Best Data Science Course In Nagpur With Placement
100% Placement Assistance | Live Online Sessions
Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format.
Enroll Before: 25 November, 2024
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Students Trained
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Placement Assistance
25 November, 2024
Start Date
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7:30 AM - 9:30 AM
Lecture Timings ( IST )
Key Highlights Of The Advance Data Science Course
- 100+ Hours Of Live Class
- 50+ Hours of Videos
- 1-on-1 Mentoring Sessions
- 20+ Industry Tools Mastery
- In-Class Live Presentations
- 3+ Live Projects & Practice Assignments
- 100% Placement Assistance
- Resume & Interview Training
Syllabus for Data Science Course
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.
Term 1
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 :
- Â Why python
- Python IDE
- Basics of programming
- Variables , Data Types
- Conditional statements
- Loops
- Logical Thinking
- Data Structures
- Functions and types of arguments
- Lambda Functions
- memory Management
- garbage collector
- Copies - shallow copy, deep copy
- Higher Order Functions - Map , Reduce , Filter
- Iterable , Iterator , generator
- Exception handling
- Programming interview questions
Python OOPS :
- Class
- constructor and its types , Destructor
- Types of variables - instance , static
- Inheritance - Single , Multiple , Multilevel , Hierarchical
- polymorphism
- duck typing
- Overloading - method , Operator , constructor
- overriding - method , Constructor
- Super Function
- Encapsulation
- access Modifiers
- Abstraction
- monkey patching
Pandas :
- Introduction to Pandas
Series Data Structure - Data Frame Data Structure
- Merging DataFrame
- Read Complex CSV , JSON , excel Files using pandas
- Write to File
- Data Frame Manipulation - head , Tail , Describe , shape ,Drop , inplace
- loc & I=iloc
- Apply Function
- Value count
- Add Column
- Add Row To DataFrame - using concat,Append
- Order By Operation
- Sort Values
- Group by operation
- Pivot Table
- Date/ Time Functionality
- Example Manipulating DataFrame
Matplotlib :
- line graph
- bar Plot
- scatter plot
- pie chart
- other function
Numpy :
- Introduction to Numpy
- Creating Arrays , Indexing , Slicing
- Data Types
- Copy vs View
- Array Shape & Reshape
- Arrays Split & Joins
- Arrays Filter
- Seaborn Model
Data Engineering in Python :
- Handling Missing Data
- Techniques to inpute missing Values
- Meaningful Data transformation
- Encoding Data
Data Visualization in Python - Read Json , CSV's, excels
Term 2
In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S
MYSQL :
- DBMS & RDBMS
- Data Types
- DQL
- DDL
- DML
- TCL
- DCL
- Key Constraints
- Operators
- Clouses
- Aggregate Functions
- Indexes
- Views
- Triggers
- JOINS
- Sub Queries & Nested Queries
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- Use of AWS
- Cloud computing models
- S3
- AWS Data Pipeline
- EMR
- AWS Glue
- Athena
- Redshift
Azure devops :
- Use Of Devops
- CI/ CD Pipeline
- work item
- sprints
- repository
- state of task
- Repose Clone
- pull request
Git :
- Use of Git
- feature branch
- clone
- Add
- Commit
- Push
IDE :
- PowerBI :
- Dashboards
- Application
- DBeaver :
- Connection Process
- DB Manipulation
- Jupyter Notebook :
- Google Colaboratory
- Pycharm
Term 3
In this term, you will learn how to ace Framework and industry projects
Pyspark :
- Use of Pyspark For Data Science
- Spark Session & RDD
- Timestamp
- Schema
- Parallelize
- Broadcast Variable
- Create DataFrame
- Transformations & actions
- Empty DataFrame
- Structure type and structure field
- Select
- Collect
- WithColumn
- Where & Filter
- Drop & Drop Duplicate
- orderby and sortby
- Groupby
- Joins
- union and union all
- union byname
- map , flatmap
- Sample by vs Sample
- Pivote
- maptype
- Aggregate Functions
- Windows Function
- Read and Write in CSV
- When
- Split
- collect
- Row number
- dense rank
1) E-Commerce
2) Banking Domain
The Career Opportunities After Completing a Data Science Course in Nagpur
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.
Tools You’ll Master
Data Engineer Salary in India (INR)
| 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+ |
Mentors
Python Trainer
4+ Years ITExperience
4+ Years Teaching Experience
Project Trainer
5+ Years IT Experience
5+Teaching Experience
Digital Marketing
3+ Years IT Experience
2+Teaching Experience
Manual Trainer
5+ Years IT Experience
5+Teaching Experience
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Bug Spotter Reviews
Introduction to Data Science
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.
Why Data Science Matters
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.
Key Components of Data Science
Data science is made up of several steps that work together to create meaningful insights from data.
Data Collection and Data Sources
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.
Data Cleaning: The Foundation of Accurate 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.
Data Analysis: Turning Data into Knowledge
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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The Process of Data Science
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.
How Data Science Projects Are Structured
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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The Role of a Data Scientist
Data scientists are professionals who interpret data to help make decisions. They combine skills from statistics, computer science, and business to solve complex problems.
Key Responsibilities of Data Scientists
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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Applications of Data Science
Data science is everywhere! Here are some of the most exciting applications:
Data Science in Healthcare
In healthcare, data science is used to predict disease outbreaks, improve patient care, and develop personalized treatments.
Data Science in E-commerce
E-commerce companies use data science to recommend products, forecast trends, and improve customer experiences.
Data Science in Finance
Data science helps financial institutions detect fraud, manage risks, and make investment decisions.
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How to Get Started in Data Science
Interested in data science? Here are some tips to get started:
Courses and Resources for Beginners
Many online courses cover data science basics. Look for reputable platforms that offer beginner-friendly courses.
Practical Experience and Projects
Working on real-life projects is one of the best ways to learn. Many beginners start with projects that analyze publicly available data.
Eligibility
for Data Science Course​
1. Any Graduate Background
- You can come from any educational background—whether you’re an engineer, from commerce, humanities, or any other field. A degree is the only basic requirement.
2. Interest in Coding
- While prior coding experience isn’t mandatory, having a basic interest in coding will help. During the course, you’ll learn programming languages like Python, R, and SQL. Don’t worry if you don’t know coding yet—these skills can be picked up as you go!
3. Time Commitment
- A minimum of 3-4 hours per day is required to complete the course, which includes time for lectures, assignments, and practical exercises. Consistency and commitment are key to your success.
4. No Prior Experience Required
- No prior data science experience is required. The course is designed to take you from beginner-level concepts all the way to advanced topics, so you can start from scratch and still succeed.
5. Curiosity & Problem-Solving Mindset
- If you have a curious mind and enjoy solving problems, you’re already on the right path. Data Science is about exploring data and finding solutions to real-world problems, and this mindset will help you excel.
FAQs
Frequently asked questions
1. What is a Data Science course, and why should I consider it?
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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2. Who is eligible for a Data Science course?
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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3. What skills will I learn in a Data Science course?
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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4. How long does it take to complete a Data Science course?
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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5. Is a Data Science course difficult for beginners?
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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6. What are the career opportunities after completing a Data Science course?
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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7. Are there any prerequisites for joining a Data Science course?
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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8. Will I learn machine learning in a Data Science course?
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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9. How is a Data Science course different from a Data Analytics course?
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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10. Can I take a Data Science course online?
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.
11.What is the Data Science Course fees in Nagpur?
Data Engineer
Roles and Responsibility for Data Engineer
Roles and Responsibility for Data Engineer
The key responsibilities of a data engineer typically include:
- Designing and building robust, scalable and reliable data pipelines to ingest, transform and load data from various sources.
- Implementing efficient data storage solutions, such as data warehouses, data lakes or NoSQL databases, to support reporting, analytics and business intelligence needs.
- Developing data transformation logic to clean, enrich and normalize data to ensure high-quality information.
- Collaborating with data analysts and business stakeholders to understand data requirements and design appropriate data models.
- Ensuring data quality, security and compliance through monitoring, alerting and continuous improvement processes.
- Deploying and maintaining data engineering solutions, including pipelines, data stores and supporting infrastructure.
- Sharing knowledge, best practices and lessons learned with the broader data engineering community.
Course Duration
Data Science in 3 Months?​
Can I Learn 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.