Best Data Science Course In Jalna 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: 1 March, 2025
1000+
Students Trained
100%
Placement Assistance
1 March, 2025
Start Date
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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 Jalna 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 Jalna
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
Our Learners Work At
Enroll Now and get 5% Off On Course Fees
Bug Spotter Reviews
Introduction to Data Science
Data science is the art of extracting meaningful insights from data to solve problems and make informed decisions. Think of it as detective work—using data to uncover patterns, answer questions, and drive smarter choices. As technology advances, data science has become essential for analyzing large, complex datasets, enabling organizations to make data-driven decisions with confidence.
Why Data Science Matters
Data is everywhere—from social media interactions to online purchases and mobile apps. Businesses leverage data science to understand customer behavior, enhance products, and predict future trends. By transforming raw data into actionable insights, data science fuels innovation, improves efficiency, and helps industries stay competitive.
Key Components of Data Science
The data science process involves several key steps:
- Data Collection: Gathering information from sources like surveys, websites, sensors, or databases.
- Data Cleaning: Removing errors, duplicates, and inconsistencies to ensure accuracy and usability.
- Data Analysis: Identifying trends, patterns, and correlations to extract valuable insights.
The Data Science Process
A typical data science workflow includes:
- Collecting Data :Gathering raw data from multiple sources.
- Cleaning Data :Â Fixing errors, handling missing values, and filtering irrelevant information.
- Analyzing Data :Â Exploring trends and patterns to extract insights.
- Healthcare: Predicting disease outbreaks, personalizing treatments, and improving patient care.
- E-commerce: Enhancing product recommendations, forecasting trends, and optimizing customer experiences.
- Finance: Detecting fraud, assessing risks, and guiding investment decisions.
Each step builds on the previous one, forming a continuous cycle that deepens understanding and improves decision-making.
The Role of a Data Scientist
A data scientist blends expertise in statistics, programming, and business strategy to analyze and interpret data. Their role involves:
- Gathering and processing data
- Building predictive models and algorithms
- Communicating insights to stakeholders to solve business challenges.
Data scientists play a crucial role in converting raw data into meaningful strategies that drive success.
Applications of Data Science
Data science is transforming industries, including:
- Healthcare: Predicting disease outbreaks, personalizing treatments, and improving patient care.
- E-commerce: Enhancing product recommendations, forecasting trends, and optimizing customer experiences.
- Finance: Detecting fraud, assessing risks, and guiding investment decisions.
How to Get Started in Data Science
Want to explore data science? Here’s how to begin:
- Take Beginner Courses: Start with online courses on platforms like Coursera, Udemy, or Khan Academy to build foundational knowledge.
- Work on Real Projects: Apply your learning through hands-on projects to develop practical skills and gain experience.
With curiosity and practice, you can unlock the power of data science and turn insights into impact!
Eligibility
for Data Science Course​
Who Can Join?
- Open to All Graduates
No matter your background—engineering, commerce, humanities, or any other field—this course is for you. The only requirement is a degree. - Interest in Coding
No prior coding experience? No problem! While an interest in coding helps, you’ll learn programming languages like Python, R, and SQL from scratch. - Time Commitment
To succeed, plan to dedicate 3-4 hours per day for lectures, assignments, and hands-on exercises. Consistency is key! - Beginner-Friendly
No prior data science experience is required. The course starts with the basics and gradually moves to advanced concepts, making it accessible to everyone. - Curiosity & Problem-Solving Mindset
If you love solving problems and have a curious mindset, you’re already on the right path. Data science is all about exploring data to find real-world solutions.
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 large datasets and uncover valuable insights. With industries like healthcare, finance, and e-commerce relying heavily on data-driven decision-making, learning data science equips you to solve complex problems and drive business strategies.
2. Who is eligible for a Data Science course?
Anyone with a background in math, computer science, or a related field can enroll. Many courses also welcome beginners eager to learn data analysis and programming.
3. What skills will I learn in a Data Science course?
You’ll gain expertise in:
- Programming: Python, R, SQL
- Data Cleaning & Preparation: Processing raw data for analysis
- Data Visualization: Creating insightful charts and graphs
- Statistical Analysis: Understanding data trends
- Machine Learning: Building predictive models
Some advanced courses also cover data engineering and big data technologies.
4. How long does it take to complete a Data Science course?
Course durations vary:
- Short courses: A few weeks
- Comprehensive programs (diplomas/certifications): Several months
5. Is a Data Science course difficult for beginners?
Not at all! Most courses start with fundamental concepts and gradually increase in complexity. With consistent practice, beginners can successfully build strong data science skills.
6. What career opportunities are available after completing a Data Science course?
You can explore high-demand roles such as:
- Data Analyst
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Analyst
These roles are sought after across various industries.
7. Are there prerequisites for joining a Data Science course?
Some courses require basic knowledge of math and programming, but many beginner-friendly options are available. Always check the course details before enrolling.
8. Will I learn machine learning in a Data Science course?
Yes! Machine learning is a core part of most Data Science courses, helping you build predictive models and analyze trends effectively.
9. How is a Data Science course different from a Data Analytics course?
- Data Science: Covers a broad range, including machine learning and predictive modeling.
- Data Analytics: Focuses primarily on analyzing and interpreting existing data.
10. Can I take a Data Science course online?
Absolutely! Many institutions offer flexible online courses, allowing you to learn at your own pace while balancing other commitments.
11. What is the Data Science course fee in Satara?
At Bug Spotter Software Training Institute in Akola, the Data Science course fee is ₹30,000.
Data Engineer
Roles and Responsibility for Data Engineer
Roles and Responsibility for Data Engineer
The key responsibilities of a data engineer typically include:
A data engineer plays a crucial role in managing and optimizing data infrastructure. Their key responsibilities include:
- Designing and Building Data Pipelines – Developing efficient workflows to collect, transform, and load data from multiple sources.
- Implementing Scalable Storage Solutions – Setting up data warehouses, data lakes, and NoSQL databases to support analytics and reporting.
- Transforming and Standardizing Data – Cleaning, enriching, and structuring data to ensure accuracy and usability.
- Collaborating with Analysts & Business Teams – Understanding data needs and developing models that align with business objectives.
- Ensuring Data Quality & Security – Monitoring data pipelines, implementing alerts, and ensuring compliance with data governance standards.
- Deploying & Maintaining Data Infrastructure – Managing cloud and on-premise data systems for seamless operations.
- Sharing Best Practices – Contributing to the data engineering community to enhance collaboration and workflow efficiency.
Course Duration
Data Science in 3 Months?​