effectiveness can be measured by how far its positive ripple effects reach. Having a mentor has positive results in the long run, as well. They don’t just help in your career take off but ensure promotions and salary grade changes as well.

But how can you find the correct career Sensei to guide you take off on your analytics career? That’s where AI Monks comes to connect you with the trusted go-to person to address your career-specific needs. AI Monks presents its Mentorship Program, an online platform that helps you connect with the experienced data science and analytics industry veteran. A mentor will guide you through every step of your journey.

AI Monks’s Mentorship Program

AI Monks’ Mentorship program aims to give you a sneak peek into the wondrous world of analytics via one on one chat with the most qualified industry leaders. We are there to provide you the best possible experience from professionals who know what you need, have been in your place, and have traveled the same lanes you want to walk. They will equip you to tackle any real-world challenges that may come your way.

AI Monks’ Mentorship program will help you land your dream Analytics job more quickly. We aim to provide an affordable platform and offer rock-solid career advice. If you need help getting your career off the ground,  make the life-changing decision, and get mentored by AI Monks’ approved industry expert, to take on the career track you want.

Pursue your dream career with AI Monks.

Check out our Expert Mentorship Programs. For any query, reach us at info@aimonks.com

The post Take off on Your Analytics Career With A Mentorship Program appeared first on AI MONKS.

This post is a continuation of our previous post – 6 Resume Mistakes That Could Make Or Break Your Career – Part 1.

In this post, we will talk about other 3 mistakes that people often make in their resumes that make them a part of 98% who get to face rejection at the initial screening level.

Mistake 4: Declaration

A declaration states that all the information you have provided is correct to the best of your knowledge, affirming that all the information provided is the truth.

Please take a look at these examples to get a better understanding of what we mean.

1. All information included in this resume is true and correct to the best of my knowledge and belief.

2. I hereby declare that all the information mentioned above is correct and accurate.

3. I solemnly declare that all the information provided in this document is free of errors to the best of my knowledge.

4. I hereby declare that all the information provided in this resume is in accordance with facts or truths to my knowledge. I take full responsibility for the correctness of the said information.

But how significant is this, it’s like you wrote an exam, checked it yourself, and even gave yourself an A+. Obviously, you would say you did not lie on the resume. First, it isn’t good practice. Also, if caught, you would lose any credibility, and could even be blacklisted in that company’s records.

Adding a declaration used to be common practice in earlier days, but it is discouraged now since it is not that relevant. It is better to change with changing times. A declaration is usually followed by the writer’s name and the date.

The company you are applying to will most likely conduct a background verification once you are hired. So, any kind of declaration is a wastage of real estate on your resume, nothing else. And remember, real estate is very expensive – be it on your resume or otherwise.

Mistake 5: E-mail ID

Your e-mail address may create an impression you make as you apply for a job and the first establishment of your personal brand.

A strong e-mail address can help recruiters find you. If an HR recruiter or hiring manager can’t find you easily, they are not likely to spend much time searching, when they have a massive pile of applications. There goes your chance of getting an interview.

It astounds me how “nice” e-mail ids candidates put on their resumes. Let’s take a look at a few of these.

1. Stoner_420@gmail.com– Unless you are applying for a job as a *rolling* manager, this isn’t the first impression you want to make.

2. hottie_69@gmail.com – You want to look professional, and your e-mail address, not helping. Don’t use the number 69 in your e-mail id, not even if that’s your lucky number or birth.

3. SearchingForWork@gmail.com – Searching? Yeah, that is why you submitted your resume, didn’t you? Also, this makes you look quite desperate.

4. rahulgoel@abcd.com – Do you realize that the people who run the network at ABCD company now know that you are looking for a job? Only if you want your boss to see that you’re looking for a job, use a company e-mail address. So, when your head fires you and a recruiter or hiring manager wants to send you a job description, your e-mail can bounce because you’re no longer at ABCD company.

5. ramshar1987@gmail.com – Including your birth or graduation might subject you to ageism, which can work against younger as well as older candidates.

Basically, avoid e-mail ids built around Sports – (CubsFan1908@gmail.com), City – (Kanpurgal2001@gmail.com), State – (pahadidelhite75@gmail.com), School Mascot – (Dawgs83@gmail.com), Music – (OneDireaction340@gmail.com), Nickname – (Sexaaa83@gmail.com), Pet’s Name – (Tommy22@gmail.com), Car – (jaguar68@gmail.com), Children’s names – (chunnu-munny@gmail.com)… all tough to find in an e-mail search.

And, yeah strict no for email IDs that have cool, dude, rockstar, great, etc. words in it.

Mistake 6: Photograph

Along with omitting details such as your nationality, marital status, political or religious beliefs, do not add your photograph to your resume. You might be a gym freak, always looking for an opportunity to flaunt those sharp features, but your resume is not the place to do that. To help you understand why you shouldn’t, here are two reasons.

1. It is not relevant: Resume is like a trailer or teaser before the actual release of the movie. If you can’t indulge the viewer, in this case, it’s the recruiter; then there is no point. Use your resume to sell yourself, your skills, and your abilities rather than your appearance.

Why fill space unnecessarily with a photo that is not relevant to your job application (unless, of course, you’re a model or an actor)? But if you want to land a job in the analytics field, your looks might not be a contributing factor to your getting a job, your skills will certainly be.

2. Might lead to Discrimination: Whether intentional or not, a photo on your resume could lead to discrimination based on age, race, gender, or other factors. Why give an employer any reason to ignore your skills and experience by focusing on your appearance?

These are some of the examples that could be deal-breakers and could keep you from getting the job you so desperately want.

If you have been making these mistakes too, you need not worry. AI Monks presents to you our Resume Building packages at affordable prices. We can help you rectify any blunders that have been holding you back from getting your dream job.

We have helped thousands of candidates land a job with a careful, customized and focused CVs. We believe we can help you too. We care about your needs. Reach out to AI Monks at info@aimonks.com today to move a step forward towards getting that dream job.

The post 6 Resume Mistakes That Could Make Or Break Your Career – Part 2 appeared first on AI MONKS.

Mistake 1: Objective Statement

An objective statement is a short paragraph usually included at the top of a resume. A candidate uses this space to convey that, “Hey, why to waste your time on other resumes when I possess all the skills that you’ve been looking for?” It’s a well-written summary of where you’ve been so far in your career and where you aim to go.

But people often make common mistakes which backfires instead of helping them get the job. Let us see a few examples.

1. I want to work at ABC company for the XYX role.

It’s a no brainer that you submitted the resume because you wanted to apply for the role at that company. You are just wasting the space by quoting the obvious.

2. I am an experienced manager seeking a full-time position, I can apply my knowledge and skills to benefit the organization.

Did you notice where the problem lies? Don’t worry; there are many others like you who didn’t. The person, let’s call him ‘A,’ is telling the recruiter that based on his experience, he “can apply his knowledge and skills.” But it doesn’t make any sense, why do you have to mention it? The recruiter would just look over it and his mind would interpret it as “blah, blah, blah.” If you do get the job, you obviously will have to apply the skills to get the job done.

3. I want to explore the infinite possibilities in the analytics field.

You say that you want to “explore the infinite possibilities in the analytics field.” Are you sure you are ready for the job? Because once you get the job. It is going to be all work. It is not a school field trip where you get to explore new possibilities. You will be given responsibilities, and you will be paid to get the job done and not to “explore” while at the job.

4. I am interested in learning Data Science and Machine Learning required for solving business problems through computer simulation.

Hold on, are you serious? You want to learn Data Science and Machine Learning after you get the job? Why would the recruiters even consider you when they have hundreds of other capable applicants who, like any regular candidate, learned the basics before getting the job. Do you want to learn? Why don’t you do an online course, or get coaching or maybe join a college, complete your studies, and then apply for the job? A company needs qualified personnel. They haven’t opened an educational institution to teach you and then pay you for it.

5. I am “A”, I work as a XYZ, and I can do ABC work efficiently.

It’s good that you are at a particular position in your firm, involved in so and so work. What is the recruiter supposed to do with this information? It is a complete waste to use up this space just to talk about yourself. How is this information supposed to help you get the job or make your resume stand out from the rest?

Mistake 2: Personal Information

The ‘Personal Information’ section of your resume is an opportunity to introduce yourself to a recruiter. If you write the basics wrong at this stage, you can say goodbye to the chances of landing an interview. You begin with your name at the top of the resume. What follows is what might be a problem.

1. Mentioning your parents’ names or your marital status

The recruiter isn’t going to send a card adressed to your parents, skip that information maybe. The same goes for your marital status. It is a recruiter and not your next-door aunty who is interested in knowing whether you are single or married.

2. Your Address

The recruiter is not interested in knowing where you live, more so attaching “Hari Om Niwas” in front of your address is even more unnecessary. Unless you are living in Antilia, White House. or Buckingham Palace, just don’t mention it.

3. Hobbies

What you do in your leisure time least concerns the recruiter. The company will select you based on your professional skills and not on the fact that you love traveling, singing, playing basketball, cooking, or reading. Keep your interests to yourself unless you don’t have other more relevant things to mention.

Mistake 3: Skills

Skills can either make or break your resume. Skills can be the expertise you develop to perform a task or a job or your natural talent. Life skills help you deal with daily tasks in all areas of life while job skills allow you to perform specific work duties. Let us focus on the skills you should avoid mentioning in your CV.

1. I possess working knowledge of the Office suite.

Office suite would include a large collection of softwares, browse the whole list before you mention it. Also, if the job you are applying requires just basic understanding of word or excel, it is best advised to skip mentioning it in your resume altogether.

2. Using Digital Technologies to improve efficiency.

What do you mean by it? If Chandler Bing, from F.R.I.E.N.D.S. would have been in the recruiter’s place, I could imagine him say, “Could this skill BE any more vague?” How exactly is this even a skill? Just HOW?

3. Proficiency in English and Hindi.

Okay, I can try to make sense of why you mentioned English. But it seems like you are working too hard. Isn’t the whole CV written in English? Isn’t it proof enough for the recruiter to know that you are proficient in English? But why the need to mention Hindi? We live in a world where English is the second most spoken language, how is the employer going to benefit from your Hindi proficiency? No, they don’t need you to write a “Sanjay Leela Bhansaali” level movie. Mention something that could count as a skill, don’t write anything just for the sake of it. It’s like you are saying, “Yeah, I can TALK.”

4. Hardworking and Focused

Had you been hardworking and focused as you have mentioned in your resume, you would have actually focused on what a resume needs and worked hard to actually find the right skill to put on your CV. Sorry, but it seems like instead of working hard, you are hardly working to put in the effort needed to get the job.

These are some of the examples that could be deal-breakers and could keep you from getting the job you so desperately want.

If you have been making these mistakes too, you need not worry. AI Monks presents to you our Resume Building packages at affordable prices. We can help you rectify any blunders that have been holding you back from getting your dream job.

We have helped thousands of candidates land a job with a careful, customized and focused CVs. We believe we can help you too. We care about your needs. Reach out to AI Monks at info@aimonks.com today to move a step forward towards getting that dream job.

Read the second part of the series – 6 Resume Mistakes That Could Make Or Break Your Career – Part 2

The post 6 Resume Mistakes That Could Make Or Break Your Career – Part 1 appeared first on AI MONKS.

Meet Rahul. Rahul has always been proud of his name. His name has been his brand all his life. You must have heard him say at least once, “Rahul….Naam toh suna hoga!” (Rahul…You must have heard the name.) But unfortunately, the recruiters of his dream Analytics firm begged to differ. And all his dreams came crashing down just like Humpty Dumpty did from the wall.

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If you need help building a brand name for yourself. If you want to win over any recruiter with just a look at your resume. If you want to stand out of the crowd based on your resume. Reach out to AI Monks.

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You could be a novice, a working professional looking for a job transition, or an analytics enthusiast in need of bringing yourself up-to-date. AI Monks can help you tap into the world of Analytics job market, confidently land the positions you want, shorten and de-stress your job search and exponentially increase your salaries with customized and professionally curated resumes and mock interviews.

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2. Professional Resume Building—- ₹1249

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The post Meet Rahul – Rahul is Intelligent… Be Like Rahul appeared first on AI MONKS.

The report gives an insight into the advancement of analytics in various industries such as

• Financial Services
• Retail
• Technology, Media and Telecom
• Energy & Utilities
• Services
• Real Estate and Construction
• Supply Chain
• Manufacturing
• ITES
• Healthcare
• Analytics and Consulting

The report also discusses the key tools and technologies being prominent in the industry and talks in detail about the top industries leveraging different tools.

Through this report, we study the use of AI, ML, Analysis, and Data Science across areas such as Administration, Human Resources, Customer Care Support, Customer Care Assessment, Research & Development, IT, Finance & Accounting, and Sales & Marketing.

AI is prevalent in many industries and has deep-rooted itself into the basic functioning of different sectors. According to multiple sources. the data science and artificial intelligence market is expected to reach hundreds of billions of dollars in the next few years. Despite such advancements, there are still a few people belonging to various industries who are quite skeptical about including AI, ML, Analysis, and Data Science in their organizations. AI Monks gives you a deep understanding of it all through this report.

The report helps to understand the analytics trends by seeking answers to following questions:

• If data science, artificial intelligence (AI), machine learning (ML) gives a competitive edge to companies in their respective sector.
• If data science, artificial intelligence (AI), machine learning (ML) plays an essential role in an organization.
• Which sectors have significantly large analytics teams?
• What are the most widely used tools for analytics and machine learning?
• Which organizations have embraced analytics and data science?
• What are the broad areas that the Analytics teams work upon in a company?

About AI Monks:

AI Monks aims to be your guide in your journey to the analytics world. Whether you are a novice or an analytics professional, we help you take the next step in the vast world of analytics.

Owing to this increasing demand in the field of analytics and data science. AI Monks guides and mentors aspiring minds to enter the analytics industry and build a mark for themselves in their professional career.

We are a team of enthusiasts who love to delve into data and numbers, who understands the nuances of technology and coding and who also knows how business works.

You can follow us on LinkedIn, Facebook and Instagram.

The post Uncovering the Analytics Trends of 2020 appeared first on AI MONKS.