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Data Analytics, Data Science, IT Education

Data Analytics vs Data Science: Which Course Should You Choose in Pune in 2026?

Surekha Nanekar / September 16, 2026

Selecting data analytics or data science may seem like an easy thing to do until one starts reviewing course offerings. Both include topics on data, programming in Python, career options, and high salaries; however, the kind of preparation each field requires is different. In one case, you will be able to begin working with dashboards, SQL, and business reports soon, while the other way will involve going deep into Python, statistics, machine learning, and AI. Thus, asking the question of which is better, data analytics vs data science, is not enough on its own. What matters more is which option suits you in terms of your background, the speed of learning, and the nature of the work you will enjoy doing. Data Analytics vs Data Science: which should you choose in Pune? What Data Analytics Actually Is, and What the Daily Work Looks Like Picture a Tuesday morning in a Kharadi office. The sales manager walks into a meeting saying, “Revenue dropped last quarter. What happened?” Nobody expects you to deploy a neural network to answer that question. You grab the raw transactional logs, clean up missing values, compare sales across regions or products, and pinpoint where the drop came from. Your entire day revolves around SQL queries, Excel sheets, interactive dashboards, and talking to team leads. You might spend two hours fixing a broken SQL join, build a Power BI dashboard before lunch, and then present those numbers to someone from sales or operations before heading home. Finding a number is useless if you cannot explain what it means for the business. The tools are approachable Excel handles small datasets quickly. Power BI turns those raw rows into visual dashboards, while database tools like PostgreSQL help analysts pull exact records instead of fighting with massive spreadheets. Skip the fear of needing to code like a software engineer on day one. SQL uses straightforward English syntax, and Python only becomes necessary as your analysis deepens. Teknowell’s current 6-month Applied Diploma in Data Analytics includes Excel, Power BI, Python, databases, data handling, cleaning, exploratory analysis and practical projects, along with interview preparation. Who gets into analyst roles? Non-technical graduates switch into this field constantly. A BCom or BBA graduate who already understands sales, finance, or operations can pick up data tools and become useful immediately. If you are coming from a non-IT background and wondering whether getting an IT job without previous experience is realistic, this guide on how to get an IT job without experience in Pune covers the entry-level routes in more detail. Pune is packed with companies that rely on reporting and business intelligence, especially around Kharadi, Viman Nagar, and Hinjewadi. You do not need to rewrite software architectures. Taking messy operational data and turning it into clear decisions is what actually gets you hired. What Data Science Actually Is, and Where It Differs Take that exact same messy dataset, but hand it to a data scientist. They do not stop at why customers left last quarter. They ask which active customers will drop out in the next 90 days and whether a predictive model can catch them first. Predictive questions change your daily workflow completely. Python dominates your screen as you focus on data preparation, statistical formulas, machine learning algorithms, and model evaluation. A data scientist might spend three straight days tuning hyper-parameters, testing train-test splits, and checking if their model is overfitting. The programming reality Writing code is non-negotiable here. You must get comfortable writing, debugging, and refactoring Python scripts that leverage libraries like Pandas, NumPy, and Scikit-learn. Math stops being optional very quickly. No computer science degree is required, but you cannot dodge probability distributions, linear algebra, and statistical hypothesis testing when evaluating why a model fails. Teknowell’s current 9-month Data Science course covers Python, Pandas, NumPy, Machine Learning, Deep Learning, AI basics, data visualization and predictive modeling, with practical work using real datasets. What jobs can come after it? Completing this training opens up technical roles like Junior Data Scientist, Machine Learning Associate, AI Analyst, Data Science Associate, and Predictive Analytics Associate. If you want a closer look at what companies are actually expecting from data science candidates, this guide on whether data science is still a good career in 2026 breaks down the current job market, skills and hiring expectations. Signing up for a course does not automatically transform you into a senior scientist overnight. Your final designation depends on your portfolio, past education, and interview performance. How you prepare determines the market value you command. The Salary Difference: What Verified Data Actually Says Ignore the hype on social media. Real salary offers are never as clean as marketing brochures make them look. Naukri Campus released data in February 2026 showing entry-level data analysts in India earn around ₹3 to ₹6 LPA, while entry-level data scientists start around ₹5 to ₹10 LPA. They also report that data science roles generally carry a 20% to 30% pay premium over traditional analytics roles. Glassdoor India reports a similar baseline. Current submissions show data analyst base pay ranging from ₹5 to ₹10 LPA, averaging roughly ₹6 LPA. Market figures fluctuate based on employer size and specific candidate skill sets. Experience Data Analyst Data Scientist Fresher, 0 to 2 years ₹3 to ₹6 LPA ₹5 to ₹10 LPA Mid-level, 3 to 5 years ₹6 to ₹12 LPA ₹10 to ₹18 LPA Senior, 6+ years ₹12 to ₹20+ LPA ₹18 to ₹30+ LPA These ranges reflect broader Indian market trends reported by Naukri Campus and Glassdoor India. Actual offers vary depending on the hiring company, candidate background, and specific domain experience. When the salary gap is misleading High averages do not guarantee every data scientist beats every analyst. An analyst who masters SQL queries, designs complex Power BI dashboards, and understands healthcare or retail operations often earns more than a mid-level data scientist stuck writing basic Python scripts. A starting job title never locks your lifelong income trajectory. Specialized industry expertise and practical problem-solving dictate long-term growth

Is Data Science Still a Good Career in 2026? The Honest Answer

Everyone hears the same story in different versions. Data science is the future. Then data science is saturated. Then AI will take over everything anyway. All three statements are floating around at the same time, and most people trying to decide a career path in 2026 are stuck somewhere between excitement and confusion. The reality is less dramatic but more important: the field hasn’t disappeared, it has matured. What has changed is not the demand for data science, but the type of people who succeed in it. So the real question is not whether data science still exists as a career. It’s whether the version of data science most people are preparing for still exists. If you are still deciding between the two fields, our comparison of data analytics vs data science in Pune will help. Quick Verdict: Is Data Science Still Worth It in 2026? Yes, data science is still a good career in 2026, but only if the skill set matches what companies are actually hiring for now. According to NASSCOM’s State of Data Science & AI Skills in India report, India is expected to require more than 1 million AI and data professionals by 2026, while the current talent supply still falls significantly short. The field is not saturated at the top end. Generic profiles are struggling. Strong AI, ML, analytics, and domain-focused profiles are still getting hired aggressively. What the data actually says: the data science job market in India in 2026 The conversation around data science often becomes emotional very quickly. One side says there are unlimited jobs. The other says nobody is hiring freshers anymore. Neither side is fully right. The market is growing, but the hiring expectations are far higher than they were in 2020 or 2021. Data science is no longer treated as an “extra tech skill” inside companies. It has become part of core business operations across banking, healthcare, logistics, e-commerce, manufacturing, insurance, and fintech. Businesses are making decisions around prediction systems, customer behaviour analysis, automation, and AI-assisted workflows. What changed over the last few years is not demand, it’s the definition of a “hireable” candidate. Companies are moving away from generic certification-based hiring and looking more closely at applied skills, project quality, domain understanding, and AI readiness. Another major shift is that companies are hiring fewer “general learners” and more specialised profiles. Businesses increasingly want people who understand machine learning, AI workflows, analytics, and business interpretation together. The market did not disappear. It became stricter. And that’s exactly why the field still has strong long-term potential for serious learners. Will AI replace data scientists? The real answer (not the fear-based one) This is the question behind almost every career doubt in 2026. Students see ChatGPT writing SQL queries, AI tools building dashboards automatically, and platforms generating reports in seconds. Naturally, the next thought becomes: if AI can already do this, why would companies hire data scientists at all? Because most real data science work was never just about writing queries. What AI is replacing AI is definitely reducing repetitive and mechanical work: Entry-level roles that depend only on these tasks are becoming weaker every year. That part is real. What AI is increasing the demand for At the same time, AI is creating entirely new layers of demand: Companies still need humans to decide: AI can generate answers. It still cannot understand organisational context the way experienced professionals do. The biggest misunderstanding is assuming AI replaces the entire profession because it automates one layer of the work. It doesn’t. It simply pushes the industry upward. And that’s why the strongest professionals in 2026 are the ones learning how to work with AI instead of competing against it. AI will not replace data scientists.It will replace data scientists who refuse to use AI. Is data science saturated in India? Separating fact from LinkedIn noise The word “saturated” gets thrown around constantly online, but very few people explain what exactly it means. The field itself is not overcrowded. The entry-level clone profile is. For years, thousands of learners followed almost identical paths: Eventually, hiring managers started seeing the same resume repeatedly. That created frustration on both sides. Students felt there were no jobs. Recruiters felt there were very few genuinely skilled applicants. According to LinkedIn’s Economic Graph and workforce insights, AI Specialist, Data Analyst, and ML Engineer roles continue to remain among the fastest-growing job categories in India. So the issue is not a lack of hiring. The issue is a lack of differentiation. The strongest candidates in 2026 are usually the ones who combine: That combination is still surprisingly rare. Why do some data science students still struggle to get jobs This is the part many institutes avoid saying openly. A large number of learners still rely entirely on tutorial-based portfolios. Their resumes look polished, but the projects often solve no real business problem. Many candidates also jump directly toward “AI Engineer” titles without first building strong analyst fundamentals. Communication becomes another major barrier. Some students can build models but cannot explain what the model actually means to a business stakeholder. The industry is not rejecting data science. It is rejecting interchangeable profiles. Data science salary in India in 2026: What you can realistically earn Salary expectations shape most career decisions, but the range in data science is unusually wide because skills matter more than titles. Salary by experience level Experience Role Salary range (India) Notes 0–2 years Data Analyst / Jr DS ₹5–8 LPA Strong portfolios reach ₹10 LPA 2–5 years Data Scientist / ML Engineer ₹10–18 LPA Gen AI adds 30–40% premium 5–8 years Senior Data Scientist ₹18–28 LPA Domain expertise critical 8+ years Lead / Principal ₹25–50 LPA+ Product companies exceed this Remote global Data roles (US/EU clients) ₹25–45 LPA Contract-based high upside What increases salary faster than experience A pattern is becoming obvious in hiring data across India: Gen AI skills, MLOps exposure, and cloud deployment knowledge increase starting salaries more than just experience does. A