Team Sep 28, 2024 No Comments
The world always finds a way to move forward. First, it was the discovery of fire, then the agricultural revolution, the writing systems, the industrial revolution, the internet, and now, it’s generative AI.
GenAI is one of the biggest forces driving change today. It’s transforming how businesses function and reshaping how we work and live.
With such a powerful technology, what’s in demand today can become outdated tomorrow. That’s why staying updated is so important.
In this blog post, we will explore the significant generative AI trends shaping the future. You will learn what’s happening in the world and where we are headed.
A report estimates that the global generative AI market could grow at a CAGR of 42% and become valued at USD 1.3 trillion by 2032. This means we are going to see an explosion of growth in this sector. So, here are some key GenAI trends that everybody should be aware of.
This is one of the biggest trends shaped by generative AI. Almost every industry is automating repetitive and laborious tasks with AI systems. Here are some examples:
This automation is helping individuals and businesses save time, increase productivity, and reduce costs. We can now focus on creative tasks that require human thinking and problem-solving, helping us find fulfillment in work.
No doubt, one latest report predicts that generative AI could add $2.6 trillion to $4.4 trillion to the global economy annually.
GenAI is changing the way developers write code. AI tools can help you write, optimize, and debug code faster than ever possible. A McKinsey report found that generative AI can reduce completion time for writing code by up to 45 percent.
GitHub Copilot is an AI developer tool that suggests code snippets, provides context-aware coding support, and assists you in debugging effectively. This tool is powered by OpenAI Codex, which aims to translate natural language into code.
Since developers don’t have to worry about repetitive tasks, they can now focus on solving complex problems and find creative solutions. So, if you are a programmer, you must learn how to use AI and write codes faster.
Related: How To Learn Generative AI
Creating videos used to be time-consuming and costly. But generative AI is making the process much quicker. With tools like Runway, you can now generate original video content based on text prompts!
Let that sink in for a second. You can simply write a prompt and get a realistic video as output within seconds. This means you can easily make YouTube videos, market your business, or even generate clips for movies. Your imagination is the limit now.
Besides, AI tools like Vyond can help you create realistic animated character videos. You can also modify existing videos by changing colors, elements, characters, backgrounds, etc. Generative AI speeds up the editing process by automating tasks like noise reduction, color grading, and object tracking.
You can watch this video to understand career opportunities in GenAI, including roles, salaries, and future demand:
This is an interesting generative AI trend. AI assistants can now do a range of tasks, from scheduling meetings and researching information to suggesting thrilling movies to watch on the weekend. AI assistants like chatbots also help businesses support their customers around the clock with personalized and human-like responses.
Because of natural language processing, AI assistants can better understand our language and the context of conversations. This helps them understand human emotions, appear more natural, and provide relevant and accurate responses.
As they improve, AI assistants and personal robots may become a part of our daily lives. Yeah, that sounds a lot like sci-fi movies but reports estimate that there could be 244 million personal assistance robots by 2030.
Related: What’s the Future of AI
Since GenAI can automate routine tasks, it can replace humans in certain jobs. Some of those jobs are data entry, bookkeeping, telemarketing, customer support, proofreading, retail checkouts, assembly line jobs, entry-level graphic designing and content creation, etc.
A Statistica report says that the labor market will see significant transformations, which will lead to the loss of around 83 million jobs over the next five years. McKinsey estimates that US clerk jobs could decrease by 1.6 million, retail salesperson jobs by 830,000, administrative assistants jobs by 710,000, and cashier jobs by 630,000.
The good news is AI will also create new opportunities for workers to focus on more creative and strategic roles. Besides, the demand for AI and machine learning specialists, data analysts and scientists, cyber security engineers, etc, is going to increase. That’s why you need to constantly adapt to the changes to be relevant in the job market.
With the above Generative AI trends, it’s clear that this technology is here to stay and transform the world. If you want to stay ahead and be a part of this exciting revolution, learning generative AI is the key.
You can simply join Ivy Professional School’s IIT-certified Generative AI course. Created in partnership with IIT Guwahati (E&ICT Academy), this program helps you learn in-demand AI skills from IIT professors and experts from Amazon, Google, Microsoft, etc.
Over 18 weeks, you will attend live online classes and gain hands-on experience with real-world projects. From building AI-powered apps to mastering skills like machine learning, deep learning, and large language models, this course will make you a generative AI pro.
By the end of the course, you will not only earn a prestigious certification from IIT Guwahati but also get placement support to find your dream job. Visit this page to learn more about the course.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 21, 2024 No Comments
Here’s an interesting fact: The world generates 402.74 million terabytes of data every day, which will bring the total data generated this year to around 147 zettabytes.
That’s an astonishingly large amount of data. This includes all the videos uploaded on YouTube, emails sent, texts shared, Tweets on Twitter, Snaps posted on Snapchat, and so on.
If we can collect, process, and analyze this raw data, we can make data-driven decisions and solve many real-world problems effectively. This has given rise to the hundreds of data science applications we see today.
In this post, we will explore some of the most amazing data science use cases across different industries. You will understand the impact of data science and how it’s shaping the future.
Every industry has realized the importance of data science. Organizations know how it can help them make data-driven decisions, optimize processes, reduce costs, improve customer experiences, or gain a competitive edge. Here are some of those applications of data science in various fields that have seen unparalleled results:
Data science helps educational institutions keep track of the performances of teachers as well as students. For instance, you can easily analyze test papers to understand how students are performing. Similarly, you can predict the final date of course completion or how many students will drop out by analyzing the pace of teaching, engagement, attendance, etc.
Duolingo is a great example of the application of data science in education. It’s a language learning app that analyzes the strengths and weaknesses of learners to adjust the lessons and exercises. This makes learning more engaging and effective.
Besides, educational institutions can analyze industry trends and design courses that teach the latest skills. This way, students will be relevant in this fast-changing world.
You have already seen the application of data science in e-commerce. The moment you open an online shopping app like Amazon, it recommends products that you like.
That’s personalization. Amazon analyzes vast amounts of data, like browsing behavior, purchasing history, product ratings, etc., to provide recommendations based on your preference. This way, Amazon increases sales and keeps users satisfied.
And have you seen the price of products keep changing on Amazon? Well, the e-commerce giant also uses data science algorithms for dynamic pricing, which lets it change prices based on factors like demand, competition, and market trends. This helps Amazon maximize revenue.
E-commerce platforms also use advanced algorithms and machine learning models for demand forecasting. This helps them maintain an optimal inventory and avoid situations of stockouts and overstock.
One of the biggest applications of data science in finance is fraud detection. Financial institutions use algorithms that identify unusual transactional patterns, prevent fraud, and protect their assets and reputation.
Data science also helps in algorithmic trading that uses computer programming to execute trades at precise moments, taking advantage of small price fluctuations. It analyzes market trends, identifies potential risks, and, most importantly, eliminates emotions from trades.
Other use cases include providing personalized financial services, evaluating the creditworthiness of loan applicants, analyzing the performance of different investment strategies, etc.
You can watch this video to know what are data science career opportunities in finance industry:
Data science helps retailers analyze customer data, identify useful insights, and find actionable ways to keep customers engaged and interested.
For example, retailers can offer personalized product recommendations based on purchase history. This not only makes customers feel valuable but also increases the conversion rates. A McKinsey report found that 76% of consumers are more likely to purchase from a brand that personalizes.
Also, retailers can analyze online reviews, email feedback, and social media comments to understand where they are lacking and how they can improve their products and services. Similarly, they can analyze customer demand using predictive analytics and ensure their store has the optimal stock.
A few years ago, you couldn’t have imagined that the healthcare industry would use technical analysts and mathematical calculations to such an extent that it would become a necessity.
But it’s happening. Nowadays, people are using smart wearables on their wrists to collect data about their health and keep their physicians informed on a real-time basis.
Using predictive analytics, hospitals can analyze patient data to identify patterns and predict future health situations for early diagnosis.
Data science also helps in areas like drug discovery, hospital management, medical imaging, etc.
Data science applications help in optimizing the supply chain process. For instance, companies can track their goods in real-time to monitor shipments, estimate delivery times, and reduce the risk of delays or losses.
Data science also helps optimize delivery routes by considering distance, weather, traffic, and unexpected events. This not only minimizes transportation costs and makes deliveries faster but also reduces fuel consumption and carbon emissions.
You want to know about the features of this smartwatch, so you search it on Google, and the whole internet knows it. You see ads for smartwatches on YouTube, Instagram, Facebook, and almost all the apps you use. You may find a good offer this way and make a purchase.
Well, that’s an application of data science in marketing called targeted marketing. Companies analyze customer behavior and preferences to tailor their marketing campaigns to a specific group. This increases conversion rates and customer satisfaction.
Marketing professionals also analyze social media conversations to understand customer sentiments. This helps them identify strengths and weaknesses, improve products or services, and retain customers.
The incredible data science applications mentioned above show that the data science market is booming. So, if you are interested in this field, you can learn industry-relevant skills and launch your data career.
To make learning easy and quick, you can enroll in a reputed certification program like Ivy Professional School’s Data Science Course with IIT Guwahati. This course will teach you essential skills like data wrangling, analytics, visualization, machine learning, deep learning, and GenAI from scratch.
It’s a 45-week live online course where you will be mentored by IIT professors and industry experts from companies like Amazon, Google, and Microsoft. Plus, you will work on 50+ projects, earn a certification from IIT Guwahati, and be job-ready in just 45 weeks. Visit the IIT data science course page to learn more about it.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 19, 2024 No Comments
Data scientist or data analyst? What are the differences between them? Which one should you choose?
Well, those are common questions asked by people aspiring to become data experts. And I don’t blame them because these two fields are complementary and have several overlapping areas.
But at the same time, they have different roles, skill sets, and salaries that set them apart.
In this post, we will understand the difference between data scientists and data analysts. We will also see what skills they should have and what salary they earn.
These two fields have interconnected roles and are like two sides of the same coin. Data scientists ask the right questions, whereas data analysts find specific answers to those questions.
The role of data scientists is exploratory. They look for hidden patterns and develop models that predict future events. Whereas the role of data analysts is more descriptive, as they focus more on providing a description of what has already happened by looking at historical data.
A data scientist’s work involves predictive modeling, deep learning, artificial intelligence, and working with massive datasets. They often deal with uncertainty and try to uncover new patterns or relationships in data. On the other hand, data analysts analyze data and generate reports that support decision-making. Their skills revolve around data visualization, querying, and basic statistical analysis.
Both of them play a crucial part in helping businesses make data-driven decisions, but data scientists typically deal with more complexity and have a higher earning potential. We will see their salary in more detail, but let’s first understand what skill sets they need.
Related: Data Engineer vs. Data Scientist
Since they have different roles, they need different skill sets. Let’s understand this in detail:
A data scientist may have to work with complex datasets, build predictive models using ML or statistical methods, use programming to develop algorithms, evaluate models, and provide actionable insights to business stakeholders. Here are the skills they usually need:
If you want to know more about topics you should study to become a data scientist, you can check out this data science syllabus. It’s followed by Ivy Professional School’s IIT-certified Data Science Certification Course.
Also, watch this video to understand the data science journey for freshers:
A data analyst’s primary job is to analyze and visualize data to help businesses make informed decisions. They may collect and organize data, analyze it with statistical methods, identify trends, and generate reports and dashboards. Here are crucial skills they need:
You can watch this video to understand how data analysis is actually done to solve real-world problems. This is Mamta Mukherjee, an Ivy Pro student who has analyzed Netflix movies and TV shows using Excel to find valuable insights:
Businesses need data to gather important insights, enhance business performance, and evolve in the market. So, both data scientists and data analysts are in high demand. But there is a difference in the salary they earn.
The average data scientist’s salary is ₹12,00,000 per year in India. If you consider the cash bonus, commission, tips, etc., then the additional pay is ₹1,80,000 per year, which makes the average total salary of a data scientist ₹13,80,000 per year.
It’s obvious experienced data scientists earn more salary. For instance, a senior data scientist with four years of experience may earn between ₹17 lakhs to ₹31 lakhs per year.
The salary also depends on the company size. Bigger and established companies often pay more. For example, the salary of a data scientist at IBM is ₹8 lakhs to ₹20 lakhs per year, whereas in Amazon, the salary can vary between ₹9 lakhs to ₹25 lakhs per year.
The average annual salary of a data analyst in India is ₹7,00,000. Considering the cash bonus, commission, and tips, the average total pay becomes ₹8,00,000 per year.
So, it’s clear data scientists usually earn slightly more than data analysts. However, the salary depends on various factors like experience, company size, industry, location, etc. If you are a senior data analyst with four years of experience, your average annual salary could be between ₹8 lakhs to ₹17 lakhs per year.
Also, there are companies like Accenture, Amazon, Cognizant Technology Solutions, Deloitte, Google, etc., who can pay you anything between ₹4 lakhs to ₹20 lakhs per year.
But what about the future? Well, the global data analytics market is projected to grow from $51.55 billion in 2023 to $279.31 billion by 2030, with a CAGR of 27.3%. So, you can expect increasing opportunities in the job market.
Now that you know the difference between data scientists and data analysts, you can make an informed decision about what you should become.
As I said, both data scientists and data analysts are in great demand. So, if you want to land your career in any one of them, you should get a certification from a reputed institution like Ivy Professional School.
Ivy Pro is a top-ranking data science, analytics, and AI course provider in India with a legacy of 16+ years. The institute has several courses made in partnership with IIT Guwahati and IBM.
Also, Ivy Pro has trained over 29,500 learners and has helped them get jobs in Amazon, Cognizant, Deloitte, Accenture, IBM, etc. Visit this page to learn more about Ivy Pro’s courses.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 15, 2024 No Comments
Want to learn Generative AI? Well, that’s a smart choice. It’s an in-demand skill that can boost your career and help you land your dream job.
GenAI technology is spreading like wildfire. The whole world is fascinated by how it can think like humans and generate original, creative content, such as text, code, images, audio, and even videos. No wonder reports predict that the generative AI market could be valued at a tremendous $1.3 trillion by 2032.
Learning this technology now can set you apart and open up many exciting career opportunities. So, in this blog, I will tell you how to learn generative AI from scratch. You will find everything you need to get started and become a generative AI expert in 4–5 months.
Generative AI is a type of AI that can generate human-like content in response to a given prompt. It helps us write fast, generate realistic images, create quality music, do faster research, translate languages, provide 24/7 customer service, and whatnot. (Read this post to learn more about generative AI applications.)
When you start to learn GenAI, you may find topics like neural networks, machine learning algorithms, or large language models somewhat difficult. But here’s the thing: like any new skill, it starts out challenging but becomes easier as you give it some time.
The good thing is you don’t have to be a coding expert to begin. You can simply start from the basics, build your foundation, and keep learning advanced concepts. Within 4-5 months, you can be an expert. You may need the right resources and mentorship to do it easily and quickly. But as you stay consistent and curious, you will find generative AI isn’t as difficult as it seems.
Here are the most important topics you should study to master this technology. If you want more detailed topics, you can check the comprehensive generative AI syllabus.
Yes, you need to know programming. In fact, you will need a strong foundation in programming, especially in Python. So, start with the basic topics like Python data types, control flow, loops, and functions, which are essential for writing AI code. Understanding important libraries such as Pandas and Numpy for data manipulation will also help you handle data more effectively. You can watch the Python tutorial by Ivy Professional School on YouTube to develop your Python skills.
Once you know the programming basics, the next step is to understand what generative AI is and what its applications are. You also need to learn about different generative models and explore OpenAI APIs, like GPT and DALL-E. At this point, you can try to generate text with OpenAI API using Python functions.
You can’t learn generative AI without understanding the basics of machine learning. You will need to learn models like decision trees, linear models, and k-nearest neighbors (k-NN). You should study how classification and regression models work and learn to train them on different datasets. You can also learn about deep learning and understand architectures like Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), etc.
Text and image generation are two important topics in generative AI. Learn how models like GPT and tools like DALL-E generate creative text and images, respectively. You can try building apps like an AI Chatbot using Python, deploy it on a web platform, and integrate it with OpenAI’s API for real-time interactions. You can also build social media automation tools by implementing LangChain components and deploying them on a cloud platform. Next, you can learn how to build an image generation app integrating the DALL-E API.
Voice-based AI is another important area to study. Voice recognition systems analyze and process human speech, while voice generation systems like Whisper API produce natural-sounding speech. You should learn the basics of how these systems work, from feature extraction to text-to-speech algorithms. You can also integrate the Whisper API to create applications like voice assistants that can listen and speak just like humans. You should also know how to deploy the app on cloud platforms and test, monitor, and optimize its performance.
Multimodal generative AI has the capability to deal with different types of data, like text, images, audio, and video. To study this advanced topic, you should start with techniques like Early Fusion, Late Fusion, and Hybrid Fusion, which merge data from multiple sources to improve AI performance. You will also need to learn about progressive GANs, StyleGAN, and vision-and-language transformers (VLT) to handle complex multimodal tasks.
The best way to learn generative AI is to work on projects and get practical experience. You can implement the theoretical knowledge to build interesting generative AI apps like chatbots, content generation tools, voice assistants, etc. You can use APIs like OpenAI or DALL-E for tasks like text generation, image creation, or voice synthesis. The more projects you will work on, the more confident you will be. This will also build a solid portfolio to showcase your skills and expertise to employers and get a job at top MNCs.
Now, you can watch this one-hour video where I explain in detail how you can make a career in generative AI:
Here are the three best generative AI books that will help you learn what it is, how it works, what are the latest trends and how you can use it to solve real-world problems:
There are many courses that can help you learn GenAI from the basics and master advanced concepts. For example, Introduction to Generative AI is a 45-minute free course by Google Cloud that teaches the fundamentals of this technology.
However, if you are looking for a comprehensive, advanced course, you can enroll in Ivy Professional School’s GenAI Certification program in collaboration with IIT Guwahati. This live, online course will equip you with in-demand skills such as machine learning, deep learning, large language models, LangChain, RAG, and Transformers.
You will be mentored by IIT professors and receive a prestigious IIT-branded certificate. Additionally, you will work on exciting projects, building highly useful applications like an AI chatbot and a social media automation tool. And with solid career support, you will be well-prepared to launch your generative AI career. Visit the GenAI course page to learn more about the program.
Now that you know how to learn generative AI, you can begin your journey with confidence. GenAI is already helping businesses save time, reduce costs, and boost efficiency by automating repetitive tasks. This technology is only going to grow stronger over time. The sooner you master it, the more opportunities you will unlock for yourself. So don’t wait—start learning today, just as outlined in the post.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 11, 2024 No Comments
If you are searching for data analytics courses in Mumbai, you are already ahead of a lot of people. You know what you want to be and how to make it happen.
Yes, it all starts with learning. And courses are the best way to learn. They not only teach you essential technical skills but also help you know the current industry trends, improve your problem-solving skills, and find high-paying job opportunities.
So, in this post, I will share a list of the best data analyst courses in Mumbai. Whether you are a beginner or a professional, you can find the ideal course that will transform your career. But let’s first understand how much data analysts earn.
The average salary of a data analyst in Mumbai is ₹6,00,000 per year. And if you add cash bonus, commission, tips, etc., the average total pay for data analysts becomes ₹6,98,000 per year.
As you become a more experienced data analyst, your salary keeps on increasing. For instance, a senior data analyst in Mumbai with 2-4 years of experience can earn a salary between ₹8 lakhs to ₹18 lakhs per year.
The salary also depends on the industry and the company size. Companies like Cognizant Technology Solutions, Accenture, Tata Consultancy Services, Deloitte, Capgemini, etc., are some of the biggest companies that keep hiring data analysts and pay ₹5 lakhs and ₹8 lakhs per year.
You can read this post about data analyst companies in Mumbai to see which companies are hiring and what roles and responsibilities data analysts have. And now, let’s find a good course for you:
Data analysts collect, clean, and analyze data to find valuable insights and patterns, which help businesses make better decisions, increase efficiency, improve customer experience, and boost profits. And if you want to learn how to do it, these courses will teach you everything:
Provided by Ivy Professional School, it is one of the best data analysis courses in Mumbai with placement support. It has a comprehensive curriculum that focuses on industry-relevant data analytics and visualization skills.
The course teaches you how to use in-demand tools like Excel, SQL, Python, R, and Tableau. You will learn predictive modeling, business statistics, machine learning, and big data technologies like Hadoop and Spark.
This 12-14 month live online course will help you work on real-world projects, gain practical experience, and build your portfolio. You will be mentored by data science professionals from McKinsey, BlackRock, IIT, IIM, and US universities.
You will earn an industry-recognized certificate to boost your job prospects. And with additional career development support like resume-building sessions and mock interviews, you will become an ideal job candidate for data analyst roles.
Here’s an interesting example of data analytics project using Excel:
This data analytics program includes eight courses that are provided by Google on Coursera. It’s a beginner-level program that doesn’t require any degree or prior experience.
The course teaches you skills like data collection, cleansing, aggregation, analysis, visualization, etc., with SQL, R, and Tableau. You will be taught by professionals from Google and get a reputed certification. It will make you fit for jobs like data analyst, junior data analyst, and associate data analyst.
Over one lakh learners on coursera have rated this program with 4.8/5 stars, which proves the quality of the courses. And since it’s an online program with recorded lectures, you can learn it from anywhere in Mumbai at your own pace and finish it within 6 months.
This data analytics course in Mumbai is a live online program provided by Simplilearn and IBM. It covers several tools like Excel, SQL, Python, R, and Tableau.
You will learn skills like data analytics, statistical analysis, data visualization, linear and logistic regression, supervised and unsupervised learning, etc. You will also work on 20+ projects and gain hands-on experience.
Besides, you will attend masterclasses from IBM experts, participate in hackathons, and earn a valuable IBM certification. The course duration is 11 months, and it’s ideal for those who want to begin their data analyst career.
The data analytics course from IIM Skills is a comprehensive program that can launch your data career. It provides training in Excel, Python, Tableau, Power BI, and SQL, along with hands-on projects and assignments.
The course also offers a one-month internship so that you can implement your knowledge and solve practical problems. It’s perfect for beginners as well as working professionals looking to enhance their data analysis skills.
And you can complete this online course in six months. You will receive certification upon completion, lifetime access to course materials, and career support, including resume-building and job placement assistance.
The data analysis course on Udemy teaches you how to handle large datasets using Python’s Pandas library. You will learn essential techniques like sorting, filtering, pivoting, cleaning, and visualizing data.
It’s perfect for anyone familiar with Excel or Python who wants to take their data skills to the next level. It has 19.5 hours of video lectures, 32 coding exercises, dozens of real datasets for practice, and a valuable certificate of completion.
The instructor is an experienced software engineer, author, and consultant who has taught over 394,000 students and gained a 4.6/5 instructor rating on Udemy. This makes this online data science course in Mumbai worth joining.
Becoming a successful data analyst may not be that easy. But if you put in the right efforts and get the right guidance, you can do it. This is proven by Megha Julka, an inspiring alumni of Ivy Professional School.
Megha Julka went from being a Miss India Finalist to becoming a successful data analyst. Ivy Pro’s course helped her learn skills, gain practical experience, and land her dream job.
Watch this video to learn how she transformed her career, how she managed time, what experiences she had in the corporate world, and how you can become a successful data analyst:
If you also want to boost your career, you can join Ivy Professional School’s in-demand data analytics course in Mumbai. Ivy Pro is a top-ranked data science, analytics, and AI education provider in India that has trained over 29,500+ learners since 2008. Our alumni have secured placement in companies like Amazon, Accenture, Deloitte, Apple, Samsung, KPMG, Honeywell, etc. Maybe it’s your turn now.
Eeshani Agrawal holds an MS in Civil Engineering from Texas A&M University (USA) and has over 16 years of experience in data visualization, storytelling, and analytics. She has consulted for top engineering, manufacturing, and consulting firms worldwide and has coached over 9,000 professionals and students from leading institutions like IITs, IIMs, and ISI.
Team Sep 07, 2024 No Comments
Choosing a career is a big decision.
In fact, it’s one of the biggest decisions that directly impacts how you spend 33% of your life and what kind of lifestyle you have. It also impacts your income, relationships, and overall happiness.
That’s why it’s important to take your time and carefully consider your options.
However, with so many career choices, it can feel a little more overwhelming. And scary, too.
But don’t worry. We all go through this phase. And to make things easy, this post will tell you how to choose a career. You will have clarity in finding the perfect career path that you will enjoy for decades.
Let’s begin by asking the right questions. They will reveal a lot about you and help you decide on career paths you can take. So, take your time, think with a calm mind, and answer these questions carefully. I would suggest you take out a notebook and write down your answers. Ready?
The purpose of this question is to find out what you enjoy, what you are interested in, what makes you happy, and what gives you energy. Think about your hobbies. Think about what you do in your free time. Think about the tasks that make you lose track of time.
For example, if you like to solve puzzles or problems and play with numbers, data science could be a great career option for you. Similarly, if you love writing and being creative, you can be a copywriter or content creator. And if sharing knowledge and helping others learn excites you, you can be a teacher, trainer, or professor.
Doing something you love will make the work fun. So, find out what you love doing.
Just loving something isn’t enough to make a career in it. You also need skills. That’s why this second question helps you find out what skills you have or can have.
The idea is to know your strengths and weaknesses. You can study your past to see what comes naturally to you that others struggle with. Think about the past events where you excelled. You can also ask people who are close to you because other’s perspectives can be helpful.
For example, If you can easily solve complex math problems and your friends often come to you for help with their homework, then you might be good at mathematics. So, you can have career paths in fields like engineering, data science, computer science, or accounting.
If you have always been a natural leader, taking charge of projects and motivating others, you might be good at leadership and management. So, you can consider careers in business, management consulting, project management, etc.
Related: Career options after graduation
Now, you have to think about your long-term goals in life. This involves a lot of things. But here are some basic questions that will help you figure out:
For example, if you want to make a lot of money, you can choose a career path with high income skills, like artificial intelligence, machine learning, etc.
Similarly, if you want more freedom and flexibility, you can choose a freelancing career. Whatever your goals are, your career must align with them.
Now that you have answered what you love, what you are good at, and what aligns with your goals, you must have found several possible career paths. So, it’s time to consider how in-demand those career options are.
Even if you are passionate about a job, if the market for that job is shrinking, it will be really difficult to find opportunities. Hence, the idea here is that you want to be a part of a thriving market. So, look at reports about growing industries and job trends.
Forecasts can help you find roles that are likely to have long-term security and stability. For example, the global data science market is projected to grow from USD 133.12 billion in 2024 to USD 776.86 billion by 2032, with a CAGR of 24.7%. This means there will be a lot of opportunities for data scientists in the coming times.
Similarly, you can find trends and expert opinions related to your career choice to verify whether it’s worth it. And when you are done with that, we come to the next part of finding your career…
Related: Career options after engineering
You must have found some specific career options after asking those questions. Now, it’s time to act. Here, you will gather as much information as you can about a specific career. The idea is to make an informed decision. And here’s how you can do that:
Now that you know how to choose a career path, I am sure you have a clear idea of what you are made for. And if not…
We at Ivy Professional School have qualified mentors who can help you pick the right career and courses related to it. They will understand your strengths, talk to you about your career goals, and guide you every step of the way, ensuring you are making the best decision for your future.
We provide comprehensive courses on data science, generative AI, machine learning, business analytics, etc., to help you learn in-demand skills. We also provide demo classes every week, which can help you better understand a career field. You can stay connected with us on social media for updates and visit this page to learn more about Ivy Pro’s courses.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 05, 2024 No Comments
Joining a data engineering course is the best way to launch your data career.
It not only helps you learn industry-relevant skills like how to collect, store, and process data but also gain practical experience with projects and case studies. Some courses even help you land jobs through resume-building sessions, soft skills classes, and mock interviews.
But with so many courses out there, finding the right one can be overwhelming.
That’s why I have put together this list of the six best courses for data engineering to help you get started. Keep reading to find the course that suits your needs best.
A data engineer designs, develops, and maintains the systems and infrastructure needed for processing, storing, and analyzing massive datasets. They are the backbone of data-driven organizations that need data to make smart decisions. So, here are some courses that can kickstart your career as a data engineer:
This is one of the best data engineering courses that makes you a job-ready candidate. Provided by Ivy Professional School and E&ICT Academy, IIT Guwahati, this course is your opportunity to learn from IIT professors and experts from Amazon, Google, Microsoft, etc.
You can attend this 45-week course live online or in a physical classroom. Either way, you will get to interact with industry-expert instructors and clear your doubts.
The course covers high-value data engineering, AI, and ML skills with tools like Azure, Hive, MongoDB, Spark, and more. You will work on 30+ real-life projects where you will implement your knowledge and gain practical experience.
The course also provides you with essential job-oriented skills such as resume building, LinkedIn profile building, networking, communication, and success in interviews. And after you complete the program, you receive a reputed certificate from E&ICT Academy IIT Guwahati, IBM, and NASSCOM.
This 16-course series by IBM on Coursera is one of the most comprehensive data engineering certification programs. It lets you learn at your own pace and finish the courses in 6 months at a rate of 10 hours a week.
You don’t need any prior data engineering experience, as experts from IBM will teach you everything from scratch. You will learn in-demand skills like NoSQL and Big Data using MongoDB, Cassandra, Cloudant, Hadoop, Apache Spark, etc. The program also teaches you how to implement ETL & data pipelines, build data warehouses, and create BI reports and interactive dashboards.
The specialization also gives access to soft skill sessions, resume review, interview preparation, and career support. Finally, you will earn a valuable IBM certification upon completion of the courses.
This 32-week course, provided by Simplilearn and Purdue University Online, is best for professionals. It can help you master data engineering and make successful career transitions, boost career growth, or get salary hikes.
The 150+ hours of core curriculum are delivered by professionals with decades of industry experience. You will learn useful skills like real-time data processing, data pipelining, big data analytics, data visualization, data protection, data governance, etc. You also learn tools like Python, SQL, NoSQL, Snowflake, AWS, Azure, etc.
The course lets you work on 14+ projects and multiple case studies so that you can implement your knowledge in real-world business problems. Upon completion of the course, you earn a joint completion certificate from Purdue University and Simplilearn.
This is one of the best data engineering certifications, and it includes six courses provided by Google Cloud on Coursera. It’s an intermediate-level course, so you will need an understanding of query languages like SQL and how to develop apps using common programming languages.
The program starts with machine learning fundamentals, covers modernizing data lakes and data warehouses, and teaches how to build batch data pipelines. You also learn to build resilient streaming analytics systems and explore topics like smart analytics and AI.
Google Cloud provides all the training and certifications, helping you gain skills and build credibility. The course also helps you prepare for the Google Cloud Certification exam.
Related: Data Engineer vs. Data Scientist
This course by Udemy teaches the basics of data engineering, focusing on building data pipelines using tools like SQL, Python, and Apache Spark. This is an online course with 56 hours of recorded videos, two articles, and one downloadable resource.
You will learn how to write and optimize SQL queries, use Python for data processing with Pandas, build and troubleshoot data engineering applications, work with Spark SQL for big data processing, and set up and tune Spark environments on Google Cloud.
The data engineering course is perfect for IT students, database developers, BI developers, and professionals looking to transition into data engineering.
This is a two-month online program by Udacity that consists of 7 courses. The courses cover a range of key areas, including building data infrastructure, managing large datasets, and optimizing data workflows.
You will learn how to design and implement data models, construct efficient and scalable data warehouses, build ETL (Extract, Transform, Load) pipelines, and understand data lakes. Additionally, the course provides hands-on experience with tools like Apache Spark, Apache Airflow, and AWS. This way, you can apply what you learn in real-world scenarios.
This course isn’t suitable for absolute beginners. You would need a basic knowledge of relational databases, command line interfaces, and Amazon Web Service, as well as intermediate-level knowledge of Python and SQL.
Next, you can read this post to know how you can become a data engineer or watch this video:
Joining a comprehensive course lets you become an expert in a short time. Whether you are a beginner or a professional, you can go through the above best data engineering courses carefully to see which one fits your requirements. They will surely help you gain a deeper understanding of data engineering concepts, learn the industry’s best practices, stay updated with the latest technologies, and accelerate your career.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.
Team Sep 03, 2024 No Comments
“Data engineer or data scientist? Who earns more? Which one is better?”
If you have these questions in mind, you are in the right place.
You see, data engineers build and maintain architectures required for data collection and storage, whereas data scientists analyze the data to find insights and help organizations make smart decisions.
In this post, I will discuss more about the differences between data engineers and data scientists.
You will know what they do, what skills they use, how much they earn, and what career opportunities they get. This will help you decide which career path you should choose.
Though data engineers and data scientists both work with data, there are many differences in their roles and responsibilities. Let’s understand that…
A data engineer basically designs, builds, and maintains the systems and infrastructure that collect, process, and store large datasets.
They create data pipelines, build ETL processes, and manage databases and data warehouses. All this is to make sure the data is clear, reliable, and accessible for analysis.
Data engineers work closely with data scientists, business analysts, and other stakeholders to understand the organization’s data requirements and fulfill them.
They use tools like Apache, Hadoop, Spark, Kafka, SQL, and NoSQL databases (e.g. MongoDB). Their work involves a lot of coding and includes programming languages like Python, SQL, Scala, and Java.
A data scientist simply analyzes data collected by data engineers and finds valuable insights that can solve business problems. They may use statistical methods, machine learning and AI algorithms, predictive models, etc., to solve specific business problems.
They also have to communicate their findings through visualizations like dashboards, charts, graphs, etc. The purpose is to help non-technical stakeholders identify trends and patterns in complex data and make smart decisions.
Data scientists use tools like Adv. Excel, Python, R, SQL, TensorFlow, PyTorch, scikit-learn, and data visualization tools like Tableau and Power BI. Their work is more about statistics, maths, analytical thinking, and problem-solving.
Since their work is different, it’s obvious they will have different sets of skills (although there may be some overlaps):
To be a successful data engineer, you need to learn these skills:
Ivy Professional School’s IIT-certified Cloud Data Engineering course not only helps you learn the above in-demand skills but also lets you work on industry projects to gain hands-on experience. This is a 45-week live online course where you learn to use tools like Azure, Hive, MongoDB, Spark, etc., from industry experts.
You also get mentored by IIT Guwahati professors and professionals in companies like Amazon, Google, Microsoft, etc. Besides, you will earn certifications from IIT Guwahati, IBM, and NASSCOM to boost your credibility as a data engineer.
And to understand what topics you must study to become a data engineer, you can go through the latest data engineering syllabus.
You need to gain the following skills to become a successful data scientist:
If you want to master the above skills, you can join Ivy Professional School’s IIT-certified Data Science and AI course. Again, it’s a 45-week live online course, so you can join from anywhere, engage with instructors, and solve your doubts instantly.
You will be trained by experts and exposed to real-world problems through 10+ projects, 40+ case studies, and 50+ assignments. Ivy also provides CV-building sessions as well as mock interview sessions to make you completely job-ready.
And to understand what topics you must study to become a data scientist, you can go through the latest data science syllabus.
Let’s look at the most asked question about this topic: “Do data scientists earn more than data engineers?” I will simply provide you the average numbers, but remember that the salary is highly influenced by factors like location, industry, company size, etc.
The average salary of a data engineer in India is ₹8,50,000 per year. That’s the base pay amount. If you consider the bonuses and commission, the average total pay for a data engineer becomes ₹9,50,000 per year.
The salary increases as you gain more experience. For instance, the salary of a senior data engineer with 2-4 years of experience can be anything between ₹11 lakhs to ₹24 lakhs per year. And if you are the lead data engineer with 5-7 years of experience, you can expect to earn ₹ 18 lakhs to ₹32 lakhs per year.
The average salary of data scientists in India is ₹12,00,000 per year (base pay). And again, if you consider the bonuses and commission, the average total pay of data scientists is ₹13,60,000 per year.
Senior data scientists with 2-4 years of experience can earn between ₹16 lakhs to ₹30 lakhs per year. Whereas lead data scientists with 5-7 years of experience can earn ₹22 lakhs to ₹38 lakhs per year. So, it’s clear that data scientists earn more than data engineers.
The world is generating more data than ever. In fact, we produce 402.74 million terabytes of data every day. This means businesses need skilled individuals to make sense of the massive amounts of data. And that’s why both data engineers and data scientists are in high demand.
Data engineering provides you amazing career opportunities. The global big data and data engineering services market is expected to be valued at USD 163.80 billion by 2030, growing at a CAGR of 15.48%.
This type of growth means there will be more demand for skilled data engineers in the coming years. And it’s already happening: the demand for data engineers with over six years of experience increased from 27% in 2023 to 38% in 2024.
You will find more and more opportunities in data warehouse engineering, ETL development, data pipeline architecture, big data engineering, and cloud data engineering roles. All these are focused on the technical aspects of data management, like data extraction, transformation, loading, data integration, and data quality.
Related: Is Data Engineering a Good Career?
Data science is one of the high-income skills that is in huge demand. You can open a job platform like GlassDoor or LinkedIn, and you will find over 10,000 data science jobs in India.
And the demand is going to explode in the coming days. This is evident from a report that predicts that the global data science market is expected to grow from $133.12 billion in 2024 to $776.86 billion by 2032.
Data science provides you many opportunities for growth and advancement. You can be a data analyst, business intelligence analyst, machine learning engineer, data storyteller, and so on. The rise of AI will open new applications like natural language processing, computer vision, predictive analytics, etc.
So, both data engineering and data science provide exciting opportunities to grow and succeed in your career. Now comes the most important question…
Related: 7 Reasons to Become a Data Scientist
There is no right answer to that. That’s because what may be better for others may not be a better choice for you. So, the real question is- what do you enjoy, and what kind of work excites you?
If you love coding, solving technical problems, and building systems, data engineering might be the better option for you. As a data engineer, you will design, build, and maintain architectures like data pipelines and databases to collect, store, and organize data.
On the other hand, if you love playing with numbers, analyzing data, and finding useful insights, data science could be a better choice for you. As a data scientist, you will basically study a problem from different angles, think analytically, develop solutions, communicate it to business stakeholders, and help businesses make smart decisions.
Whatever excites you the most is the right career option for you.
Prateek Agrawal is the founder and director of Ivy Professional School. He is ranked among the top 20 analytics and data science academicians in India. With over 16 years of experience in consulting and analytics, Prateek has advised more than 50 leading companies worldwide and taught over 7,000 students from top universities like IIT Kharagpur, IIM Kolkata, IIT Delhi, and others.