A Career Comparison Between Machine Learning and Data Analytics

Want to pursue a career in technology but stuck between choosing a career in machine learning and data analytics? Then, you’ve got company as this is the dilemma that numerous tech aspirants that we come across every day, are in! Machine learning and data analytics are two terms that echo everywhere these days.  And why wouldn’t they? After all, every organization uses them to reap their benefits! Hence, the demand for both these skill sets is perpetually high.  But that’s where the challenge kicks in.  Choosing between two highly in-demand options is practically difficult. Aspirants often find themselves at the crossroads where they are unable to decide whether to go for a machine learning course in Pune or data analytics courses in data Pune. So, when in doubt, you must have a comprehensive comparison of both to make an informed choice. Here’s one to help you get going. Comparing Machine Learning and Data Analytics – 15 Key Factors! Aspect Machine Learning Data Analytics Basic Purpose Building models that enable machines to learn and make decisions with minimal human intervention.  Deriving meaningful insights from data to aid business decisions. Responsibilities Developing ML algorithms, building training models, optimizing performance and working with massive datasets. Cleaning and analyzing data, preparing reports, dashboards and identifying patterns and trends within datasets.  Technical Skills Programming (advanced level with R, Java, and Python), understanding of ML frameworks (PyTorch and TensorFlow) and solid mathematics and statistical proficiency. Expertise in SQL, Power BI, Tableau, Excel, basic statistics and Python/R (but optional). Educational Background/Eligibility Master degree in computer science, mathematics, AI or any other related stream. Bachelor degree in economics, business, computer science, statistics or related domains.  Employers Technology companies, finance organizations, eCommerce, healthcare, pharmaceuticals, automation solutions providers, gaming companies, automobile companies, consulting firms and more. Healthcare, BFSI, retail, eCommerce, marketing firms, advertising agencies, manufacturing companies, telecom companies, consulting firms, startups, educational organizations, and many others. Learning Curve The learning curve is considered steep, considering the profound knowledge of algorithms, high-level programming and modeling skills that the profession demands. The learning level is often moderate and with fewer technical barriers to get started. Hence, people find it easier to begin a career as a data analyst. Applications Voice assistants, fraud detection, self-driving cars, etc. Marketing analytics, business intelligence, supply chain analytics, financial analytics, customer segmentation, and many others – practically everywhere as every modern enterprise looks to leverage the data advantage. Career Growth High due to the rapid evolution of AI, the increasing dependence on AI/ML for decision-making, accelerating operations and ensuring accuracy. High on account of the extensive demand across multiple industries and the need to be more data-driven in the contemporary business environment. Working Style Research-driven, and technical, involving extensive experimentation. Collaborative and business-facing focused on actionable insights. Tools and Technologies Used Some of them include TensorFlow, Jupyter and Python. SQL, Tableau, Excel, Powe BI, R and Python. Average Salary in India (Generic figures – specific numbers may vary depending on the employer’s paying capacity, the projects available with companies, market situation, location, ML skills, experience, etc.) The average salary range of an ML professional in India varies widely. However, it is in the range of Rs. 8-9 lakhs per annum. The average salary of a data analyst in India is Rs. 6-7 lakhs per annum. Common Positions Offered ML engineer, AI engineer, ML research scientist, deep learning engineer, MLOps engineer and more. Business Intelligence (BI) analyst, data analyst, reporting analyst, financial analyst, market analyst and many others. Work-Life Balance Occasionally, longer working hours on account of the complex problem-solving, model training and deployment challenges involved.  Usually, the job involves more predictable working hours, focusing in recurring reporting and analytical cycles. However, the working hours and overall work-life balance may vary based on project deadlines, pressures and the company’s culture.  Machine Learning or Data Analytics – Who Should Choose Which Career? Choose ML as a career if you, Prefer working with algorithms and building intelligent systems Have a solid programming background Love technical challenges and want to work on the latest AI advancements Intend to create predictive models, work on automation, and develop diverse applications Choose data analytics as a career if you, Love to uncover insights from data to support business decision-making Like to create impactful visualizations and communicate your findings Want to enter a more business-centric role, influencing operational decisions Understand statistics and can work with SQL, Excel, Power BI, etc., comfortably Specific career choices and decisions may differ from person to person. Whether ML or Data Analytics – Choose Ethan’s Tech to Begin Your Career Confidently! Both are thriving career options. However, the right guidance also matters. This is where Ethan’s Tech steps in. We offer comprehensive machine learning courses in Pune and data analytics classes in Pune. Our practical exposure and learning approach prepares you for challenges and opportunities across both these domains, unlocking the doors to remunerative career opportunities. Want to know more? Call us at +91 95133 92223.

How to Build a Career in Machine Learning Without a Tech Background?

How to build career in ML

Curious to learn machine learning (ML) but don’t have a tech background? As an institute for machine learning in Pune, we receive many such ML aspirants.  While some have degrees in psychology, some are economic graduates. Many others have been accounting professionals, travel consultants, marketing experts, etc. So, if you are one, you aren’t alone! You’ve thought about the right career. A career in ML is a futuristic choice. But is it possible to build a career in it without a tech degree? Let’s explore the possibilities and answer this question! What is Machine Learning – Let’s Begin with the Basics! Most of you must have already researched what ML is. But for those who haven’t or need a better explanation of ML, here’s some help. Technically speaking, ML is a subset of AI. It allows computers to learn from data and enhance their performance without explicit programming.  The technology uses data to analyze it, identify patterns, and make decisions. Any routine examples of ML? Of course, there is! Ever wondered how your favorite OTT platform recommends your next watch? Or, how your phone instantly translates languages for you, making it easier for you to understand the content? That’s precisely where ML is at play. Interesting, isn’t it? And what’s even more intriguing is that you, as an ML professional, could be the magician behind this.  But then, a voice creeps in, reminding you that you don’t have a technical degree to support your aspirations. So, can you not become an ML professional without a tech certificate? Can You Become an ML Professional Without a Tech Degree? The answer is a resounding YES! Entering ML with a technical degree is conventional. It is more natural for people with statistics, programming, and computer science to enter the burgeoning field of ML. But guess what, ML is evolving. And it is doing so at a pace quicker than you think.  This evolution is creating space for people from different domains and backgrounds so that they can contribute more meaningfully. It is because ML isn’t merely about writing code. It extends to understanding problems, devising solutions, and communicating insights. Thus, ML would have room for you even if you aren’t a technical expert.  So, let’s say you are an airline professional. Your understanding of the field and experience in it can form valuable data to enrich ML’s application in the airline domain. But how do you bridge the gap between your expertise and the door to ML? Let’s explore. Ready to dive into the world of Machine Learning? Start your data science journey today and unlock the power of intelligent insights with expert guidance! Believe in Your Potential We know this sounds generic. But what if we tell you this is the key to your entry in ML? Yes. Your non-technical background isn’t a hindrance but an advantage.  It can bring a fresh perspective, domain expertise and communication and strong problem-solving skills, valuable in real-world ML applications. Develop Technical Skills You cannot fully overlook the technical aspect as that’s integral to becoming an ML expert. But instead of being paranoid about it, make it your friend and develop the required skills. Begin with the basics like the fundamentals of Python, data structures, basic syntax, and libraries like NumPy, and Pandas for data manipulation. Besides, learn statistics concepts. You don’t need an advanced degree. However, learning concepts like hypothesis testing, probability distributions, and regression is critical. Furthermore, you must understand the core concepts behind ML, common algorithms, and concepts like bias-variance trade-off and model evaluation. Imagine you are learning a new language and this is the basic grammar for it! And where to learn all this – Ethan’s Tech’s machine learning course in Pune. Use Your Domain Expertise You aren’t a tech ninja. Fine! But you are a domain expert. And that’s one of the keys to unlocking an exciting and fruitful career in Ml. Recall every challenge you faced and how you tackled every situation. Leverage your technical expertise to provide solutions that can transform the domain. For instance, if you are a marketing expert, and you understand customer behavior well, you can learn ML to develop recommendation systems or forecast churn. Begin Small Practical experience is crucial in the early stages. So, begin with smaller projects or assignments to get hands-on experience and develop preliminary expertise. For example, you can begin with small datasets and attempt to apply ML concepts while learning. Platforms like Kaggle offer several free datasets and beginner contests. Further, focus on building a robust and diverse portfolio and showcase your platforms like GitHub. This will serve as proof that you can apply your knowledge. Join a course that offers abundant practical experience. Ethan’s machine learning training in Pune can be a great choice in this regard. Build Your Network Don’t work in isolation. ML keeps evolving with every passing day. Thus, the more you stay connected with the world, the better. You can do so by joining online communities through platforms like Reddit and LinkedIn groups to ask questions and seek answers. On the other hand, you can attend ML conferences and webinars to gain insights into the industry and broaden the scope of your knowledge. Develop Soft Skills Soft skills are a must for every profession, including ML. Some prominent soft skills to develop include problem-solving, critical thinking, teamwork, and communication.  Additionally, you must continue developing your domain expertise to ensure a constant supply of updated information and develop competent solutions. Prepared to Take the Leap? Then, enroll with Ethan’s, one of the leading names for a machine learning course in Pune.  We offer what it takes to make you an empowered ML professional even if you don’t have a technical background. We help you use your domain expertise and knowledge to become an ML professional with a difference. Our course, faculty members, learning environment, practical exposure, and placement assistance – everything is targeted to give you your first break as an ML expert. Want to … Read more