Prateek Agrawal Aug 29, 2026 No Comments
If you have been researching data careers, chances are you have typed “best data engineering course with placement” into Google more than once. That is a good instinct, data engineering has quietly become one of the highest-paying, most stable roles in the Indian tech industry, and picking the right program can make or break how quickly you land your first job in the field. In this guide, we will break down exactly what to look for, what a strong curriculum should include, and how a placement-focused program can take you from beginner to hired data engineer.
Data engineers build and maintain the pipelines, warehouses, and infrastructure that allow organizations to collect, clean, and use data at scale. Every dashboard a business analyst opens, every machine learning model a data scientist trains, and every recommendation engine an e-commerce site runs depends on a data engineer having built reliable systems behind the scenes.
As Indian companies — from fintech startups to global capability centers — accelerate their adoption of cloud computing, real-time analytics, and generative AI, the demand for skilled data engineers has surged well beyond the supply of qualified professionals. Job portals consistently list data engineering among the fastest-growing tech roles, and salaries have risen accordingly. This gap between demand and skilled supply is precisely why so many career switchers and freshers are now searching for the best data engineering course with placement rather than trying to piece together skills through scattered YouTube tutorials.
The catch is that data engineering is not a “watch a few videos and you’re job-ready” kind of field. It requires structured learning across programming, databases, distributed systems, cloud platforms, and orchestration tools — which is exactly why a well-designed, mentor-led course with genuine placement support matters so much.
Not every course that advertises “placement assistance” delivers on it. Before enrolling, evaluate a program against these criteria:
When you filter your shortlist using these six criteria, you naturally narrow down to programs that can genuinely call themselves the best data engineering course with placement — as opposed to marketing copy dressed up as a guarantee.
A placement-ready data engineering curriculum should be built in layers, moving from fundamentals to production-grade systems:
A course missing several of these layers is unlikely to prepare you for real interviews, regardless of how it markets itself. This is the benchmark to hold any program to when you’re comparing options for the best data engineering course with placement.
At Ivy Pro School, the data engineering program is designed around exactly the curriculum layers above, taught by practitioners rather than purely academic instructors. A few things set the program apart:
Combine institutional pedigree with a genuine placement engine, and you get a program that doesn’t just teach data engineering concepts — it engineers outcomes. That combination of curriculum depth, mentorship, and accountability is what makes this the best data engineering course with placement for learners who are serious about breaking into the field, whether they’re fresh graduates or working professionals pivoting from adjacent roles like software development, QA, or business analytics.
The phrase “placement assistance” gets used loosely across the ed-tech industry, so it’s worth understanding what genuine placement support actually looks like:
This is the layer that separates a course from a career transition. Plenty of programs teach Spark and Airflow reasonably well; far fewer put in the structural work to get you hired afterward. Spend as much time scrutinizing this section of a program as you do the technical syllabus, since it is usually the deciding factor between a completed certificate and an actual job offer.
Even motivated learners waste months and money by making avoidable mistakes during the selection process:
Avoiding these pitfalls up front saves both time and tuition, and it usually points you straight toward programs with a genuine track record of outcomes rather than marketing promises.
Data engineering offers several career trajectories once you have foundational skills in place:
Salaries in this field have grown steadily as demand has outpaced the supply of trained professionals, with entry-level data engineers in India commanding competitive packages compared to many other tech roles, and experienced engineers with cloud and big data expertise commanding significantly higher compensation. This growth trajectory is a major reason so many professionals are actively pursuing structured training instead of relying on self-study alone — the field rewards demonstrable, hands-on expertise, and employers pay a premium for candidates who can prove it through real pipeline projects and credible certification rather than a list of watched tutorials.
Beyond base salary, data engineers often see faster promotion cycles than adjacent roles, since the skill set transfers directly into senior positions like data architect, platform lead, or engineering manager for data infrastructure teams. Companies also increasingly value data engineers who understand the basics of machine learning workflows, since MLOps and AI infrastructure roles are growing out of traditional data engineering teams as generative AI adoption accelerates across industries.
Both formats can lead to the same outcome, but they suit different learners:
Classroom or hybrid learning works well if you value in-person mentorship, peer accountability, and a structured daily routine — which is why Ivy Pro School maintains physical centers in Kolkata, Bangalore, and Delhi alongside its online offering.
Live online learning works well for working professionals who need flexibility around existing job commitments but still want real-time instructor interaction rather than pre-recorded, self-paced content.
What matters more than the format is whether the program includes live doubt-clearing sessions, project reviews, and direct placement support — self-paced, pre-recorded courses with no human accountability structure rarely deliver strong placement outcomes, regardless of how comprehensive the video library looks on paper.
Recruiters screening resumes for data engineering roles tend to look for a specific, recognizable toolkit, and a strong course should give you working proficiency — not just theoretical familiarity — across most of the following:
A program that only teaches two or three items from this list will leave graduates under-prepared for technical interviews, where hiring managers routinely test candidates on how these tools interact rather than testing each one in isolation. This is also why capstone projects matter so much — they force learners to combine several of these tools into a single working pipeline, which is a far closer approximation of real job responsibilities than isolated exercises ever could be.
Q: How long does it take to complete a data engineering course with placement? Most structured programs run between 6 and 9 months, depending on whether you’re studying full-time or alongside a job, with placement support typically continuing for several months after course completion.
Q: Do I need a coding background to enroll? No. A good program builds programming and SQL skills from the ground up, though basic logical reasoning and comfort with computers will help you move faster through the early modules.
Q: Is data engineering harder to break into than data science? Not necessarily — it’s a different skill set, more focused on systems, pipelines, and infrastructure than statistics and modeling. Many learners find the more engineering-oriented, less mathematically heavy curriculum easier to grasp.
Q: What makes Pay After Placement different from a regular fee structure? It shifts financial risk away from the learner and ties the institute’s revenue directly to your getting placed, which tends to correlate with stronger, more hands-on placement support.
Q: Can working professionals switch careers into data engineering? Yes — professionals from software development, QA, database administration, and even business analytics backgrounds frequently transition into data engineering roles, often bringing relevant domain knowledge that strengthens their candidacy.
Q: Which cloud platform should I learn first for data engineering? Most learners start with AWS, since it has the largest market share in India’s hiring landscape, though a strong program will expose you to the core concepts of Azure and GCP as well, since many enterprises run multi-cloud environments.
Q: Does Ivy Pro School offer both classroom and online batches for data engineering? Yes — the program runs at physical centers in Kolkata, Bangalore, and Delhi, alongside live online batches for learners who need remote flexibility while keeping real-time instructor interaction and doubt-clearing support.
Choosing the best data engineering course with placement comes down to three things: a curriculum that genuinely covers the tools employers use, instructors who have worked with those tools in production, and a placement system that goes far beyond a vague promise in the brochure. Ivy Pro School’s data engineering program combines all three — an 18-year institutional track record, NASSCOM and IBM industry partnerships, and a Pay After Placement model designed to keep the institute accountable for your outcome, not just your attendance.
If you’re serious about building a career in data engineering, it’s worth attending a free career counseling session at Ivy Pro School’s Kolkata, Bangalore, or Delhi centers, or joining a live online orientation, to see the curriculum, meet instructors, and understand exactly how the placement process works before you commit.
Prateek Agrawal Aug 22, 2026 No Comments
If you’re weighing your next career move, chances are you’ve typed some version of “data science vs data analytics 2026” into a search bar more than once. It’s one of the most common questions among students, working professionals, and career switchers trying to break into the data industry — and for good reason. Both fields are booming, both pay well, and both are often confused with one another. But they are not the same career path, and picking the wrong one for your goals, skills, and personality can cost you months of misdirected effort.
This guide breaks down the real differences between the two fields — the skills, tools, salaries, career trajectories, and day-to-day realities of each role — so you can make a confident, informed decision about which one suits you in the year ahead.
Data analytics is the practice of examining existing data to answer specific business questions. A data analyst looks backward and sideways — what happened, why did it happen, and what does it mean for the business right now? Analysts work heavily with dashboards, spreadsheets, SQL queries, and visualization tools like Power BI and Tableau to turn raw numbers into decisions marketing, sales, and operations teams can act on.
Typical data analyst responsibilities include:
Data analytics is often the more accessible entry point into the data world. The learning curve is gentler, the tools are more business-friendly, and the career transition — especially for commerce, business, or non-engineering graduates — tends to be faster. Many analysts also enjoy a shorter feedback loop: a dashboard built on Monday can influence a marketing decision by Friday, which makes the day-to-day work feel immediately tied to business outcomes rather than abstract experimentation.
Data science goes a step further. Instead of just interpreting what already happened, data scientists build predictive models that estimate what’s likely to happen next. This involves statistics, machine learning, and programming, along with a strong intuition for business context. A data scientist might build a model that predicts customer churn, forecasts demand, or powers a recommendation engine.
Typical data scientist responsibilities include:
Where analytics is about interpreting the past, data science is about predicting and shaping the future. That extra layer of complexity is exactly why so many learners struggle to tell the two roles apart — on the surface they look similar, but underneath they demand very different skill sets, tools, and ways of thinking about problems.
Let’s lay out the head-to-head comparison that defines this landscape heading into the new year.
| Aspect | Data Analytics | Data Science |
| Core focus | Descriptive & diagnostic (“what happened”) | Predictive & prescriptive (“what will happen”) |
| Key tools | SQL, Excel, Power BI, Tableau | Python, R, TensorFlow, Scikit-learn |
| Math depth | Basic statistics | Advanced statistics, probability, linear algebra |
| Coding requirement | Light to moderate | Heavy, production-grade code |
| Typical output | Dashboards, reports | ML models, predictive systems |
| Entry difficulty | Easier for beginners | Steeper learning curve |
| Career ceiling | Analytics Manager, BI Lead | Data Science Lead, ML Engineer, AI Architect |
This table is the fastest way to visualize the decision, but numbers on a page don’t tell you which path actually fits your temperament, background, or long-term ambitions. Let’s go deeper.
To succeed as a data analyst in 2026, you’ll want to build proficiency in:
To succeed as a data scientist in 2026, the bar is higher and more technical:
This is where the data science vs data analytics 2026 gap becomes most obvious: analytics rewards business fluency and tool proficiency, while data science demands genuine engineering and mathematical depth.
Compensation is one of the biggest deciding factors for most career switchers, and it’s a major part of any honest comparison between these two fields. Numbers shift year to year as demand fluctuates, but the general gap between the two roles has held steady for the past several hiring cycles, and 2026 is no exception.
Data Analyst salaries (India, 2026 estimates):
Data Scientist salaries (India, 2026 estimates):
Data science roles generally command a salary premium because of the technical depth and business impact of predictive modeling. That said, experienced analysts who specialize — particularly in domains like fintech, healthcare, or supply chain — can close much of that gap, and senior analysts with strong SQL, dashboarding, and stakeholder-management skills are rarely short of opportunities. Salary isn’t the only factor to weigh here, but it’s a meaningful one, especially for career switchers trying to calculate the return on investment of several months of upskilling before they commit their time and money.
A common misconception among students comparing these two careers is that one path is strictly “better” than the other. In reality, they lead to different destinations, and the “better” choice is entirely a function of what kind of work you actually enjoy doing every day:
Data Analytics career path: Data Analyst → Senior Analyst → Analytics Manager → BI Lead / Head of Analytics. Many analysts also pivot laterally into product management, growth, or operations roles, since the business-facing skills translate well.
Data Science career path: Data Scientist → Senior Data Scientist → ML Engineer / AI Specialist → Data Science Lead → Chief Data Officer. Data scientists with strong engineering chops can also transition into MLOps, AI product development, or research-oriented roles.
Notably, in 2026, the boundary between the two paths is getting blurrier. Many analytics roles now expect basic Python and even light machine learning knowledge, while many data science teams rely on analysts to handle the exploratory groundwork before a model gets built. Understanding this convergence is essential to making a smart decision about which career to pursue — the “pure” versions of either role are becoming rarer, and hybrid skill sets are increasingly the norm across hiring listings, job descriptions, and internal role definitions at both startups and large enterprises.
The industries hiring for these roles overlap heavily, but the specific job titles and day-to-day expectations shift depending on the sector.
Industries that lean heavily on data analysts:
Industries that lean heavily on data scientists:
Almost every industry now employs both roles side by side, often on the same team. A retail company, for instance, might have analysts monitoring daily sales dashboards while data scientists build the demand-forecasting model those dashboards eventually feed into. Rather than competing career tracks, the two roles frequently function as two halves of the same data pipeline — which is worth keeping in mind if you’re choosing based purely on “which field is more in demand,” since the honest answer is: both are, in nearly every sector.
There’s no universal right answer to this question — the right path depends on your background, strengths, and goals. Here’s a practical way to decide.
Choose Data Analytics if you:
Choose Data Science if you:
If you’re still unsure, a good rule of thumb: start with data analytics fundamentals — SQL, Excel, visualization — since these skills are foundational to both paths anyway. Once you have that base, you can decide whether to specialize further into analytics or level up into data science with Python, statistics, and machine learning.
No comparison of these two careers would be complete without addressing generative AI’s impact on both roles. GenAI tools are now assisting analysts with writing SQL queries, generating dashboard summaries, and automating repetitive reporting tasks — effectively raising the productivity bar for entry-level analysts. On the data science side, GenAI is changing how models get built entirely, with teams increasingly fine-tuning large language models, building retrieval-augmented generation (RAG) systems, and integrating AI agents into existing data pipelines.
This means that regardless of which side of the divide you choose, a working understanding of generative AI is quickly becoming a baseline expectation rather than a nice-to-have. Professionals who ignore this shift risk falling behind peers who actively upskill in GenAI alongside their core analytics or data science training.
Making the right call in this career debate is only half the battle — the other half is getting trained by people who actually understand where the industry is heading. Ivy Pro School is an 18-year-old institute with IIT Guwahati E&ICT Academy certification, partnerships with NASSCOM and IBM, and a track record of 37,500+ alumni placed through 500+ hiring partners. With a Pay After Placement model, you don’t pay full fees until you’re actually employed — which keeps the incentive structure aligned with your success, not just enrollment numbers.
Whether you’re leaning toward analytics or data science, Ivy Pro School offers two standout ways to start exploring hands-on, right now:
🎓 FREE Live Gen AI Session — Experience two full classes before you commit. If the GenAI angle of this comparison caught your attention, this is the easiest way to see what building with generative AI actually feels like, with zero upfront cost for the trial classes.
🎓 AI for Entrepreneurs Course with Prateek Agrawal — Built for professionals who want to apply AI and data skills directly to business growth, rather than purely technical roles. If your interest in data science or analytics is really about making smarter business decisions, this course bridges that gap directly.
Both programs are backed by Ivy Pro School’s Pay After Placement model, and a proven placement network — so you’re not just learning in a vacuum, you’re building toward an actual outcome.
Is data science better than data analytics in 2026?
Neither is objectively “better” — data science typically offers a higher salary ceiling and more technical depth, while data analytics offers a faster entry point and strong business-facing career growth. The right choice depends on your skills and goals.
Can I switch from data analytics to data science later?
Yes. Many professionals start as data analysts, build strong SQL and business analysis foundations, and later transition into data science by learning Python, statistics, and machine learning. This is one of the most common — and practical — paths in the industry.
Do I need a coding background for data analytics?
Not necessarily. Data analytics is more accessible to non-technical professionals, since much of the work involves SQL, Excel, and visualization tools rather than heavy programming.
Is data science harder to learn than data analytics?
Generally, yes. Data science requires a stronger foundation in statistics, programming, and machine learning, making the learning curve steeper compared to data analytics.
Which pays more, data science or data analytics, in 2026?
On average, data science roles pay more due to their technical complexity and direct impact on predictive business outcomes. However, experienced, specialized analysts can still command competitive salaries.
How long does it take to become job-ready in either field?
With focused training, most learners can become job-ready in data analytics within 4–6 months, while data science typically requires 6–12 months given its steeper technical requirements.
Do I need a degree in computer science or statistics to enter either field?
No. Both fields are increasingly skills-based rather than degree-based. Bootcamps, certification programs, and portfolio projects are widely accepted by employers as proof of capability, particularly for analytics roles and entry-level data science positions.
The data science vs data analytics 2026 decision ultimately comes down to how you want to work with data — interpreting the past and guiding present-day decisions, or building systems that predict and shape the future. Both careers are in high demand, both are being reshaped by generative AI, and both offer real, well-paying career paths for people willing to put in the work. Start with your strengths, be honest about how much technical depth you’re willing to build, and choose the path that keeps you motivated to keep learning — because in this industry, the learning never really stops.
Prateek Agrawal Jul 11, 2026 No Comments
Every digital business depends on reliable data. Customer transactions, website events, financial records, application logs and sensor readings must be collected, cleaned, organised and delivered to the people and systems that need them. Data engineers build the infrastructure that makes this possible.
A strong data engineering skillset goes beyond knowing a programming language or operating a cloud tool. It combines database knowledge, software engineering, data architecture, distributed processing, security, problem-solving and business understanding.
This guide explains the core data engineer skills employers expect, the tools associated with each skill and a practical roadmap for developing job-ready capability.
A data engineering skillset is the combination of technical, analytical and professional abilities required to design, build, operate and improve data systems.
A data engineer may extract data from databases, APIs and applications; transform raw data into consistent datasets; build batch and streaming pipelines; design warehouses and lakes; maintain data quality; and support analytics, machine learning and AI applications.
| Skill area | What you should know | Common tools |
| SQL | Queries, joins, windows and optimisation | PostgreSQL, MySQL, SQL Server |
| Programming | Automation, APIs, transformation and testing | Python, Java, Scala |
| Data modelling | Schemas, facts, dimensions and normalisation | dbt, modelling tools |
| Pipelines | ETL, ELT, retries and incremental loading | Airflow, ADF, AWS Glue |
| Distributed processing | Large-scale data processing | Spark, Databricks |
| Streaming | Events, producers, consumers and offsets | Kafka, Kinesis |
| Cloud | Storage, compute, identity and cost control | AWS, Azure, Google Cloud |
| Quality and governance | Testing, lineage and access control | Great Expectations, Purview |
| DevOps | Version control and automated deployment | Git, Docker, CI/CD tools |
| Business skills | Requirements, documentation and communication | Jira, Confluence |
SQL is the foundation of the data engineering skillset. Engineers use it to inspect source systems, transform data, validate results, create warehouse models and troubleshoot pipeline failures.
A job-ready professional should be comfortable with joins, common table expressions, subqueries, aggregate functions, window functions, date and string functions, deduplication, views, stored procedures, indexes, partitions and query execution plans.
Returning the correct result is only the first step. A data engineer must also consider how a query performs when a table contains millions of records. This requires knowledge of filtering, indexing, partition pruning, data distribution and unnecessary data movement.
Python is commonly used for data ingestion, transformation, API integration, automation and testing. Its standard data structures and extensive ecosystem make it practical for reusable data workflows.
Important skills include:
Libraries such as pandas are useful for moderate-sized data, while PySpark supports distributed processing. Python should be learned through practical tasks such as extracting paginated API data, validating schemas, processing files and loading results into databases.
Pipelines create value only when their outputs are structured for business use. Data modelling is therefore a core part of the data engineering skillset.
Engineers should understand:
Operational databases are designed for frequent inserts and updates. Analytical systems are designed for large scans, aggregations and historical analysis. Recognising this difference helps engineers build models that are both accurate and efficient.
Modern frameworks such as dbt allow teams to create modular SQL models and combine them with testing, documentation, lineage and version-controlled workflows.
ETL means extract, transform and load. ELT means extract, load and transform. Both patterns move data from source systems into analytical platforms.
A capable data engineer should know how to:
Reliable pipelines should be idempotent, meaning they can be rerun safely without creating duplicates or corrupting results. They should also account for late files, network failures, API rate limits and changing source schemas.
A complete data engineering skillset includes the major patterns used to store analytical data.
A data warehouse stores curated data optimised for reporting and analysis. Common examples include Snowflake, Google BigQuery, Amazon Redshift and Azure-based warehouse services.
A data lake stores large volumes of structured, semi-structured and unstructured data, typically in object storage.
A lakehouse combines the flexibility of data-lake storage with warehouse-style performance, management and governance.
Important storage concepts include:
The objective is not to memorise every product interface. It is to understand why a storage pattern suits a workload and what trade-offs it creates in performance, governance and cost.
When data becomes too large or processing too complex for one machine, distributed computing becomes necessary. Apache Spark is widely used for data engineering, data science and machine learning workloads.
A practical Spark skillset includes:
Many beginners focus only on PySpark syntax. Employers need engineers who can diagnose why a job is slow, why a join creates excessive shuffling or why thousands of small files reduce performance.
Batch pipelines process data at scheduled intervals. Streaming systems process events continuously or in short windows.
Apache Kafka is a distributed event-streaming platform used to publish, retain and process streams of events.
Engineers working with streaming data should understand:
Managed alternatives include Amazon Kinesis and Azure Event Hubs. AWS describes Kinesis as a service for collecting, processing and analysing real-time streaming data.
A practical streaming project could process e-commerce clickstream events, calculate rolling product views and store the resulting metrics for a dashboard.
Production data platforms contain dependent tasks that must run in the correct order. Orchestration tools schedule these tasks, manage dependencies and monitor their status.
Apache Airflow is designed for developing, scheduling and monitoring batch-oriented workflows, typically represented as directed acyclic graphs or DAGs.
Relevant orchestration skills include:
Other options include Azure Data Factory, Google Cloud Composer, Databricks Workflows and Microsoft Fabric Data Factory.
The core skill is understanding how workflows behave when tasks fail, data arrives late or historical periods must be reprocessed.
Most modern data engineering roles require exposure to at least one major cloud platform: Microsoft Azure, Amazon Web Services or Google Cloud.
A cloud-ready data engineering skillset should cover:
AWS, for example, provides analytics services across querying, processing, governance, warehousing and streaming, including Athena, EMR, Glue, Redshift, Lake Formation and Kinesis. Azure and Google Cloud offer comparable capability categories under different service names.
Beginners should not attempt to master all three clouds simultaneously. Choose one ecosystem, build an end-to-end project and then map the concepts to equivalent services elsewhere.
A pipeline is not successful merely because it finishes without an error. It must deliver accurate, complete, timely and trustworthy data.
Engineers should create checks for:
Testing operates at several levels. Unit tests validate code components. Integration tests confirm that systems work together. Data tests verify the properties of output datasets.
Observability extends this approach by monitoring pipeline duration, failure rates, data freshness, volume changes, lineage and downstream impact.
Engineers must also decide what happens when a check fails. Depending on the level of risk, the pipeline may stop, quarantine records, issue a warning or continue with an audit flag.
Data engineering is a software engineering discipline. Production pipelines should not depend on manually edited scripts stored on individual computers.
Core engineering practices include:
dbt explicitly applies software engineering practices such as version control, modularity, testing, CI/CD and documentation to data transformation workflows.
Data engineers frequently work with financial, customer, employee and operational data. Security must be built into the system.
Relevant skills include:
Governance ensures users understand where data came from, what it means, who owns it and who may access it. A catalogue, business glossary and lineage system make data easier to discover while reducing misuse.
Engineers do not need to act as legal experts, but they must translate security and governance requirements into technical controls.
Technical skill alone does not create useful data products. Data engineers must understand the business meaning behind the requested data.
If a stakeholder asks for “daily sales data,” the engineer must clarify:
Useful professional skills include requirement gathering, documentation, estimation, prioritisation, stakeholder communication and incident reporting.
Engineers should be able to explain technical trade-offs in terms of reliability, time, risk and cost.
AI coding assistants can generate SQL, explain unfamiliar code, create documentation and accelerate troubleshooting. Data platforms are also adding AI-supported development features.
dbt, for example, documents AI capabilities grounded in project context such as lineage, tests, contracts and metric definitions.
However, generated code may be inefficient or apply incorrect business logic. Suggested configurations may also create security, performance or cost issues.
The emerging skill is not simply using AI. It is using AI with sufficient context, review, testing and governance. Engineers who understand the fundamentals can use these tools to move faster without sacrificing reliability.
Beginners do not need to master every technology before applying for roles. A practical sequence is:
This sequence develops depth before breadth. It is more effective than gaining superficial exposure to dozens of tools.
Experienced professionals should move beyond tool operation towards architecture, platform reliability, cost optimisation, governance, reusable components and technical leadership.
A portfolio should demonstrate complete workflows and engineering decisions.
Extract order and customer data from an API, store raw files in object storage, transform the data, load warehouse tables and produce daily sales metrics.
Generate website events, publish them to a streaming platform, calculate windowed metrics and store the results for analysis.
Create configurable checks for duplicates, nulls, row counts, freshness and source-to-target totals. Produce an audit report after every pipeline run.
Build a dimensional model, implement incremental loading, add role-based access and document cost-optimisation decisions.
Each project should contain a README, architecture diagram, data model, source code, tests, sample outputs and deployment instructions. Explain trade-offs and limitations instead of presenting the work as flawless.
Common mistakes when building data engineer skills include:
A balanced data engineering skillset combines conceptual depth, implementation ability and operational discipline.
The most important skills are SQL, Python, data modelling, ETL or ELT, databases, cloud platforms, orchestration, data quality, Git and communication. Spark and streaming tools become important for large-scale or real-time systems.
No. Python is valuable, but data engineering also requires SQL, database concepts, modelling, pipeline design, cloud services, testing and operational skills.
Deep machine learning expertise is not mandatory for most roles. However, understanding model-training data, feature pipelines and production inference helps when supporting AI and machine learning teams.
Azure, AWS and Google Cloud all support enterprise data engineering. The best starting platform depends on your target employers, existing experience and technology environment. Learn one platform deeply before covering all three.
Yes. Entry-level candidates should focus on SQL, Python, databases, ETL, Git and one cloud ecosystem, then demonstrate these skills through complete projects.
The timeline depends on prior programming and database experience. Readiness is better measured by whether you can independently design, build, test, explain and troubleshoot an end-to-end pipeline.
The ideal data engineering skillset is not a checklist of fashionable tools. It is the ability to build data systems that are accurate, scalable, secure, maintainable and useful to the business.
Start with SQL, Python, databases and data modelling. Progress to pipelines, cloud platforms, orchestration, distributed processing, data quality and governance. Reinforce each stage with practical projects and professional engineering practices.
As organisations expand analytics, machine learning and enterprise AI, reliable data engineering becomes increasingly valuable. Professionals who combine technical depth with business understanding will be better positioned to build the trusted data foundation these systems require.
To develop these capabilities through structured training and hands-on projects, explore the Data Engineering Programme at Ivy Professional School.
Prateek Agrawal May 16, 2026 No Comments
Many people believe that data science is only for software engineers, coders, or people from a computer science background. This is one of the biggest myths stopping talented professionals from entering the field. The truth is simple: Can a non-IT person learn data science? Yes, absolutely.
Data science is not just about writing code. It is about understanding data, asking the right questions, finding patterns, solving business problems, and communicating insights clearly. In fact, many non-IT professionals already use data in their daily work without calling it “data science.” Sales teams analyze targets, finance teams study costs, HR teams review attrition, marketing teams track campaigns, and operations teams monitor performance. Data science simply gives structure, tools, and advanced techniques to do this better.
So, if you are from commerce, management, economics, statistics, engineering, HR, sales, finance, operations, or even a completely different background, this blog will help you understand how you can enter the field confidently.
Before answering Can a non-IT person learn data science, it is important to understand what data science really is.
Data science is the process of collecting, cleaning, analyzing, visualizing, and interpreting data to solve problems or support decision-making. It combines different skills such as statistics, business understanding, programming, machine learning, and communication.
For example, a retail company may want to know why sales dropped in a particular region. A data science approach would include collecting sales data, comparing it across locations and time periods, finding possible reasons, visualizing the trends, and recommending business actions.
Similarly, a bank may use data science to identify customers who are likely to default on loans. A hospital may use it to predict patient demand. An e-commerce company may use it to recommend products. A manufacturing company may use it to forecast defects or machine downtime.
This shows that data science is not limited to IT companies. It is used across industries and functions.
The most common fear beginners have is coding. Many people think, “I am not from IT, so how will I learn Python, SQL, or machine learning?”
Here is the reality: coding is a skill, not a background requirement. Nobody is born knowing Python or SQL. Even IT professionals learn them step by step.
So, Can a non-IT person learn data science without coding experience? Yes. You can start with beginner-friendly tools and gradually move toward programming.
A good learning path usually begins with Excel, statistics, and business problem-solving. Then you can learn SQL for working with databases. After that, Python becomes easier because you already understand what you want to do with data.
Python for data science is not the same as advanced software development. You do not need to build complex applications at the beginning. You mainly need to learn how to import data, clean it, analyze it, create charts, and build basic models.
For many learners, the fear of coding disappears once they start applying it to real examples.
A non-IT background can become a strength in data science, especially if you already understand business processes.
For example, a finance professional understands revenue, cost, profit, margins, and risk. A marketing professional understands customer behavior, campaign performance, segmentation, and conversion. An HR professional understands hiring, attrition, employee engagement, and performance. A supply chain professional understands inventory, logistics, demand, and vendor performance.
These domain skills are extremely valuable.
Many technical learners know how to build models but may struggle to understand the business context. On the other hand, a non-IT professional may understand the business problem better and can learn the required tools to analyze it.
This is why the answer to Can a non-IT person learn data science is not only yes, but also that they may bring a unique advantage.
To become good at data science, you need a combination of technical and analytical skills. You do not need to master everything on day one. You can build these skills gradually.
Statistics is the foundation of data science. You should understand concepts like average, median, percentage, variance, correlation, probability, hypothesis testing, and distribution.
The good news is that you do not need advanced mathematics at the beginner stage. Most real business problems require practical statistical thinking rather than complicated formulas.
Excel is a great starting point for non-IT learners. It helps you understand rows, columns, formulas, filters, pivot tables, charts, and basic analysis.
If you are already comfortable with Excel, you already have a strong foundation for data science.
SQL is used to extract and work with data from databases. It is one of the most important skills for data analysts and data scientists.
SQL is easier than most programming languages because it uses a structured query format. You can learn basic SQL queries like SELECT, WHERE, GROUP BY, JOIN, and ORDER BY within a few weeks of practice.
Python is widely used in data science because it is simple and powerful. Libraries like Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn help you clean data, analyze it, visualize it, and build machine learning models.
For beginners, the focus should be on Python for data analysis, not advanced software development.
A data scientist must know how to present insights clearly. Tools like Power BI, Tableau, Excel dashboards, and Python visualization libraries are useful here.
Good visualization helps decision-makers understand what the data is saying.
Machine learning helps computers learn patterns from data. As a beginner, you can start with simple concepts like regression, classification, clustering, and decision trees.
You do not need to become a machine learning researcher. You need to understand how models work, when to use them, and how to evaluate their performance.
This is where non-IT learners can shine. Data science is valuable only when it solves real problems. You should learn how to convert a business question into a data question.
For example, “Why are customers leaving?” becomes a churn analysis problem. “Which product should we promote?” becomes a sales and customer segmentation problem.
If you are wondering Can a non-IT person learn data science in a structured way, follow this practical path.
Start with Excel and basic statistics. Learn how to clean data, create pivot tables, calculate key metrics, and build simple dashboards. Then move to SQL and learn how to extract data from databases.
Once you are comfortable with SQL, start Python. Focus on Python basics first, then move to Pandas for data cleaning and analysis. After this, learn visualization using Power BI, Tableau, or Python libraries.
Then move to machine learning basics. Start with simple projects like predicting house prices, classifying customers, forecasting sales, or analyzing employee attrition.
Finally, build a project portfolio. This is extremely important for career transition. Employers want to see whether you can apply your skills to real-world problems.
Learning data science as a non-IT person is possible, but it does come with challenges.
The first challenge is fear of coding. Many learners give up before they even start because Python looks unfamiliar. The solution is to learn coding through practical examples rather than theory.
The second challenge is trying to learn too much at once. Data science has many topics, and beginners often feel overwhelmed. The solution is to follow a step-by-step roadmap.
The third challenge is lack of practice. Watching videos is not enough. You need to work on datasets, solve problems, and build projects.
The fourth challenge is not connecting data science with business use cases. Many learners focus only on tools and forget the problem-solving part. This makes their learning incomplete.
The fifth challenge is comparison. Non-IT learners often compare themselves with coders. This is unnecessary. Your journey will be different, but it can still be successful.
The timeline depends on your background, consistency, and learning approach.
If you study regularly for 8 to 10 hours per week, you can build a strong foundation in 6 to 9 months. This includes Excel, SQL, Python, statistics, visualization, and basic machine learning.
If you already know Excel, business analytics, finance, or statistics, your journey may be faster. If you are completely new to data, it may take longer.
But the real answer is not just about duration. The quality of practice matters more. A learner who completes 5 strong projects in 6 months may be more job-ready than someone who watches videos for one year without applying anything.
So, when people ask Can a non-IT person learn data science, the better question is: Are they willing to practice consistently?
Data science opens up multiple career paths. You do not have to become a data scientist immediately. Many non-IT professionals begin with roles that match their current strengths.
Some popular roles include:
| Role | Suitable For |
| Data Analyst | Beginners, Excel users, business professionals |
| Business Analyst | Management, operations, finance, sales backgrounds |
| BI Analyst | People interested in dashboards and reporting |
| Marketing Analyst | Marketing and digital campaign professionals |
| HR Analyst | HR and talent management professionals |
| Financial Analyst | Commerce, finance, accounting backgrounds |
| Machine Learning Analyst | Learners comfortable with Python and models |
| Data Scientist | Learners with stronger statistics, coding, and ML skills |
This means you do not need to jump directly into the most advanced role. You can enter through analytics and gradually grow into data science.
Many non-IT backgrounds are suitable for data science.
Commerce students can understand business numbers, accounting, finance, and reporting. MBA graduates can connect data with strategy and decision-making. Economics students often have good analytical and statistical thinking. Engineers from non-computer branches can bring logical thinking and process understanding. HR, sales, marketing, and operations professionals bring domain knowledge.
Even teachers, researchers, entrepreneurs, and consultants can learn data science if they follow the right roadmap.
So, Can a non-IT person learn data science from any background? Yes, provided they are ready to learn the tools, practice regularly, and build projects.
A portfolio is one of the most important parts of your career transition. It shows employers that you can work with real data.
Your portfolio should include projects from different areas such as sales analysis, customer segmentation, financial analysis, HR attrition analysis, inventory analysis, social media analysis, and predictive modeling.
Each project should clearly explain the business problem, dataset used, steps followed, tools applied, insights found, and recommendations given.
Do not simply upload code. Tell a story through your project. Recruiters and hiring managers should be able to understand what problem you solved and what value your analysis created.
A strong portfolio can help non-IT learners compete with technical candidates.
Start small. Do not begin with advanced machine learning or deep learning. Build your foundation first.
Learn one tool at a time. For example, do not try to learn Excel, SQL, Python, Power BI, and machine learning all in the same week.
Practice on real datasets. Use business datasets whenever possible because they are easier to relate to.
Focus on problem-solving. Tools will keep changing, but analytical thinking will always remain valuable.
Build projects and publish them on LinkedIn or a portfolio website. Visibility matters.
Learn how to explain your work. A data professional must communicate insights, not just produce charts or code.
Yes. Can a non-IT person learn data science? Definitely. Data science is not reserved for IT professionals. It is open to anyone who is curious, analytical, consistent, and willing to learn.
You do not need to know coding before starting. You do not need a computer science degree. You do not need to be a mathematics genius. What you need is a structured roadmap, regular practice, real projects, and the ability to connect data with business problems.
In fact, non-IT professionals often bring valuable domain knowledge that can make them stronger data professionals. A finance person can become a finance analytics expert. A marketing person can become a marketing analyst. An HR person can become an HR analytics specialist. An operations professional can become a supply chain analytics expert.
The best way to start is simple: learn Excel and statistics, move to SQL, then Python, then visualization and machine learning. Build projects at every stage.
So, the next time someone asks Can a non-IT person learn data science, the answer is clear: yes, and with the right guidance, they can build a strong and rewarding career in the data field.
Yes, a non-IT person can learn data science with the right roadmap. You can start with Excel, basic statistics, and business analysis before moving to SQL, Python, dashboards, and machine learning.
No, you do not need coding knowledge to start. Coding can be learned step by step. Many beginners first learn Excel, SQL, and basic analytics before learning Python.
Data science may feel challenging in the beginning, but it becomes easier when you learn through practical examples and real projects. The key is to follow a structured learning path instead of trying to learn everything at once.
Students and professionals from commerce, economics, statistics, management, finance, marketing, HR, operations, and engineering backgrounds can all learn data science. A strong business understanding can actually be an advantage.
With regular practice, a non-IT learner can build a strong foundation in around 6 to 9 months. The timeline depends on your current skills, learning consistency, and project practice.
A non-IT beginner should start with Excel, basic statistics, and data interpretation. After that, they can learn SQL, Python, data visualization, and machine learning basics.
Yes, you can get a data-related job without an IT degree if you build strong practical skills and a good project portfolio. Many learners start with roles like Data Analyst, Business Analyst, BI Analyst, or Marketing Analyst before moving into advanced data science roles.
Python is not compulsory at the very beginning, but it is highly recommended for long-term growth in data science. It is widely used for data cleaning, analysis, visualization, and machine learning.
Non-IT learners should build business-focused projects such as sales analysis, customer segmentation, HR attrition analysis, financial analysis, marketing campaign analysis, inventory analysis, and basic prediction models.
Yes, a non-IT person can become a data scientist by learning the right skills, practicing consistently, building projects, and gaining confidence in statistics, SQL, Python, machine learning, and business problem-solving.
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.
Prateek Agrawal May 02, 2026 No Comments
Prateek Agrawal Apr 18, 2026 No Comments
A few years ago, enrolling in a data science program almost guaranteed a job. But in today’s evolving landscape, the question has changed.
In 2026, the real question is not whether data science is in demand—it is whether a data science course in 2026 will actually deliver meaningful ROI for your career.
Let’s break this down across three key dimensions: ROI, salary, and long-term career growth.
Before evaluating whether a data science course in 2026 is worth it, you need to understand one major shift.
Data science hasn’t declined—it has matured.
Companies today are not hiring people who just know tools. They are hiring professionals who can use data and AI to drive decisions.
What has changed:
This means that simply completing a data science course in 2026 is not enough—you need to bring real-world value.
Let’s address the most practical concern—earning potential.
The average salary is around ₹10 LPA.
However, completing a data science course in 2026 does not guarantee these numbers.
Your salary depends on:
A certificate alone does not create value—execution does.
Let’s evaluate the ROI of a data science course in 2026 in practical terms.
A well-designed data science course in 2026 offers:
The key is not the course—it’s how you leverage it.
One of the strongest arguments for a data science course in 2026 is long-term career flexibility.
A data science course in 2026 is not just about a job—it is a foundation for the AI economy.
Let’s be direct.
A data science course in 2026 is NOT worth it if:
But it is worth it if:
This is the difference between:
₹4 LPA candidate vs ₹15 LPA candidate
A common concern is whether AI will replace data scientists.
The reality is different.
AI is:
Companies now expect professionals to:
A data science course in 2026 should prepare you for this shift.
The future role is not just a “data scientist”—it is an AI-enabled decision-maker.
A data science course in 2026 is ideal for:
Short answer: Yes—but only if done right.
A data science course in 2026 is worth it if:
Because:
But one thing is clear:
The shortcut era is over.
The “learn → build → apply → communicate” era has begun.
If you treat a data science course in 2026 as:
❌ Just a certificate → Not worth it
✅ A career transformation tool → One of the best investments you can make
Success in 2026 is not about knowing more tools.
It’s about creating real impact with data.
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.
Prateek Agrawal Apr 16, 2026 No Comments
A few years ago, becoming a data scientist meant learning Python, a few machine learning algorithms, and building some dashboards.
That playbook is broken.
In 2026, companies are no longer hiring “data scientists.”
They are hiring decision-makers who can use data and AI to move the business forward.
This is why data scientist skills 2026 look very different today.
So instead of listing generic skills, let’s answer a better question:
What skills make someone valuable in today’s data-driven organizations?
Data science is no longer a support function. It is now directly tied to revenue, efficiency, and strategy.
Three major shifts are redefining the role:
This shift is exactly why data scientist skills 2026 are becoming more business-focused than ever before.
Let’s break this down in a way that actually reflects real-world expectations of data scientist skills 2026.
Before you touch data, you need to define the problem correctly.
Most professionals jump straight into analysis. The best ones step back and ask:
If you get this wrong, even the best model won’t help. This is one of the most critical data scientist skills 2026.
Yes, statistics is important. But what defines data scientist skills 2026 is data intuition.
This means:
Python is still essential, but expectations have changed in data scientist skills 2026.
The focus is now on:
Clean datasets are a myth.
A core part of data science skills 2026 is handling:
Machine learning is still relevant, but companies don’t need academic experts.
They need professionals who reflect real-world data scientist skills 2026:
This is no longer optional.
Modern data scientist skills 2026 include working with AI systems effectively.
This includes:
In 2026, visualization is about decision clarity.
A key part of data scientist skills 2026 is asking:
What should the user do after seeing this?
Companies now expect ownership.
This shift defines data scientist skills 2026:
A major shift in data scientist skills 2026 is understanding systems, not just datasets.
You should know:
Top performers use AI to build workflows, not just ask questions.
This is what separates average vs top-tier data scientist skills 2026.
It’s not about how many tools you know.
It’s about how you combine them.
The real power of data scientist skills 2026 lies in skill stacking:
Data science is evolving rapidly.
What’s changing:
This reinforces why data scientist skills 2026 are focused on impact, not just tools.
The biggest mistake people make is preparing for yesterday’s roles.
If you build the right data scientist skills 2026, you won’t just stay relevant, you’ll become indispensable.
Because the future doesn’t belong to people who know tools.
It belongs to people who know how to use data to make decisions.
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 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.