Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format.
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Get familiar with our online Python Data Science course syllabus.
Online Advance Data Science Course in Pune is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s.
In this term, you will learn how to ace Python Basics, Python OOPS and Python Libraries like Pandas, Matplotlib, Numpy, etc…Â Â
Python Basics :
Python OOPS :
Pandas :
Matplotlib :
Numpy :
Data Engineering in Python :
In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S
MYSQL :
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Azure devops :
Git :
IDE :
In this term, you will learn how to ace Framework and industry projects
Pyspark :
1) E-Commerce
2) Banking Domain
Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL.
Quantitative Analyst: Applying quantitative techniques to financial and business data, leveraging Python for data manipulation and analysis.
Data Analyst: Performing data analysis and visualization using tools like Pandas, Matplotlib, and PowerBI, and creating actionable insights from data.
Cloud Data Engineer: Working with cloud services and data pipelines, including AWS services like S3, EMR, and Athena.
DevOps Engineer: Implementing CI/CD pipelines and managing version control with Git, focusing on automation and integration in data engineering projects.
Experience Level | Salary Range (INR) |
---|---|
Freshers (0-1 years) | ₹4,00,000 - ₹7,00,000 |
2-3 years | ₹7,00,000 - ₹12,00,000 |
3-6 years | ₹12,00,000 - ₹18,00,000 |
6-10 years | ₹18,00,000 - ₹25,00,000 |
10-15 years | ₹25,00,000 - ₹35,00,000 |
15+ years | ₹35,00,000 - ₹50,00,000+ |
Data science is a field that helps us make sense of data and gain insights to solve real-world problems. It’s like having a detective who uses data to uncover patterns, trends, and facts that help businesses make better decisions. With the rise of technology, data science has become a crucial part of how we understand and interpret massive amounts of information.
Data science matters because we are surrounded by data in our daily lives. Every time you shop online, browse social media, or even walk around with your smartphone, data is being collected. This data can help businesses understand customer behavior, improve products, and forecast trends. Data science turns all this data into valuable insights that guide decisions in nearly every industry.
Data science is made up of several steps that work together to create meaningful insights from data.
The first step in data science is gathering data from various sources, like surveys, social media, and sensors. This step is essential because quality data leads to quality insights.
Raw data often has errors or irrelevant information, which needs to be cleaned before analysis. Data cleaning involves removing duplicates, fixing errors, and making sure the data is organized.
Once the data is prepared, it’s time for analysis. Data scientists use different methods to analyze data, finding patterns, and making sense of it all. This is where insights are discovered, and it’s a critical part of the process.
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Data science follows a structured process, which includes data collection, cleaning, analysis, modeling, and interpretation. Each step builds on the previous one, helping data scientists find useful information that can influence business decisions.
Data science projects typically start with a problem to solve, like predicting sales or identifying customer trends. Data scientists gather and prepare data, analyze it, and finally present the findings in a way that others can understand and use.
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Data scientists are professionals who interpret data to help make decisions. They combine skills from statistics, computer science, and business to solve complex problems.
A data scientist’s work involves gathering and analyzing data, building models, and communicating findings. They often work closely with business leaders to provide insights that drive strategic decisions.
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Data science is everywhere! Here are some of the most exciting applications:
In healthcare, data science is used to predict disease outbreaks, improve patient care, and develop personalized treatments.
E-commerce companies use data science to recommend products, forecast trends, and improve customer experiences.
Data science helps financial institutions detect fraud, manage risks, and make investment decisions.
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Interested in data science? Here are some tips to get started:
Many online courses cover data science basics. Look for reputable platforms that offer beginner-friendly courses.
Working on real-life projects is one of the best ways to learn. Many beginners start with projects that analyze publicly available data.
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Frequently asked questions
A Data Science course teaches you how to analyze and interpret large data sets to draw meaningful insights. This field is in high demand as data-driven decision-making becomes crucial in industries like healthcare, finance, and e-commerce.
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A Data Science course is suitable for anyone with a background in mathematics, computer science, or a related field. However, many courses also welcome beginners who are eager to learn about data analysis and programming.
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In a Data Science course, you’ll gain skills in programming (using languages like Python and R), data cleaning, data visualization, statistical analysis, and machine learning. Some courses also cover advanced topics like data engineering and big data tools.
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The duration of a Data Science course varies. Short courses may take a few weeks, while more comprehensive programs, like diplomas or certifications, can take several months.
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While a Data Science course involves technical concepts, many courses are designed for beginners and build foundational knowledge before moving on to advanced topics. The key is to start with a beginner-friendly course and practice regularly.
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After completing a Data Science course, you can explore careers such as Data Analyst, Data Scientist, Machine Learning Engineer, and Business Intelligence Analyst. These roles are highly sought after across various industries.
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Some Data Science courses require knowledge of basic mathematics and programming, while others start from scratch. Always check the course prerequisites before enrolling to ensure it matches your current skill level.
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Yes, most Data Science courses cover machine learning as part of the curriculum. Machine learning is a vital part of data science, allowing you to create predictive models and analyze trends.
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While both fields involve working with data, a Data Science course usually covers a broader range of topics, including machine learning, data modeling, and programming, whereas Data Analytics focuses mainly on analyzing existing data.
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Absolutely! Many top institutions and online platforms offer comprehensive Data Science courses online. These courses often provide flexibility, allowing you to study at your own pace.
Roles and Responsibility for Data Engineer
Data Science in 3 Months?​
Yes, absolutely! It is entirely possible to learn Data Science in 3 months, but it requires a high level of commitment and consistency. To make this happen, you’ll need to dedicate a minimum of 3-4 hours daily to studying and practicing Data Science. This time should be spent not just on watching lectures, but also on working through problems, hands-on exercises, and applying the concepts you learn to real-world scenarios. Regular practice is key to mastering important topics like Machine Learning, Data Analysis, Statistics, and Data Visualization.
Moreover, it’s important to fill any gaps in your learning by working on multiple projects. These projects help reinforce your understanding and give you practical experience, which is invaluable for job applications. Building a strong portfolio of real-world projects will make you stand out when applying for jobs. The more projects you complete, the more confident you will be in your skills.
However, learning Data Science in 3 months isn’t just about following a study schedule—it’s about consistency and focus. If you stay dedicated and stick to your routine without letting distractions get in the way, you can cover the essential topics in Data Science within this time frame. By the end of 3 months, with the right mindset and effort, you will not only have a solid understanding of the core concepts but also a portfolio of projects that can help you land a job in the field.
In short, if you’re willing to put in the work and follow a structured plan, it’s definitely possible to learn Data Science and be ready for a job in just 3 months.
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