Best Data Science Course In Kolhapur 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: 07 December, 2024
1000+
Students Trained
100%
Placement Assistance
07 December 2024
Start Date
0%
EMI Available
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
Â
- 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 Kolhapur
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 Science Salary in India
The salary for Data Scientists in India depends on experience, location, and the company. Here's a breakdown:
- Entry-level (0-2 years): ₹4,00,000 - ₹8,00,000 per year
- Mid-level (2-5 years): ₹8,00,000 - ₹12,00,000 per year
- Senior-level (5+ years): ₹12,00,000 - ₹20,00,000 per year
The salary can be even higher for those with advanced skills in machine learning, deep learning, or big data technologies, as well as for those working in top companies in major cities like Bengaluru, Hyderabad, and Mumbai.
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
Our Learners Work At
Enroll Now and get 5% Off On Course Fees
Bug Spotter Reviews
Eligibility
for Data Science Course​
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 gather, clean, analyze, and interpret large amounts of data to find meaningful insights. In today’s world, businesses and organizations rely on data to make decisions in areas like healthcare, finance, e-commerce, and more. Taking a data science course can help you learn how to use data to solve real-world problems and improve decision-making.
2. Who is Eligible for a Data Science Course?
Anyone can take a Data Science course, but it’s especially useful for people with a background in fields like mathematics, computer science, or related subjects. That said, many courses are designed for beginners with no prior experience in programming or data analysis. Whether you’re new to the field or looking to switch careers, a Data Science course can be a great starting point.
3. What Skills Will I Learn in a Data Science Course?
In a Data Science course, you’ll learn a variety of important skills, including:
- Programming: Using languages like Python, R, and SQL.
- Data Cleaning: How to fix and organize messy data.
- Data Visualization: Creating charts and graphs using tools like Tableau, Matplotlib, or Seaborn.
- Statistical Analysis: Understanding data trends and performing tests to make decisions.
- Machine Learning: Using algorithms to predict outcomes and classify data.
- Big Data Tools: Working with technologies like Hadoop and Spark to handle large datasets.
Some courses also teach about data engineering (building systems to process data) and deep learning (a type of advanced machine learning).
4. How Long Does it Take to Complete a Data Science Course?
The duration depends on the course type:
- Short bootcamps or workshops can last a few weeks to a couple of months.
- Comprehensive certification programs can take several months.
- Self-paced online courses may take longer, depending on how much time you can dedicate to learning.
5. Is a Data Science Course Difficult for Beginners?
Data Science can be complex, but many courses are designed to be beginner-friendly. They start with the basics—like basic statistics, programming, and data visualization—and gradually move into more advanced topics like machine learning. With consistent practice and learning, beginners can succeed.
6. What are the Career Opportunities After Completing a Data Science Course?
Data Science is in high demand, and there are many job opportunities, such as:
- Data Analyst: Analyzing data to provide insights.
- Data Scientist: Using data to build models and make predictions.
- Machine Learning Engineer: Building systems that learn from data.
- Business Intelligence Analyst: Helping businesses make decisions based on data.
- Data Engineer: Creating infrastructure to manage large datasets.
These roles offer good pay and career growth potential.
7. Are There Any Prerequisites for Joining a Data Science Course?
Some courses may require a basic understanding of mathematics (like statistics or linear algebra) and programming. However, there are also many beginner courses that don’t require any prior knowledge, so you can start from scratch if you’re new to the field.
8. Will I Learn Machine Learning in a Data Science Course?
Yes, machine learning is an important part of most Data Science courses. You’ll learn how to use algorithms to make predictions, classify data, and find patterns. Topics like supervised learning, unsupervised learning, and deep learning are usually covered in advanced sections of the course.
9. How is a Data Science Course Different from a Data Analytics Course?
Both fields involve working with data, but they are different:
- Data Science is broader—it includes data analysis, but also programming, machine learning, and predictive modeling.
- Data Analytics focuses more on interpreting existing data to make business decisions, without as much emphasis on creating models or predictions.
In short, Data Science is more about using algorithms and programming to explore and predict future trends, while Data Analytics is about analyzing current data to understand what’s happening.
10. Can I Take a Data Science Course Online?
Yes, you can find many online Data Science courses. These courses are offered by platforms like Coursera, edX, and Udemy, as well as universities. Online learning gives you flexibility, so you can study at your own pace from anywhere.
11. What is the Fee for a Data Science Course in Kolhapur?
The fee for a Data Science course can vary. For example, at Bug Spotter Software Training Institute in Mumbai, the fee is ₹30,000. Prices can differ based on the institution, course duration, and specialization. It’s a good idea to compare different options to find a course that fits your budget and learning goals.
Data Engineer
Roles and Responsibility for Data Engineer
Roles and Responsibility for Data Engineer
A Data Engineer typically has the following key responsibilities:
- Designing and building scalable, reliable data pipelines to ingest, transform, and load data from multiple sources.
- Implementing data storage solutions (e.g., data warehouses, data lakes, NoSQL databases) for reporting, analytics, and business intelligence.
- Developing data transformation logic to clean, enrich, and normalize data, ensuring high-quality information.
- Collaborating with data analysts and business stakeholders to understand data requirements and design effective data models.
- Ensuring data quality, security, and compliance through monitoring, alerting, and continuous improvement.
- Deploying and maintaining data engineering solutions, including pipelines, data stores, and infrastructure.
- Sharing knowledge and best practices with the data engineering community.
Course Duration
Data Science in 3 Months?​
Can I Learn Data Science in 3 Months?
Yes, it’s absolutely possible to learn Data Science in 3 months, but it will require dedication and consistent effort. You’ll need to commit at least 3-4 hours per day to study and practice key concepts like Machine Learning, Data Analysis, Statistics, and Data Visualization. It’s important not just to watch lectures but also to engage in hands-on exercises and apply your learning to real-world projects.
Completing several projects is crucial for building a strong portfolio that can help you stand out to potential employers. Focus, consistency, and a structured study schedule are essential to mastering the core skills in a short time. By the end of 3 months, with the right effort, you’ll have a solid understanding of Data Science and practical experience that makes you job-ready.
With the right mindset and dedication, you can definitely position yourself for a career in Data Science in just 3 months!