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Tag: career in data science

How Covid-19 Crisis Can Work In Your Favour When Starting A Career In Data Science?

Posted on by Imarticus Learning

How has Covid-19 Impacted the World Economy?

Coronavirus widespread has brought about many drastic changes in the economy of the world. There are some serious threats of downfall to even some of the renowned companies of the world. But some of the Entrepreneurs have tried to look at the brighter side of this miserable pandemic and have established pretty decent businesses to sustain themselves.

Some of the serious impacts of Covid-19 on the World Economy can be understood by the following points: 

  1. A Vicious Circle

 

Data Science CareerConsidering the current situation, the economy has more or less become a vicious circle. Because of no money in the market, there are no sales.

No sales give rise to a situation where the sustainability of the business along with the payment of salaries to employees becomes impossible.

This whole situation has no disposable income in the market to be processed.

  1. Loss of Jobs

There is a huge population in the world which has lost its job in the times of Coronavirus Pandemic.

Data ScienceApparently in times where people are being laid off, expecting to get hired somewhere sounds like an arduous task. This has been the worst hit on the economy so far.

  1. Pending Payments

People who owe the banks are currently unable to deal with the situation and banks, on the other hand, are not been able to dispense cash to their customers because of the delays in their timely receipts.

This whole situation has become chaos and it’s very hard to understand the future course of action in terms of Financial Management.

Starting Career in Data Science in times of Covid-19 Pandemic

Although the Covid-19 period has not been easy for anyone on the planet, still things are meant to get back to their place. For all the budding Data Scientists aspirant to kick-off their careers in Data Science, this current period can prove to be beneficial. They need to focus on the problems that are being faced by the whole world at large.

 

Moving even a step forward in the direction of the solution can be a great achievement and budding Data Scientists must grab all the opportunities that come their way.

Following points can be considered to start research:

  1. Automated Sanitizer Doorways

To get rid of the Coronavirus bacteria, Sanitization is a must and thinking something in this respect can prove to be a success. Automated Sanitizer doorways can be installed at the entrances of all the buildings so that nobody enters inside being infected by the virus.

  1. Body Temperature Wrist Watches

People all over the world are worried about their and their family’s health. Automatic wristwatches can be developed which can display your body temperature on the dial at all times.

Data Science

  1. Face Detecting Cameras

The Coronavirus is spreading because it is contagious. However, wearing masks can be beneficial for all human beings living on this planet. Face detecting Cameras can be developed which can automatically detect people without masks and send a ticket at their e-mail addresses.

  1. Currency Notes Sanitizer Machines

Most of Coronavirus spread has taken place because of the Currency notes. If there is a machine that can take up notes from one side and after sanitizing them, dispenses them from the other side, there is nothing better than that. These machines could be installed at Public Places and specifically in Banks. Instead of Sanitizers, UV lights can also be used. Data science will find use in all of the areas given above.

Overview

Data Science Online CourseData Science is a field that needs brainstorming at every moment. Considering the current situation, the above-mentioned points can be the base for research and these gadgets can create a monopoly in the market.

One can turn these challenging times into something productive that could lead to a stable career.

For an established Career in Data Science, analysts can take up the Data Science Course to be proficient in their areas of work.

Pursue a Career in Data Science: Why Is This The Perfect Time (COVID – Pandemic)?

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In a recent article published by LinkedIn, the organization reported a 25% increase in the number of data science professionals in India alone. On a global scale, this number is close to 37%. If you have been wanting to pursue a career in data science, then now is the right time to chase that dream, and in today’s article, we will tell you why?

Let’s get started.

Why Should You Pursue a Career in Data Science in 2020?

Post the COVID-19 crisis, the world has shifted to a completely remote work environment, and as predicted, the amount of data that is available now for collection has increased rapidly. As companies keep collecting a variety of different data sets, the need for expert data scientists are swiftly on the rise.

Career in Data Science in COVID 19 PandemicThe key concept behind this rise being, companies, need experts to analyze the data that is being collected and conclude decisions which not only contribute to short term gains but also long term business advances for the business.

Along with this, since the demand for such roles is on the rise, companies are willing to spend more to hire the best talent in the market, thus increasing the overall pay of the profession.

 

Some of the most common designations you can explore in this field include the following:

  1. Data Engineer
  2. Data Analyst
  3. AI Product Manager
  4. Data and Analytics Manager
  5. Database Administrator
  6. Business Analyst

How to Get Started With a Career in Data Science?

Now that you know the why of why you should pursue a career in data science, along with a few of the designations you should pursue, let us explore how you can kick start your career.

21st century is one of the hottest times to pursue a career in data science since millions of job openings are being posted on the regular. While having a degree in science or engineering is a good foundation to pursue a career in data science, if you truly want to stand out, one of the best things to do is to get a professional certification from any of the top recognized companies.

While one of the most obvious advantages of having a certification in data science is the edge it gives you over thousands of applications; the underrated advantage is making it easy for recruiters to spot your talent and choose for the right role.

Conclusion

2020 is a cornerstone in shaping how big data analytics will be used in the future, and thus the decisions you make today on how to pursue and shape your career in data science will determine your success in the future. With technologies such as big data, machine learning and artificial intelligence being readily used by the small to medium scale businesses around the world to increase their capabilities, the need for skilled professionals, who can swiftly analyze this data and extract meaningful insights will be on a constant rise.

In 2020, if you choose to pursue a career in data science, it can easily be estimated that your future will be secure for the next generation.

We offer data science courses at our centers in Mumbai, Thane, Pune, Ahmedabad, Jaipur, Delhi, Gurgaon, Bangalore, Chennai, Hyderabad, Coimbatore.

The Increase in Data Science Education in India, Explained!

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Data science jobs and related roles are increasingly becoming some of the most coveted jobs across industries. This is partly due to how the data science field can cut across industries to be of value, but also thanks to its resilience in tough times and the needs it has responded to.

Data ScienceOver the past few months, colleges and academic institutions have seen a significant rise in enrollment in data science courses in India. The choice is wide– potential students can choose from full-time, part-time or short and snappy online courses to either fill a gap in their skillset or experiment outside their comfort zones.

Although the potential for online learning had been realised by many even a few years ago, certain situations contributed to its exponential rise in recent times.

WFH and Remote Learning During the Coronavirus Pandemic

As lockdowns and shelter-in-place restrictions were imposed on countries all over the world, schools and colleges also had to pull down the shutters. Learning was taken online; in many institutions, exams and lessons were replaced by the opportunity to take online courses that otherwise wouldn’t have been accessible. Whether as a result of this or to fuel this trend, online education providers also reduced or waived off subscription fees and made certain courses available to all regardless of budget or geographies.

As a result, there was a surge in remote and online learning, not just from universities that students were enrolled in but also from coveted universities on the other side of the world. With the demand for data scientists expected to increase, professionals see new opportunities for growth. This, in turn, fueled the desire for upskilling and even pivoting careers as the economy slowed down.

Exposure to Global Universities and Opportunities

Online learning has made courses available in virtually any country from international universities and institutions. By making education accessible globally, online learning significantly increases the scope of the curricula as well as the teaching standards. Another benefit of this exposure is also the ability of graduates and professionals to connect with industry experts in other countries.

Data Science

Enrolling for data science courses in India that are offered by global universities is also a fantastic learning opportunity.

It exposes students to data science landscapes in other countries as well as lays bare the scope and possibilities they have well within their reach.

Once countries open up and travel restarts, students might also consider physically enrolling in these universities to explore topics further. Having a certificate or two in your portfolio indicates to the interviewer or the recruiter that you are interested and have done preliminary research which has only served to whet your appetite further.

Completely Online Courses

Until very recently, full-fledged online courses weren’t popular or even encouraged by governmental departments in India. Indian universities and colleges have not been permitted to deliver over 20 per cent of a degree online for several years. However, in the first move of its kind, the government gave the green signal for fully online courses in order to democratize education and erase barriers to learning caused by transport, accommodation and overall access.

The approach to fully online degrees is still cautious and restricted to particular subject areas. That said, it is still a welcome shift, especially for those looking to find data science jobs but lacking the access to opportunities that a lot of metropolitan cities and countries enjoy.

Conclusion

Online learning has significantly cut down barriers to entry that involve finance and access. It is a welcome step towards democratizing knowledge and making certain domains of the job market accessible to virtually anyone with a smartphone and a stable internet connection.

Seeing as data science jobs are set to increase in number, now is the ideal time for this surge in data science education, so that students are well-prepared for roles of the future.

5 Tips To Successfully Start a Data Science Job Remotely!

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While the news of mass layoffs has inundated the market, certain industries continue to hire with one eye on the future. The data science realm is one such job market. Quite a lot about the recruitment and onboarding processes have changed; this makes transitioning into a new role a lot more complicated.

Keeping all this in mind, it is imperative that, as a candidate, you take things into their own hands. You can prepare an action plan to approach the first day of your remote data science career with enthusiasm– and this post will help you along the way.

Tip #1: Ask for A Preview of the Process

Proactively arm yourself with a blueprint of the onboarding process– this is especially relevant in current remote working scenarios. Depending on the job role you’ve been hired for, your onboarding process may be elaborate or short and snappy. Understanding what it will look like for you is a great way to avoid spreading yourself out too thin in the first few days or virtually walking in without a clue. It will also highlight any gaps you may need to fill in your skillset, in which case you might need to enrol in a data science course.

Tip #2: Reach Out to Your Teammates

It’s much harder than usual to connect with first-time teammates and colleagues in a virtual environment; however, since someone has to do it, it can be you. Not only will this allow you to establish your presence and role in the team, but it will also paint a favorable picture of you in times when first impressions are rather restricted to screens and voice calls. Try to gauge how best your team works, what communication tools they use and what they do outside of work. This personal rapport will go a long way.

Tip #3: Ensure You Have Continuous Access to Technology

Technology is the backbone of the remote working process– especially so for data science roles. Before your first day, it is a good idea to take stock of all the tools you have and how you can add to them if required. You can first start with hardware– laptops or desktops, sufficient working space, additional accessories– before moving on to software. If you find that you need something to perform your role, it is always advisable that you reach out to the onboarding team and see if they can help.

Tip #4: Be Forthcoming in Your Questions and Help

A virtual environment makes it significantly more difficult to read and react to facial or virtual clues. If you’re curious about something or don’t understand a task, it is best to be forthcoming about it. This tactic leaves no grey areas or causes for misunderstanding. Similarly, don’t hesitate to offer help where you feel like you have more to offer. This tip will make you a more valued member of your team as well as cement the skills and talents you bring to the table.

Tip #5: Weigh in Your Emotional Responses, Too

When starting a data science career remotely, it is easy to feel lonely and disconnected with your teammates despite working on the same projects. However, it is always recommended that you check in with yourself periodically and understand if you are adjusting. Reach out to colleagues to build a friendly rapport with them. Take time away from the computer and stick to strict work hours as much as possible so you don’t burn out.

Industries across the board have shifted operations to a work-from-home basis in order to cease the spread of COVID19. If you’ve been lucky enough to land a remote data science job, it’s best to head into it with a determined mind and an action plan in hand!

Does Data Science Require Coding?

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Data science has been gaining significant traction over the past few years. Myriads of people working in several areas including, business and IT look to shift to this emerging career option. Moreover, individuals with immense expertise (over 10 years) want to switch to data science.

Going for a data science course begins with numerous queries such as “Does data science require coding?”, or “What are the requisites to learn data science?” It is not necessary to be an expert coder to become a data scientist. Insufficient coding skills should not preclude people from pursuing a data science career. There has been a notion recently that people need excellent coder to become a data scientist.

Undoubtedly, coding is essential in data science but that does not imply that you need to be a hardcore coder to go for a career in the field. Industry executives reckon that anyone who knows the fundamentals of coding – functions, loops, and programming logic – can flourish as a data scientist. Having coding skills already is a plus point in a data scientist job but not compulsory. Then, what about those who have never learned to code earlier? Is there any other way they can become data scientists?

Tips for Non-Coders Learning Data Science

Become an Excellent Storyteller

If you think that vital business decisions rely on data and other quantitative parameters, you are wrong. Even after a machine learning (ML) model is developed and assessment is done by people expert in coding, somebody has to present the outcomes to the shareholders who are well oblivious to programming languages or statistical models. This accents for the need of a story woven around the insights to convince shareholders quickly. You can become that individual with exceptional storytelling abilities in spite of having mediocre programming skills.

Get a Grip on GUI-based Tools

If you are not much of a coding person, then the first thing you can do is to learn the application of GUI-based tools. There are many graphical user interfaces (GUI) supported data science tools that exclude the coding aspect and offer a user-centric interface that aids everyone with the fundamentals of algorithms.

The tools are quite easy to use to develop top-notch ML models sans coding. The majority of these GUI-based tools can be accessed for free and allow you to assess and elucidate data via charts, graphs, and other special graphics.  You do not have to display exceptional coding skills to efficiently leverage these tools but instead having a knack of visualization does help.

Enhance your Credibility with Business Intelligence

If you an expert in insurance or have comprehensive experience working in the retail sector, it is good news for you. You certainly are aware of the nitty-gritty and intricacies of businesses compared to expert coders. If you are highly skilled in areas such as healthcare, and e-commerce, you will be an asset to any company. No certification of expert coding skills can beat business intelligence in a particular area for a long period. Capitalize on your domain expertise and abilities and become the data science wizard.

Final Words

At present, the success mantra to grab a data scientist job in any company is, “The More You Know, The Better It Is.” Although companies prefer professionals with specialized coding knowledge, they are increasingly channelizing their attention towards candidates exhibiting a diverse skill set.

To wrap up, you need not be a die-hard coder or programmer to become a great data scientist.

Is Data Science a Good Career in India?

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In India, a lot many academic decisions are taken on the basis of how good a career one will have after graduation. This is all the more true for STEM streams like data science and data analytics that make up some of the hottest courses (and professions) in the country.

Aspiring students are signing up for these courses across universities in large numbers. But is a data science career in India a good bet? Can you lead a prosperous and content life by becoming a data scientist? Let’s find out.

Is Data Science Career in India Promising?

The short answer is yes.

There is enough research to support that a career in data science is promising and that it is a high-demand skill right now. Why? Because it’s a stream that has seen a spike in demand globally since the start of the 2010s decade.

A lot of data is being generated, but the number of people who can process them is still relatively negligible. Although tech startups are mushrooming in every corner of the world, there is still a gap in demand and supply. New technologies, tools, and systems are being invented in the stream but there is still a lack of talented and skilled professionals who can drive them. This points to the reason why there is a growing need for data scientists.

Companies across sectors need insights to aid in their decision-making. This is why industry analysts around the world are regularly commenting on the gap between demand and supply. The demand is only growing – from behemoths like Amazon.com to smaller local startups needing data and insights – while the supply of professionals remains truncated.

A reason for the dim supply is the overall poor understanding of data science as an academic field. It is a complex course and one that requires in-depth academic proficiency and experience.

What Does a Career in Data Science Look Like?

Before becoming a data scientist in India, one should understand what the stream is and what all will entail in the profession. Only then can one make a decision if they are fit for the role.

As noted above, it is a STEM degree. And according to Northeastern University, a data scientist is a ‘computing professional who has the skills for collecting, shaping, storing, managing, and analyzing data [as an] important resource for organizations to allow for data-driven decision making.’ It then takes the example of Amazon as one of the largest organizations utilizing data science for its business development.

Take, for example, all the purchases made on Amazon.in in a single day. The data that is generated from these purchases – types of product, prices, the weight of the package, locations, etc. – are then collated to understand user buying behavior among other things. These insights are then used to better the process.

When you become a data scientist, you will begin as an analyst and mainly work with streams of huge data. It can be purchases made on an eCommerce website as in the example above or the data about Covid-19 cases in a specific geography. Whatever it may be, your role as a data scientist will involve collating and analyzing data. Tools like Microsoft Excel, Tableau, Power BI, MATLAB, and Apache Spark will be heavily used.

To conclude, a data science career in India is a good bet considering the world is quickly moving into a data-driven world. If you have an academic background in mathematics and engineering, then it’s time to take it up seriously and become a data scientist.

Sandeep’s Review of Imarticus’ Data Science Course

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We caught up with Sandeep, a recent graduate of the Post Graduate program in Analytics, for a quick chat to get his perspective on the program, the curriculum, Imarticus Learning’s placement process and more.
Tell us a little bit about yourself.

Sandeep: My name is Sandeep Singh. I recently completed my B.Sc. in Computer Science and was looking for an avenue to enhance my analytics skills and start my career.

Data Science Course in MumbaiI came across Imarticus’ data science course and, after thorough research, decided to enroll for it. I completed the course and have been placed at M Technologies through Imarticus.

How has your experience been with Imarticus Learning?
Sandeep: My experience with Imarticus Learning was super! The course focused on practical training with hands-on learning of various analytical tools and thorough practice with numerous datasets.

Looking back, I see the importance of actually applying Analytical tools and techniques to the projects I worked on because it gave me a running start when I began working.

What has changed since you joined Imarticus Learning?
Sandeep: Since the day I joined Imarticus my confidence has been boosted to a very high level. Through the practice of various analytical tools such as R, Python, SAS, Tableau, etc. I’ve come to believe in myself. My soft skills have also been elevated with the help of business communication workshops, mock interviews, and soft skill sessions throughout the course.

Would you recommend the program to someone else?
Sandeep: While researching various institutes, I came across some reviews that say Imarticus Learning is fake. Well, I wanted to see for myself and now that I have, I would definitely recommend Imarticus. If you’re looking for an institute, the first thing that comes to mind is the faculty and the learning material.

The faculty and staff are very cooperative and help you both inside and outside the classroom. The learning material is extensive and covers every aspect of data analytics. The best part is all of the lectures, notes, datasets, and quizzes are stored in an online Learning Management system and is available to students anytime, anywhere.

What do you like most about Imarticus?
The best thing about Imarticus Learning was the course content, the cooperative staff and the informative notes that are easily accessible. The resume building workshops and mock interviews definitely prepared me for the placement drives and I was able to crack the interview and land a job at M Technologies.

Looking to get started on your data science career, Speak with a counselor and get matched with the best course for you.

Where Data Science Will Be 5 years From Now?

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Data is everywhere and data science is the perfect m mixture of algorithms, programming, deploying statistics, deductive reasoning, and data interference.

Data is the amalgamation of statistics, programming, mathematics, reasoning, and more importantly, a data scientist is a field that comprises everything that related to data cleaning, preparation, and analysis.

But when thinking about where data science will be 5 years from now, it’s useful to know how data science has made its unique position in the science field over the past five years.

Why is it hard to imagine a world without data?

As of late, advanced data have become so unavoidable and essential that we’ve nearly turned out to be unwilling to deal with anything that isn’t in data. To request that an information researcher takes a shot at something that isn’t digitized. Give them a table scribbled on a wrinkly bit of paper. Or then again, to more replicate the size of what we will discuss, whole libraries of thick books, flooding with tables of data.

Stack them around their work area and they’d most likely run away and never return. It is because the digital codes of information have become essentials and valuable. We cannot do modern work without them.  That’s the reason digitalization of the data is the whole story that makes our business work easier.

What data scientists do on a regular basis?

Data scientist begins their day by converting a business case into the algorithm, analytic agenda, develop codes, and exploring pattern to calculate which impact they will have on the business. They utilize business analytics to not just clarify what impact the information will have on an organization later on, however, can likewise help devise solutions that will assist the organization in moving forward.

So if you are perfect in statistics for data science, mathematics calculations, algorithms, and resolve highly complex business problems efficiently than the position of a data scientist is a round of clock available for you.

If we talk about data science salary, the job, and salary of the data scientist always on the top on in India but all over the world. A career in information particularly appeals to the youthful IT experts due to the positive relationship between the long periods of work experience and higher data science salary.

What does a data scientist actually need?

If you want to explore your career in data science, you are in the right place. Here we suggest you how to learn data science and statistics for data science along with the kind of skills recruiters expecting from you.

First and foremost, before entering in the data science choose the best data science online course. Because with the help of online courses you can build your skills easily and efficiently. Secondly, there are many roles in data science, so pick the one that depends on your background and work experience.

So, now you have decided on your job role and subscribed to the data science online course. The next thing you need to do is when you take up the course is learn data science go through actively, always follow the instructor instructions, the reason we are saying to follow the course regularly because it gives you a clear picture regarding data science skills.

The demand for data science is enormous and businesses are putting huge time and money into Data Scientists. So making the correct strides will prompt an exponential development. This guide gives tips that can kick you off and assist you in avoiding some expensive mistakes.

Data science is the core of the business because all the operations related to the business depend on the data science from statistics to decision making companies are using data science and its story not end here.

Which is better for data analysis: R or Python or else?

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Data sciences have become a crucial part of everyday jobs. The availability of data, advanced computing software, and a focus on decisions that are analytics-driven has made data sciences a booming field. Jobs abound in this field and hence large interest also exists on which languages to learn. 

R and Python are the most popular tools for data science work. Both are flexible, open source, and evolved just over a decade ago.R is used for statistical analysis while Python is a programming language that can be termed general-purpose. These are both in combination essential for data analysis where you are involved in working with large data sets, machine learning, and creating data visualization insights based on complexities involving data sciences.

The process of Data Science:
 Very simply put the course on data science involve the four subdivisions discussed below. Let’s compare the two for the following.

Data Collection:
Python is supportive of different data formats. You can use CSVs, JSON and SQL tables directly in your code. You can even find Python solutions when stuck on Google. Rvest, magrittr, and beautiful soup packages in Python resolve issues in parsing, web scraping, requests etc.

Data can be imported from CSV, Excel, text files etc. Minitab or SPSS file formats can be converted into R data frames. R is not as efficient in getting web information but handles data from common sources just as well.

Data Exploration:
One can hold large volumes of data, sort, display data and filter large amounts of data using Pandas without the lag of Excel. Data frames can be redefined and defined throughout a project. You can clean data and scan it before you clean up empirical sense data.

R is an ace at numerical and statistical analysis of large datasets. You can apply statistical tests, build probability distributions, and use standard ML and data mining techniques. Signal processing, optimization, basics of analytics, statistical processing, random number generation, and ML tasks are easy to perform from its rather limited libraries.

Data Modeling:
 Numerical modelling analysis with Numpy, scientific computing with SciPy and the scikit-learncode library with machine learning algorithms are some excellent working features in Python.

The R’s core functionality and specific modelling analysis are rather limited and compatible packages may have to be used.

Data Visualization:
 The Anaconda enabled IPython Notebook, the Matplotlib library, Plot.ly, Python API, nbconvert function and many more are great tools available in Python.

ggplot2, statistical analysis abilities, saving of files in various formats like jpg, pdf etc, the base graphics module and graphical displays make R the best tool for statistical analysis complexities.

Before choosing, ask these questions
• Do you have programming experience?
• Do you want to do a Python course for business analytics or a business analytics course?
• Do you want to go into research and teaching or work in the industry?
• Do you want to learn ML or statistical learning in data sciences?
• Do you want to do software engineering?
• Do you want to visualize data in graphics?

Research well and you will find that depending on what functions you need both are excellent languages to learn for a career in data science.

How is MySQL Used In Data Science

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Data Science is considered to be the most sought-after profession of the 21st century. With lucrative opportunities and large pay scales, this profession has been attracting IT professionals around the world. Various tools and techniques are used in Data science to handle data. This article talks about MySQL and how it is used in data science.
What is MySQL
In short words, MySQL is a Relational Database Management System or RDBMS that use Structured Query Language (SQL) to do so. MySQL is used for many applications, especially in web servers. Websites with pages that access data from databases use MySQL. These pages are known as “Dynamic Pages” since their contents are generated from the database as the page loads.
Using MySQL for Data Science
Data science requires data to be stored in an easily accessible and analyzable way. Even though there are various methods to store data, databases are considered to be the most convenient method for data science.
A database is a structured collection of data. It can contain anything from a simple shopping list to a huge chunk of data of a multinational corporation. In order to add, access and process the data stored in a database, we need a database management system. As mentioned MySQL is an open-source relational database management system with easier operations enabling us to carry out data analysis on a database.
We can use MySQL for collecting, Cleaning and visualizing the data.  We will discuss how it is done.
1. Collecting the Data
The first part of any data science analysis is collecting the massive amount of data of data. The Sheer volume of data often causes some insights to be lost or overlooked. So, it is important to aggregate data from various sources to facilitate fruitful analysis. MySQL is capable of importing data to the database from various sources such as CSV, XLS, XML and many more. LOAD DATA INFILE and INTO TABLE are the statements mostly used for this purpose.
2. Clean the Tables
Once the data is loaded to the MySQL database,  the cleaning process or correcting the inaccurate datasets can be done. Also deleting the dirty data is also part of this step. The dirty data are the incomplete or irrelevant parts of the data.
The following SQL functions can be used to clean the data.

  • LIKE() – the simple pattern matching
  • TRIM() – Removing the leading and trailing spaces.
  • REPLACE() – To replace the specified string.
  • CASE WHEN field is empty THEN xxx ELSE field END  – To evaluate conditions and return value when the first one is met.

3. Analyze and visualize data
After the cleaning process, it is time to analyze and visualize the meaningful insights from the data. Using the standard SQL queries, you can find relevant answers to the specific questions.
Some analysis examples are given below:

  • Using query with a DESC function, you can limit the results only to the top values.
  • Display details of sales according to the country, gender or product.
  • Calculate rates, evolution, growth and retention.

If you would like to know more about MySQL and its use in Data Science join the data science course offered by the Imarticus. This Genpact data science course offers a great opening to the career opportunities in Data Science. Check out the course and join right away.